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llvm/llvm-project/a257e2aa4eb47ad340915136228cd28d918e8dc2/./llvm/lib/Transforms/Vectorize/LoopVectorize.cpp
blob: 6535f2dc6c72c5901821634262ce3020c9ab3f17 [file]
//===- 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/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 "VPlanAnalysis.h"
#include "VPlanCFG.h"
#include "VPlanHelpers.h"
#include "VPlanPatternMatch.h"
#include "VPlanTransforms.h"
#include "VPlanUtils.h"
#include "VPlanVerifier.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/STLExtras.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/TypeSwitch.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/OptimizationRemarkEmitter.h"
#include "llvm/Analysis/ProfileSummaryInfo.h"
#include "llvm/Analysis/ScalarEvolution.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/ScalarEvolutionPatternMatch.h"
#include "llvm/Analysis/TargetLibraryInfo.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/Analysis/ValueTracking.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/DebugInfo.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/MDBuilder.h"
#include "llvm/IR/Metadata.h"
#include "llvm/IR/Module.h"
#include "llvm/IR/Operator.h"
#include "llvm/IR/PatternMatch.h"
#include "llvm/IR/ProfDataUtils.h"
#include "llvm/IR/Type.h"
#include "llvm/IR/Use.h"
#include "llvm/IR/User.h"
#include "llvm/IR/Value.h"
#include "llvm/IR/Verifier.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/InstructionCost.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/NativeFormatting.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/InjectTLIMappings.h"
#include "llvm/Transforms/Utils/Local.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 <cmath>
#include <cstdint>
#include <functional>
#include <iterator>
#include <limits>
#include <memory>
#include <string>
#include <tuple>
#include <utility>
using namespace llvm;
using namespace SCEVPatternMatch;
#define LV_NAME "loop-vectorize"
#define DEBUG_TYPE LV_NAME
#ifndef NDEBUG
const char VerboseDebug[] = DEBUG_TYPE "-verbose";
#endif
STATISTIC(LoopsVectorized, "Number of loops vectorized");
STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization");
STATISTIC(LoopsEpilogueVectorized, "Number of epilogues vectorized");
STATISTIC(LoopsEarlyExitVectorized, "Number of early exit loops vectorized");
static cl::opt<bool> EnableEpilogueVectorization(
"enable-epilogue-vectorization", cl::init(true), cl::Hidden,
cl::desc("Enable vectorization of epilogue loops."));
static cl::opt<unsigned> EpilogueVectorizationForceVF(
"epilogue-vectorization-force-VF", cl::init(1), cl::Hidden,
cl::desc("When epilogue vectorization is enabled, and a value greater than "
"1 is specified, forces the given VF for all applicable epilogue "
"loops."));
static cl::opt<unsigned> EpilogueVectorizationMinVF(
"epilogue-vectorization-minimum-VF", cl::Hidden,
cl::desc("Only loops with vectorization factor equal to or larger than "
"the specified value are considered for epilogue 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."));
static cl::opt<unsigned> VectorizeMemoryCheckThreshold(
"vectorize-memory-check-threshold", cl::init(128), cl::Hidden,
cl::desc("The maximum allowed number of runtime memory checks"));
/// Option tail-folding-policy indicates that an epilogue is undesired, that
/// tail folding 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:
enum class TailFoldingPolicyTy { None = 0, PreferFoldTail, MustFoldTail };
static cl::opt<TailFoldingPolicyTy> TailFoldingPolicy(
"tail-folding-policy", cl::init(TailFoldingPolicyTy::None), cl::Hidden,
cl::desc("Tail-folding preferences over creating an epilogue loop."),
cl::values(
clEnumValN(TailFoldingPolicyTy::None, "dont-fold-tail",
"Don't tail-fold loops."),
clEnumValN(TailFoldingPolicyTy::PreferFoldTail, "prefer-fold-tail",
"prefer tail-folding, otherwise create an epilogue when "
"appropriate."),
clEnumValN(TailFoldingPolicyTy::MustFoldTail, "must-fold-tail",
"always tail-fold, don't attempt vectorization if "
"tail-folding fails.")));
static cl::opt<TailFoldingStyle> ForceTailFoldingStyle(
"force-tail-folding-style", cl::desc("Force the tail folding style"),
cl::init(TailFoldingStyle::None),
cl::values(
clEnumValN(TailFoldingStyle::None, "none", "Disable tail folding"),
clEnumValN(
TailFoldingStyle::Data, "data",
"Create lane mask for data only, using active.lane.mask intrinsic"),
clEnumValN(TailFoldingStyle::DataWithoutLaneMask,
"data-without-lane-mask",
"Create lane mask with compare/stepvector"),
clEnumValN(TailFoldingStyle::DataAndControlFlow, "data-and-control",
"Create lane mask using active.lane.mask intrinsic, and use "
"it for both data and control flow"),
clEnumValN(TailFoldingStyle::DataWithEVL, "data-with-evl",
"Use predicated EVL instructions for tail folding. If EVL "
"is unsupported, fallback to data-without-lane-mask.")));
cl::opt<bool> llvm::EnableWideActiveLaneMask(
"enable-wide-lane-mask", cl::init(false), cl::Hidden,
cl::desc("Enable use of wide lane masks when used for control flow in "
"tail-folded loops"));
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> 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."));
cl::opt<unsigned> llvm::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"));
/// The number of stores in a loop that are allowed to need predication.
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<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> ForceOrderedReductions(
"force-ordered-reductions", cl::init(false), cl::Hidden,
cl::desc("Enable the vectorisation of loops with in-order (strict) "
"FP reductions"));
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> llvm::EnableVPlanNativePath(
"enable-vplan-native-path", cl::Hidden,
cl::desc("Enable VPlan-native vectorization path with "
"support for outer loop vectorization."));
cl::opt<bool>
llvm::VerifyEachVPlan("vplan-verify-each",
#ifdef EXPENSIVE_CHECKS
cl::init(true),
#else
cl::init(false),
#endif
cl::Hidden,
cl::desc("Verify VPlans after VPlan transforms."));
#if !defined(NDEBUG) || defined(LLVM_ENABLE_DUMP)
cl::opt<bool> llvm::VPlanPrintAfterAll(
"vplan-print-after-all", cl::init(false), cl::Hidden,
cl::desc("Print VPlans after all VPlan transformations."));
cl::list<std::string> llvm::VPlanPrintAfterPasses(
"vplan-print-after", cl::Hidden,
cl::desc("Print VPlans after specified VPlan transformations (regexp)."));
cl::opt<bool> llvm::VPlanPrintVectorRegionScope(
"vplan-print-vector-region-scope", cl::init(false), cl::Hidden,
cl::desc("Limit VPlan printing to vector loop region in "
"`-vplan-print-after*` if the plan has one."));
#endif
// 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"));
static cl::opt<cl::boolOrDefault> ForceSafeDivisor(
"force-widen-divrem-via-safe-divisor", cl::Hidden,
cl::desc(
"Override cost based safe divisor widening for div/rem instructions"));
static cl::opt<bool> EnableEarlyExitVectorization(
"enable-early-exit-vectorization", cl::init(true), cl::Hidden,
cl::desc(
"Enable vectorization of early exit loops with uncountable exits."));
// Likelyhood of bypassing the vectorized loop because there are zero trips left
// after prolog. See `emitIterationCountCheck`.
static constexpr uint32_t MinItersBypassWeights[] = {1, 127};
/// 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.
static bool hasIrregularType(Type *Ty, const DataLayout &DL) {
// Determine if an array of N elements of type Ty is "bitcast compatible"
// with a <N x Ty> vector.
// This is only true if there is no padding between the array elements.
return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty);
}
/// A version of ScalarEvolution::getSmallConstantTripCount that returns an
/// ElementCount to include loops whose trip count is a function of vscale.
static ElementCount getSmallConstantTripCount(ScalarEvolution *SE,
const Loop *L) {
if (unsigned ExpectedTC = SE->getSmallConstantTripCount(L))
return ElementCount::getFixed(ExpectedTC);
const SCEV *BTC = SE->getBackedgeTakenCount(L);
if (isa<SCEVCouldNotCompute>(BTC))
return ElementCount::getFixed(0);
const SCEV *ExitCount = SE->getTripCountFromExitCount(BTC, BTC->getType(), L);
if (isa<SCEVVScale>(ExitCount))
return ElementCount::getScalable(1);
const APInt *Scale;
if (match(ExitCount, m_scev_Mul(m_scev_APInt(Scale), m_SCEVVScale())))
if (cast<SCEVMulExpr>(ExitCount)->hasNoUnsignedWrap())
if (Scale->getActiveBits() <= 32)
return ElementCount::getScalable(Scale->getZExtValue());
return ElementCount::getFixed(0);
}
/// Get the maximum trip count for \p L from the SCEV unsigned range, excluding
/// zero from the range. Only valid when not folding the tail, as the minimum
/// iteration count check guards against a zero trip count. Returns 0 if
/// unknown.
static unsigned getMaxTCFromNonZeroRange(PredicatedScalarEvolution &PSE,
Loop *L) {
const SCEV *BTC = PSE.getBackedgeTakenCount();
if (isa<SCEVCouldNotCompute>(BTC))
return 0;
ScalarEvolution *SE = PSE.getSE();
const SCEV *TripCount = SE->getTripCountFromExitCount(BTC, BTC->getType(), L);
ConstantRange TCRange = SE->getUnsignedRange(TripCount);
APInt MaxTCFromRange = TCRange.getUnsignedMax();
if (!MaxTCFromRange.isZero() && MaxTCFromRange.getActiveBits() <= 32)
return MaxTCFromRange.getZExtValue();
return 0;
}
/// Returns "best known" trip count, which is either a valid positive trip count
/// or std::nullopt when an estimate cannot be made (including when the trip
/// count would overflow), 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 known, and if \p CanUseConstantMax.
/// 4) Returns the maximum trip count from the SCEV range excluding zero,
/// if \p CanUseConstantMax and \p CanExcludeZeroTrips.
/// 5) Returns std::nullopt if all of the above failed.
static std::optional<ElementCount>
getSmallBestKnownTC(PredicatedScalarEvolution &PSE, Loop *L,
bool CanUseConstantMax = true,
bool CanExcludeZeroTrips = false) {
// Check if exact trip count is known.
if (auto ExpectedTC = getSmallConstantTripCount(PSE.getSE(), L))
return ExpectedTC;
// Check if there is an expected trip count available from profile data.
if (LoopVectorizeWithBlockFrequency)
if (auto EstimatedTC = getLoopEstimatedTripCount(L))
return ElementCount::getFixed(*EstimatedTC);
if (!CanUseConstantMax)
return std::nullopt;
// Check if upper bound estimate is known.
if (unsigned ExpectedTC = PSE.getSmallConstantMaxTripCount())
return ElementCount::getFixed(ExpectedTC);
// Get the maximum trip count from the SCEV range excluding zero. This is
// only safe when not folding the tail, as the minimum iteration count check
// prevents entering the vector loop with a zero trip count.
if (CanUseConstantMax && CanExcludeZeroTrips)
if (unsigned RefinedTC = getMaxTCFromNonZeroRange(PSE, L))
return ElementCount::getFixed(RefinedTC);
return std::nullopt;
}
namespace {
// Forward declare GeneratedRTChecks.
class GeneratedRTChecks;
using SCEV2ValueTy = DenseMap<const SCEV *, Value *>;
} // namespace
namespace llvm {
AnalysisKey ShouldRunExtraVectorPasses::Key;
/// 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 TargetTransformInfo *TTI, AssumptionCache *AC,
ElementCount VecWidth, unsigned UnrollFactor,
LoopVectorizationCostModel *CM,
GeneratedRTChecks &RTChecks, VPlan &Plan)
: OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TTI(TTI), AC(AC),
VF(VecWidth), UF(UnrollFactor), Builder(PSE.getSE()->getContext()),
Cost(CM), RTChecks(RTChecks), Plan(Plan),
VectorPHVPBB(cast<VPBasicBlock>(
Plan.getVectorLoopRegion()->getSinglePredecessor())) {}
virtual ~InnerLoopVectorizer() = default;
/// Creates a basic block for the scalar preheader. Both
/// EpilogueVectorizerMainLoop and EpilogueVectorizerEpilogueLoop overwrite
/// the method to create additional blocks and checks needed for epilogue
/// vectorization.
virtual BasicBlock *createVectorizedLoopSkeleton();
/// Fix the vectorized code, taking care of header phi's, and more.
void fixVectorizedLoop(VPTransformState &State);
/// Fix the non-induction PHIs in \p Plan.
void fixNonInductionPHIs(VPTransformState &State);
protected:
friend class LoopVectorizationPlanner;
/// Create and return a new IR basic block for the scalar preheader whose name
/// is prefixed with \p Prefix.
BasicBlock *createScalarPreheader(StringRef Prefix);
/// Allow subclasses to override and print debug traces before/after vplan
/// execution, when trace information is requested.
virtual void printDebugTracesAtStart() {}
virtual void printDebugTracesAtEnd() {}
/// 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;
/// Target Transform Info.
const TargetTransformInfo *TTI;
/// Assumption Cache.
AssumptionCache *AC;
/// 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 profitablity analysis.
LoopVectorizationCostModel *Cost;
/// Structure to hold information about generated runtime checks, responsible
/// for cleaning the checks, if vectorization turns out unprofitable.
GeneratedRTChecks &RTChecks;
VPlan &Plan;
/// The vector preheader block of \p Plan, used as target for check blocks
/// introduced during skeleton creation.
VPBasicBlock *VectorPHVPBB;
};
/// Encapsulate information regarding vectorization of a loop and its epilogue.
/// This information is meant to be updated and used across two stages of
/// epilogue vectorization.
struct EpilogueLoopVectorizationInfo {
ElementCount MainLoopVF = ElementCount::getFixed(0);
unsigned MainLoopUF = 0;
ElementCount EpilogueVF = ElementCount::getFixed(0);
unsigned EpilogueUF = 0;
BasicBlock *MainLoopIterationCountCheck = nullptr;
BasicBlock *EpilogueIterationCountCheck = nullptr;
Value *VectorTripCount = nullptr;
VPlan &EpiloguePlan;
EpilogueLoopVectorizationInfo(ElementCount MVF, unsigned MUF,
ElementCount EVF, unsigned EUF,
VPlan &EpiloguePlan)
: MainLoopVF(MVF), MainLoopUF(MUF), EpilogueVF(EVF), EpilogueUF(EUF),
EpiloguePlan(EpiloguePlan) {
assert(EUF == 1 &&
"A high UF for the epilogue loop is likely not beneficial.");
}
};
/// An extension of the inner loop vectorizer that creates a skeleton for a
/// vectorized loop that has its epilogue (residual) also vectorized.
/// The idea is to run the vplan on a given loop twice, firstly to setup the
/// skeleton and vectorize the main loop, and secondly to complete the skeleton
/// from the first step and vectorize the epilogue. This is achieved by
/// deriving two concrete strategy classes from this base class and invoking
/// them in succession from the loop vectorizer planner.
class InnerLoopAndEpilogueVectorizer : public InnerLoopVectorizer {
public:
InnerLoopAndEpilogueVectorizer(
Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI,
DominatorTree *DT, const TargetTransformInfo *TTI, AssumptionCache *AC,
EpilogueLoopVectorizationInfo &EPI, LoopVectorizationCostModel *CM,
GeneratedRTChecks &Checks, VPlan &Plan, ElementCount VecWidth,
ElementCount MinProfitableTripCount, unsigned UnrollFactor)
: InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TTI, AC, VecWidth,
UnrollFactor, CM, Checks, Plan),
EPI(EPI), MinProfitableTripCount(MinProfitableTripCount) {}
/// Holds and updates state information required to vectorize the main loop
/// and its epilogue in two separate passes. This setup helps us avoid
/// regenerating and recomputing runtime safety checks. It also helps us to
/// shorten the iteration-count-check path length for the cases where the
/// iteration count of the loop is so small that the main vector loop is
/// completely skipped.
EpilogueLoopVectorizationInfo &EPI;
protected:
ElementCount MinProfitableTripCount;
};
/// A specialized derived class of inner loop vectorizer that performs
/// vectorization of *main* loops in the process of vectorizing loops and their
/// epilogues.
class EpilogueVectorizerMainLoop : public InnerLoopAndEpilogueVectorizer {
public:
EpilogueVectorizerMainLoop(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
LoopInfo *LI, DominatorTree *DT,
const TargetTransformInfo *TTI,
AssumptionCache *AC,
EpilogueLoopVectorizationInfo &EPI,
LoopVectorizationCostModel *CM,
GeneratedRTChecks &Check, VPlan &Plan)
: InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TTI, AC, EPI, CM,
Check, Plan, EPI.MainLoopVF,
EPI.MainLoopVF, EPI.MainLoopUF) {}
protected:
void printDebugTracesAtStart() override;
void printDebugTracesAtEnd() override;
};
// A specialized derived class of inner loop vectorizer that performs
// vectorization of *epilogue* loops in the process of vectorizing loops and
// their epilogues.
class EpilogueVectorizerEpilogueLoop : public InnerLoopAndEpilogueVectorizer {
public:
EpilogueVectorizerEpilogueLoop(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
LoopInfo *LI, DominatorTree *DT,
const TargetTransformInfo *TTI,
AssumptionCache *AC,
EpilogueLoopVectorizationInfo &EPI,
LoopVectorizationCostModel *CM,
GeneratedRTChecks &Checks, VPlan &Plan)
: InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TTI, AC, EPI, CM,
Checks, Plan, EPI.EpilogueVF,
EPI.EpilogueVF, EPI.EpilogueUF) {}
/// Implements the interface for creating a vectorized skeleton using the
/// *epilogue loop* strategy (i.e., the second pass of VPlan execution).
BasicBlock *createVectorizedLoopSkeleton() final;
protected:
void printDebugTracesAtStart() override;
void printDebugTracesAtEnd() override;
};
} // end namespace llvm
/// Look for a meaningful debug location on the instruction or its operands.
static DebugLoc getDebugLocFromInstOrOperands(Instruction *I) {
if (!I)
return DebugLoc::getUnknown();
DebugLoc Empty;
if (I->getDebugLoc() != Empty)
return I->getDebugLoc();
for (Use &Op : I->operands()) {
if (Instruction *OpInst = dyn_cast<Instruction>(Op))
if (OpInst->getDebugLoc() != Empty)
return OpInst->getDebugLoc();
}
return I->getDebugLoc();
}
/// Write a \p DebugMsg about vectorization to the debug output stream. If \p I
/// is passed, the message relates to that particular instruction.
#ifndef NDEBUG
static void debugVectorizationMessage(const StringRef Prefix,
const StringRef DebugMsg,
Instruction *I) {
dbgs() << "LV: " << Prefix << 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. If \p DL is passed, use it as debug location for
/// the remark. \return the remark object that can be streamed to.
static OptimizationRemarkAnalysis
createLVAnalysis(const char *PassName, StringRef RemarkName,
const Loop *TheLoop, Instruction *I, DebugLoc DL = {}) {
BasicBlock *CodeRegion = I ? I->getParent() : TheLoop->getHeader();
// If debug location is attached to the instruction, use it. Otherwise if DL
// was not provided, use the loop's.
if (I && I->getDebugLoc())
DL = I->getDebugLoc();
else if (!DL)
DL = TheLoop->getStartLoc();
return OptimizationRemarkAnalysis(PassName, RemarkName, DL, CodeRegion);
}
namespace llvm {
/// Return the runtime value for VF.
Value *getRuntimeVF(IRBuilderBase &B, Type *Ty, ElementCount VF) {
return B.CreateElementCount(Ty, VF);
}
void reportVectorizationFailure(const StringRef DebugMsg,
const StringRef OREMsg, const StringRef ORETag,
OptimizationRemarkEmitter *ORE, Loop *TheLoop,
Instruction *I) {
LLVM_DEBUG(debugVectorizationMessage("Not vectorizing: ", DebugMsg, I));
LoopVectorizeHints Hints(TheLoop, false /* doesn't matter */, *ORE);
ORE->emit(
createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop, I)
<< "loop not vectorized: " << OREMsg);
}
void reportVectorizationInfo(const StringRef Msg, const StringRef ORETag,
OptimizationRemarkEmitter *ORE,
const Loop *TheLoop, Instruction *I, DebugLoc DL) {
LLVM_DEBUG(debugVectorizationMessage("", Msg, I));
LoopVectorizeHints Hints(TheLoop, false /* doesn't matter */, *ORE);
ORE->emit(createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop,
I, DL)
<< Msg);
}
/// Report successful vectorization of the loop. In case an outer loop is
/// vectorized, prepend "outer" to the vectorization remark.
static void reportVectorization(OptimizationRemarkEmitter *ORE, Loop *TheLoop,
VectorizationFactor VF, unsigned IC) {
LLVM_DEBUG(debugVectorizationMessage(
"Vectorizing: ", TheLoop->isInnermost() ? "innermost loop" : "outer loop",
nullptr));
StringRef LoopType = TheLoop->isInnermost() ? "" : "outer ";
ORE->emit([&]() {
return OptimizationRemark(LV_NAME, "Vectorized", TheLoop->getStartLoc(),
TheLoop->getHeader())
<< "vectorized " << LoopType << "loop (vectorization width: "
<< ore::NV("VectorizationFactor", VF.Width)
<< ", interleaved count: " << ore::NV("InterleaveCount", IC) << ")";
});
}
} // end namespace llvm
namespace llvm {
// Loop vectorization cost-model hints how the epilogue/tail loop should be
// lowered.
enum EpilogueLowering {
// The default: allowing epilogues.
CM_EpilogueAllowed,
// Vectorization with OptForSize: don't allow epilogues.
CM_EpilogueNotAllowedOptSize,
// 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_EpilogueNotAllowedLowTripLoop,
// Loop hint indicating an epilogue is undesired, apply tail folding.
CM_EpilogueNotNeededFoldTail,
// Directive indicating we must either fold the epilogue/tail or not vectorize
CM_EpilogueNotAllowedFoldTail
};
/// 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 {
friend class LoopVectorizationPlanner;
public:
LoopVectorizationCostModel(EpilogueLowering SEL, Loop *L,
PredicatedScalarEvolution &PSE, LoopInfo *LI,
LoopVectorizationLegality *Legal,
const TargetTransformInfo &TTI,
const TargetLibraryInfo *TLI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE,
std::function<BlockFrequencyInfo &()> GetBFI,
const Function *F, const LoopVectorizeHints *Hints,
InterleavedAccessInfo &IAI,
VFSelectionContext &Config)
: Config(Config), EpilogueLoweringStatus(SEL), TheLoop(L), PSE(PSE),
LI(LI), Legal(Legal), TTI(TTI), TLI(TLI), AC(AC), ORE(ORE),
GetBFI(GetBFI), TheFunction(F), Hints(Hints), InterleaveInfo(IAI) {}
/// \return An upper bound for the vectorization factors (both fixed and
/// scalable). If the factors are 0, vectorization and interleaving should be
/// avoided up front.
FixedScalableVFPair computeMaxVF(ElementCount UserVF, unsigned UserIC);
/// 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 call may be vectorized in different ways depending on whether we have
/// vectorized variants available and whether the target supports masking.
/// This function analyzes all calls in the function at the supplied VF,
/// makes a decision based on the costs of available options, and stores that
/// decision in a map for use in planning and plan execution.
void setVectorizedCallDecision(ElementCount VF);
/// Collect values we want to ignore in the cost model.
void collectValuesToIgnore();
/// \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.");
assert(
TheLoop->isInnermost() &&
"cost-model should not be used for outer loops (in VPlan-native path)");
auto Scalars = InstsToScalarize.find(VF);
assert(Scalars != InstsToScalarize.end() &&
"VF not yet analyzed for scalarization profitability");
return Scalars->second.contains(I);
}
/// Returns true if \p I is known to be uniform after vectorization.
bool isUniformAfterVectorization(Instruction *I, ElementCount VF) const {
assert(
TheLoop->isInnermost() &&
"cost-model should not be used for outer loops (in VPlan-native path)");
// If VF is scalar, then all instructions are trivially uniform.
if (VF.isScalar())
return true;
// Pseudo probes must be duplicated per vector lane so that the
// profiled loop trip count is not undercounted.
if (isa<PseudoProbeInst>(I))
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 {
assert(
TheLoop->isInnermost() &&
"cost-model should not be used for outer loops (in VPlan-native path)");
if (VF.isScalar())
return true;
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 {
const auto &MinBWs = Config.getMinimalBitwidths();
// Truncs must truncate at most to their destination type.
if (isa_and_nonnull<TruncInst>(I) && MinBWs.contains(I) &&
I->getType()->getScalarSizeInBits() < MinBWs.lookup(I))
return false;
return VF.isVector() && MinBWs.contains(I) &&
!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,
CM_VectorCall,
CM_IntrinsicCall
};
/// 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,
InstructionCost Cost) {
assert(VF.isVector() && "Expected VF >=2");
WideningDecisions[{I, VF}] = {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,
InstructionCost Cost) {
assert(VF.isVector() && "Expected VF >=2");
/// Broadcast this decicion to all instructions inside the group.
/// When interleaving, the cost will only be assigned one instruction, the
/// insert position. For other cases, add the appropriate fraction of the
/// total cost to each instruction. This ensures accurate costs are used,
/// even if the insert position instruction is not used.
InstructionCost InsertPosCost = Cost;
InstructionCost OtherMemberCost = 0;
if (W != CM_Interleave)
OtherMemberCost = InsertPosCost = Cost / Grp->getNumMembers();
;
for (auto *I : Grp->members()) {
if (Grp->getInsertPos() == I)
WideningDecisions[{I, VF}] = {W, InsertPosCost};
else
WideningDecisions[{I, VF}] = {W, OtherMemberCost};
}
}
/// 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) const {
assert(VF.isVector() && "Expected VF to be a vector VF");
assert(
TheLoop->isInnermost() &&
"cost-model should not be used for outer loops (in VPlan-native path)");
std::pair<Instruction *, ElementCount> InstOnVF(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.
InstructionCost getWideningCost(Instruction *I, ElementCount VF) {
assert(VF.isVector() && "Expected VF >=2");
std::pair<Instruction *, ElementCount> InstOnVF(I, VF);
assert(WideningDecisions.contains(InstOnVF) &&
"The cost is not calculated");
return WideningDecisions[InstOnVF].second;
}
struct CallWideningDecision {
InstWidening Kind;
Function *Variant;
Intrinsic::ID IID;
std::optional<unsigned> MaskPos;
InstructionCost Cost;
};
void setCallWideningDecision(CallInst *CI, ElementCount VF, InstWidening Kind,
Function *Variant, Intrinsic::ID IID,
std::optional<unsigned> MaskPos,
InstructionCost Cost) {
assert(!VF.isScalar() && "Expected vector VF");
CallWideningDecisions[{CI, VF}] = {Kind, Variant, IID, MaskPos, Cost};
}
CallWideningDecision getCallWideningDecision(CallInst *CI,
ElementCount VF) const {
assert(!VF.isScalar() && "Expected vector VF");
auto I = CallWideningDecisions.find({CI, VF});
if (I == CallWideningDecisions.end())
return {CM_Unknown, nullptr, Intrinsic::not_intrinsic, std::nullopt, 0};
return I->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(Trunc->getSrcTy(), VF);
Type *DestTy = toVectorTy(Trunc->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 values that will not be widened, including Uniforms, Scalars, and
/// Instructions to Scalarize 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.
/// Also make a decision on what to do about call instructions in the loop
/// at that VF -- scalarize, call a known vector routine, or call a
/// vector intrinsic.
void collectNonVectorizedAndSetWideningDecisions(ElementCount VF) {
// Do the analysis once.
if (VF.isScalar() || Uniforms.contains(VF))
return;
setCostBasedWideningDecision(VF);
collectLoopUniforms(VF);
setVectorizedCallDecision(VF);
collectLoopScalars(VF);
collectInstsToScalarize(VF);
}
/// Given costs for both strategies, return true if the scalar predication
/// lowering should be used for div/rem. This incorporates an override
/// option so it is not simply a cost comparison.
bool isDivRemScalarWithPredication(InstructionCost ScalarCost,
InstructionCost SafeDivisorCost) const {
switch (ForceSafeDivisor) {
case cl::BOU_UNSET:
return ScalarCost < SafeDivisorCost;
case cl::BOU_TRUE:
return false;
case cl::BOU_FALSE:
return true;
}
llvm_unreachable("impossible case value");
}
/// Returns true if \p I is an instruction which requires predication and
/// for which our chosen predication strategy is scalarization (i.e. we
/// don't have an alternate strategy such as masking available).
/// \p VF is the vectorization factor that will be used to vectorize \p I.
bool isScalarWithPredication(Instruction *I, ElementCount VF);
/// Wrapper function for LoopVectorizationLegality::isMaskRequired,
/// that passes the Instruction \p I and if we fold tail.
bool isMaskRequired(Instruction *I) const;
/// Returns true if \p I is an instruction that needs to be predicated
/// at runtime. The result is independent of the predication mechanism.
/// Superset of instructions that return true for isScalarWithPredication.
bool isPredicatedInst(Instruction *I) const;
/// A helper function that returns how much we should divide the cost of a
/// predicated block by. Typically this is the reciprocal of the block
/// probability, i.e. if we return X we are assuming the predicated block will
/// execute once for every X iterations of the loop header so the block should
/// only contribute 1/X of its cost to the total cost calculation, but when
/// optimizing for code size it will just be 1 as code size costs don't depend
/// on execution probabilities.
///
/// Note that if a block wasn't originally predicated but was predicated due
/// to tail folding, the divisor will still be 1 because it will execute for
/// every iteration of the loop header.
inline uint64_t
getPredBlockCostDivisor(TargetTransformInfo::TargetCostKind CostKind,
const BasicBlock *BB);
/// Returns true if an artificially high cost for emulated masked memrefs
/// should be used.
bool useEmulatedMaskMemRefHack(Instruction *I, ElementCount VF);
/// Return the costs for our two available strategies for lowering a
/// div/rem operation which requires speculating at least one lane.
/// First result is for scalarization (will be invalid for scalable
/// vectors); second is for the safe-divisor strategy.
std::pair<InstructionCost, InstructionCost>
getDivRemSpeculationCost(Instruction *I, ElementCount VF);
/// Returns true if \p I is a memory instruction with consecutive memory
/// access that can be widened.
bool memoryInstructionCanBeWidened(Instruction *I, ElementCount VF);
/// 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) const;
/// Check if \p Instr belongs to any interleaved access group.
bool isAccessInterleaved(Instruction *Instr) const {
return InterleaveInfo.isInterleaved(Instr);
}
/// Get the interleaved access group that \p Instr belongs to.
const InterleaveGroup<Instruction> *
getInterleavedAccessGroup(Instruction *Instr) const {
return InterleaveInfo.getInterleaveGroup(Instr);
}
/// Returns true if we're required to use a scalar epilogue for at least
/// the final iteration of the original loop.
bool requiresScalarEpilogue(bool IsVectorizing) const {
if (!isEpilogueAllowed()) {
LLVM_DEBUG(dbgs() << "LV: Loop does not require scalar epilogue\n");
return false;
}
// If we might exit from anywhere but the latch and early exit vectorization
// is disabled, we must run the exiting iteration in scalar form.
if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch() &&
!(EnableEarlyExitVectorization && Legal->hasUncountableEarlyExit())) {
LLVM_DEBUG(dbgs() << "LV: Loop requires scalar epilogue: not exiting "
"from latch block\n");
return true;
}
if (IsVectorizing && InterleaveInfo.requiresScalarEpilogue()) {
LLVM_DEBUG(dbgs() << "LV: Loop requires scalar epilogue: "
"interleaved group requires scalar epilogue\n");
return true;
}
LLVM_DEBUG(dbgs() << "LV: Loop does not require scalar epilogue\n");
return false;
}
/// Returns true if an epilogue is allowed (e.g., not prevented by
/// optsize or a loop hint annotation).
bool isEpilogueAllowed() const {
return EpilogueLoweringStatus == CM_EpilogueAllowed;
}
/// Returns true if tail-folding is preferred over an epilogue.
bool preferTailFoldedLoop() const {
return EpilogueLoweringStatus == CM_EpilogueNotNeededFoldTail ||
EpilogueLoweringStatus == CM_EpilogueNotAllowedFoldTail;
}
/// Returns the TailFoldingStyle that is best for the current loop.
TailFoldingStyle getTailFoldingStyle() const {
return ChosenTailFoldingStyle;
}
/// Selects and saves TailFoldingStyle.
/// \param IsScalableVF true if scalable vector factors enabled.
/// \param UserIC User specific interleave count.
void setTailFoldingStyle(bool IsScalableVF, unsigned UserIC) {
assert(ChosenTailFoldingStyle == TailFoldingStyle::None &&
"Tail folding must not be selected yet.");
if (!Legal->canFoldTailByMasking()) {
ChosenTailFoldingStyle = TailFoldingStyle::None;
return;
}
// Default to TTI preference, but allow command line override.
ChosenTailFoldingStyle = TTI.getPreferredTailFoldingStyle();
if (ForceTailFoldingStyle.getNumOccurrences())
ChosenTailFoldingStyle = ForceTailFoldingStyle.getValue();
if (ChosenTailFoldingStyle != TailFoldingStyle::DataWithEVL)
return;
// Override EVL styles if needed.
// FIXME: Investigate opportunity for fixed vector factor.
bool EVLIsLegal = UserIC <= 1 && IsScalableVF &&
TTI.hasActiveVectorLength() && !EnableVPlanNativePath;
if (EVLIsLegal)
return;
// If for some reason EVL mode is unsupported, fallback to an epilogue
// if it's allowed, or DataWithoutLaneMask otherwise.
if (EpilogueLoweringStatus == CM_EpilogueAllowed ||
EpilogueLoweringStatus == CM_EpilogueNotNeededFoldTail)
ChosenTailFoldingStyle = TailFoldingStyle::None;
else
ChosenTailFoldingStyle = TailFoldingStyle::DataWithoutLaneMask;
LLVM_DEBUG(
dbgs() << "LV: Preference for VP intrinsics indicated. Will "
"not try to generate VP Intrinsics "
<< (UserIC > 1
? "since interleave count specified is greater than 1.\n"
: "due to non-interleaving reasons.\n"));
}
/// Returns true if all loop blocks should be masked to fold tail loop.
bool foldTailByMasking() const {
return getTailFoldingStyle() != TailFoldingStyle::None;
}
/// Returns true if the use of wide lane masks is requested and the loop is
/// using tail-folding with a lane mask for control flow.
bool useWideActiveLaneMask() const {
if (!EnableWideActiveLaneMask)
return false;
return getTailFoldingStyle() == TailFoldingStyle::DataAndControlFlow;
}
/// Returns true if the instructions in this block requires predication
/// for any reason, e.g. because tail folding now requires a predicate
/// or because the block in the original loop was predicated.
bool blockNeedsPredicationForAnyReason(BasicBlock *BB) const {
return foldTailByMasking() || Legal->blockNeedsPredication(BB);
}
/// Returns true if VP intrinsics with explicit vector length support should
/// be generated in the tail folded loop.
bool foldTailWithEVL() const {
return getTailFoldingStyle() == TailFoldingStyle::DataWithEVL;
}
/// Returns true if the predicated reduction select should be used to set the
/// incoming value for the reduction phi.
bool usePredicatedReductionSelect(RecurKind RecurrenceKind) const {
// Force to use predicated reduction select since the EVL of the
// second-to-last iteration might not be VF*UF.
if (foldTailWithEVL())
return true;
// Note: For FindLast recurrences we prefer a predicated select to simplify
// matching in handleFindLastReductions(), rather than handle multiple
// cases.
if (RecurrenceDescriptor::isFindLastRecurrenceKind(RecurrenceKind))
return true;
return PreferPredicatedReductionSelect ||
TTI.preferPredicatedReductionSelect();
}
/// 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.
InstructionCost getVectorIntrinsicCost(CallInst *CI, ElementCount VF) const;
/// 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.
InstructionCost getVectorCallCost(CallInst *CI, ElementCount VF) const;
/// Invalidates decisions already taken by the cost model.
void invalidateCostModelingDecisions() {
WideningDecisions.clear();
CallWideningDecisions.clear();
Uniforms.clear();
Scalars.clear();
}
/// 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.
InstructionCost expectedCost(ElementCount VF);
/// Returns true if epilogue vectorization is considered profitable, and
/// false otherwise.
/// \p VF is the vectorization factor chosen for the original loop.
/// \p Multiplier is an aditional scaling factor applied to VF before
/// comparing to EpilogueVectorizationMinVF.
bool isEpilogueVectorizationProfitable(const ElementCount VF,
const unsigned IC) const;
/// Returns the execution time cost of an instruction for a given vector
/// width. Vector width of one means scalar.
InstructionCost getInstructionCost(Instruction *I, ElementCount VF);
/// Return the cost of instructions in an inloop reduction pattern, if I is
/// part of that pattern.
std::optional<InstructionCost> getReductionPatternCost(Instruction *I,
ElementCount VF,
Type *VectorTy) const;
/// Returns true if \p Op should be considered invariant and if it is
/// trivially hoistable.
bool shouldConsiderInvariant(Value *Op);
private:
unsigned NumPredStores = 0;
/// VF selection state independent of cost-modeling decisions.
VFSelectionContext &Config;
/// Calculate vectorization cost of memory instruction \p I.
InstructionCost getMemoryInstructionCost(Instruction *I, ElementCount VF);
/// The cost computation for scalarized memory instruction.
InstructionCost getMemInstScalarizationCost(Instruction *I, ElementCount VF);
/// The cost computation for interleaving group of memory instructions.
InstructionCost getInterleaveGroupCost(Instruction *I, ElementCount VF);
/// The cost computation for Gather/Scatter instruction.
InstructionCost getGatherScatterCost(Instruction *I, ElementCount VF);
/// The cost computation for widening instruction \p I with consecutive
/// memory access.
InstructionCost 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)
InstructionCost getUniformMemOpCost(Instruction *I, ElementCount VF);
/// Estimate the overhead of scalarizing an instruction. This is a
/// convenience wrapper for the type-based getScalarizationOverhead API.
InstructionCost getScalarizationOverhead(Instruction *I,
ElementCount VF) const;
/// A type representing the costs for instructions if they were to be
/// scalarized rather than vectorized. The entries are Instruction-Cost
/// pairs.
using ScalarCostsTy = MapVector<Instruction *, InstructionCost>;
/// A set containing all BasicBlocks that are known to present after
/// vectorization as a predicated block.
DenseMap<ElementCount, 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.
EpilogueLowering EpilogueLoweringStatus = CM_EpilogueAllowed;
/// Control finally chosen tail folding style.
TailFoldingStyle ChosenTailFoldingStyle = TailFoldingStyle::None;
/// 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.
MapVector<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;
/// 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.
InstructionCost 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. collectLoopScalars should only add non-uniform nodes
/// to the list if they are used by a load/store instruction that is marked as
/// CM_Scalarize. 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, InstructionCost>>;
DecisionList WideningDecisions;
using CallDecisionList =
DenseMap<std::pair<CallInst *, ElementCount>, CallWideningDecision>;
CallDecisionList CallWideningDecisions;
/// 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) ||
getWideningDecision(I, VF) == CM_Scalarize ||
(isa<CallInst>(I) &&
getCallWideningDecision(cast<CallInst>(I), VF).Kind == CM_Scalarize))
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.contains(VF) || !isScalarAfterVectorization(I, VF);
};
/// Returns a range containing only operands needing to be extracted.
SmallVector<Value *, 4> filterExtractingOperands(Instruction::op_range Ops,
ElementCount VF) const {
SmallPtrSet<const Value *, 4> UniqueOperands;
SmallVector<Value *, 4> Res;
for (Value *Op : Ops) {
if (isa<Constant>(Op) || !UniqueOperands.insert(Op).second ||
!needsExtract(Op, VF))
continue;
Res.push_back(Op);
}
return Res;
}
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;
/// Assumption cache.
AssumptionCache *AC;
/// Interface to emit optimization remarks.
OptimizationRemarkEmitter *ORE;
/// A function to lazily fetch BlockFrequencyInfo. This avoids computing it
/// unless necessary, e.g. when the loop isn't legal to vectorize or when
/// there is no predication.
std::function<BlockFrequencyInfo &()> GetBFI;
/// The BlockFrequencyInfo returned from GetBFI.
BlockFrequencyInfo *BFI = nullptr;
/// Returns the BlockFrequencyInfo for the function if cached, otherwise
/// fetches it via GetBFI. Avoids an indirect call to the std::function.
BlockFrequencyInfo &getBFI() {
if (!BFI)
BFI = &GetBFI();
return *BFI;
}
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
namespace {
/// Helper struct to manage generating runtime checks for vectorization.
///
/// The runtime checks are created up-front in temporary blocks to allow better
/// estimating the cost and un-linked from the existing IR. After deciding to
/// vectorize, the checks are moved back. If deciding not to vectorize, the
/// temporary blocks are completely removed.
class GeneratedRTChecks {
/// Basic block which contains the generated SCEV checks, if any.
BasicBlock *SCEVCheckBlock = nullptr;
/// The value representing the result of the generated SCEV checks. If it is
/// nullptr no SCEV checks have been generated.
Value *SCEVCheckCond = nullptr;
/// Basic block which contains the generated memory runtime checks, if any.
BasicBlock *MemCheckBlock = nullptr;
/// The value representing the result of the generated memory runtime checks.
/// If it is nullptr no memory runtime checks have been generated.
Value *MemRuntimeCheckCond = nullptr;
DominatorTree *DT;
LoopInfo *LI;
TargetTransformInfo *TTI;
SCEVExpander SCEVExp;
SCEVExpander MemCheckExp;
bool CostTooHigh = false;
Loop *OuterLoop = nullptr;
PredicatedScalarEvolution &PSE;
/// The kind of cost that we are calculating
TTI::TargetCostKind CostKind;
public:
GeneratedRTChecks(PredicatedScalarEvolution &PSE, DominatorTree *DT,
LoopInfo *LI, TargetTransformInfo *TTI,
TTI::TargetCostKind CostKind)
: DT(DT), LI(LI), TTI(TTI),
SCEVExp(*PSE.getSE(), "scev.check", /*PreserveLCSSA=*/false),
MemCheckExp(*PSE.getSE(), "scev.check", /*PreserveLCSSA=*/false),
PSE(PSE), CostKind(CostKind) {}
/// Generate runtime checks in SCEVCheckBlock and MemCheckBlock, so we can
/// accurately estimate the cost of the runtime checks. The blocks are
/// un-linked from the IR and are added back during vector code generation. If
/// there is no vector code generation, the check blocks are removed
/// completely.
void create(Loop *L, const LoopAccessInfo &LAI,
const SCEVPredicate &UnionPred, ElementCount VF, unsigned IC,
OptimizationRemarkEmitter &ORE) {
// Hard cutoff to limit compile-time increase in case a very large number of
// runtime checks needs to be generated.
// TODO: Skip cutoff if the loop is guaranteed to execute, e.g. due to
// profile info.
CostTooHigh =
LAI.getNumRuntimePointerChecks() > VectorizeMemoryCheckThreshold;
if (CostTooHigh) {
// Mark runtime checks as never succeeding when they exceed the threshold.
MemRuntimeCheckCond = ConstantInt::getTrue(L->getHeader()->getContext());
SCEVCheckCond = ConstantInt::getTrue(L->getHeader()->getContext());
ORE.emit([&]() {
return OptimizationRemarkAnalysisAliasing(
DEBUG_TYPE, "TooManyMemoryRuntimeChecks", L->getStartLoc(),
L->getHeader())
<< "loop not vectorized: too many memory checks needed";
});
LLVM_DEBUG(dbgs() << "LV: Too many memory checks needed.\n");
return;
}
BasicBlock *LoopHeader = L->getHeader();
BasicBlock *Preheader = L->getLoopPreheader();
// Use SplitBlock to create blocks for SCEV & memory runtime checks to
// ensure the blocks are properly added to LoopInfo & DominatorTree. Those
// may be used by SCEVExpander. The blocks will be un-linked from their
// predecessors and removed from LI & DT at the end of the function.
if (!UnionPred.isAlwaysTrue()) {
SCEVCheckBlock = SplitBlock(Preheader, Preheader->getTerminator(), DT, LI,
nullptr, "vector.scevcheck");
SCEVCheckCond = SCEVExp.expandCodeForPredicate(
&UnionPred, SCEVCheckBlock->getTerminator());
if (isa<Constant>(SCEVCheckCond)) {
// Clean up directly after expanding the predicate to a constant, to
// avoid further expansions re-using anything left over from SCEVExp.
SCEVExpanderCleaner SCEVCleaner(SCEVExp);
SCEVCleaner.cleanup();
}
}
const auto &RtPtrChecking = *LAI.getRuntimePointerChecking();
if (RtPtrChecking.Need) {
auto *Pred = SCEVCheckBlock ? SCEVCheckBlock : Preheader;
MemCheckBlock = SplitBlock(Pred, Pred->getTerminator(), DT, LI, nullptr,
"vector.memcheck");
auto DiffChecks = RtPtrChecking.getDiffChecks();
if (DiffChecks) {
Value *RuntimeVF = nullptr;
MemRuntimeCheckCond = addDiffRuntimeChecks(
MemCheckBlock->getTerminator(), *DiffChecks, MemCheckExp,
[VF, &RuntimeVF](IRBuilderBase &B, unsigned Bits) {
if (!RuntimeVF)
RuntimeVF = getRuntimeVF(B, B.getIntNTy(Bits), VF);
return RuntimeVF;
},
IC);
} else {
MemRuntimeCheckCond = addRuntimeChecks(
MemCheckBlock->getTerminator(), L, RtPtrChecking.getChecks(),
MemCheckExp, VectorizerParams::HoistRuntimeChecks);
}
assert(MemRuntimeCheckCond &&
"no RT checks generated although RtPtrChecking "
"claimed checks are required");
}
SCEVExp.eraseDeadInstructions(SCEVCheckCond);
if (!MemCheckBlock && !SCEVCheckBlock)
return;
// Unhook the temporary block with the checks, update various places
// accordingly.
if (SCEVCheckBlock)
SCEVCheckBlock->replaceAllUsesWith(Preheader);
if (MemCheckBlock)
MemCheckBlock->replaceAllUsesWith(Preheader);
if (SCEVCheckBlock) {
SCEVCheckBlock->getTerminator()->moveBefore(
Preheader->getTerminator()->getIterator());
auto *UI = new UnreachableInst(Preheader->getContext(), SCEVCheckBlock);
UI->setDebugLoc(DebugLoc::getTemporary());
Preheader->getTerminator()->eraseFromParent();
}
if (MemCheckBlock) {
MemCheckBlock->getTerminator()->moveBefore(
Preheader->getTerminator()->getIterator());
auto *UI = new UnreachableInst(Preheader->getContext(), MemCheckBlock);
UI->setDebugLoc(DebugLoc::getTemporary());
Preheader->getTerminator()->eraseFromParent();
}
DT->changeImmediateDominator(LoopHeader, Preheader);
if (MemCheckBlock) {
DT->eraseNode(MemCheckBlock);
LI->removeBlock(MemCheckBlock);
}
if (SCEVCheckBlock) {
DT->eraseNode(SCEVCheckBlock);
LI->removeBlock(SCEVCheckBlock);
}
// Outer loop is used as part of the later cost calculations.
OuterLoop = L->getParentLoop();
}
InstructionCost getCost() {
if (SCEVCheckBlock || MemCheckBlock)
LLVM_DEBUG(dbgs() << "Calculating cost of runtime checks:\n");
if (CostTooHigh) {
InstructionCost Cost;
Cost.setInvalid();
LLVM_DEBUG(dbgs() << " number of checks exceeded threshold\n");
return Cost;
}
InstructionCost RTCheckCost = 0;
if (SCEVCheckBlock)
for (Instruction &I : *SCEVCheckBlock) {
if (SCEVCheckBlock->getTerminator() == &I)
continue;
InstructionCost C = TTI->getInstructionCost(&I, CostKind);
LLVM_DEBUG(dbgs() << " " << C << " for " << I << "\n");
RTCheckCost += C;
}
if (MemCheckBlock) {
InstructionCost MemCheckCost = 0;
for (Instruction &I : *MemCheckBlock) {
if (MemCheckBlock->getTerminator() == &I)
continue;
InstructionCost C = TTI->getInstructionCost(&I, CostKind);
LLVM_DEBUG(dbgs() << " " << C << " for " << I << "\n");
MemCheckCost += C;
}
// If the runtime memory checks are being created inside an outer loop
// we should find out if these checks are outer loop invariant. If so,
// the checks will likely be hoisted out and so the effective cost will
// reduce according to the outer loop trip count.
if (OuterLoop) {
ScalarEvolution *SE = MemCheckExp.getSE();
// TODO: If profitable, we could refine this further by analysing every
// individual memory check, since there could be a mixture of loop
// variant and invariant checks that mean the final condition is
// variant.
const SCEV *Cond = SE->getSCEV(MemRuntimeCheckCond);
if (SE->isLoopInvariant(Cond, OuterLoop)) {
// It seems reasonable to assume that we can reduce the effective
// cost of the checks even when we know nothing about the trip
// count. Assume that the outer loop executes at least twice.
unsigned BestTripCount = 2;
// Get the best known TC estimate.
if (auto EstimatedTC = getSmallBestKnownTC(
PSE, OuterLoop, /* CanUseConstantMax = */ false))
if (EstimatedTC->isFixed())
BestTripCount = EstimatedTC->getFixedValue();
InstructionCost NewMemCheckCost = MemCheckCost / BestTripCount;
// Let's ensure the cost is always at least 1.
NewMemCheckCost = std::max(NewMemCheckCost.getValue(),
(InstructionCost::CostType)1);
if (BestTripCount > 1)
LLVM_DEBUG(dbgs()
<< "We expect runtime memory checks to be hoisted "
<< "out of the outer loop. Cost reduced from "
<< MemCheckCost << " to " << NewMemCheckCost << '\n');
MemCheckCost = NewMemCheckCost;
}
}
RTCheckCost += MemCheckCost;
}
if (SCEVCheckBlock || MemCheckBlock)
LLVM_DEBUG(dbgs() << "Total cost of runtime checks: " << RTCheckCost
<< "\n");
return RTCheckCost;
}
/// Remove the created SCEV & memory runtime check blocks & instructions, if
/// unused.
~GeneratedRTChecks() {
SCEVExpanderCleaner SCEVCleaner(SCEVExp);
SCEVExpanderCleaner MemCheckCleaner(MemCheckExp);
bool SCEVChecksUsed = !SCEVCheckBlock || !pred_empty(SCEVCheckBlock);
bool MemChecksUsed = !MemCheckBlock || !pred_empty(MemCheckBlock);
if (SCEVChecksUsed)
SCEVCleaner.markResultUsed();
if (MemChecksUsed) {
MemCheckCleaner.markResultUsed();
} else {
auto &SE = *MemCheckExp.getSE();
// Memory runtime check generation creates compares that use expanded
// values. Remove them before running the SCEVExpanderCleaners.
for (auto &I : make_early_inc_range(reverse(*MemCheckBlock))) {
if (MemCheckExp.isInsertedInstruction(&I))
continue;
SE.forgetValue(&I);
I.eraseFromParent();
}
}
MemCheckCleaner.cleanup();
SCEVCleaner.cleanup();
if (!SCEVChecksUsed)
SCEVCheckBlock->eraseFromParent();
if (!MemChecksUsed)
MemCheckBlock->eraseFromParent();
}
/// Retrieves the SCEVCheckCond and SCEVCheckBlock that were generated as IR
/// outside VPlan.
std::pair<Value *, BasicBlock *> getSCEVChecks() const {
using namespace llvm::PatternMatch;
if (!SCEVCheckCond || match(SCEVCheckCond, m_ZeroInt()))
return {nullptr, nullptr};
return {SCEVCheckCond, SCEVCheckBlock};
}
/// Retrieves the MemCheckCond and MemCheckBlock that were generated as IR
/// outside VPlan.
std::pair<Value *, BasicBlock *> getMemRuntimeChecks() const {
using namespace llvm::PatternMatch;
if (MemRuntimeCheckCond && match(MemRuntimeCheckCond, m_ZeroInt()))
return {nullptr, nullptr};
return {MemRuntimeCheckCond, MemCheckBlock};
}
/// Return true if any runtime checks have been added
bool hasChecks() const {
return getSCEVChecks().first || getMemRuntimeChecks().first;
}
};
} // namespace
static bool useActiveLaneMask(TailFoldingStyle Style) {
return Style == TailFoldingStyle::Data ||
Style == TailFoldingStyle::DataAndControlFlow;
}
static bool useActiveLaneMaskForControlFlow(TailFoldingStyle Style) {
return Style == TailFoldingStyle::DataAndControlFlow;
}
// 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);
}
//===----------------------------------------------------------------------===//
// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
// LoopVectorizationCostModel and LoopVectorizationPlanner.
//===----------------------------------------------------------------------===//
/// For the given VF and UF and maximum trip count computed for the loop, return
/// whether the induction variable might overflow in the vectorized loop. If not,
/// then we know a runtime overflow check always evaluates to false and can be
/// removed.
static bool isIndvarOverflowCheckKnownFalse(
const LoopVectorizationCostModel *Cost,
ElementCount VF, std::optional<unsigned> UF = std::nullopt) {
// Always be conservative if we don't know the exact unroll factor.
unsigned MaxUF = UF ? *UF : Cost->TTI.getMaxInterleaveFactor(VF);
IntegerType *IdxTy = Cost->Legal->getWidestInductionType();
APInt MaxUIntTripCount = IdxTy->getMask();
// We know the runtime overflow check is known false iff the (max) trip-count
// is known and (max) trip-count + (VF * UF) does not overflow in the type of
// the vector loop induction variable.
if (unsigned TC = Cost->PSE.getSmallConstantMaxTripCount()) {
uint64_t MaxVF = VF.getKnownMinValue();
if (VF.isScalable()) {
std::optional<unsigned> MaxVScale =
getMaxVScale(*Cost->TheFunction, Cost->TTI);
if (!MaxVScale)
return false;
MaxVF *= *MaxVScale;
}
return (MaxUIntTripCount - TC).ugt(MaxVF * MaxUF);
}
return false;
}
// 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();
}
/// Replace \p VPBB with a VPIRBasicBlock wrapping \p IRBB. All recipes from \p
/// VPBB are moved to the end of the newly created VPIRBasicBlock. All
/// predecessors and successors of VPBB, if any, are rewired to the new
/// VPIRBasicBlock. If \p VPBB may be unreachable, \p Plan must be passed.
static VPIRBasicBlock *replaceVPBBWithIRVPBB(VPBasicBlock *VPBB,
BasicBlock *IRBB,
VPlan *Plan = nullptr) {
if (!Plan)
Plan = VPBB->getPlan();
VPIRBasicBlock *IRVPBB = Plan->createVPIRBasicBlock(IRBB);
auto IP = IRVPBB->begin();
for (auto &R : make_early_inc_range(VPBB->phis()))
R.moveBefore(*IRVPBB, IP);
for (auto &R :
make_early_inc_range(make_range(VPBB->getFirstNonPhi(), VPBB->end())))
R.moveBefore(*IRVPBB, IRVPBB->end());
VPBlockUtils::reassociateBlocks(VPBB, IRVPBB);
// VPBB is now dead and will be cleaned up when the plan gets destroyed.
return IRVPBB;
}
BasicBlock *InnerLoopVectorizer::createScalarPreheader(StringRef Prefix) {
BasicBlock *VectorPH = OrigLoop->getLoopPreheader();
assert(VectorPH && "Invalid loop structure");
assert((OrigLoop->getUniqueLatchExitBlock() ||
Cost->requiresScalarEpilogue(VF.isVector())) &&
"loops not exiting via the latch without required epilogue?");
// NOTE: The Plan's scalar preheader VPBB isn't replaced with a VPIRBasicBlock
// wrapping the newly created scalar preheader here at the moment, because the
// Plan's scalar preheader may be unreachable at this point. Instead it is
// replaced in executePlan.
return SplitBlock(VectorPH, VectorPH->getTerminator(), DT, LI, nullptr,
Twine(Prefix) + "scalar.ph");
}
/// Knowing that loop \p L executes a single vector iteration, add instructions
/// that will get simplified and thus should not have any cost to \p
/// InstsToIgnore.
static void addFullyUnrolledInstructionsToIgnore(
Loop *L, const LoopVectorizationLegality::InductionList &IL,
SmallPtrSetImpl<Instruction *> &InstsToIgnore) {
auto *Cmp = L->getLatchCmpInst();
if (Cmp)
InstsToIgnore.insert(Cmp);
for (const auto &KV : IL) {
// Extract the key by hand so that it can be used in the lambda below. Note
// that captured structured bindings are a C++20 extension.
const PHINode *IV = KV.first;
// Get next iteration value of the induction variable.
Instruction *IVInst =
cast<Instruction>(IV->getIncomingValueForBlock(L->getLoopLatch()));
if (all_of(IVInst->users(),
[&](const User *U) { return U == IV || U == Cmp; }))
InstsToIgnore.insert(IVInst);
}
}
BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() {
// Create a new IR basic block for the scalar preheader.
BasicBlock *ScalarPH = createScalarPreheader("");
return ScalarPH->getSinglePredecessor();
}
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->operand_values()));
}
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
/// FIXME: This legacy common-subexpression-elimination routine is scheduled for
/// removal, in favor of the VPlan-based one.
static void legacyCSE(BasicBlock *BB) {
// Perform simple cse.
SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap;
for (Instruction &In : llvm::make_early_inc_range(*BB)) {
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;
}
}
/// This function attempts to return a value that represents the ElementCount
/// at runtime. For fixed-width VFs we know this precisely at compile
/// time, but for scalable VFs we calculate it based on an estimate of the
/// vscale value.
static unsigned estimateElementCount(ElementCount VF,
std::optional<unsigned> VScale) {
unsigned EstimatedVF = VF.getKnownMinValue();
if (VF.isScalable())
if (VScale)
EstimatedVF *= *VScale;
assert(EstimatedVF >= 1 && "Estimated VF shouldn't be less than 1");
return EstimatedVF;
}
InstructionCost
LoopVectorizationCostModel::getVectorCallCost(CallInst *CI,
ElementCount VF) const {
// We only need to calculate a cost if the VF is scalar; for actual vectors
// we should already have a pre-calculated cost at each VF.
if (!VF.isScalar())
return getCallWideningDecision(CI, VF).Cost;
Type *RetTy = CI->getType();
if (RecurrenceDescriptor::isFMulAddIntrinsic(CI))
if (auto RedCost = getReductionPatternCost(CI, VF, RetTy))
return *RedCost;
SmallVector<Type *, 4> Tys;
for (auto &ArgOp : CI->args())
Tys.push_back(ArgOp->getType());
InstructionCost ScalarCallCost = TTI.getCallInstrCost(
CI->getCalledFunction(), RetTy, Tys, Config.CostKind);
// If this is an intrinsic we may have a lower cost for it.
if (getVectorIntrinsicIDForCall(CI, TLI)) {
InstructionCost IntrinsicCost = getVectorIntrinsicCost(CI, VF);
return std::min(ScalarCallCost, IntrinsicCost);
}
return ScalarCallCost;
}
static Type *maybeVectorizeType(Type *Ty, ElementCount VF) {
if (VF.isScalar() || !canVectorizeTy(Ty))
return Ty;
return toVectorizedTy(Ty, VF);
}
InstructionCost
LoopVectorizationCostModel::getVectorIntrinsicCost(CallInst *CI,
ElementCount VF) const {
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
assert(ID && "Expected intrinsic call!");
Type *RetTy = maybeVectorizeType(CI->getType(), VF);
FastMathFlags FMF;
if (auto *FPMO = dyn_cast<FPMathOperator>(CI))
FMF = FPMO->getFastMathFlags();
SmallVector<const Value *> Arguments(CI->args());
FunctionType *FTy = CI->getCalledFunction()->getFunctionType();
SmallVector<Type *> ParamTys;
std::transform(FTy->param_begin(), FTy->param_end(),
std::back_inserter(ParamTys),
[&](Type *Ty) { return maybeVectorizeType(Ty, VF); });
IntrinsicCostAttributes CostAttrs(ID, RetTy, Arguments, ParamTys, FMF,
dyn_cast<IntrinsicInst>(CI),
InstructionCost::getInvalid());
return TTI.getIntrinsicInstrCost(CostAttrs, Config.CostKind);
}
void InnerLoopVectorizer::fixVectorizedLoop(VPTransformState &State) {
// Fix widened non-induction PHIs by setting up the PHI operands.
fixNonInductionPHIs(State);
// Don't apply optimizations below when no (vector) loop remains, as they all
// require one at the moment.
VPBasicBlock *HeaderVPBB =
vputils::getFirstLoopHeader(*State.Plan, State.VPDT);
if (!HeaderVPBB)
return;
BasicBlock *HeaderBB = State.CFG.VPBB2IRBB[HeaderVPBB];
// Remove redundant induction instructions.
legacyCSE(HeaderBB);
}
void InnerLoopVectorizer::fixNonInductionPHIs(VPTransformState &State) {
auto Iter = vp_depth_first_shallow(Plan.getEntry());
for (VPBasicBlock *VPBB : VPBlockUtils::blocksOnly<VPBasicBlock>(Iter)) {
for (VPRecipeBase &P : VPBB->phis()) {
VPWidenPHIRecipe *VPPhi = dyn_cast<VPWidenPHIRecipe>(&P);
if (!VPPhi)
continue;
PHINode *NewPhi = cast<PHINode>(State.get(VPPhi));
// Make sure the builder has a valid insert point.
Builder.SetInsertPoint(NewPhi);
for (const auto &[Inc, VPBB] : VPPhi->incoming_values_and_blocks())
NewPhi->addIncoming(State.get(Inc), State.CFG.VPBB2IRBB[VPBB]);
}
}
}
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.contains(VF) &&
"This function should not be visited twice for the same VF");
// This avoids any chances of creating a REPLICATE recipe during planning
// since that would result in generation of scalarized code during execution,
// which is not supported for scalable vectors.
if (VF.isScalable()) {
Scalars[VF].insert_range(Uniforms[VF]);
return;
}
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 getelementptr
// instruction contained in the loop.
auto IsLoopVaryingGEP = [&](Value *V) {
return isa<GetElementPtrInst>(V) && !TheLoop->isLoopInvariant(V);
};
// A helper that evaluates a memory access's use of a pointer. 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) {
// We only care about bitcast and getelementptr instructions contained in
// the loop.
if (!IsLoopVaryingGEP(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) &&
all_of(I->users(), IsaPred<LoadInst, StoreInst>))
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_range(Uniforms[VF]);
// (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 unless the operation is a gather or scatter.
// 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 VPWidenPHIRecipe() 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) {
LLVM_DEBUG(dbgs() << "LV: Found (forced) scalar instruction: " << *I << "\n");
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 (!IsLoopVaryingGEP(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 (const 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;
// Returns true if \p Indvar is a pointer induction that is used directly by
// load/store instruction \p I.
auto IsDirectLoadStoreFromPtrIndvar = [&](Instruction *Indvar,
Instruction *I) {
return Induction.second.getKind() ==
InductionDescriptor::IK_PtrInduction &&
(isa<LoadInst>(I) || isa<StoreInst>(I)) &&
Indvar == getLoadStorePointerOperand(I) && IsScalarUse(I, Indvar);
};
// Determine if all users of the induction variable are scalar after
// vectorization.
bool ScalarInd = all_of(Ind->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) ||
IsDirectLoadStoreFromPtrIndvar(Ind, I);
});
if (!ScalarInd)
continue;
// If the induction variable update is a fixed-order recurrence, neither the
// induction variable or its update should be marked scalar after
// vectorization.
auto *IndUpdatePhi = dyn_cast<PHINode>(IndUpdate);
if (IndUpdatePhi && Legal->isFixedOrderRecurrence(IndUpdatePhi))
continue;
// Determine if all users of the induction variable update instruction are
// scalar after vectorization.
bool ScalarIndUpdate = all_of(IndUpdate->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == Ind || !TheLoop->contains(I) || Worklist.count(I) ||
IsDirectLoadStoreFromPtrIndvar(IndUpdate, 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_range(Worklist);
}
bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I,
ElementCount VF) {
if (!isPredicatedInst(I))
return false;
// Do we have a non-scalar lowering for this predicated
// instruction? No - it is scalar with predication.
switch(I->getOpcode()) {
default:
return true;
case Instruction::Call:
if (VF.isScalar())
return true;
return getCallWideningDecision(cast<CallInst>(I), VF).Kind == CM_Scalarize;
case Instruction::Load:
case Instruction::Store: {
return !Config.isLegalMaskedLoadOrStore(I, VF) &&
!Config.isLegalGatherOrScatter(I, VF);
}
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::SRem:
case Instruction::URem: {
// We have the option to use the safe-divisor idiom to avoid predication.
// The cost based decision here will always select safe-divisor for
// scalable vectors as scalarization isn't legal.
const auto [ScalarCost, SafeDivisorCost] = getDivRemSpeculationCost(I, VF);
return isDivRemScalarWithPredication(ScalarCost, SafeDivisorCost);
}
}
}
bool LoopVectorizationCostModel::isMaskRequired(Instruction *I) const {
return Legal->isMaskRequired(I, foldTailByMasking());
}
// TODO: Fold into LoopVectorizationLegality::isMaskRequired.
bool LoopVectorizationCostModel::isPredicatedInst(Instruction *I) const {
// TODO: We can use the loop-preheader as context point here and get
// context sensitive reasoning for isSafeToSpeculativelyExecute.
if (isSafeToSpeculativelyExecute(I) ||
(isa<LoadInst, StoreInst, CallInst>(I) && !isMaskRequired(I)) ||
isa<UncondBrInst, CondBrInst, SwitchInst, PHINode, AllocaInst>(I))
return false;
// If the instruction was executed conditionally in the original scalar loop,
// predication is needed with a mask whose lanes are all possibly inactive.
if (Legal->blockNeedsPredication(I->getParent()))
return true;
// If we're not folding the tail by masking, predication is unnecessary.
if (!foldTailByMasking())
return false;
// All that remain are instructions with side-effects originally executed in
// the loop unconditionally, but now execute under a tail-fold mask (only)
// having at least one active lane (the first). If the side-effects of the
// instruction are invariant, executing it w/o (the tail-folding) mask is safe
// - it will cause the same side-effects as when masked.
switch(I->getOpcode()) {
default:
llvm_unreachable(
"instruction should have been considered by earlier checks");
case Instruction::Call:
// Side-effects of a Call are assumed to be non-invariant, needing a
// (fold-tail) mask.
assert(isMaskRequired(I) &&
"should have returned earlier for calls not needing a mask");
return true;
case Instruction::Load:
// If the address is loop invariant no predication is needed.
return !Legal->isInvariant(getLoadStorePointerOperand(I));
case Instruction::Store: {
// For stores, we need to prove both speculation safety (which follows from
// the same argument as loads), but also must prove the value being stored
// is correct. The easiest form of the later is to require that all values
// stored are the same.
return !(Legal->isInvariant(getLoadStorePointerOperand(I)) &&
TheLoop->isLoopInvariant(cast<StoreInst>(I)->getValueOperand()));
}
case Instruction::UDiv:
case Instruction::URem:
// If the divisor is loop-invariant no predication is needed.
return !Legal->isInvariant(I->getOperand(1));
case Instruction::SDiv:
case Instruction::SRem:
// Conservative for now, since masked-off lanes may be poison and could
// trigger signed overflow.
return true;
}
}
uint64_t LoopVectorizationCostModel::getPredBlockCostDivisor(
TargetTransformInfo::TargetCostKind CostKind, const BasicBlock *BB) {
if (CostKind == TTI::TCK_CodeSize)
return 1;
// If the block wasn't originally predicated then return early to avoid
// computing BlockFrequencyInfo unnecessarily.
if (!Legal->blockNeedsPredication(BB))
return 1;
uint64_t HeaderFreq =
getBFI().getBlockFreq(TheLoop->getHeader()).getFrequency();
uint64_t BBFreq = getBFI().getBlockFreq(BB).getFrequency();
assert(HeaderFreq >= BBFreq &&
"Header has smaller block freq than dominated BB?");
return std::round((double)HeaderFreq / BBFreq);
}
std::pair<InstructionCost, InstructionCost>
LoopVectorizationCostModel::getDivRemSpeculationCost(Instruction *I,
ElementCount VF) {
assert(I->getOpcode() == Instruction::UDiv ||
I->getOpcode() == Instruction::SDiv ||
I->getOpcode() == Instruction::SRem ||
I->getOpcode() == Instruction::URem);
assert(!isSafeToSpeculativelyExecute(I));
// Scalarization isn't legal for scalable vector types
InstructionCost ScalarizationCost = InstructionCost::getInvalid();
if (!VF.isScalable()) {
// 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.
ScalarizationCost = 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.
ScalarizationCost += VF.getFixedValue() *
TTI.getCFInstrCost(Instruction::PHI, Config.CostKind);
// The cost of the non-predicated instruction.
ScalarizationCost +=
VF.getFixedValue() * TTI.getArithmeticInstrCost(
I->getOpcode(), I->getType(), Config.CostKind);
// The cost of insertelement and extractelement instructions needed for
// scalarization.
ScalarizationCost += 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.
ScalarizationCost =
ScalarizationCost /
getPredBlockCostDivisor(Config.CostKind, I->getParent());
}
InstructionCost SafeDivisorCost = 0;
auto *VecTy = toVectorTy(I->getType(), VF);
// The cost of the select guard to ensure all lanes are well defined
// after we speculate above any internal control flow.
SafeDivisorCost +=
TTI.getCmpSelInstrCost(Instruction::Select, VecTy,
toVectorTy(Type::getInt1Ty(I->getContext()), VF),
CmpInst::BAD_ICMP_PREDICATE, Config.CostKind);
SmallVector<const Value *, 4> Operands(I->operand_values());
SafeDivisorCost += TTI.getArithmeticInstrCost(
I->getOpcode(), VecTy, Config.CostKind,
{TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None},
{TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None},
Operands, I);
return {ScalarizationCost, SafeDivisorCost};
}
bool LoopVectorizationCostModel::interleavedAccessCanBeWidened(
Instruction *I, ElementCount VF) const {
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.");
unsigned InterleaveFactor = Group->getFactor();
// If the instruction's allocated size doesn't equal its type size, it
// requires padding and will be scalarized.
auto &DL = I->getDataLayout();
auto *ScalarTy = getLoadStoreType(I);
if (hasIrregularType(ScalarTy, DL))
return false;
// For scalable vectors, the interleave factors must be <= 8 since we require
// the (de)interleaveN intrinsics instead of shufflevectors.
if (VF.isScalable() && InterleaveFactor > 8)
return false;
// If the group involves a non-integral pointer, we may not be able to
// losslessly cast all values to a common type.
bool ScalarNI = DL.isNonIntegralPointerType(ScalarTy);
for (Instruction *Member : Group->members()) {
auto *MemberTy = getLoadStoreType(Member);
bool MemberNI = DL.isNonIntegralPointerType(MemberTy);
// Don't coerce non-integral pointers to integers or vice versa.
if (MemberNI != ScalarNI)
// TODO: Consider adding special nullptr value case here
return false;
if (MemberNI && ScalarNI &&
ScalarTy->getPointerAddressSpace() !=
MemberTy->getPointerAddressSpace())
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
// (either a gap at the end of a load-access that may result in a speculative
// load, or any gaps in a store-access).
bool PredicatedAccessRequiresMasking =
blockNeedsPredicationForAnyReason(I->getParent()) && isMaskRequired(I);
bool LoadAccessWithGapsRequiresEpilogMasking =
isa<LoadInst>(I) && Group->requiresScalarEpilogue() &&
!isEpilogueAllowed();
bool StoreAccessWithGapsRequiresMasking =
isa<StoreInst>(I) && !Group->isFull();
if (!PredicatedAccessRequiresMasking &&
!LoadAccessWithGapsRequiresEpilogMasking &&
!StoreAccessWithGapsRequiresMasking)
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.");
if (Group->isReverse())
return false;
// TODO: Support interleaved access that requires a gap mask for scalable VFs.
bool NeedsMaskForGaps = LoadAccessWithGapsRequiresEpilogMasking ||
StoreAccessWithGapsRequiresMasking;
if (VF.isScalable() && NeedsMaskForGaps)
return false;
auto *Ty = getLoadStoreType(I);
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
return isa<LoadInst>(I) ? TTI.isLegalMaskedLoad(Ty, Alignment, AS)
: TTI.isLegalMaskedStore(Ty, Alignment, AS);
}
bool LoopVectorizationCostModel::memoryInstructionCanBeWidened(
Instruction *I, ElementCount VF) {
// Get and ensure we have a valid memory instruction.
assert((isa<LoadInst, StoreInst>(I)) && "Invalid memory instruction");
auto *Ptr = getLoadStorePointerOperand(I);
auto *ScalarTy = getLoadStoreType(I);
// In order to be widened, the pointer should be consecutive, first of all.
if (!Legal->isConsecutivePtr(ScalarTy, Ptr))
return false;
// If the instruction is a store located in a predicated block, it will be
// scalarized.
if (isScalarWithPredication(I, VF))
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->getDataLayout();
if (hasIrregularType(ScalarTy, DL))
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.contains(VF) &&
"This function should not be visited twice for the same VF");
// Visit the list of Uniforms. If we find no uniform value, we won't
// analyze again. Uniforms.count(VF) will return 1.
Uniforms[VF].clear();
// Now we 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));
};
// Worklist containing uniform instructions demanding lane 0.
SetVector<Instruction *> Worklist;
// Add uniform instructions demanding lane 0 to the worklist. Instructions
// that require 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.
auto AddToWorklistIfAllowed = [&](Instruction *I) -> void {
if (IsOutOfScope(I)) {
LLVM_DEBUG(dbgs() << "LV: Found not uniform due to scope: "
<< *I << "\n");
return;
}
if (isPredicatedInst(I)) {
LLVM_DEBUG(
dbgs() << "LV: Found not uniform due to requiring predication: " << *I
<< "\n");
return;
}
LLVM_DEBUG(dbgs() << "LV: Found uniform instruction: " << *I << "\n");
Worklist.insert(I);
};
// Start with the conditional branches exiting the loop. If the branch
// condition is an instruction contained in the loop that is only used by the
// branch, it is uniform. Note conditions from uncountable early exits are not
// uniform.
SmallVector<BasicBlock *> Exiting;
TheLoop->getExitingBlocks(Exiting);
for (BasicBlock *E : Exiting) {
if (Legal->hasUncountableEarlyExit() && TheLoop->getLoopLatch() != E)
continue;
auto *Cmp = dyn_cast<Instruction>(E->getTerminator()->getOperand(0));
if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse())
AddToWorklistIfAllowed(Cmp);
}
auto PrevVF = VF.divideCoefficientBy(2);
// Return true if all lanes perform the same memory operation, and we can
// thus choose to execute only one.
auto IsUniformMemOpUse = [&](Instruction *I) {
// If the value was already known to not be uniform for the previous
// (smaller VF), it cannot be uniform for the larger VF.
if (PrevVF.isVector()) {
auto Iter = Uniforms.find(PrevVF);
if (Iter != Uniforms.end() && !Iter->second.contains(I))
return false;
}
if (!Legal->isUniformMemOp(*I, VF))
return false;
if (isa<LoadInst>(I))
// Loading the same address always produces the same result - at least
// assuming aliasing and ordering which have already been checked.
return true;
// Storing the same value on every iteration.
return TheLoop->isLoopInvariant(cast<StoreInst>(I)->getValueOperand());
};
auto IsUniformDecision = [&](Instruction *I, ElementCount VF) {
InstWidening WideningDecision = getWideningDecision(I, VF);
assert(WideningDecision != CM_Unknown &&
"Widening decision should be ready at this moment");
if (IsUniformMemOpUse(I))
return true;
return (WideningDecision == CM_Widen ||
WideningDecision == CM_Widen_Reverse ||
WideningDecision == CM_Interleave);
};
// Returns true if Ptr is the pointer operand of a memory access instruction
// I, I is known to not require scalarization, and the pointer is not also
// stored.
auto IsVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool {
if (isa<StoreInst>(I) && I->getOperand(0) == Ptr)
return false;
return getLoadStorePointerOperand(I) == Ptr &&
(IsUniformDecision(I, VF) || Legal->isInvariant(Ptr));
};
// Holds a list of values which are known to have at least one uniform use.
// Note that there may be other uses which aren't uniform. A "uniform use"
// here is something which only demands lane 0 of the unrolled iterations;
// it does not imply that all lanes produce the same value (e.g. this is not
// the usual meaning of uniform)
SetVector<Value *> HasUniformUse;
// Scan the loop for instructions which are either a) known to have only
// lane 0 demanded or b) are uses which demand only lane 0 of their operand.
for (auto *BB : TheLoop->blocks())
for (auto &I : *BB) {
if (IntrinsicInst *II = dyn_cast<IntrinsicInst>(&I)) {
switch (II->getIntrinsicID()) {
case Intrinsic::sideeffect:
case Intrinsic::experimental_noalias_scope_decl:
case Intrinsic::assume:
case Intrinsic::lifetime_start:
case Intrinsic::lifetime_end:
if (TheLoop->hasLoopInvariantOperands(&I))
AddToWorklistIfAllowed(&I);
break;
default:
break;
}
}
if (auto *EVI = dyn_cast<ExtractValueInst>(&I)) {
if (IsOutOfScope(EVI->getAggregateOperand())) {
AddToWorklistIfAllowed(EVI);
continue;
}
// Only ExtractValue instructions where the aggregate value comes from a
// call are allowed to be non-uniform.
assert(isa<CallInst>(EVI->getAggregateOperand()) &&
"Expected aggregate value to be call return value");
}
// If there's no pointer operand, there's nothing to do.
auto *Ptr = getLoadStorePointerOperand(&I);
if (!Ptr)
continue;
// If the pointer can be proven to be uniform, always add it to the
// worklist.
if (isa<Instruction>(Ptr) && Legal->isUniform(Ptr, VF))
AddToWorklistIfAllowed(cast<Instruction>(Ptr));
if (IsUniformMemOpUse(&I))
AddToWorklistIfAllowed(&I);
if (IsVectorizedMemAccessUse(&I, Ptr))
HasUniformUse.insert(Ptr);
}
// Add to the worklist any operands which have *only* uniform (e.g. lane 0
// demanding) users. Since loops are assumed to be in LCSSA form, this
// disallows uses outside the loop as well.
for (auto *V : HasUniformUse) {
if (IsOutOfScope(V))
continue;
auto *I = cast<Instruction>(V);
bool UsersAreMemAccesses = all_of(I->users(), [&](User *U) -> bool {
auto *UI = cast<Instruction>(U);
return TheLoop->contains(UI) && IsVectorizedMemAccessUse(UI, V);
});
if (UsersAreMemAccesses)
AddToWorklistIfAllowed(I);
}
// 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->isFixedOrderRecurrence(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) || IsVectorizedMemAccessUse(J, OI);
}))
AddToWorklistIfAllowed(OI);
}
}
// 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.
BasicBlock *Latch = TheLoop->getLoopLatch();
for (const 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.
bool UniformInd = 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.
bool UniformIndUpdate = all_of(IndUpdate->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == Ind || 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_range(Worklist);
}
FixedScalableVFPair
LoopVectorizationCostModel::computeMaxVF(ElementCount UserVF, unsigned UserIC) {
if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) {
// TODO: It may be 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 FixedScalableVFPair::getNone();
}
ScalarEvolution *SE = PSE.getSE();
ElementCount TC = getSmallConstantTripCount(SE, TheLoop);
unsigned MaxTC = PSE.getSmallConstantMaxTripCount();
if (!MaxTC && EpilogueLoweringStatus == CM_EpilogueAllowed)
MaxTC = getMaxTCFromNonZeroRange(PSE, TheLoop);
LLVM_DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n');
if (TC != ElementCount::getFixed(MaxTC))
LLVM_DEBUG(dbgs() << "LV: Found maximum trip count: " << MaxTC << '\n');
if (TC.isScalar()) {
reportVectorizationFailure("Single iteration (non) loop",
"loop trip count is one, irrelevant for vectorization",
"SingleIterationLoop", ORE, TheLoop);
return FixedScalableVFPair::getNone();
}
// If BTC matches the widest induction type and is -1 then the trip count
// computation will wrap to 0 and the vector trip count will be 0. Do not try
// to vectorize.
const SCEV *BTC = SE->getBackedgeTakenCount(TheLoop);
if (!isa<SCEVCouldNotCompute>(BTC) &&
BTC->getType()->getScalarSizeInBits() >=
Legal->getWidestInductionType()->getScalarSizeInBits() &&
SE->isKnownPredicate(CmpInst::ICMP_EQ, BTC,
SE->getMinusOne(BTC->getType()))) {
reportVectorizationFailure(
"Trip count computation wrapped",
"backedge-taken count is -1, loop trip count wrapped to 0",
"TripCountWrapped", ORE, TheLoop);
return FixedScalableVFPair::getNone();
}
assert(WideningDecisions.empty() && CallWideningDecisions.empty() &&
Uniforms.empty() && Scalars.empty() &&
"No cost-modeling decisions should have been taken at this point");
switch (EpilogueLoweringStatus) {
case CM_EpilogueAllowed:
return Config.computeFeasibleMaxVF(MaxTC, UserVF, UserIC, false,
requiresScalarEpilogue(true));
case CM_EpilogueNotAllowedFoldTail:
[[fallthrough]];
case CM_EpilogueNotNeededFoldTail:
LLVM_DEBUG(dbgs() << "LV: tail-folding hint/switch found.\n"
<< "LV: Not allowing epilogue, creating tail-folded "
<< "vector loop.\n");
break;
case CM_EpilogueNotAllowedLowTripLoop:
// fallthrough as a special case of OptForSize
case CM_EpilogueNotAllowedOptSize:
if (EpilogueLoweringStatus == CM_EpilogueNotAllowedOptSize)
LLVM_DEBUG(dbgs() << "LV: Not allowing epilogue due to -Os/-Oz.\n");
else
LLVM_DEBUG(dbgs() << "LV: Not allowing epilogue due to low trip "
<< "count.\n");
// Bail if runtime checks are required, which are not good when optimising
// for size.
if (Config.runtimeChecksRequired())
return FixedScalableVFPair::getNone();
break;
}
// Now try the tail folding
// Invalidate interleave groups that require an epilogue if we can't mask
// the interleave-group.
if (!useMaskedInterleavedAccesses(TTI)) {
// Note: There is no need to invalidate any cost modeling decisions here, as
// none were taken so far (see assertion above).
InterleaveInfo.invalidateGroupsRequiringScalarEpilogue();
}
FixedScalableVFPair MaxFactors = Config.computeFeasibleMaxVF(
MaxTC, UserVF, UserIC, true, requiresScalarEpilogue(true));
// Avoid tail folding if the trip count is known to be a multiple of any VF
// we choose.
std::optional<unsigned> MaxPowerOf2RuntimeVF =
MaxFactors.FixedVF.getFixedValue();
if (MaxFactors.ScalableVF) {
std::optional<unsigned> MaxVScale = getMaxVScale(*TheFunction, TTI);
if (MaxVScale) {
MaxPowerOf2RuntimeVF = std::max<unsigned>(
*MaxPowerOf2RuntimeVF,
*MaxVScale * MaxFactors.ScalableVF.getKnownMinValue());
} else
MaxPowerOf2RuntimeVF = std::nullopt; // Stick with tail-folding for now.
}
auto NoScalarEpilogueNeeded = [this, &UserIC](unsigned MaxVF) {
// Return false if the loop is neither a single-latch-exit loop nor an
// early-exit loop as tail-folding is not supported in that case.
if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch() &&
!Legal->hasUncountableEarlyExit())
return false;
unsigned MaxVFtimesIC = UserIC ? MaxVF * UserIC : MaxVF;
ScalarEvolution *SE = PSE.getSE();
// Calling getSymbolicMaxBackedgeTakenCount enables support for loops
// with uncountable exits. For countable loops, the symbolic maximum must
// remain identical to the known back-edge taken count.
const SCEV *BackedgeTakenCount = PSE.getSymbolicMaxBackedgeTakenCount();
assert((Legal->hasUncountableEarlyExit() ||
BackedgeTakenCount == PSE.getBackedgeTakenCount()) &&
"Invalid loop count");
const SCEV *ExitCount = SE->getAddExpr(
BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType()));
const SCEV *Rem = SE->getURemExpr(
SE->applyLoopGuards(ExitCount, TheLoop),
SE->getConstant(BackedgeTakenCount->getType(), MaxVFtimesIC));
return Rem->isZero();
};
if (MaxPowerOf2RuntimeVF > 0u) {
assert((UserVF.isNonZero() || isPowerOf2_32(*MaxPowerOf2RuntimeVF)) &&
"MaxFixedVF must be a power of 2");
if (NoScalarEpilogueNeeded(*MaxPowerOf2RuntimeVF)) {
// Accept MaxFixedVF if we do not have a tail.
LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n");
return MaxFactors;
}
}
auto ExpectedTC = getSmallBestKnownTC(PSE, TheLoop);
if (ExpectedTC && ExpectedTC->isFixed() &&
ExpectedTC->getFixedValue() <=
TTI.getMinTripCountTailFoldingThreshold()) {
if (MaxPowerOf2RuntimeVF > 0u) {
// If we have a low-trip-count, and the fixed-width VF is known to divide
// the trip count but the scalable factor does not, use the fixed-width
// factor in preference to allow the generation of a non-predicated loop.
if (EpilogueLoweringStatus == CM_EpilogueNotAllowedLowTripLoop &&
NoScalarEpilogueNeeded(MaxFactors.FixedVF.getFixedValue())) {
LLVM_DEBUG(dbgs() << "LV: Picking a fixed-width so that no tail will "
"remain for any chosen VF.\n");
MaxFactors.ScalableVF = ElementCount::getScalable(0);
return MaxFactors;
}
}
reportVectorizationFailure(
"The trip count is below the minial threshold value.",
"loop trip count is too low, avoiding vectorization", "LowTripCount",
ORE, TheLoop);
return FixedScalableVFPair::getNone();
}
// 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.
bool ContainsScalableVF = MaxFactors.ScalableVF.isNonZero();
setTailFoldingStyle(ContainsScalableVF, UserIC);
if (foldTailByMasking()) {
if (foldTailWithEVL()) {
LLVM_DEBUG(
dbgs()
<< "LV: tail is folded with EVL, forcing unroll factor to be 1. Will "
"try to generate VP Intrinsics with scalable vector "
"factors only.\n");
// Tail folded loop using VP intrinsics restricts the VF to be scalable
// for now.
// TODO: extend it for fixed vectors, if required.
assert(ContainsScalableVF && "Expected scalable vector factor.");
MaxFactors.FixedVF = ElementCount::getFixed(1);
}
return MaxFactors;
}
// If there was a tail-folding hint/switch, but we can't fold the tail by
// masking, fallback to a vectorization with an epilogue.
if (EpilogueLoweringStatus == CM_EpilogueNotNeededFoldTail) {
LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with an "
"epilogue instead.\n");
EpilogueLoweringStatus = CM_EpilogueAllowed;
return MaxFactors;
}
if (EpilogueLoweringStatus == CM_EpilogueNotAllowedFoldTail) {
LLVM_DEBUG(dbgs() << "LV: Can't fold tail by masking: don't vectorize\n");
return FixedScalableVFPair::getNone();
}
if (TC.isZero()) {
reportVectorizationFailure(
"unable to calculate the loop count due to complex control flow",
"UnknownLoopCountComplexCFG", ORE, TheLoop);
return FixedScalableVFPair::getNone();
}
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 FixedScalableVFPair::getNone();
}
bool LoopVectorizationPlanner::isMoreProfitable(const VectorizationFactor &A,
const VectorizationFactor &B,
const unsigned MaxTripCount,
bool HasTail,
bool IsEpilogue) const {
InstructionCost CostA = A.Cost;
InstructionCost CostB = B.Cost;
// When there is a hint to always prefer scalable vectors, honour that hint.
if (Hints.isScalableVectorizationAlwaysPreferred())
if (A.Width.isScalable() && CostA.isValid() && !B.Width.isScalable() &&
!B.Width.isScalar())
return true;
// Improve estimate for the vector width if it is scalable.
unsigned EstimatedWidthA = A.Width.getKnownMinValue();
unsigned EstimatedWidthB = B.Width.getKnownMinValue();
if (std::optional<unsigned> VScale = Config.getVScaleForTuning()) {
if (A.Width.isScalable())
EstimatedWidthA *= *VScale;
if (B.Width.isScalable())
EstimatedWidthB *= *VScale;
}
// When optimizing for size choose whichever is smallest, which will be the
// one with the smallest cost for the whole loop. On a tie pick the larger
// vector width, on the assumption that throughput will be greater.
if (Config.CostKind == TTI::TCK_CodeSize)
return CostA < CostB ||
(CostA == CostB && EstimatedWidthA > EstimatedWidthB);
// Assume vscale may be larger than 1 (or the value being tuned for),
// so that scalable vectorization is slightly favorable over fixed-width
// vectorization.
bool PreferScalable = !TTI.preferFixedOverScalableIfEqualCost(IsEpilogue) &&
A.Width.isScalable() && !B.Width.isScalable();
auto CmpFn = [PreferScalable](const InstructionCost &LHS,
const InstructionCost &RHS) {
return PreferScalable ? LHS <= RHS : LHS < RHS;
};
// To avoid the need for FP division:
// (CostA / EstimatedWidthA) < (CostB / EstimatedWidthB)
// <=> (CostA * EstimatedWidthB) < (CostB * EstimatedWidthA)
bool LowerCostWithoutTC =
CmpFn(CostA * EstimatedWidthB, CostB * EstimatedWidthA);
if (!MaxTripCount)
return LowerCostWithoutTC;
auto GetCostForTC = [MaxTripCount, HasTail](unsigned VF,
InstructionCost VectorCost,
InstructionCost ScalarCost) {
// If the trip count is a known (possibly small) constant, the trip count
// will be rounded up to an integer number of iterations under
// FoldTailByMasking. The total cost in that case will be
// VecCost*ceil(TripCount/VF). When not folding the tail, the total
// cost will be VecCost*floor(TC/VF) + ScalarCost*(TC%VF). There will be
// some extra overheads, but for the purpose of comparing the costs of
// different VFs we can use this to compare the total loop-body cost
// expected after vectorization.
if (HasTail)
return VectorCost * (MaxTripCount / VF) +
ScalarCost * (MaxTripCount % VF);
return VectorCost * divideCeil(MaxTripCount, VF);
};
auto RTCostA = GetCostForTC(EstimatedWidthA, CostA, A.ScalarCost);
auto RTCostB = GetCostForTC(EstimatedWidthB, CostB, B.ScalarCost);
bool LowerCostWithTC = CmpFn(RTCostA, RTCostB);
LLVM_DEBUG(if (LowerCostWithTC != LowerCostWithoutTC) {
dbgs() << "LV: VF " << (LowerCostWithTC ? A.Width : B.Width)
<< " has lower cost than VF "
<< (LowerCostWithTC ? B.Width : A.Width)
<< " when taking the cost of the remaining scalar loop iterations "
"into consideration for a maximum trip count of "
<< MaxTripCount << ".\n";
});
return LowerCostWithTC;
}
bool LoopVectorizationPlanner::isMoreProfitable(const VectorizationFactor &A,
const VectorizationFactor &B,
bool HasTail,
bool IsEpilogue) const {
const unsigned MaxTripCount = PSE.getSmallConstantMaxTripCount();
return LoopVectorizationPlanner::isMoreProfitable(A, B, MaxTripCount, HasTail,
IsEpilogue);
}
void LoopVectorizationPlanner::emitInvalidCostRemarks(
OptimizationRemarkEmitter *ORE) {
using RecipeVFPair = std::pair<VPRecipeBase *, ElementCount>;
SmallVector<RecipeVFPair> InvalidCosts;
for (const auto &Plan : VPlans) {
for (ElementCount VF : Plan->vectorFactors()) {
// The VPlan-based cost model is designed for computing vector cost.
// Querying VPlan-based cost model with a scarlar VF will cause some
// errors because we expect the VF is vector for most of the widen
// recipes.
if (VF.isScalar())
continue;
VPCostContext CostCtx(CM.TTI, *CM.TLI, *Plan, CM, Config.CostKind, CM.PSE,
OrigLoop);
precomputeCosts(*Plan, VF, CostCtx);
auto Iter = vp_depth_first_deep(Plan->getVectorLoopRegion()->getEntry());
for (VPBasicBlock *VPBB : VPBlockUtils::blocksOnly<VPBasicBlock>(Iter)) {
for (auto &R : *VPBB) {
if (!R.cost(VF, CostCtx).isValid())
InvalidCosts.emplace_back(&R, VF);
}
}
}
}
if (InvalidCosts.empty())
return;
// Emit a report of VFs with invalid costs in the loop.
// Group the remarks per recipe, keeping the recipe order from InvalidCosts.
DenseMap<VPRecipeBase *, unsigned> Numbering;
unsigned I = 0;
for (auto &Pair : InvalidCosts)
if (Numbering.try_emplace(Pair.first, I).second)
++I;
// Sort the list, first on recipe(number) then on VF.
sort(InvalidCosts, [&Numbering](RecipeVFPair &A, RecipeVFPair &B) {
unsigned NA = Numbering[A.first];
unsigned NB = Numbering[B.first];
if (NA != NB)
return NA < NB;
return ElementCount::isKnownLT(A.second, B.second);
});
// For a list of ordered recipe-VF pairs:
// [(load, VF1), (load, VF2), (store, VF1)]
// group the recipes together to emit separate remarks for:
// load (VF1, VF2)
// store (VF1)
auto Tail = ArrayRef<RecipeVFPair>(InvalidCosts);
auto Subset = ArrayRef<RecipeVFPair>();
do {
if (Subset.empty())
Subset = Tail.take_front(1);
VPRecipeBase *R = Subset.front().first;
unsigned Opcode =
TypeSwitch<const VPRecipeBase *, unsigned>(R)
.Case([](const VPHeaderPHIRecipe *R) { return Instruction::PHI; })
.Case(
[](const VPWidenStoreRecipe *R) { return Instruction::Store; })
.Case([](const VPWidenLoadRecipe *R) { return Instruction::Load; })
.Case<VPWidenCallRecipe, VPWidenIntrinsicRecipe>(
[](const auto *R) { return Instruction::Call; })
.Case<VPInstruction, VPWidenRecipe, VPReplicateRecipe,
VPWidenCastRecipe>(
[](const auto *R) { return R->getOpcode(); })
.Case([](const VPInterleaveRecipe *R) {
return R->getStoredValues().empty() ? Instruction::Load
: Instruction::Store;
})
.Case([](const VPReductionRecipe *R) {
return RecurrenceDescriptor::getOpcode(R->getRecurrenceKind());
});
// If the next recipe is different, or if there are no other pairs,
// emit a remark for the collated subset. e.g.
// [(load, VF1), (load, VF2))]
// to emit:
// remark: invalid costs for 'load' at VF=(VF1, VF2)
if (Subset == Tail || Tail[Subset.size()].first != R) {
std::string OutString;
raw_string_ostream OS(OutString);
assert(!Subset.empty() && "Unexpected empty range");
OS << "Recipe with invalid costs prevented vectorization at VF=(";
for (const auto &Pair : Subset)
OS << (Pair.second == Subset.front().second ? "" : ", ") << Pair.second;
OS << "):";
if (Opcode == Instruction::Call) {
StringRef Name = "";
if (auto *Int = dyn_cast<VPWidenIntrinsicRecipe>(R)) {
Name = Int->getIntrinsicName();
} else {
auto *WidenCall = dyn_cast<VPWidenCallRecipe>(R);
Function *CalledFn =
WidenCall ? WidenCall->getCalledScalarFunction()
: cast<Function>(R->getOperand(R->getNumOperands() - 1)
->getLiveInIRValue());
Name = CalledFn->getName();
}
OS << " call to " << Name;
} else
OS << " " << Instruction::getOpcodeName(Opcode);
reportVectorizationInfo(OutString, "InvalidCost", ORE, OrigLoop, nullptr,
R->getDebugLoc());
Tail = Tail.drop_front(Subset.size());
Subset = {};
} else
// Grow the subset by one element
Subset = Tail.take_front(Subset.size() + 1);
} while (!Tail.empty());
}
/// Check if any recipe of \p Plan will generate a vector value, which will be
/// assigned a vector register.
static bool willGenerateVectors(VPlan &Plan, ElementCount VF,
const TargetTransformInfo &TTI) {
assert(VF.isVector() && "Checking a scalar VF?");
VPTypeAnalysis TypeInfo(Plan);
DenseSet<VPRecipeBase *> EphemeralRecipes;
collectEphemeralRecipesForVPlan(Plan, EphemeralRecipes);
// Set of already visited types.
DenseSet<Type *> Visited;
for (VPBasicBlock *VPBB : VPBlockUtils::blocksOnly<VPBasicBlock>(
vp_depth_first_shallow(Plan.getVectorLoopRegion()->getEntry()))) {
for (VPRecipeBase &R : *VPBB) {
if (EphemeralRecipes.contains(&R))
continue;
// Continue early if the recipe is considered to not produce a vector
// result. Note that this includes VPInstruction where some opcodes may
// produce a vector, to preserve existing behavior as VPInstructions model
// aspects not directly mapped to existing IR instructions.
switch (R.getVPRecipeID()) {
case VPRecipeBase::VPDerivedIVSC:
case VPRecipeBase::VPScalarIVStepsSC:
case VPRecipeBase::VPReplicateSC:
case VPRecipeBase::VPInstructionSC:
case VPRecipeBase::VPCurrentIterationPHISC:
case VPRecipeBase::VPVectorPointerSC:
case VPRecipeBase::VPVectorEndPointerSC:
case VPRecipeBase::VPExpandSCEVSC:
case VPRecipeBase::VPPredInstPHISC:
case VPRecipeBase::VPBranchOnMaskSC:
continue;
case VPRecipeBase::VPReductionSC:
case VPRecipeBase::VPActiveLaneMaskPHISC:
case VPRecipeBase::VPWidenCallSC:
case VPRecipeBase::VPWidenCanonicalIVSC:
case VPRecipeBase::VPWidenCastSC:
case VPRecipeBase::VPWidenGEPSC:
case VPRecipeBase::VPWidenIntrinsicSC:
case VPRecipeBase::VPWidenSC:
case VPRecipeBase::VPBlendSC:
case VPRecipeBase::VPFirstOrderRecurrencePHISC:
case VPRecipeBase::VPHistogramSC:
case VPRecipeBase::VPWidenPHISC:
case VPRecipeBase::VPWidenIntOrFpInductionSC:
case VPRecipeBase::VPWidenPointerInductionSC:
case VPRecipeBase::VPReductionPHISC:
case VPRecipeBase::VPInterleaveEVLSC:
case VPRecipeBase::VPInterleaveSC:
case VPRecipeBase::VPWidenLoadEVLSC:
case VPRecipeBase::VPWidenLoadSC:
case VPRecipeBase::VPWidenStoreEVLSC:
case VPRecipeBase::VPWidenStoreSC:
break;
default:
llvm_unreachable("unhandled recipe");
}
auto WillGenerateTargetVectors = [&TTI, VF](Type *VectorTy) {
unsigned NumLegalParts = TTI.getNumberOfParts(VectorTy);
if (!NumLegalParts)
return false;
if (VF.isScalable()) {
// <vscale x 1 x iN> is assumed to be profitable over iN because
// scalable registers are a distinct register class from scalar
// ones. If we ever find a target which wants to lower scalable
// vectors back to scalars, we'll need to update this code to
// explicitly ask TTI about the register class uses for each part.
return NumLegalParts <= VF.getKnownMinValue();
}
// Two or more elements that share a register - are vectorized.
return NumLegalParts < VF.getFixedValue();
};
// If no def nor is a store, e.g., branches, continue - no value to check.
if (R.getNumDefinedValues() == 0 &&
!isa<VPWidenStoreRecipe, VPWidenStoreEVLRecipe, VPInterleaveBase>(&R))
continue;
// For multi-def recipes, currently only interleaved loads, suffice to
// check first def only.
// For stores check their stored value; for interleaved stores suffice
// the check first stored value only. In all cases this is the second
// operand.
VPValue *ToCheck =
R.getNumDefinedValues() >= 1 ? R.getVPValue(0) : R.getOperand(1);
Type *ScalarTy = TypeInfo.inferScalarType(ToCheck);
if (!Visited.insert({ScalarTy}).second)
continue;
Type *WideTy = toVectorizedTy(ScalarTy, VF);
if (any_of(getContainedTypes(WideTy), WillGenerateTargetVectors))
return true;
}
}
return false;
}
static bool hasReplicatorRegion(VPlan &Plan) {
return any_of(VPBlockUtils::blocksOnly<VPRegionBlock>(vp_depth_first_shallow(
Plan.getVectorLoopRegion()->getEntry())),
[](auto *VPRB) { return VPRB->isReplicator(); });
}
/// Returns true if the VPlan contains a VPReductionPHIRecipe with
/// FindLast recurrence kind.
static bool hasFindLastReductionPhi(VPlan &Plan) {
return any_of(Plan.getVectorLoopRegion()->getEntryBasicBlock()->phis(),
[](VPRecipeBase &R) {
auto *RedPhi = dyn_cast<VPReductionPHIRecipe>(&R);
return RedPhi &&
RecurrenceDescriptor::isFindLastRecurrenceKind(
RedPhi->getRecurrenceKind());
});
}
/// Returns true if the VPlan contains header phi recipes that are not currently
/// supported for epilogue vectorization.
static bool hasUnsupportedHeaderPhiRecipe(VPlan &Plan) {
return any_of(
Plan.getVectorLoopRegion()->getEntryBasicBlock()->phis(),
[](VPRecipeBase &R) {
switch (R.getVPRecipeID()) {
case VPRecipeBase::VPFirstOrderRecurrencePHISC:
// TODO: Add support for fixed-order recurrences.
return true;
case VPRecipeBase::VPWidenIntOrFpInductionSC:
return !cast<VPWidenIntOrFpInductionRecipe>(&R)->getPHINode();
case VPRecipeBase::VPReductionPHISC: {
auto *RedPhi = cast<VPReductionPHIRecipe>(&R);
// TODO: Support FMinNum/FMaxNum, FindLast reductions, and reductions
// without underlying values.
RecurKind Kind = RedPhi->getRecurrenceKind();
if (RecurrenceDescriptor::isFPMinMaxNumRecurrenceKind(Kind) ||
RecurrenceDescriptor::isFindLastRecurrenceKind(Kind) ||
!RedPhi->getUnderlyingValue())
return true;
// TODO: Add support for FindIV reductions with sunk expressions: the
// resume value from the main loop is in expression domain (e.g.,
// mul(ReducedIV, 3)), but the epilogue tracks raw IV values. A sunk
// expression is identified by a non-VPInstruction user of
// ComputeReductionResult.
if (RecurrenceDescriptor::isFindIVRecurrenceKind(Kind)) {
auto *RdxResult = vputils::findComputeReductionResult(RedPhi);
assert(RdxResult &&
"FindIV reduction must have ComputeReductionResult");
return any_of(RdxResult->users(),
std::not_fn(IsaPred<VPInstruction>));
}
return false;
}
default:
return false;
};
});
}
bool LoopVectorizationPlanner::isCandidateForEpilogueVectorization(
VPlan &MainPlan) const {
// Bail out if the plan contains header phi recipes not yet supported
// for epilogue vectorization.
if (hasUnsupportedHeaderPhiRecipe(MainPlan))
return false;
// Epilogue vectorization code has not been auditted to ensure it handles
// non-latch exits properly. It may be fine, but it needs auditted and
// tested.
// TODO: Add support for loops with an early exit.
if (OrigLoop->getExitingBlock() != OrigLoop->getLoopLatch())
return false;
return true;
}
bool LoopVectorizationCostModel::isEpilogueVectorizationProfitable(
const ElementCount VF, const unsigned IC) const {
// FIXME: We need a much better cost-model to take different parameters such
// as register pressure, code size increase and cost of extra branches into
// account. For now we apply a very crude heuristic and only consider loops
// with vectorization factors larger than a certain value.
// Allow the target to opt out.
if (!TTI.preferEpilogueVectorization(VF * IC))
return false;
unsigned MinVFThreshold = EpilogueVectorizationMinVF.getNumOccurrences() > 0
? EpilogueVectorizationMinVF
: TTI.getEpilogueVectorizationMinVF();
return estimateElementCount(VF * IC, Config.getVScaleForTuning()) >=
MinVFThreshold;
}
std::unique_ptr<VPlan> LoopVectorizationPlanner::selectBestEpiloguePlan(
VPlan &MainPlan, ElementCount MainLoopVF, unsigned IC) {
if (!EnableEpilogueVectorization) {
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is disabled.\n");
return nullptr;
}
if (!CM.isEpilogueAllowed()) {
LLVM_DEBUG(dbgs() << "LEV: Unable to vectorize epilogue because no "
"epilogue is allowed.\n");
return nullptr;
}
// Not really a cost consideration, but check for unsupported cases here to
// simplify the logic.
if (!isCandidateForEpilogueVectorization(MainPlan)) {
LLVM_DEBUG(dbgs() << "LEV: Unable to vectorize epilogue because the loop "
"is not a supported candidate.\n");
return nullptr;
}
if (EpilogueVectorizationForceVF > 1) {
if (EpilogueVectorizationForceVF >=
IC * estimateElementCount(MainLoopVF, Config.getVScaleForTuning())) {
// Note that the main loop leaves IC * MainLoopVF iterations iff a scalar
// epilogue is required, but then the epilogue loop also requires a scalar
// epilogue.
LLVM_DEBUG(dbgs() << "LEV: Forced epilogue VF results in dead epilogue "
"vector loop, skipping vectorizing epilogue.\n");
return nullptr;
}
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization factor is forced.\n");
ElementCount ForcedEC = ElementCount::getFixed(EpilogueVectorizationForceVF);
if (hasPlanWithVF(ForcedEC)) {
std::unique_ptr<VPlan> Clone(getPlanFor(ForcedEC).duplicate());
Clone->setVF(ForcedEC);
return Clone;
}
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization forced factor is not "
"viable.\n");
return nullptr;
}
if (OrigLoop->getHeader()->getParent()->hasOptSize()) {
LLVM_DEBUG(
dbgs() << "LEV: Epilogue vectorization skipped due to opt for size.\n");
return nullptr;
}
if (!CM.isEpilogueVectorizationProfitable(MainLoopVF, IC)) {
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is not profitable for "
"this loop\n");
return nullptr;
}
// Check if a plan's vector loop processes fewer iterations than VF (e.g. when
// interleave groups have been narrowed) narrowInterleaveGroups) and return
// the adjusted, effective VF.
using namespace VPlanPatternMatch;
auto GetEffectiveVF = [](VPlan &Plan, ElementCount VF) -> ElementCount {
auto *Exiting = Plan.getVectorLoopRegion()->getExitingBasicBlock();
if (match(&Exiting->back(),
m_BranchOnCount(m_Add(m_CanonicalIV(), m_Specific(&Plan.getUF())),
m_VPValue())))
return ElementCount::get(1, VF.isScalable());
return VF;
};
// Check if the main loop processes fewer than MainLoopVF elements per
// iteration (e.g. due to narrowing interleave groups). Adjust MainLoopVF
// as needed.
MainLoopVF = GetEffectiveVF(MainPlan, MainLoopVF);
// If MainLoopVF = vscale x 2, and vscale is expected to be 4, then we know
// the main loop handles 8 lanes per iteration. We could still benefit from
// vectorizing the epilogue loop with VF=4.
ElementCount EstimatedRuntimeVF = ElementCount::getFixed(
estimateElementCount(MainLoopVF, Config.getVScaleForTuning()));
Type *TCType = Legal->getWidestInductionType();
const SCEV *RemainingIterations = nullptr;
unsigned MaxTripCount = 0;
const SCEV *TC = vputils::getSCEVExprForVPValue(MainPlan.getTripCount(), PSE);
assert(!isa<SCEVCouldNotCompute>(TC) && "Trip count SCEV must be computable");
const SCEV *KnownMinTC;
bool ScalableTC = match(TC, m_scev_c_Mul(m_SCEV(KnownMinTC), m_SCEVVScale()));
bool ScalableRemIter = false;
ScalarEvolution &SE = *PSE.getSE();
// Use versions of TC and VF in which both are either scalable or fixed.
if (ScalableTC == MainLoopVF.isScalable()) {
ScalableRemIter = ScalableTC;
RemainingIterations =
SE.getURemExpr(TC, SE.getElementCount(TCType, MainLoopVF * IC));
} else if (ScalableTC) {
const SCEV *EstimatedTC = SE.getMulExpr(
KnownMinTC,
SE.getConstant(TCType, Config.getVScaleForTuning().value_or(1)));
RemainingIterations = SE.getURemExpr(
EstimatedTC, SE.getElementCount(TCType, MainLoopVF * IC));
} else
RemainingIterations =
SE.getURemExpr(TC, SE.getElementCount(TCType, EstimatedRuntimeVF * IC));
// No iterations left to process in the epilogue.
if (RemainingIterations->isZero())
return nullptr;
if (MainLoopVF.isFixed()) {
MaxTripCount = MainLoopVF.getFixedValue() * IC - 1;
if (SE.isKnownPredicate(CmpInst::ICMP_ULT, RemainingIterations,
SE.getConstant(TCType, MaxTripCount))) {
MaxTripCount = SE.getUnsignedRangeMax(RemainingIterations).getZExtValue();
}
LLVM_DEBUG(dbgs() << "LEV: Maximum Trip Count for Epilogue: "
<< MaxTripCount << "\n");
}
auto SkipVF = [&](const SCEV *VF, const SCEV *RemIter) -> bool {
return SE.isKnownPredicate(CmpInst::ICMP_UGT, VF, RemIter);
};
VectorizationFactor Result = VectorizationFactor::Disabled();
VPlan *BestPlan = nullptr;
for (auto &NextVF : ProfitableVFs) {
// Skip candidate VFs without a corresponding VPlan.
if (!hasPlanWithVF(NextVF.Width))
continue;
VPlan &CurrentPlan = getPlanFor(NextVF.Width);
ElementCount EffectiveVF = GetEffectiveVF(CurrentPlan, NextVF.Width);
// Skip candidate VFs with widths >= the (estimated) runtime VF (scalable
// vectors) or > the VF of the main loop (fixed vectors).
if ((!EffectiveVF.isScalable() && MainLoopVF.isScalable() &&
ElementCount::isKnownGE(EffectiveVF, EstimatedRuntimeVF)) ||
(EffectiveVF.isScalable() &&
ElementCount::isKnownGE(EffectiveVF, MainLoopVF)) ||
(!EffectiveVF.isScalable() && !MainLoopVF.isScalable() &&
ElementCount::isKnownGT(EffectiveVF, MainLoopVF)))
continue;
// If EffectiveVF is greater than the number of remaining iterations, the
// epilogue loop would be dead. Skip such factors. If the epilogue plan
// also has narrowed interleave groups, use the effective VF since
// the epilogue step will be reduced to its IC.
// TODO: We should also consider comparing against a scalable
// RemainingIterations when SCEV be able to evaluate non-canonical
// vscale-based expressions.
if (!ScalableRemIter) {
// Handle the case where EffectiveVF and RemainingIterations are in
// different numerical spaces.
if (EffectiveVF.isScalable())
EffectiveVF = ElementCount::getFixed(
estimateElementCount(EffectiveVF, Config.getVScaleForTuning()));
if (SkipVF(SE.getElementCount(TCType, EffectiveVF), RemainingIterations))
continue;
}
if (Result.Width.isScalar() ||
isMoreProfitable(NextVF, Result, MaxTripCount, !CM.foldTailByMasking(),
/*IsEpilogue*/ true)) {
Result = NextVF;
BestPlan = &CurrentPlan;
}
}
if (!BestPlan)
return nullptr;
LLVM_DEBUG(dbgs() << "LEV: Vectorizing epilogue loop with VF = "
<< Result.Width << "\n");
std::unique_ptr<VPlan> Clone(BestPlan->duplicate());
Clone->setVF(Result.Width);
return Clone;
}
unsigned
LoopVectorizationPlanner::selectInterleaveCount(VPlan &Plan, ElementCount VF,
InstructionCost 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.
// Only interleave tail-folded loops if wide lane masks are requested, as the
// overhead of multiple instructions to calculate the predicate is likely
// not beneficial. If an epilogue is not allowed for any other reason,
// do not interleave.
if (!CM.isEpilogueAllowed() &&
!(CM.preferTailFoldedLoop() && CM.useWideActiveLaneMask()))
return 1;
if (any_of(Plan.getVectorLoopRegion()->getEntryBasicBlock()->phis(),
IsaPred<VPCurrentIterationPHIRecipe>)) {
LLVM_DEBUG(dbgs() << "LV: Loop requires variable-length step. "
"Unroll factor forced to be 1.\n");
return 1;
}
// We used the distance for the interleave count.
if (!Legal->isSafeForAnyVectorWidth())
return 1;
// We don't attempt to perform interleaving for loops with uncountable early
// exits because the VPInstruction::AnyOf code cannot currently handle
// multiple parts.
if (Plan.hasEarlyExit())
return 1;
const bool HasReductions =
any_of(Plan.getVectorLoopRegion()->getEntryBasicBlock()->phis(),
IsaPred<VPReductionPHIRecipe>);
// FIXME: implement interleaving for FindLast transform correctly.
if (hasFindLastReductionPhi(Plan))
return 1;
VPRegisterUsage R =
calculateRegisterUsageForPlan(Plan, {VF}, TTI, CM.ValuesToIgnore)[0];
// 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) {
if (VF.isScalar())
LoopCost = CM.expectedCost(VF);
else
LoopCost = cost(Plan, VF, &R);
assert(LoopCost.isValid() && "Expected to have chosen a VF with valid cost");
// Loop body is free and there is no need for interleaving.
if (LoopCost == 0)
return 1;
}
// 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 (const 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.contains(Pair.first))
LoopInvariantRegs = R.LoopInvariantRegs[Pair.first];
unsigned TmpIC = llvm::bit_floor((TargetNumRegisters - LoopInvariantRegs) /
MaxLocalUsers);
// Don't count the induction variable as interleaved.
if (EnableIndVarRegisterHeur) {
TmpIC = llvm::bit_floor((TargetNumRegisters - LoopInvariantRegs - 1) /
std::max(1U, (MaxLocalUsers - 1)));
}
IC = std::min(IC, TmpIC);
}
// Clamp the interleave ranges to reasonable counts.
unsigned MaxInterleaveCount = TTI.getMaxInterleaveFactor(VF);
LLVM_DEBUG(dbgs() << "LV: MaxInterleaveFactor for the target is "
<< MaxInterleaveCount << "\n");
// Check if the user has overridden the max.
if (VF.isScalar()) {
if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor;
} else {
if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor;
}
// Try to get the exact trip count, or an estimate based on profiling data or
// ConstantMax from PSE, failing that.
auto BestKnownTC =
getSmallBestKnownTC(PSE, OrigLoop,
/*CanUseConstantMax=*/true,
/*CanExcludeZeroTrips=*/CM.isEpilogueAllowed());
// For fixed length VFs treat a scalable trip count as unknown.
if (BestKnownTC && (BestKnownTC->isFixed() || VF.isScalable())) {
// Re-evaluate trip counts and VFs to be in the same numerical space.
unsigned AvailableTC =
estimateElementCount(*BestKnownTC, Config.getVScaleForTuning());
unsigned EstimatedVF =
estimateElementCount(VF, Config.getVScaleForTuning());
// At least one iteration must be scalar when this constraint holds. So the
// maximum available iterations for interleaving is one less.
if (CM.requiresScalarEpilogue(VF.isVector()))
--AvailableTC;
unsigned InterleaveCountLB = bit_floor(std::max(
1u, std::min(AvailableTC / (EstimatedVF * 2), MaxInterleaveCount)));
if (getSmallConstantTripCount(PSE.getSE(), OrigLoop).isNonZero()) {
// If the best known trip count is exact, we select between two
// prospective ICs, where
//
// 1) the aggressive IC is capped by the trip count divided by VF
// 2) the conservative IC is capped by the trip count divided by (VF * 2)
//
// The final IC is selected in a way that the epilogue loop trip count is
// minimized while maximizing the IC itself, so that we either run the
// vector loop at least once if it generates a small epilogue loop, or
// else we run the vector loop at least twice.
unsigned InterleaveCountUB = bit_floor(std::max(
1u, std::min(AvailableTC / EstimatedVF, MaxInterleaveCount)));
MaxInterleaveCount = InterleaveCountLB;
if (InterleaveCountUB != InterleaveCountLB) {
unsigned TailTripCountUB =
(AvailableTC % (EstimatedVF * InterleaveCountUB));
unsigned TailTripCountLB =
(AvailableTC % (EstimatedVF * InterleaveCountLB));
// If both produce same scalar tail, maximize the IC to do the same work
// in fewer vector loop iterations
if (TailTripCountUB == TailTripCountLB)
MaxInterleaveCount = InterleaveCountUB;
}
} else {
// If trip count is an estimated compile time constant, limit the
// IC to be capped by the trip count divided by VF * 2, such that the
// vector loop runs at least twice to make interleaving seem profitable
// when there is an epilogue loop present. Since exact Trip count is not
// known we choose to be conservative in our IC estimate.
MaxInterleaveCount = InterleaveCountLB;
}
}
assert(MaxInterleaveCount > 0 &&
"Maximum interleave count must be greater than 0");
// 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
// Make sure IC is greater than 0.
IC = std::max(1u, IC);
assert(IC > 0 && "Interleave count must be greater than 0.");
// 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;
}
// For any scalar loop that either requires runtime checks or tail-folding we
// are better off leaving this to the unroller. 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 ScalarInterleavingRequiresPredication =
(VF.isScalar() && any_of(OrigLoop->blocks(), [this](BasicBlock *BB) {
return Legal->blockNeedsPredication(BB);
}));
bool ScalarInterleavingRequiresRuntimePointerCheck =
(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 << '\n');
const bool AggressivelyInterleave =
TTI.enableAggressiveInterleaving(HasReductions);
if (!ScalarInterleavingRequiresRuntimePointerCheck &&
!ScalarInterleavingRequiresPredication && 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)llvm::bit_floor<uint64_t>(
SmallLoopCost / LoopCost.getValue()));
// Interleave until store/load ports (estimated by max interleave count) are
// saturated.
unsigned NumStores = 0;
unsigned NumLoads = 0;
for (VPBasicBlock *VPBB : VPBlockUtils::blocksOnly<VPBasicBlock>(
vp_depth_first_deep(Plan.getVectorLoopRegion()->getEntry()))) {
for (VPRecipeBase &R : *VPBB) {
if (isa<VPWidenLoadRecipe, VPWidenLoadEVLRecipe>(&R)) {
NumLoads++;
continue;
}
if (isa<VPWidenStoreRecipe, VPWidenStoreEVLRecipe>(&R)) {
NumStores++;
continue;
}
if (auto *InterleaveR = dyn_cast<VPInterleaveRecipe>(&R)) {
if (unsigned StoreOps = InterleaveR->getNumStoreOperands())
NumStores += StoreOps;
else
NumLoads += InterleaveR->getNumDefinedValues();
continue;
}
if (auto *RepR = dyn_cast<VPReplicateRecipe>(&R)) {
NumLoads += isa<LoadInst>(RepR->getUnderlyingInstr());
NumStores += isa<StoreInst>(RepR->getUnderlyingInstr());
continue;
}
if (isa<VPHistogramRecipe>(&R)) {
NumLoads++;
NumStores++;
continue;
}
}
}
unsigned StoresIC = IC / (NumStores ? NumStores : 1);
unsigned LoadsIC = IC / (NumLoads ? NumLoads : 1);
// There is little point in interleaving for reductions containing selects
// and compares when VF=1 since it may just create more overhead than it's
// worth for loops with small trip counts. This is because we still have to
// do the final reduction after the loop.
bool HasSelectCmpReductions =
HasReductions &&
any_of(Plan.getVectorLoopRegion()->getEntryBasicBlock()->phis(),
[](VPRecipeBase &R) {
auto *RedR = dyn_cast<VPReductionPHIRecipe>(&R);
return RedR && (RecurrenceDescriptor::isAnyOfRecurrenceKind(
RedR->getRecurrenceKind()) ||
RecurrenceDescriptor::isFindIVRecurrenceKind(
RedR->getRecurrenceKind()));
});
if (HasSelectCmpReductions) {
LLVM_DEBUG(dbgs() << "LV: Not interleaving select-cmp reductions.\n");
return 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. For tree-wise reductions
// set the limit to 2, and for ordered reductions it's best to disable
// interleaving entirely.
if (HasReductions && OrigLoop->getLoopDepth() > 1) {
bool HasOrderedReductions =
any_of(Plan.getVectorLoopRegion()->getEntryBasicBlock()->phis(),
[](VPRecipeBase &R) {
auto *RedR = dyn_cast<VPReductionPHIRecipe>(&R);
return RedR && RedR->isOrdered();
});
if (HasOrderedReductions) {
LLVM_DEBUG(
dbgs() << "LV: Not interleaving scalar ordered reductions.\n");
return 1;
}
unsigned F = 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 (VF.isScalar() && AggressivelyInterleave) {
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);
}
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 (AggressivelyInterleave) {
LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
return IC;
}
LLVM_DEBUG(dbgs() << "LV: Not Interleaving.\n");
return 1;
}
bool LoopVectorizationCostModel::useEmulatedMaskMemRefHack(Instruction *I,
ElementCount VF) {
// 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) {
assert(VF.isVector() && "Expected VF >= 2");
// If we've already collected the instructions to scalarize or the predicated
// BBs after vectorization, 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 (InstsToScalarize.contains(VF) ||
PredicatedBBsAfterVectorization.contains(VF))
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 (!blockNeedsPredicationForAnyReason(BB))
continue;
for (Instruction &I : *BB)
if (isScalarWithPredication(&I, VF)) {
ScalarCostsTy ScalarCosts;
// Do not apply discount logic for:
// 1. Scalars after vectorization, as there will only be a single copy
// of the instruction.
// 2. Scalable VF, as that would lead to invalid scalarization costs.
// 3. Emulated masked memrefs, if a hacked cost is needed.
if (!isScalarAfterVectorization(&I, VF) && !VF.isScalable() &&
!useEmulatedMaskMemRefHack(&I, VF) &&
computePredInstDiscount(&I, ScalarCosts, VF) >= 0) {
for (const auto &[I, IC] : ScalarCosts)
ScalarCostsVF.insert({I, IC});
// Check if we decided to scalarize a call. If so, update the widening
// decision of the call to CM_Scalarize with the computed scalar cost.
for (const auto &[I, Cost] : ScalarCosts) {
auto *CI = dyn_cast<CallInst>(I);
if (!CI || !CallWideningDecisions.contains({CI, VF}))
continue;
CallWideningDecisions[{CI, VF}].Kind = CM_Scalarize;
CallWideningDecisions[{CI, VF}].Cost = Cost;
}
}
// Remember that BB will remain after vectorization.
PredicatedBBsAfterVectorization[VF].insert(BB);
for (auto *Pred : predecessors(BB)) {
if (Pred->getSingleSuccessor() == BB)
PredicatedBBsAfterVectorization[VF].insert(Pred);
}
}
}
}
InstructionCost LoopVectorizationCostModel::computePredInstDiscount(
Instruction *PredInst, ScalarCostsTy &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.
InstructionCost 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, VF))
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.contains(I))
continue;
// Cannot scalarize fixed-order recurrence phis at the moment.
if (isa<PHINode>(I) && Legal->isFixedOrderRecurrence(cast<PHINode>(I)))
continue;
// Compute the cost of the vector instruction. Note that this cost already
// includes the scalarization overhead of the predicated instruction.
InstructionCost VectorCost = getInstructionCost(I, VF);
// 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.
InstructionCost ScalarCost =
VF.getFixedValue() * getInstructionCost(I, ElementCount::getFixed(1));
// Compute the scalarization overhead of needed insertelement instructions
// and phi nodes.
if (isScalarWithPredication(I, VF) && !I->getType()->isVoidTy()) {
Type *WideTy = toVectorizedTy(I->getType(), VF);
for (Type *VectorTy : getContainedTypes(WideTy)) {
ScalarCost += TTI.getScalarizationOverhead(
cast<VectorType>(VectorTy), APInt::getAllOnes(VF.getFixedValue()),
/*Insert=*/true,
/*Extract=*/false, Config.CostKind);
}
ScalarCost += VF.getFixedValue() *
TTI.getCFInstrCost(Instruction::PHI, Config.CostKind);
}
// 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(canVectorizeTy(J->getType()) &&
"Instruction has non-scalar type");
if (CanBeScalarized(J))
Worklist.push_back(J);
else if (needsExtract(J, VF)) {
Type *WideTy = toVectorizedTy(J->getType(), VF);
for (Type *VectorTy : getContainedTypes(WideTy)) {
ScalarCost += TTI.getScalarizationOverhead(
cast<VectorType>(VectorTy),
APInt::getAllOnes(VF.getFixedValue()), /*Insert*/ false,
/*Extract*/ true, Config.CostKind);
}
}
}
// Scale the total scalar cost by block probability.
ScalarCost /= getPredBlockCostDivisor(Config.CostKind, I->getParent());
// 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;
}
InstructionCost LoopVectorizationCostModel::expectedCost(ElementCount VF) {
InstructionCost Cost;
assert(VF.isScalar() && "must only be called for scalar VFs");
// For each block.
for (BasicBlock *BB : TheLoop->blocks()) {
InstructionCost BlockCost;
// For each instruction in the old loop.
for (Instruction &I : *BB) {
// Skip ignored values.
if (ValuesToIgnore.count(&I) ||
(VF.isVector() && VecValuesToIgnore.count(&I)))
continue;
InstructionCost C = getInstructionCost(&I, VF);
// Check if we should override the cost.
if (C.isValid() && ForceTargetInstructionCost.getNumOccurrences() > 0)
C = InstructionCost(ForceTargetInstructionCost);
BlockCost += C;
LLVM_DEBUG(dbgs() << "LV: Found an estimated cost of " << C << " for VF "
<< VF << " For instruction: " << I << '\n');
}
// In the scalar loop, we may not always execute the predicated block, if it
// is an if-else block. Thus, scale the block's cost by the probability of
// executing it. getPredBlockCostDivisor will return 1 for blocks that are
// only predicated by the header mask when folding the tail.
Cost += BlockCost / getPredBlockCostDivisor(Config.CostKind, BB);
}
return Cost;
}
/// Gets the address access SCEV for Ptr, if it should be used for cost modeling
/// according to isAddressSCEVForCost.
///
/// This SCEV can be sent to the Target in order to estimate the address
/// calculation cost.
static const SCEV *getAddressAccessSCEV(
Value *Ptr,
PredicatedScalarEvolution &PSE,
const Loop *TheLoop) {
const SCEV *Addr = PSE.getSCEV(Ptr);
return vputils::isAddressSCEVForCost(Addr, *PSE.getSE(), TheLoop) ? Addr
: nullptr;
}
InstructionCost
LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I,
ElementCount VF) {
assert(VF.isVector() &&
"Scalarization cost of instruction implies vectorization.");
if (VF.isScalable())
return InstructionCost::getInvalid();
Type *ValTy = getLoadStoreType(I);
auto *SE = PSE.getSE();
unsigned AS = getLoadStoreAddressSpace(I);
Value *Ptr = getLoadStorePointerOperand(I);
Type *PtrTy = toVectorTy(Ptr->getType(), VF);
// NOTE: PtrTy is a vector to signal `TTI::getAddressComputationCost`
// that it is being called from this specific place.
// Figure out whether the access is strided and get the stride value
// if it's known in compile time
const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, PSE, TheLoop);
// Get the cost of the scalar memory instruction and address computation.
InstructionCost Cost =
VF.getFixedValue() *
TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV, Config.CostKind);
// 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);
TTI::OperandValueInfo OpInfo = TTI::getOperandInfo(I->getOperand(0));
Cost += VF.getFixedValue() *
TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment,
AS, Config.CostKind, OpInfo);
// Get the overhead of the extractelement and insertelement instructions
// we might create due to scalarization.
Cost += getScalarizationOverhead(I, VF);
// If we have a predicated load/store, it will need extra i1 extracts and
// conditional branches, but may not be executed for each vector lane. Scale
// the cost by the probability of executing the predicated block.
if (isPredicatedInst(I)) {
Cost /= getPredBlockCostDivisor(Config.CostKind, I->getParent());
// Add the cost of an i1 extract and a branch
auto *VecI1Ty =
VectorType::get(IntegerType::getInt1Ty(ValTy->getContext()), VF);
Cost += TTI.getScalarizationOverhead(
VecI1Ty, APInt::getAllOnes(VF.getFixedValue()),
/*Insert=*/false, /*Extract=*/true, Config.CostKind);
Cost += TTI.getCFInstrCost(Instruction::CondBr, Config.CostKind);
if (useEmulatedMaskMemRefHack(I, VF))
// Artificially setting to a high enough value to practically disable
// vectorization with such operations.
Cost = 3000000;
}
return Cost;
}
InstructionCost
LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getLoadStoreType(I);
auto *VectorTy = cast<VectorType>(toVectorTy(ValTy, VF));
Value *Ptr = getLoadStorePointerOperand(I);
unsigned AS = getLoadStoreAddressSpace(I);
int ConsecutiveStride = Legal->isConsecutivePtr(ValTy, Ptr);
assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
"Stride should be 1 or -1 for consecutive memory access");
const Align Alignment = getLoadStoreAlignment(I);
InstructionCost Cost = 0;
if (isMaskRequired(I)) {
unsigned IID = I->getOpcode() == Instruction::Load
? Intrinsic::masked_load
: Intrinsic::masked_store;
Cost += TTI.getMemIntrinsicInstrCost(
MemIntrinsicCostAttributes(IID, VectorTy, Alignment, AS),
Config.CostKind);
} else {
TTI::OperandValueInfo OpInfo = TTI::getOperandInfo(I->getOperand(0));
Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS,
Config.CostKind, OpInfo, I);
}
bool Reverse = ConsecutiveStride < 0;
if (Reverse)
Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy,
VectorTy, {}, Config.CostKind, 0);
return Cost;
}
InstructionCost
LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I,
ElementCount VF) {
assert(Legal->isUniformMemOp(*I, VF));
Type *ValTy = getLoadStoreType(I);
Type *PtrTy = getLoadStorePointerOperand(I)->getType();
auto *VectorTy = cast<VectorType>(toVectorTy(ValTy, VF));
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
if (isa<LoadInst>(I)) {
return TTI.getAddressComputationCost(PtrTy, nullptr, nullptr,
Config.CostKind) +
TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS,
Config.CostKind) +
TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy,
VectorTy, {}, Config.CostKind);
}
StoreInst *SI = cast<StoreInst>(I);
bool IsLoopInvariantStoreValue = Legal->isInvariant(SI->getValueOperand());
// TODO: We have existing tests that request the cost of extracting element
// VF.getKnownMinValue() - 1 from a scalable vector. This does not represent
// the actual generated code, which involves extracting the last element of
// a scalable vector where the lane to extract is unknown at compile time.
InstructionCost Cost =
TTI.getAddressComputationCost(PtrTy, nullptr, nullptr, Config.CostKind) +
TTI.getMemoryOpCost(Instruction::Store, ValTy, Alignment, AS,
Config.CostKind);
if (!IsLoopInvariantStoreValue)
Cost += TTI.getIndexedVectorInstrCostFromEnd(Instruction::ExtractElement,
VectorTy, Config.CostKind, 0);
return Cost;
}
InstructionCost
LoopVectorizationCostModel::getGatherScatterCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getLoadStoreType(I);
auto *VectorTy = cast<VectorType>(toVectorTy(ValTy, VF));
const Align Alignment = getLoadStoreAlignment(I);
Value *Ptr = getLoadStorePointerOperand(I);
Type *PtrTy = Ptr->getType();
if (!Legal->isUniform(Ptr, VF))
PtrTy = toVectorTy(PtrTy, VF);
unsigned IID = I->getOpcode() == Instruction::Load
? Intrinsic::masked_gather
: Intrinsic::masked_scatter;
return TTI.getAddressComputationCost(PtrTy, nullptr, nullptr,
Config.CostKind) +
TTI.getMemIntrinsicInstrCost(
MemIntrinsicCostAttributes(IID, VectorTy, Ptr, isMaskRequired(I),
Alignment, I),
Config.CostKind);
}
InstructionCost
LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I,
ElementCount VF) {
const auto *Group = getInterleavedAccessGroup(I);
assert(Group && "Fail to get an interleaved access group.");
Instruction *InsertPos = Group->getInsertPos();
Type *ValTy = getLoadStoreType(InsertPos);
auto *VectorTy = cast<VectorType>(toVectorTy(ValTy, VF));
unsigned AS = getLoadStoreAddressSpace(InsertPos);
unsigned InterleaveFactor = Group->getFactor();
auto *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor);
// Holds the indices of existing members in the interleaved group.
SmallVector<unsigned, 4> Indices;
for (unsigned IF = 0; IF < InterleaveFactor; IF++)
if (Group->getMember(IF))
Indices.push_back(IF);
// Calculate the cost of the whole interleaved group.
bool UseMaskForGaps =
(Group->requiresScalarEpilogue() && !isEpilogueAllowed()) ||
(isa<StoreInst>(I) && !Group->isFull());
InstructionCost Cost = TTI.getInterleavedMemoryOpCost(
InsertPos->getOpcode(), WideVecTy, Group->getFactor(), Indices,
Group->getAlign(), AS, Config.CostKind, isMaskRequired(I),
UseMaskForGaps);
if (Group->isReverse()) {
// TODO: Add support for reversed masked interleaved access.
assert(!isMaskRequired(I) &&
"Reverse masked interleaved access not supported.");
Cost += Group->getNumMembers() *
TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy,
VectorTy, {}, Config.CostKind, 0);
}
return Cost;
}
std::optional<InstructionCost>
LoopVectorizationCostModel::getReductionPatternCost(Instruction *I,
ElementCount VF,
Type *Ty) const {
using namespace llvm::PatternMatch;
// Early exit for no inloop reductions
if (Config.getInLoopReductions().empty() || VF.isScalar() ||
!isa<VectorType>(Ty))
return std::nullopt;
auto *VectorTy = cast<VectorType>(Ty);
// We are looking for a pattern of, and finding the minimal acceptable cost:
// reduce(mul(ext(A), ext(B))) or
// reduce(mul(A, B)) or
// reduce(ext(A)) or
// reduce(A).
// The basic idea is that we walk down the tree to do that, finding the root
// reduction instruction in InLoopReductionImmediateChains. From there we find
// the pattern of mul/ext and test the cost of the entire pattern vs the cost
// of the components. If the reduction cost is lower then we return it for the
// reduction instruction and 0 for the other instructions in the pattern. If
// it is not we return an invalid cost specifying the orignal cost method
// should be used.
Instruction *RetI = I;
if (match(RetI, m_ZExtOrSExt(m_Value()))) {
if (!RetI->hasOneUser())
return std::nullopt;
RetI = RetI->user_back();
}
if (match(RetI, m_OneUse(m_Mul(m_Value(), m_Value()))) &&
RetI->user_back()->getOpcode() == Instruction::Add) {
RetI = RetI->user_back();
}
// Test if the found instruction is a reduction, and if not return an invalid
// cost specifying the parent to use the original cost modelling.
Instruction *LastChain = Config.getInLoopReductionImmediateChain(RetI);
if (!LastChain)
return std::nullopt;
// Find the reduction this chain is a part of and calculate the basic cost of
// the reduction on its own.
Instruction *ReductionPhi = LastChain;
while (!isa<PHINode>(ReductionPhi))
ReductionPhi = Config.getInLoopReductionImmediateChain(ReductionPhi);
const RecurrenceDescriptor &RdxDesc =
Legal->getRecurrenceDescriptor(cast<PHINode>(ReductionPhi));
InstructionCost BaseCost;
RecurKind RK = RdxDesc.getRecurrenceKind();
if (RecurrenceDescriptor::isMinMaxRecurrenceKind(RK)) {
Intrinsic::ID MinMaxID = getMinMaxReductionIntrinsicOp(RK);
BaseCost = TTI.getMinMaxReductionCost(
MinMaxID, VectorTy, RdxDesc.getFastMathFlags(), Config.CostKind);
} else {
BaseCost = TTI.getArithmeticReductionCost(RdxDesc.getOpcode(), VectorTy,
RdxDesc.getFastMathFlags(),
Config.CostKind);
}
// For a call to the llvm.fmuladd intrinsic we need to add the cost of a
// normal fmul instruction to the cost of the fadd reduction.
if (RK == RecurKind::FMulAdd)
BaseCost += TTI.getArithmeticInstrCost(Instruction::FMul, VectorTy,
Config.CostKind);
// If we're using ordered reductions then we can just return the base cost
// here, since getArithmeticReductionCost calculates the full ordered
// reduction cost when FP reassociation is not allowed.
if (Config.useOrderedReductions(RdxDesc))
return BaseCost;
// Get the operand that was not the reduction chain and match it to one of the
// patterns, returning the better cost if it is found.
Instruction *RedOp = RetI->getOperand(1) == LastChain
? dyn_cast<Instruction>(RetI->getOperand(0))
: dyn_cast<Instruction>(RetI->getOperand(1));
VectorTy = VectorType::get(I->getOperand(0)->getType(), VectorTy);
Instruction *Op0, *Op1;
if (RedOp && RdxDesc.getOpcode() == Instruction::Add &&
match(RedOp,
m_ZExtOrSExt(m_Mul(m_Instruction(Op0), m_Instruction(Op1)))) &&
match(Op0, m_ZExtOrSExt(m_Value())) &&
Op0->getOpcode() == Op1->getOpcode() &&
Op0->getOperand(0)->getType() == Op1->getOperand(0)->getType() &&
!TheLoop->isLoopInvariant(Op0) && !TheLoop->isLoopInvariant(Op1) &&
(Op0->getOpcode() == RedOp->getOpcode() || Op0 == Op1)) {
// Matched reduce.add(ext(mul(ext(A), ext(B)))
// Note that the extend opcodes need to all match, or if A==B they will have
// been converted to zext(mul(sext(A), sext(A))) as it is known positive,
// which is equally fine.
bool IsUnsigned = isa<ZExtInst>(Op0);
auto *ExtType = VectorType::get(Op0->getOperand(0)->getType(), VectorTy);
auto *MulType = VectorType::get(Op0->getType(), VectorTy);
InstructionCost ExtCost =
TTI.getCastInstrCost(Op0->getOpcode(), MulType, ExtType,
TTI::CastContextHint::None, Config.CostKind, Op0);
InstructionCost MulCost =
TTI.getArithmeticInstrCost(Instruction::Mul, MulType, Config.CostKind);
InstructionCost Ext2Cost = TTI.getCastInstrCost(
RedOp->getOpcode(), VectorTy, MulType, TTI::CastContextHint::None,
Config.CostKind, RedOp);
InstructionCost RedCost = TTI.getMulAccReductionCost(
IsUnsigned, RdxDesc.getOpcode(), RdxDesc.getRecurrenceType(), ExtType,
Config.CostKind);
if (RedCost.isValid() &&
RedCost < ExtCost * 2 + MulCost + Ext2Cost + BaseCost)
return I == RetI ? RedCost : 0;
} else if (RedOp && match(RedOp, m_ZExtOrSExt(m_Value())) &&
!TheLoop->isLoopInvariant(RedOp)) {
// Matched reduce(ext(A))
bool IsUnsigned = isa<ZExtInst>(RedOp);
auto *ExtType = VectorType::get(RedOp->getOperand(0)->getType(), VectorTy);
InstructionCost RedCost = TTI.getExtendedReductionCost(
RdxDesc.getOpcode(), IsUnsigned, RdxDesc.getRecurrenceType(), ExtType,
RdxDesc.getFastMathFlags(), Config.CostKind);
InstructionCost ExtCost = TTI.getCastInstrCost(
RedOp->getOpcode(), VectorTy, ExtType, TTI::CastContextHint::None,
Config.CostKind, RedOp);
if (RedCost.isValid() && RedCost < BaseCost + ExtCost)
return I == RetI ? RedCost : 0;
} else if (RedOp && RdxDesc.getOpcode() == Instruction::Add &&
match(RedOp, m_Mul(m_Instruction(Op0), m_Instruction(Op1)))) {
if (match(Op0, m_ZExtOrSExt(m_Value())) &&
Op0->getOpcode() == Op1->getOpcode() &&
!TheLoop->isLoopInvariant(Op0) && !TheLoop->isLoopInvariant(Op1)) {
bool IsUnsigned = isa<ZExtInst>(Op0);
Type *Op0Ty = Op0->getOperand(0)->getType();
Type *Op1Ty = Op1->getOperand(0)->getType();
Type *LargestOpTy =
Op0Ty->getIntegerBitWidth() < Op1Ty->getIntegerBitWidth() ? Op1Ty
: Op0Ty;
auto *ExtType = VectorType::get(LargestOpTy, VectorTy);
// Matched reduce.add(mul(ext(A), ext(B))), where the two ext may be of
// different sizes. We take the largest type as the ext to reduce, and add
// the remaining cost as, for example reduce(mul(ext(ext(A)), ext(B))).
InstructionCost ExtCost0 = TTI.getCastInstrCost(
Op0->getOpcode(), VectorTy, VectorType::get(Op0Ty, VectorTy),
TTI::CastContextHint::None, Config.CostKind, Op0);
InstructionCost ExtCost1 = TTI.getCastInstrCost(
Op1->getOpcode(), VectorTy, VectorType::get(Op1Ty, VectorTy),
TTI::CastContextHint::None, Config.CostKind, Op1);
InstructionCost MulCost = TTI.getArithmeticInstrCost(
Instruction::Mul, VectorTy, Config.CostKind);
InstructionCost RedCost = TTI.getMulAccReductionCost(
IsUnsigned, RdxDesc.getOpcode(), RdxDesc.getRecurrenceType(), ExtType,
Config.CostKind);
InstructionCost ExtraExtCost = 0;
if (Op0Ty != LargestOpTy || Op1Ty != LargestOpTy) {
Instruction *ExtraExtOp = (Op0Ty != LargestOpTy) ? Op0 : Op1;
ExtraExtCost = TTI.getCastInstrCost(
ExtraExtOp->getOpcode(), ExtType,
VectorType::get(ExtraExtOp->getOperand(0)->getType(), VectorTy),
TTI::CastContextHint::None, Config.CostKind, ExtraExtOp);
}
if (RedCost.isValid() &&
(RedCost + ExtraExtCost) < (ExtCost0 + ExtCost1 + MulCost + BaseCost))
return I == RetI ? RedCost : 0;
} else if (!match(I, m_ZExtOrSExt(m_Value()))) {
// Matched reduce.add(mul())
InstructionCost MulCost = TTI.getArithmeticInstrCost(
Instruction::Mul, VectorTy, Config.CostKind);
InstructionCost RedCost = TTI.getMulAccReductionCost(
true, RdxDesc.getOpcode(), RdxDesc.getRecurrenceType(), VectorTy,
Config.CostKind);
if (RedCost.isValid() && RedCost < MulCost + BaseCost)
return I == RetI ? RedCost : 0;
}
}
return I == RetI ? std::optional<InstructionCost>(BaseCost) : std::nullopt;
}
InstructionCost
LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I,
ElementCount VF) {
// Calculate scalar cost only. Vectorization cost should be ready at this
// moment.
if (VF.isScalar()) {
Type *ValTy = getLoadStoreType(I);
Type *PtrTy = getLoadStorePointerOperand(I)->getType();
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
TTI::OperandValueInfo OpInfo = TTI::getOperandInfo(I->getOperand(0));
return TTI.getAddressComputationCost(PtrTy, nullptr, nullptr,
Config.CostKind) +
TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS,
Config.CostKind, OpInfo, I);
}
return getWideningCost(I, VF);
}
InstructionCost
LoopVectorizationCostModel::getScalarizationOverhead(Instruction *I,
ElementCount VF) const {
// There is no mechanism yet to create a scalable scalarization loop,
// so this is currently Invalid.
if (VF.isScalable())
return InstructionCost::getInvalid();
if (VF.isScalar())
return 0;
InstructionCost Cost = 0;
Type *RetTy = toVectorizedTy(I->getType(), VF);
if (!RetTy->isVoidTy() &&
(!isa<LoadInst>(I) || !TTI.supportsEfficientVectorElementLoadStore())) {
TTI::VectorInstrContext VIC = TTI::VectorInstrContext::None;
if (isa<LoadInst>(I))
VIC = TTI::VectorInstrContext::Load;
else if (isa<StoreInst>(I))
VIC = TTI::VectorInstrContext::Store;
for (Type *VectorTy : getContainedTypes(RetTy)) {
Cost += TTI.getScalarizationOverhead(
cast<VectorType>(VectorTy), APInt::getAllOnes(VF.getFixedValue()),
/*Insert=*/true, /*Extract=*/false, Config.CostKind,
/*ForPoisonSrc=*/true, {}, VIC);
}
}
// 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->args() : I->operands();
// Skip operands that do not require extraction/scalarization and do not incur
// any overhead.
SmallVector<Type *> Tys;
for (auto *V : filterExtractingOperands(Ops, VF))
Tys.push_back(maybeVectorizeType(V->getType(), VF));
TTI::VectorInstrContext OperandVIC = isa<StoreInst>(I)
? TTI::VectorInstrContext::Store
: TTI::VectorInstrContext::None;
return Cost +
TTI.getOperandsScalarizationOverhead(Tys, Config.CostKind, OperandVIC);
}
void LoopVectorizationCostModel::setCostBasedWideningDecision(ElementCount VF) {
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, VF))
NumPredStores++;
if (Legal->isUniformMemOp(I, VF)) {
auto IsLegalToScalarize = [&]() {
if (!VF.isScalable())
// Scalarization of fixed length vectors "just works".
return true;
// We have dedicated lowering for unpredicated uniform loads and
// stores. Note that even with tail folding we know that at least
// one lane is active (i.e. generalized predication is not possible
// here), and the logic below depends on this fact.
if (!foldTailByMasking())
return true;
// For scalable vectors, a uniform memop load is always
// uniform-by-parts and we know how to scalarize that.
if (isa<LoadInst>(I))
return true;
// A uniform store isn't neccessarily uniform-by-part
// and we can't assume scalarization.
auto &SI = cast<StoreInst>(I);
return TheLoop->isLoopInvariant(SI.getValueOperand());
};
const InstructionCost GatherScatterCost =
Config.isLegalGatherOrScatter(&I, VF)
? getGatherScatterCost(&I, VF)
: InstructionCost::getInvalid();
// Load: Scalar load + broadcast
// Store: Scalar store + isLoopInvariantStoreValue ? 0 : extract
// FIXME: This cost is a significant under-estimate for tail folded
// memory ops.
const InstructionCost ScalarizationCost =
IsLegalToScalarize() ? getUniformMemOpCost(&I, VF)
: InstructionCost::getInvalid();
// Choose better solution for the current VF, Note that Invalid
// costs compare as maximumal large. If both are invalid, we get
// scalable invalid which signals a failure and a vectorization abort.
if (GatherScatterCost < ScalarizationCost)
setWideningDecision(&I, VF, CM_GatherScatter, GatherScatterCost);
else
setWideningDecision(&I, VF, CM_Scalarize, ScalarizationCost);
continue;
}
// We assume that widening is the best solution when possible.
if (memoryInstructionCanBeWidened(&I, VF)) {
InstructionCost Cost = getConsecutiveMemOpCost(&I, VF);
int ConsecutiveStride = Legal->isConsecutivePtr(
getLoadStoreType(&I), 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.
InstructionCost InterleaveCost = InstructionCost::getInvalid();
unsigned NumAccesses = 1;
if (isAccessInterleaved(&I)) {
const 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);
}
InstructionCost GatherScatterCost =
Config.isLegalGatherOrScatter(&I, VF)
? getGatherScatterCost(&I, VF) * NumAccesses
: InstructionCost::getInvalid();
InstructionCost ScalarizationCost =
getMemInstScalarizationCost(&I, VF) * NumAccesses;
// Choose better solution for the current VF,
// write down this decision and use it during vectorization.
InstructionCost 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 (const auto *Group = getInterleavedAccessGroup(&I)) {
if (Decision == CM_Scalarize) {
for (Instruction *I : Group->members())
setWideningDecision(I, VF, Decision,
getMemInstScalarizationCost(I, VF));
} else {
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;
append_range(Worklist, AddrDefs);
while (!Worklist.empty()) {
Instruction *I = Worklist.pop_back_val();
for (auto &Op : I->operands())
if (auto *InstOp = dyn_cast<Instruction>(Op))
if (TheLoop->contains(InstOp) && !isa<PHINode>(InstOp) &&
AddrDefs.insert(InstOp).second)
Worklist.push_back(InstOp);
}
auto UpdateMemOpUserCost = [this, VF](LoadInst *LI) {
// If there are direct memory op users of the newly scalarized load,
// their cost may have changed because there's no scalarization
// overhead for the operand. Update it.
for (User *U : LI->users()) {
if (!isa<LoadInst, StoreInst>(U))
continue;
if (getWideningDecision(cast<Instruction>(U), VF) != CM_Scalarize)
continue;
setWideningDecision(
cast<Instruction>(U), VF, CM_Scalarize,
getMemInstScalarizationCost(cast<Instruction>(U), VF));
}
};
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 (!isPredicatedInst(I) &&
(Decision == CM_Widen || Decision == CM_Widen_Reverse ||
(!Legal->isUniformMemOp(*I, VF) && Decision == CM_Scalarize))) {
// Scalarize a widened load of address or update the cost of a scalar
// load of an address.
setWideningDecision(
I, VF, CM_Scalarize,
(VF.getKnownMinValue() *
getMemoryInstructionCost(I, ElementCount::getFixed(1))));
UpdateMemOpUserCost(cast<LoadInst>(I));
} else if (const auto *Group = getInterleavedAccessGroup(I)) {
// Scalarize all members of this interleaved group when any member
// is used as an address. The address-used load skips scalarization
// overhead, other members include it.
for (Instruction *Member : Group->members()) {
InstructionCost Cost = AddrDefs.contains(Member)
? (VF.getKnownMinValue() *
getMemoryInstructionCost(
Member, ElementCount::getFixed(1)))
: getMemInstScalarizationCost(Member, VF);
setWideningDecision(Member, VF, CM_Scalarize, Cost);
UpdateMemOpUserCost(cast<LoadInst>(Member));
}
}
} else {
// Cannot scalarize fixed-order recurrence phis at the moment.
if (isa<PHINode>(I) && Legal->isFixedOrderRecurrence(cast<PHINode>(I)))
continue;
// Make sure I gets scalarized and a cost estimate without
// scalarization overhead.
ForcedScalars[VF].insert(I);
}
}
}
void LoopVectorizationCostModel::setVectorizedCallDecision(ElementCount VF) {
assert(!VF.isScalar() &&
"Trying to set a vectorization decision for a scalar VF");
auto ForcedScalar = ForcedScalars.find(VF);
for (BasicBlock *BB : TheLoop->blocks()) {
// For each instruction in the old loop.
for (Instruction &I : *BB) {
CallInst *CI = dyn_cast<CallInst>(&I);
if (!CI)
continue;
InstructionCost ScalarCost = InstructionCost::getInvalid();
InstructionCost VectorCost = InstructionCost::getInvalid();
InstructionCost IntrinsicCost = InstructionCost::getInvalid();
Function *ScalarFunc = CI->getCalledFunction();
Type *ScalarRetTy = CI->getType();
SmallVector<Type *, 4> Tys, ScalarTys;
for (auto &ArgOp : CI->args())
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.
if (VF.isFixed()) {
InstructionCost ScalarCallCost = TTI.getCallInstrCost(
ScalarFunc, ScalarRetTy, ScalarTys, Config.CostKind);
// Compute costs of unpacking argument values for the scalar calls and
// packing the return values to a vector.
InstructionCost ScalarizationCost = getScalarizationOverhead(CI, VF);
ScalarCost = ScalarCallCost * VF.getKnownMinValue() + ScalarizationCost;
} else {
// There is no point attempting to calculate the scalar cost for a
// scalable VF as we know it will be Invalid.
assert(!getScalarizationOverhead(CI, VF).isValid() &&
"Unexpected valid cost for scalarizing scalable vectors");
ScalarCost = InstructionCost::getInvalid();
}
// Honor ForcedScalars and UniformAfterVectorization decisions.
// TODO: For calls, it might still be more profitable to widen. Use
// VPlan-based cost model to compare different options.
if (VF.isVector() && ((ForcedScalar != ForcedScalars.end() &&
ForcedScalar->second.contains(CI)) ||
isUniformAfterVectorization(CI, VF))) {
setCallWideningDecision(CI, VF, CM_Scalarize, nullptr,
Intrinsic::not_intrinsic, std::nullopt,
ScalarCost);
continue;
}
bool MaskRequired = isMaskRequired(CI);
// Compute corresponding vector type for return value and arguments.
Type *RetTy = toVectorizedTy(ScalarRetTy, VF);
for (Type *ScalarTy : ScalarTys)
Tys.push_back(toVectorizedTy(ScalarTy, VF));
// An in-loop reduction using an fmuladd intrinsic is a special case;
// we don't want the normal cost for that intrinsic.
if (RecurrenceDescriptor::isFMulAddIntrinsic(CI))
if (auto RedCost = getReductionPatternCost(CI, VF, RetTy)) {
setCallWideningDecision(CI, VF, CM_IntrinsicCall, nullptr,
getVectorIntrinsicIDForCall(CI, TLI),
std::nullopt, *RedCost);
continue;
}
// Find the cost of vectorizing the call, if we can find a suitable
// vector variant of the function.
VFInfo FuncInfo;
Function *VecFunc = nullptr;
// Search through any available variants for one we can use at this VF.
for (VFInfo &Info : VFDatabase::getMappings(*CI)) {
// Must match requested VF.
if (Info.Shape.VF != VF)
continue;
// Must take a mask argument if one is required
if (MaskRequired && !Info.isMasked())
continue;
// Check that all parameter kinds are supported
bool ParamsOk = true;
for (VFParameter Param : Info.Shape.Parameters) {
switch (Param.ParamKind) {
case VFParamKind::Vector:
break;
case VFParamKind::OMP_Uniform: {
Value *ScalarParam = CI->getArgOperand(Param.ParamPos);
// Make sure the scalar parameter in the loop is invariant.
if (!PSE.getSE()->isLoopInvariant(PSE.getSCEV(ScalarParam),
TheLoop))
ParamsOk = false;
break;
}
case VFParamKind::OMP_Linear: {
Value *ScalarParam = CI->getArgOperand(Param.ParamPos);
// Find the stride for the scalar parameter in this loop and see if
// it matches the stride for the variant.
// TODO: do we need to figure out the cost of an extract to get the
// first lane? Or do we hope that it will be folded away?
ScalarEvolution *SE = PSE.getSE();
if (!match(SE->getSCEV(ScalarParam),
m_scev_AffineAddRec(
m_SCEV(), m_scev_SpecificSInt(Param.LinearStepOrPos),
m_SpecificLoop(TheLoop))))
ParamsOk = false;
break;
}
case VFParamKind::GlobalPredicate:
break;
default:
ParamsOk = false;
break;
}
}
if (!ParamsOk)
continue;
// Found a suitable candidate, stop here.
VecFunc = CI->getModule()->getFunction(Info.VectorName);
FuncInfo = Info;
break;
}
if (TLI && VecFunc && !CI->isNoBuiltin())
VectorCost = TTI.getCallInstrCost(nullptr, RetTy, Tys, Config.CostKind);
// Find the cost of an intrinsic; some targets may have instructions that
// perform the operation without needing an actual call.
Intrinsic::ID IID = getVectorIntrinsicIDForCall(CI, TLI);
if (IID != Intrinsic::not_intrinsic)
IntrinsicCost = getVectorIntrinsicCost(CI, VF);
InstructionCost Cost = ScalarCost;
InstWidening Decision = CM_Scalarize;
if (VectorCost.isValid() && VectorCost <= Cost) {
Cost = VectorCost;
Decision = CM_VectorCall;
}
if (IntrinsicCost.isValid() && IntrinsicCost <= Cost) {
Cost = IntrinsicCost;
Decision = CM_IntrinsicCall;
}
setCallWideningDecision(CI, VF, Decision, VecFunc, IID,
FuncInfo.getParamIndexForOptionalMask(), Cost);
}
}
}
bool LoopVectorizationCostModel::shouldConsiderInvariant(Value *Op) {
if (!Legal->isInvariant(Op))
return false;
// Consider Op invariant, if it or its operands aren't predicated
// instruction in the loop. In that case, it is not trivially hoistable.
auto *OpI = dyn_cast<Instruction>(Op);
return !OpI || !TheLoop->contains(OpI) ||
(!isPredicatedInst(OpI) &&
(!isa<PHINode>(OpI) || OpI->getParent() != TheLoop->getHeader()) &&
all_of(OpI->operands(),
[this](Value *Op) { return shouldConsiderInvariant(Op); }));
}
InstructionCost
LoopVectorizationCostModel::getInstructionCost(Instruction *I,
ElementCount VF) {
// 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 InstsToScalarize[VF][I];
// 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 getInstructionCost(I, ElementCount::getFixed(1)) *
VF.getKnownMinValue();
}
const auto &MinBWs = Config.getMinimalBitwidths();
uint64_t InstrMinBWs = MinBWs.lookup(I);
Type *RetTy = I->getType();
if (canTruncateToMinimalBitwidth(I, VF))
RetTy = IntegerType::get(RetTy->getContext(), InstrMinBWs);
auto *SE = PSE.getSE();
Type *VectorTy;
if (isScalarAfterVectorization(I, VF)) {
[[maybe_unused]] auto HasSingleCopyAfterVectorization =
[this](Instruction *I, ElementCount VF) -> bool {
if (VF.isScalar())
return true;
auto Scalarized = InstsToScalarize.find(VF);
assert(Scalarized != InstsToScalarize.end() &&
"VF not yet analyzed for scalarization profitability");
return !Scalarized->second.count(I) &&
llvm::all_of(I->users(), [&](User *U) {
auto *UI = cast<Instruction>(U);
return !Scalarized->second.count(UI);
});
};
// With the exception of GEPs and PHIs, after scalarization there should
// only be one copy of the instruction generated in the loop. This is
// because the VF is either 1, or any instructions that need scalarizing
// have already been dealt with by the time we get here. As a result,
// it means we don't have to multiply the instruction cost by VF.
assert(I->getOpcode() == Instruction::GetElementPtr ||
I->getOpcode() == Instruction::PHI ||
(I->getOpcode() == Instruction::BitCast &&
I->getType()->isPointerTy()) ||
HasSingleCopyAfterVectorization(I, VF));
VectorTy = RetTy;
} else
VectorTy = toVectorizedTy(RetTy, VF);
if (VF.isVector() && VectorTy->isVectorTy() &&
!TTI.getNumberOfParts(VectorTy))
return InstructionCost::getInvalid();
// 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::UncondBr:
case Instruction::CondBr: {
// 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.
// Note that the conditional branch from the loop latch will be replaced by
// a single branch controlling the loop, so there is no extra overhead from
// scalarization.
bool ScalarPredicatedBB = false;
CondBrInst *BI = dyn_cast<CondBrInst>(I);
if (VF.isVector() && BI &&
(PredicatedBBsAfterVectorization[VF].count(BI->getSuccessor(0)) ||
PredicatedBBsAfterVectorization[VF].count(BI->getSuccessor(1))) &&
BI->getParent() != TheLoop->getLoopLatch())
ScalarPredicatedBB = true;
if (ScalarPredicatedBB) {
// Not possible to scalarize scalable vector with predicated instructions.
if (VF.isScalable())
return InstructionCost::getInvalid();
// Return cost for branches around scalarized and predicated blocks.
auto *VecI1Ty =
VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
return (TTI.getScalarizationOverhead(
VecI1Ty, APInt::getAllOnes(VF.getFixedValue()),
/*Insert*/ false, /*Extract*/ true, Config.CostKind) +
(TTI.getCFInstrCost(Instruction::CondBr, Config.CostKind) *
VF.getFixedValue()));
}
if (I->getParent() == TheLoop->getLoopLatch() || VF.isScalar())
// The back-edge branch will remain, as will all scalar branches.
return TTI.getCFInstrCost(Instruction::UncondBr, Config.CostKind);
// 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::Switch: {
if (VF.isScalar())
return TTI.getCFInstrCost(Instruction::Switch, Config.CostKind);
auto *Switch = cast<SwitchInst>(I);
return Switch->getNumCases() *
TTI.getCmpSelInstrCost(
Instruction::ICmp,
toVectorTy(Switch->getCondition()->getType(), VF),
toVectorTy(Type::getInt1Ty(I->getContext()), VF),
CmpInst::ICMP_EQ, Config.CostKind);
}
case Instruction::PHI: {
auto *Phi = cast<PHINode>(I);
// First-order recurrences are replaced by vector shuffles inside the loop.
if (VF.isVector() && Legal->isFixedOrderRecurrence(Phi)) {
return TTI.getShuffleCost(
TargetTransformInfo::SK_Splice, cast<VectorType>(VectorTy),
cast<VectorType>(VectorTy), {}, Config.CostKind, -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()) {
Type *ResultTy = Phi->getType();
// All instructions in an Any-of reduction chain are narrowed to bool.
// Check if that is the case for this phi node.
auto *HeaderUser = cast_if_present<PHINode>(
find_singleton<User>(Phi->users(), [this](User *U, bool) -> User * {
auto *Phi = dyn_cast<PHINode>(U);
if (Phi && Phi->getParent() == TheLoop->getHeader())
return Phi;
return nullptr;
}));
if (HeaderUser) {
auto &ReductionVars = Legal->getReductionVars();
auto Iter = ReductionVars.find(HeaderUser);
if (Iter != ReductionVars.end() &&
RecurrenceDescriptor::isAnyOfRecurrenceKind(
Iter->second.getRecurrenceKind()))
ResultTy = Type::getInt1Ty(Phi->getContext());
}
return (Phi->getNumIncomingValues() - 1) *
TTI.getCmpSelInstrCost(
Instruction::Select, toVectorTy(ResultTy, VF),
toVectorTy(Type::getInt1Ty(Phi->getContext()), VF),
CmpInst::BAD_ICMP_PREDICATE, Config.CostKind);
}
// When tail folding with EVL, if the phi is part of an out of loop
// reduction then it will be transformed into a wide vp_merge.
if (VF.isVector() && foldTailWithEVL() &&
Legal->getReductionVars().contains(Phi) &&
!Config.isInLoopReduction(Phi)) {
IntrinsicCostAttributes ICA(
Intrinsic::vp_merge, toVectorTy(Phi->getType(), VF),
{toVectorTy(Type::getInt1Ty(Phi->getContext()), VF)});
return TTI.getIntrinsicInstrCost(ICA, Config.CostKind);
}
return TTI.getCFInstrCost(Instruction::PHI, Config.CostKind);
}
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::URem:
case Instruction::SRem:
if (VF.isVector() && isPredicatedInst(I)) {
const auto [ScalarCost, SafeDivisorCost] = getDivRemSpeculationCost(I, VF);
return isDivRemScalarWithPredication(ScalarCost, SafeDivisorCost) ?
ScalarCost : SafeDivisorCost;
}
// We've proven all lanes safe to speculate, fall through.
[[fallthrough]];
case Instruction::Add:
case Instruction::Sub: {
auto Info = Legal->getHistogramInfo(I);
if (Info && VF.isVector()) {
const HistogramInfo *HGram = Info.value();
// Assume that a non-constant update value (or a constant != 1) requires
// a multiply, and add that into the cost.
InstructionCost MulCost = TTI::TCC_Free;
ConstantInt *RHS = dyn_cast<ConstantInt>(I->getOperand(1));
if (!RHS || RHS->getZExtValue() != 1)
MulCost = TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy,
Config.CostKind);
// Find the cost of the histogram operation itself.
Type *PtrTy = VectorType::get(HGram->Load->getPointerOperandType(), VF);
Type *ScalarTy = I->getType();
Type *MaskTy = VectorType::get(Type::getInt1Ty(I->getContext()), VF);
IntrinsicCostAttributes ICA(Intrinsic::experimental_vector_histogram_add,
Type::getVoidTy(I->getContext()),
{PtrTy, ScalarTy, MaskTy});
// Add the costs together with the add/sub operation.
return TTI.getIntrinsicInstrCost(ICA, Config.CostKind) + MulCost +
TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy,
Config.CostKind);
}
[[fallthrough]];
}
case Instruction::FAdd:
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: {
// If we're speculating on the stride being 1, the multiplication may
// fold away. We can generalize this for all operations using the notion
// of neutral elements. (TODO)
if (I->getOpcode() == Instruction::Mul &&
((TheLoop->isLoopInvariant(I->getOperand(0)) &&
PSE.getSCEV(I->getOperand(0))->isOne()) ||
(TheLoop->isLoopInvariant(I->getOperand(1)) &&
PSE.getSCEV(I->getOperand(1))->isOne())))
return 0;
// Detect reduction patterns
if (auto RedCost = getReductionPatternCost(I, VF, VectorTy))
return *RedCost;
// 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);
if (!isa<Constant>(Op2) && TheLoop->isLoopInvariant(Op2) &&
PSE.getSE()->isSCEVable(Op2->getType()) &&
isa<SCEVConstant>(PSE.getSCEV(Op2))) {
Op2 = cast<SCEVConstant>(PSE.getSCEV(Op2))->getValue();
}
auto Op2Info = TTI.getOperandInfo(Op2);
if (Op2Info.Kind == TargetTransformInfo::OK_AnyValue &&
shouldConsiderInvariant(Op2))
Op2Info.Kind = TargetTransformInfo::OK_UniformValue;
SmallVector<const Value *, 4> Operands(I->operand_values());
return TTI.getArithmeticInstrCost(
I->getOpcode(), VectorTy, Config.CostKind,
{TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None},
Op2Info, Operands, I, TLI);
}
case Instruction::FNeg: {
return TTI.getArithmeticInstrCost(
I->getOpcode(), VectorTy, Config.CostKind,
{TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None},
{TargetTransformInfo::OK_AnyValue, 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));
const Value *Op0, *Op1;
using namespace llvm::PatternMatch;
if (!ScalarCond && (match(I, m_LogicalAnd(m_Value(Op0), m_Value(Op1))) ||
match(I, m_LogicalOr(m_Value(Op0), m_Value(Op1))))) {
// select x, y, false --> x & y
// select x, true, y --> x | y
const auto [Op1VK, Op1VP] = TTI::getOperandInfo(Op0);
const auto [Op2VK, Op2VP] = TTI::getOperandInfo(Op1);
assert(Op0->getType()->getScalarSizeInBits() == 1 &&
Op1->getType()->getScalarSizeInBits() == 1);
return TTI.getArithmeticInstrCost(
match(I, m_LogicalOr()) ? Instruction::Or : Instruction::And,
VectorTy, Config.CostKind, {Op1VK, Op1VP}, {Op2VK, Op2VP}, {Op0, Op1},
I);
}
Type *CondTy = SI->getCondition()->getType();
if (!ScalarCond)
CondTy = VectorType::get(CondTy, VF);
CmpInst::Predicate Pred = CmpInst::BAD_ICMP_PREDICATE;
if (auto *Cmp = dyn_cast<CmpInst>(SI->getCondition()))
Pred = Cmp->getPredicate();
return TTI.getCmpSelInstrCost(
I->getOpcode(), VectorTy, CondTy, Pred, Config.CostKind,
{TTI::OK_AnyValue, TTI::OP_None}, {TTI::OK_AnyValue, TTI::OP_None}, I);
}
case Instruction::ICmp:
case Instruction::FCmp: {
Type *ValTy = I->getOperand(0)->getType();
if (canTruncateToMinimalBitwidth(I, VF)) {
[[maybe_unused]] Instruction *Op0AsInstruction =
dyn_cast<Instruction>(I->getOperand(0));
assert((!canTruncateToMinimalBitwidth(Op0AsInstruction, VF) ||
InstrMinBWs == MinBWs.lookup(Op0AsInstruction)) &&
"if both the operand and the compare are marked for "
"truncation, they must have the same bitwidth");
ValTy = IntegerType::get(ValTy->getContext(), InstrMinBWs);
}
VectorTy = toVectorTy(ValTy, VF);
return TTI.getCmpSelInstrCost(
I->getOpcode(), VectorTy, CmpInst::makeCmpResultType(VectorTy),
cast<CmpInst>(I)->getPredicate(), Config.CostKind,
{TTI::OK_AnyValue, TTI::OP_None}, {TTI::OK_AnyValue, TTI::OP_None}, 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 (getWideningCost(I, VF) == InstructionCost::getInvalid())
return InstructionCost::getInvalid();
if (Decision == CM_Scalarize)
Width = ElementCount::getFixed(1);
}
VectorTy = toVectorTy(getLoadStoreType(I), Width);
return getMemoryInstructionCost(I, VF);
}
case Instruction::BitCast:
if (I->getType()->isPointerTy())
return 0;
[[fallthrough]];
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: {
// 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 isPredicatedInst(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?");
case LoopVectorizationCostModel::CM_VectorCall:
case LoopVectorizationCostModel::CM_IntrinsicCall:
llvm_unreachable_internal("Instr has invalid widening decision");
}
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, Config.CostKind,
Trunc);
}
// Detect reduction patterns
if (auto RedCost = getReductionPatternCost(I, VF, VectorTy))
return *RedCost;
Type *SrcScalarTy = I->getOperand(0)->getType();
Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0));
if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF))
SrcScalarTy = IntegerType::get(SrcScalarTy->getContext(),
MinBWs.lookup(Op0AsInstruction));
Type *SrcVecTy =
VectorTy->isVectorTy() ? toVectorTy(SrcScalarTy, VF) : SrcScalarTy;
if (canTruncateToMinimalBitwidth(I, VF)) {
// If the result type is <= the source type, there will be no extend
// after truncating the users to the minimal required bitwidth.
if (VectorTy->getScalarSizeInBits() <= SrcVecTy->getScalarSizeInBits() &&
(I->getOpcode() == Instruction::ZExt ||
I->getOpcode() == Instruction::SExt))
return 0;
}
return TTI.getCastInstrCost(Opcode, VectorTy, SrcVecTy, CCH,
Config.CostKind, I);
}
case Instruction::Call:
return getVectorCallCost(cast<CallInst>(I), VF);
case Instruction::ExtractValue:
return TTI.getInstructionCost(I, Config.CostKind);
case Instruction::Alloca:
// We cannot easily widen alloca to a scalable alloca, as
// the result would need to be a vector of pointers.
if (VF.isScalable())
return InstructionCost::getInvalid();
return TTI.getArithmeticInstrCost(Instruction::Mul, RetTy, Config.CostKind);
default:
// This opcode is unknown. Assume that it is the same as 'mul'.
return TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy,
Config.CostKind);
} // end of switch.
}
void LoopVectorizationCostModel::collectValuesToIgnore() {
// Ignore ephemeral values.
CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore);
SmallVector<Value *, 4> DeadInterleavePointerOps;
SmallVector<Value *, 4> DeadOps;
// If a scalar epilogue is required, users outside the loop won't use
// live-outs from the vector loop but from the scalar epilogue. Ignore them if
// that is the case.
bool RequiresScalarEpilogue = requiresScalarEpilogue(true);
auto IsLiveOutDead = [this, RequiresScalarEpilogue](User *U) {
return RequiresScalarEpilogue &&
!TheLoop->contains(cast<Instruction>(U)->getParent());
};
LoopBlocksDFS DFS(TheLoop);
DFS.perform(LI);
for (BasicBlock *BB : reverse(make_range(DFS.beginRPO(), DFS.endRPO())))
for (Instruction &I : reverse(*BB)) {
if (VecValuesToIgnore.contains(&I) || ValuesToIgnore.contains(&I))
continue;
// Add instructions that would be trivially dead and are only used by
// values already ignored to DeadOps to seed worklist.
if (wouldInstructionBeTriviallyDead(&I, TLI) &&
all_of(I.users(), [this, IsLiveOutDead](User *U) {
return VecValuesToIgnore.contains(U) ||
ValuesToIgnore.contains(U) || IsLiveOutDead(U);
}))
DeadOps.push_back(&I);
// For interleave groups, we only create a pointer for the start of the
// interleave group. Queue up addresses of group members except the insert
// position for further processing.
if (isAccessInterleaved(&I)) {
auto *Group = getInterleavedAccessGroup(&I);
if (Group->getInsertPos() == &I)
continue;
Value *PointerOp = getLoadStorePointerOperand(&I);
DeadInterleavePointerOps.push_back(PointerOp);
}
// Queue branches for analysis. They are dead, if their successors only
// contain dead instructions.
if (isa<CondBrInst>(&I))
DeadOps.push_back(&I);
}
// Mark ops feeding interleave group members as free, if they are only used
// by other dead computations.
for (unsigned I = 0; I != DeadInterleavePointerOps.size(); ++I) {
auto *Op = dyn_cast<Instruction>(DeadInterleavePointerOps[I]);
if (!Op || !TheLoop->contains(Op) || any_of(Op->users(), [this](User *U) {
Instruction *UI = cast<Instruction>(U);
return !VecValuesToIgnore.contains(U) &&
(!isAccessInterleaved(UI) ||
getInterleavedAccessGroup(UI)->getInsertPos() == UI);
}))
continue;
VecValuesToIgnore.insert(Op);
append_range(DeadInterleavePointerOps, Op->operands());
}
// Mark ops that would be trivially dead and are only used by ignored
// instructions as free.
BasicBlock *Header = TheLoop->getHeader();
// Returns true if the block contains only dead instructions. Such blocks will
// be removed by VPlan-to-VPlan transforms and won't be considered by the
// VPlan-based cost model, so skip them in the legacy cost-model as well.
auto IsEmptyBlock = [this](BasicBlock *BB) {
return all_of(*BB, [this](Instruction &I) {
return ValuesToIgnore.contains(&I) || VecValuesToIgnore.contains(&I) ||
isa<UncondBrInst>(&I);
});
};
for (unsigned I = 0; I != DeadOps.size(); ++I) {
auto *Op = dyn_cast<Instruction>(DeadOps[I]);
// Check if the branch should be considered dead.
if (auto *Br = dyn_cast_or_null<CondBrInst>(Op)) {
BasicBlock *ThenBB = Br->getSuccessor(0);
BasicBlock *ElseBB = Br->getSuccessor(1);
// Don't considers branches leaving the loop for simplification.
if (!TheLoop->contains(ThenBB) || !TheLoop->contains(ElseBB))
continue;
bool ThenEmpty = IsEmptyBlock(ThenBB);
bool ElseEmpty = IsEmptyBlock(ElseBB);
if ((ThenEmpty && ElseEmpty) ||
(ThenEmpty && ThenBB->getSingleSuccessor() == ElseBB &&
ElseBB->phis().empty()) ||
(ElseEmpty && ElseBB->getSingleSuccessor() == ThenBB &&
ThenBB->phis().empty())) {
VecValuesToIgnore.insert(Br);
DeadOps.push_back(Br->getCondition());
}
continue;
}
// Skip any op that shouldn't be considered dead.
if (!Op || !TheLoop->contains(Op) ||
(isa<PHINode>(Op) && Op->getParent() == Header) ||
!wouldInstructionBeTriviallyDead(Op, TLI) ||
any_of(Op->users(), [this, IsLiveOutDead](User *U) {
return !VecValuesToIgnore.contains(U) &&
!ValuesToIgnore.contains(U) && !IsLiveOutDead(U);
}))
continue;
// If all of Op's users are in ValuesToIgnore, add it to ValuesToIgnore
// which applies for both scalar and vector versions. Otherwise it is only
// dead in vector versions, so only add it to VecValuesToIgnore.
if (all_of(Op->users(),
[this](User *U) { return ValuesToIgnore.contains(U); }))
ValuesToIgnore.insert(Op);
VecValuesToIgnore.insert(Op);
append_range(DeadOps, Op->operands());
}
// Ignore type-promoting instructions we identified during reduction
// detection.
for (const auto &Reduction : Legal->getReductionVars()) {
const RecurrenceDescriptor &RedDes = Reduction.second;
const SmallPtrSetImpl<Instruction *> &Casts = RedDes.getCastInsts();
VecValuesToIgnore.insert_range(Casts);
}
// Ignore type-casting instructions we identified during induction
// detection.
for (const auto &Induction : Legal->getInductionVars()) {
const InductionDescriptor &IndDes = Induction.second;
VecValuesToIgnore.insert_range(IndDes.getCastInsts());
}
}
// This function will select a scalable VF if the target supports scalable
// vectors and a fixed one otherwise.
// 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 ElementCount determineVPlanVF(const TargetTransformInfo &TTI,
VFSelectionContext &Config) {
unsigned WidestType = Config.getSmallestAndWidestTypes().second;
TargetTransformInfo::RegisterKind RegKind =
TTI.enableScalableVectorization()
? TargetTransformInfo::RGK_ScalableVector
: TargetTransformInfo::RGK_FixedWidthVector;
TypeSize RegSize = TTI.getRegisterBitWidth(RegKind);
unsigned N = RegSize.getKnownMinValue() / WidestType;
return ElementCount::get(N, RegSize.isScalable());
}
VectorizationFactor
LoopVectorizationPlanner::planInVPlanNativePath(ElementCount UserVF) {
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 = determineVPlanVF(TTI, Config);
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);
}
} else if (UserVF.isScalable() && !Config.supportsScalableVectors()) {
LLVM_DEBUG(dbgs() << "LV: Not vectorizing. Scalable VF requested, but "
<< "not supported by the target.\n");
reportVectorizationFailure(
"Scalable vectorization requested but not supported by the target",
"the scalable user-specified vectorization width for outer-loop "
"vectorization cannot be used because the target does not support "
"scalable vectors.",
"ScalableVFUnfeasible", ORE, OrigLoop);
return VectorizationFactor::Disabled();
}
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, VF);
if (VPlans.empty())
return VectorizationFactor::Disabled();
// For VPlan build stress testing, we bail out after VPlan construction.
if (VPlanBuildStressTest)
return VectorizationFactor::Disabled();
return {VF, 0 /*Cost*/, 0 /* ScalarCost */};
}
LLVM_DEBUG(
dbgs() << "LV: Not vectorizing. Inner loops aren't supported in the "
"VPlan-native path.\n");
return VectorizationFactor::Disabled();
}
void LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) {
assert(OrigLoop->isInnermost() && "Inner loop expected.");
CM.collectValuesToIgnore();
Config.collectElementTypesForWidening(&CM.ValuesToIgnore);
FixedScalableVFPair MaxFactors = CM.computeMaxVF(UserVF, UserIC);
if (!MaxFactors) // Cases that should not to be vectorized nor interleaved.
return;
// Compute the minimal bitwidths required for integer operations in the loop
// for later use by the cost model.
Config.computeMinimalBitwidths();
// Invalidate interleave groups if all blocks of loop will be predicated.
if (CM.blockNeedsPredicationForAnyReason(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 (CM.foldTailByMasking())
Legal->prepareToFoldTailByMasking();
ElementCount MaxUserVF =
UserVF.isScalable() ? MaxFactors.ScalableVF : MaxFactors.FixedVF;
if (UserVF) {
if (!ElementCount::isKnownLE(UserVF, MaxUserVF)) {
reportVectorizationInfo(
"UserVF ignored because it may be larger than the maximal safe VF",
"InvalidUserVF", ORE, OrigLoop);
} else {
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.
Config.collectInLoopReductions();
CM.collectNonVectorizedAndSetWideningDecisions(UserVF);
ElementCount EpilogueUserVF =
ElementCount::getFixed(EpilogueVectorizationForceVF);
if (EpilogueUserVF.isVector() &&
ElementCount::isKnownLT(EpilogueUserVF, UserVF)) {
CM.collectNonVectorizedAndSetWideningDecisions(EpilogueUserVF);
buildVPlansWithVPRecipes(EpilogueUserVF, EpilogueUserVF);
}
buildVPlansWithVPRecipes(UserVF, UserVF);
if (!VPlans.empty() && VPlans.back()->getSingleVF() == UserVF) {
// For scalar VF, skip VPlan cost check as VPlan cost is designed for
// vector VFs only.
if (UserVF.isScalar() ||
cost(*VPlans.back(), UserVF, /*RU=*/nullptr).isValid()) {
LLVM_DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
LLVM_DEBUG(printPlans(dbgs()));
return;
}
}
VPlans.clear();
reportVectorizationInfo("UserVF ignored because of invalid costs.",
"InvalidCost", ORE, OrigLoop);
}
}
// Collect the Vectorization Factor Candidates.
SmallVector<ElementCount> VFCandidates;
for (auto VF = ElementCount::getFixed(1);
ElementCount::isKnownLE(VF, MaxFactors.FixedVF); VF *= 2)
VFCandidates.push_back(VF);
for (auto VF = ElementCount::getScalable(1);
ElementCount::isKnownLE(VF, MaxFactors.ScalableVF); VF *= 2)
VFCandidates.push_back(VF);
Config.collectInLoopReductions();
for (const auto &VF : VFCandidates) {
// Collect Uniform and Scalar instructions after vectorization with VF.
CM.collectNonVectorizedAndSetWideningDecisions(VF);
}
buildVPlansWithVPRecipes(ElementCount::getFixed(1), MaxFactors.FixedVF);
buildVPlansWithVPRecipes(ElementCount::getScalable(1), MaxFactors.ScalableVF);
LLVM_DEBUG(printPlans(dbgs()));
}
InstructionCost VPCostContext::getLegacyCost(Instruction *UI,
ElementCount VF) const {
InstructionCost Cost = CM.getInstructionCost(UI, VF);
if (Cost.isValid() && ForceTargetInstructionCost.getNumOccurrences())
return InstructionCost(ForceTargetInstructionCost);
return Cost;
}
bool VPCostContext::skipCostComputation(Instruction *UI, bool IsVector) const {
return CM.ValuesToIgnore.contains(UI) ||
(IsVector && CM.VecValuesToIgnore.contains(UI)) ||
SkipCostComputation.contains(UI);
}
uint64_t VPCostContext::getPredBlockCostDivisor(BasicBlock *BB) const {
return CM.getPredBlockCostDivisor(CostKind, BB);
}
InstructionCost
LoopVectorizationPlanner::precomputeCosts(VPlan &Plan, ElementCount VF,
VPCostContext &CostCtx) const {
InstructionCost Cost;
// Cost modeling for inductions is inaccurate in the legacy cost model
// compared to the recipes that are generated. To match here initially during
// VPlan cost model bring up directly use the induction costs from the legacy
// cost model. Note that we do this as pre-processing; the VPlan may not have
// any recipes associated with the original induction increment instruction
// and may replace truncates with VPWidenIntOrFpInductionRecipe. We precompute
// the cost of induction phis and increments (both that are represented by
// recipes and those that are not), to avoid distinguishing between them here,
// and skip all recipes that represent induction phis and increments (the
// former case) later on, if they exist, to avoid counting them twice.
// Similarly we pre-compute the cost of any optimized truncates.
// TODO: Switch to more accurate costing based on VPlan.
for (const auto &[IV, IndDesc] : Legal->getInductionVars()) {
Instruction *IVInc = cast<Instruction>(
IV->getIncomingValueForBlock(OrigLoop->getLoopLatch()));
SmallVector<Instruction *> IVInsts = {IVInc};
for (unsigned I = 0; I != IVInsts.size(); I++) {
for (Value *Op : IVInsts[I]->operands()) {
auto *OpI = dyn_cast<Instruction>(Op);
if (Op == IV || !OpI || !OrigLoop->contains(OpI) || !Op->hasOneUse())
continue;
IVInsts.push_back(OpI);
}
}
IVInsts.push_back(IV);
for (User *U : IV->users()) {
auto *CI = cast<Instruction>(U);
if (!CostCtx.CM.isOptimizableIVTruncate(CI, VF))
continue;
IVInsts.push_back(CI);
}
// If the vector loop gets executed exactly once with the given VF, ignore
// the costs of comparison and induction instructions, as they'll get
// simplified away.
// TODO: Remove this code after stepping away from the legacy cost model and
// adding code to simplify VPlans before calculating their costs.
auto TC = getSmallConstantTripCount(PSE.getSE(), OrigLoop);
if (TC == VF && !CM.foldTailByMasking())
addFullyUnrolledInstructionsToIgnore(OrigLoop, Legal->getInductionVars(),
CostCtx.SkipCostComputation);
for (Instruction *IVInst : IVInsts) {
if (CostCtx.skipCostComputation(IVInst, VF.isVector()))
continue;
InstructionCost InductionCost = CostCtx.getLegacyCost(IVInst, VF);
LLVM_DEBUG({
dbgs() << "Cost of " << InductionCost << " for VF " << VF
<< ": induction instruction " << *IVInst << "\n";
});
Cost += InductionCost;
CostCtx.SkipCostComputation.insert(IVInst);
}
}
/// Compute the cost of all exiting conditions of the loop using the legacy
/// cost model. This is to match the legacy behavior, which adds the cost of
/// all exit conditions. Note that this over-estimates the cost, as there will
/// be a single condition to control the vector loop.
SmallVector<BasicBlock *> Exiting;
CM.TheLoop->getExitingBlocks(Exiting);
SetVector<Instruction *> ExitInstrs;
// Collect all exit conditions.
for (BasicBlock *EB : Exiting) {
auto *Term = dyn_cast<CondBrInst>(EB->getTerminator());
if (!Term || CostCtx.skipCostComputation(Term, VF.isVector()))
continue;
if (auto *CondI = dyn_cast<Instruction>(Term->getOperand(0))) {
ExitInstrs.insert(CondI);
}
}
// Compute the cost of all instructions only feeding the exit conditions.
for (unsigned I = 0; I != ExitInstrs.size(); ++I) {
Instruction *CondI = ExitInstrs[I];
if (!OrigLoop->contains(CondI) ||
!CostCtx.SkipCostComputation.insert(CondI).second)
continue;
InstructionCost CondICost = CostCtx.getLegacyCost(CondI, VF);
LLVM_DEBUG({
dbgs() << "Cost of " << CondICost << " for VF " << VF
<< ": exit condition instruction " << *CondI << "\n";
});
Cost += CondICost;
for (Value *Op : CondI->operands()) {
auto *OpI = dyn_cast<Instruction>(Op);
if (!OpI || CostCtx.skipCostComputation(OpI, VF.isVector()) ||
any_of(OpI->users(), [&ExitInstrs](User *U) {
return !ExitInstrs.contains(cast<Instruction>(U));
}))
continue;
ExitInstrs.insert(OpI);
}
}
// Pre-compute the costs for branches except for the backedge, as the number
// of replicate regions in a VPlan may not directly match the number of
// branches, which would lead to different decisions.
// TODO: Compute cost of branches for each replicate region in the VPlan,
// which is more accurate than the legacy cost model.
for (BasicBlock *BB : OrigLoop->blocks()) {
if (CostCtx.skipCostComputation(BB->getTerminator(), VF.isVector()))
continue;
CostCtx.SkipCostComputation.insert(BB->getTerminator());
if (BB == OrigLoop->getLoopLatch())
continue;
auto BranchCost = CostCtx.getLegacyCost(BB->getTerminator(), VF);
Cost += BranchCost;
}
// Don't apply special costs when instruction cost is forced to make sure the
// forced cost is used for each recipe.
if (ForceTargetInstructionCost.getNumOccurrences())
return Cost;
// Pre-compute costs for instructions that are forced-scalar or profitable to
// scalarize. For most such instructions, their scalarization costs are
// accounted for here using the legacy cost model. However, some opcodes
// are excluded from these precomputed scalarization costs and are instead
// modeled later by the VPlan cost model (see UseVPlanCostModel below).
for (Instruction *ForcedScalar : CM.ForcedScalars[VF]) {
if (CostCtx.skipCostComputation(ForcedScalar, VF.isVector()))
continue;
CostCtx.SkipCostComputation.insert(ForcedScalar);
InstructionCost ForcedCost = CostCtx.getLegacyCost(ForcedScalar, VF);
LLVM_DEBUG({
dbgs() << "Cost of " << ForcedCost << " for VF " << VF
<< ": forced scalar " << *ForcedScalar << "\n";
});
Cost += ForcedCost;
}
auto UseVPlanCostModel = [](Instruction *I) -> bool {
switch (I->getOpcode()) {
case Instruction::SDiv:
case Instruction::UDiv:
case Instruction::SRem:
case Instruction::URem:
return true;
default:
return false;
}
};
for (const auto &[Scalarized, ScalarCost] : CM.InstsToScalarize[VF]) {
if (UseVPlanCostModel(Scalarized) ||
CostCtx.skipCostComputation(Scalarized, VF.isVector()))
continue;
CostCtx.SkipCostComputation.insert(Scalarized);
LLVM_DEBUG({
dbgs() << "Cost of " << ScalarCost << " for VF " << VF
<< ": profitable to scalarize " << *Scalarized << "\n";
});
Cost += ScalarCost;
}
return Cost;
}
InstructionCost LoopVectorizationPlanner::cost(VPlan &Plan, ElementCount VF,
VPRegisterUsage *RU) const {
VPCostContext CostCtx(CM.TTI, *CM.TLI, Plan, CM, Config.CostKind, PSE,
OrigLoop);
InstructionCost Cost = precomputeCosts(Plan, VF, CostCtx);
// Now compute and add the VPlan-based cost.
Cost += Plan.cost(VF, CostCtx);
// Add the cost of spills due to excess register usage
if (RU && Config.shouldConsiderRegPressureForVF(VF))
Cost += RU->spillCost(CM.TTI, Config.CostKind, ForceTargetNumVectorRegs);
#ifndef NDEBUG
unsigned EstimatedWidth =
estimateElementCount(VF, Config.getVScaleForTuning());
LLVM_DEBUG(dbgs() << "Cost for VF " << VF << ": " << Cost
<< " (Estimated cost per lane: ");
if (Cost.isValid()) {
double CostPerLane = double(Cost.getValue()) / EstimatedWidth;
LLVM_DEBUG(dbgs() << format("%.1f", CostPerLane));
} else /* No point dividing an invalid cost - it will still be invalid */
LLVM_DEBUG(dbgs() << "Invalid");
LLVM_DEBUG(dbgs() << ")\n");
#endif
return Cost;
}
std::pair<VectorizationFactor, VPlan *>
LoopVectorizationPlanner::computeBestVF() {
if (VPlans.empty())
return {VectorizationFactor::Disabled(), nullptr};
// If there is a single VPlan with a single VF, return it directly.
VPlan &FirstPlan = *VPlans[0];
ElementCount UserVF = Hints.getWidth();
if (hasPlanWithVF(UserVF)) {
if (VPlans.size() == 1) {
assert(FirstPlan.getSingleVF() == UserVF &&
"UserVF must match single VF");
return {VectorizationFactor(FirstPlan.getSingleVF(), 0, 0), &FirstPlan};
}
if (EpilogueVectorizationForceVF > 1) {
assert(VPlans.size() == 2 && "Must have exactly 2 VPlans built");
assert(VPlans[0]->getSingleVF() ==
ElementCount::getFixed(EpilogueVectorizationForceVF) &&
"expected first plan to be for the forced epilogue VF");
assert(VPlans[1]->getSingleVF() == UserVF &&
"expected second plan to be for the forced UserVF");
return {VectorizationFactor(UserVF, 0, 0), VPlans[1].get()};
}
}
LLVM_DEBUG(dbgs() << "LV: Computing best VF using cost kind: "
<< (Config.CostKind == TTI::TCK_RecipThroughput
? "Reciprocal Throughput\n"
: Config.CostKind == TTI::TCK_Latency
? "Instruction Latency\n"
: Config.CostKind == TTI::TCK_CodeSize ? "Code Size\n"
: Config.CostKind == TTI::TCK_SizeAndLatency
? "Code Size and Latency\n"
: "Unknown\n"));
ElementCount ScalarVF = ElementCount::getFixed(1);
assert(FirstPlan.hasVF(ScalarVF) &&
"More than a single plan/VF w/o any plan having scalar VF");
// TODO: Compute scalar cost using VPlan-based cost model.
InstructionCost ScalarCost = CM.expectedCost(ScalarVF);
LLVM_DEBUG(dbgs() << "LV: Scalar loop costs: " << ScalarCost << ".\n");
VectorizationFactor ScalarFactor(ScalarVF, ScalarCost, ScalarCost);
VectorizationFactor BestFactor = ScalarFactor;
bool ForceVectorization = Hints.getForce() == LoopVectorizeHints::FK_Enabled;
if (ForceVectorization) {
// Ignore scalar width, because the user explicitly wants vectorization.
// Initialize cost to max so that VF = 2 is, at least, chosen during cost
// evaluation.
BestFactor.Cost = InstructionCost::getMax();
}
VPlan *PlanForBestVF = &FirstPlan;
for (auto &P : VPlans) {
ArrayRef<ElementCount> VFs(P->vectorFactors().begin(),
P->vectorFactors().end());
SmallVector<VPRegisterUsage, 8> RUs;
bool ConsiderRegPressure = any_of(VFs, [this](ElementCount VF) {
return Config.shouldConsiderRegPressureForVF(VF);
});
if (ConsiderRegPressure)
RUs = calculateRegisterUsageForPlan(*P, VFs, TTI, CM.ValuesToIgnore);
for (unsigned I = 0; I < VFs.size(); I++) {
ElementCount VF = VFs[I];
if (VF.isScalar())
continue;
if (!ForceVectorization && !willGenerateVectors(*P, VF, TTI)) {
LLVM_DEBUG(
dbgs()
<< "LV: Not considering vector loop of width " << VF
<< " because it will not generate any vector instructions.\n");
continue;
}
if (Config.OptForSize && !ForceVectorization && hasReplicatorRegion(*P)) {
LLVM_DEBUG(
dbgs()
<< "LV: Not considering vector loop of width " << VF
<< " because it would cause replicated blocks to be generated,"
<< " which isn't allowed when optimizing for size.\n");
continue;
}
InstructionCost Cost =
cost(*P, VF, ConsiderRegPressure ? &RUs[I] : nullptr);
VectorizationFactor CurrentFactor(VF, Cost, ScalarCost);
if (isMoreProfitable(CurrentFactor, BestFactor, P->hasScalarTail())) {
BestFactor = CurrentFactor;
PlanForBestVF = P.get();
}
// If profitable add it to ProfitableVF list.
if (isMoreProfitable(CurrentFactor, ScalarFactor, P->hasScalarTail()))
ProfitableVFs.push_back(CurrentFactor);
}
}
VPlan &BestPlan = *PlanForBestVF;
assert((BestFactor.Width.isScalar() || BestFactor.ScalarCost > 0) &&
"when vectorizing, the scalar cost must be computed.");
LLVM_DEBUG(dbgs() << "LV: Selecting VF: " << BestFactor.Width << ".\n");
return {BestFactor, &BestPlan};
}
DenseMap<const SCEV *, Value *> LoopVectorizationPlanner::executePlan(
ElementCount BestVF, unsigned BestUF, VPlan &BestVPlan,
InnerLoopVectorizer &ILV, DominatorTree *DT,
EpilogueVectorizationKind EpilogueVecKind) {
assert(BestVPlan.hasVF(BestVF) &&
"Trying to execute plan with unsupported VF");
assert(BestVPlan.hasUF(BestUF) &&
"Trying to execute plan with unsupported UF");
if (BestVPlan.hasEarlyExit())
++LoopsEarlyExitVectorized;
VPlanTransforms::replaceWideCanonicalIVWithWideIV(
BestVPlan, *PSE.getSE(), CM.TTI, Config.CostKind, BestVF, BestUF,
CM.ValuesToIgnore);
// TODO: Move to VPlan transform stage once the transition to the VPlan-based
// cost model is complete for better cost estimates.
RUN_VPLAN_PASS(VPlanTransforms::unrollByUF, BestVPlan, BestUF);
RUN_VPLAN_PASS(VPlanTransforms::materializePacksAndUnpacks, BestVPlan);
RUN_VPLAN_PASS(VPlanTransforms::materializeBroadcasts, BestVPlan);
RUN_VPLAN_PASS(VPlanTransforms::replicateByVF, BestVPlan, BestVF);
bool HasBranchWeights =
hasBranchWeightMD(*OrigLoop->getLoopLatch()->getTerminator());
if (HasBranchWeights) {
std::optional<unsigned> VScale = Config.getVScaleForTuning();
RUN_VPLAN_PASS(VPlanTransforms::addBranchWeightToMiddleTerminator,
BestVPlan, BestVF, VScale);
}
// Retrieving VectorPH now when it's easier while VPlan still has Regions.
VPBasicBlock *VectorPH = cast<VPBasicBlock>(BestVPlan.getVectorPreheader());
RUN_VPLAN_PASS(VPlanTransforms::materializeConstantVectorTripCount, BestVPlan,
BestVF, BestUF, PSE);
RUN_VPLAN_PASS(VPlanTransforms::optimizeForVFAndUF, BestVPlan, BestVF, BestUF,
PSE);
RUN_VPLAN_PASS(VPlanTransforms::simplifyRecipes, BestVPlan);
if (EpilogueVecKind == EpilogueVectorizationKind::None)
RUN_VPLAN_PASS(VPlanTransforms::removeBranchOnConst, BestVPlan,
/*OnlyLatches=*/false);
if (BestVPlan.getEntry()->getSingleSuccessor() ==
BestVPlan.getScalarPreheader()) {
// TODO: The vector loop would be dead, should not even try to vectorize.
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationDead",
OrigLoop->getStartLoc(),
OrigLoop->getHeader())
<< "Created vector loop never executes due to insufficient trip "
"count.";
});
return DenseMap<const SCEV *, Value *>();
}
RUN_VPLAN_PASS(VPlanTransforms::removeDeadRecipes, BestVPlan);
RUN_VPLAN_PASS(VPlanTransforms::convertToConcreteRecipes, BestVPlan);
// Convert the exit condition to AVLNext == 0 for EVL tail folded loops.
RUN_VPLAN_PASS(VPlanTransforms::convertEVLExitCond, BestVPlan);
// Regions are dissolved after optimizing for VF and UF, which completely
// removes unneeded loop regions first.
VPlanTransforms::dissolveLoopRegions(BestVPlan);
// Expand BranchOnTwoConds after dissolution, when latch has direct access to
// its successors.
VPlanTransforms::expandBranchOnTwoConds(BestVPlan);
// Convert loops with variable-length stepping after regions are dissolved.
VPlanTransforms::convertToVariableLengthStep(BestVPlan);
// Remove dead back-edges for single-iteration loops with BranchOnCond(true).
// Only process loop latches to avoid removing edges from the middle block,
// which may be needed for epilogue vectorization.
VPlanTransforms::removeBranchOnConst(BestVPlan, /*OnlyLatches=*/true);
VPlanTransforms::materializeBackedgeTakenCount(BestVPlan, VectorPH);
std::optional<uint64_t> MaxRuntimeStep;
if (auto MaxVScale = getMaxVScale(*CM.TheFunction, CM.TTI))
MaxRuntimeStep = uint64_t(*MaxVScale) * BestVF.getKnownMinValue() * BestUF;
VPlanTransforms::materializeVectorTripCount(
BestVPlan, VectorPH, CM.foldTailByMasking(),
CM.requiresScalarEpilogue(BestVF.isVector()), &BestVPlan.getVFxUF(),
MaxRuntimeStep);
VPlanTransforms::materializeFactors(BestVPlan, VectorPH, BestVF);
VPlanTransforms::cse(BestVPlan);
VPlanTransforms::simplifyRecipes(BestVPlan);
VPlanTransforms::simplifyKnownEVL(BestVPlan, BestVF, PSE);
// 0. Generate SCEV-dependent code in the entry, including TripCount, before
// making any changes to the CFG.
DenseMap<const SCEV *, Value *> ExpandedSCEVs =
VPlanTransforms::expandSCEVs(BestVPlan, *PSE.getSE());
// Perform the actual loop transformation.
VPTransformState State(&TTI, BestVF, LI, DT, ILV.AC, ILV.Builder, &BestVPlan,
OrigLoop->getParentLoop(),
Legal->getWidestInductionType());
#ifdef EXPENSIVE_CHECKS
assert(DT->verify(DominatorTree::VerificationLevel::Fast));
#endif
// 1. Set up the skeleton for vectorization, including vector pre-header and
// middle block. The vector loop is created during VPlan execution.
State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton();
if (VPBasicBlock *ScalarPH = BestVPlan.getScalarPreheader())
replaceVPBBWithIRVPBB(ScalarPH, State.CFG.PrevBB->getSingleSuccessor(),
&BestVPlan);
VPlanTransforms::removeDeadRecipes(BestVPlan);
assert(verifyVPlanIsValid(BestVPlan) && "final VPlan is invalid");
// After vectorization, the exit blocks of the original loop will have
// additional predecessors. Invalidate SCEVs for the exit phis in case SE
// looked through single-entry phis.
ScalarEvolution &SE = *PSE.getSE();
for (VPIRBasicBlock *Exit : BestVPlan.getExitBlocks()) {
if (!Exit->hasPredecessors())
continue;
for (VPRecipeBase &PhiR : Exit->phis())
SE.forgetLcssaPhiWithNewPredecessor(OrigLoop,
&cast<VPIRPhi>(PhiR).getIRPhi());
}
// Forget the original loop and block dispositions.
SE.forgetLoop(OrigLoop);
SE.forgetBlockAndLoopDispositions();
ILV.printDebugTracesAtStart();
//===------------------------------------------------===//
//
// Notice: any optimization or new instruction that go
// into the code below should also be implemented in
// the cost-model.
//
//===------------------------------------------------===//
// Retrieve loop information before executing the plan, which may remove the
// original loop, if it becomes unreachable.
MDNode *LID = OrigLoop->getLoopID();
unsigned OrigLoopInvocationWeight = 0;
std::optional<unsigned> OrigAverageTripCount =
getLoopEstimatedTripCount(OrigLoop, &OrigLoopInvocationWeight);
BestVPlan.execute(&State);
// 2.6. Maintain Loop Hints
// Keep all loop hints from the original loop on the vector loop (we'll
// replace the vectorizer-specific hints below).
VPBasicBlock *HeaderVPBB = vputils::getFirstLoopHeader(BestVPlan, State.VPDT);
// 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.
bool DisableRuntimeUnroll = !ILV.RTChecks.hasChecks() && !BestVF.isScalar();
updateLoopMetadataAndProfileInfo(
HeaderVPBB ? LI->getLoopFor(State.CFG.VPBB2IRBB.lookup(HeaderVPBB))
: nullptr,
HeaderVPBB, BestVPlan,
EpilogueVecKind == EpilogueVectorizationKind::Epilogue, LID,
OrigAverageTripCount, OrigLoopInvocationWeight,
estimateElementCount(BestVF * BestUF, Config.getVScaleForTuning()),
DisableRuntimeUnroll);
// 3. Fix the vectorized code: take care of header phi's, live-outs,
// predication, updating analyses.
ILV.fixVectorizedLoop(State);
ILV.printDebugTracesAtEnd();
return ExpandedSCEVs;
}
//===--------------------------------------------------------------------===//
// EpilogueVectorizerMainLoop
//===--------------------------------------------------------------------===//
void EpilogueVectorizerMainLoop::printDebugTracesAtStart() {
LLVM_DEBUG({
dbgs() << "Create Skeleton for epilogue vectorized loop (first pass)\n"
<< "Main Loop VF:" << EPI.MainLoopVF
<< ", Main Loop UF:" << EPI.MainLoopUF
<< ", Epilogue Loop VF:" << EPI.EpilogueVF
<< ", Epilogue Loop UF:" << EPI.EpilogueUF << "\n";
});
}
void EpilogueVectorizerMainLoop::printDebugTracesAtEnd() {
DEBUG_WITH_TYPE(VerboseDebug, {
dbgs() << "intermediate fn:\n"
<< *OrigLoop->getHeader()->getParent() << "\n";
});
}
//===--------------------------------------------------------------------===//
// EpilogueVectorizerEpilogueLoop
//===--------------------------------------------------------------------===//
/// This function creates a new scalar preheader, using the previous one as
/// entry block to the epilogue VPlan. The minimum iteration check is being
/// represented in VPlan.
BasicBlock *EpilogueVectorizerEpilogueLoop::createVectorizedLoopSkeleton() {
BasicBlock *NewScalarPH = createScalarPreheader("vec.epilog.");
BasicBlock *OriginalScalarPH = NewScalarPH->getSinglePredecessor();
OriginalScalarPH->setName("vec.epilog.iter.check");
VPIRBasicBlock *NewEntry = Plan.createVPIRBasicBlock(OriginalScalarPH);
VPBasicBlock *OldEntry = Plan.getEntry();
for (auto &R : make_early_inc_range(*OldEntry)) {
// Skip moving VPIRInstructions (including VPIRPhis), which are unmovable by
// defining.
if (isa<VPIRInstruction>(&R))
continue;
R.moveBefore(*NewEntry, NewEntry->end());
}
VPBlockUtils::reassociateBlocks(OldEntry, NewEntry);
Plan.setEntry(NewEntry);
// OldEntry is now dead and will be cleaned up when the plan gets destroyed.
return OriginalScalarPH;
}
void EpilogueVectorizerEpilogueLoop::printDebugTracesAtStart() {
LLVM_DEBUG({
dbgs() << "Create Skeleton for epilogue vectorized loop (second pass)\n"
<< "Epilogue Loop VF:" << EPI.EpilogueVF
<< ", Epilogue Loop UF:" << EPI.EpilogueUF << "\n";
});
}
void EpilogueVectorizerEpilogueLoop::printDebugTracesAtEnd() {
DEBUG_WITH_TYPE(VerboseDebug, {
dbgs() << "final fn:\n" << *OrigLoop->getHeader()->getParent() << "\n";
});
}
VPRecipeBase *VPRecipeBuilder::tryToWidenMemory(VPInstruction *VPI,
VFRange &Range) {
assert((VPI->getOpcode() == Instruction::Load ||
VPI->getOpcode() == Instruction::Store) &&
"Must be called with either a load or store");
Instruction *I = VPI->getUnderlyingInstr();
auto WillWiden = [&](ElementCount VF) -> bool {
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;
// If a mask is not required, drop it - use unmasked version for safe loads.
// TODO: Determine if mask is needed in VPlan.
VPValue *Mask = CM.isMaskRequired(I) ? VPI->getMask() : nullptr;
// Determine if the pointer operand of the access is either consecutive or
// reverse consecutive.
LoopVectorizationCostModel::InstWidening Decision =
CM.getWideningDecision(I, Range.Start);
bool Reverse = Decision == LoopVectorizationCostModel::CM_Widen_Reverse;
bool Consecutive =
Reverse || Decision == LoopVectorizationCostModel::CM_Widen;
VPValue *Ptr = VPI->getOpcode() == Instruction::Load ? VPI->getOperand(0)
: VPI->getOperand(1);
if (Consecutive) {
GEPNoWrapFlags Flags = vputils::getGEPFlagsForPtr(Ptr);
VPSingleDefRecipe *VectorPtr;
if (Reverse) {
// When folding the tail, we may compute an address that we don't in the
// original scalar loop: drop the GEP no-wrap flags in this case.
// Otherwise preserve existing flags without no-unsigned-wrap, as we will
// emit negative indices.
GEPNoWrapFlags ReverseFlags = CM.foldTailByMasking()
? GEPNoWrapFlags::none()
: Flags.withoutNoUnsignedWrap();
VectorPtr = new VPVectorEndPointerRecipe(
Ptr, &Plan.getVF(), getLoadStoreType(I),
/*Stride*/ -1, ReverseFlags, VPI->getDebugLoc());
} else {
VectorPtr = new VPVectorPointerRecipe(Ptr, getLoadStoreType(I), Flags,
VPI->getDebugLoc());
}
Builder.setInsertPoint(VPI);
Builder.insert(VectorPtr);
Ptr = VectorPtr;
}
if (Reverse && Mask)
Mask = Builder.createNaryOp(VPInstruction::Reverse, Mask, I->getDebugLoc());
if (VPI->getOpcode() == Instruction::Load) {
auto *Load = cast<LoadInst>(I);
auto *LoadR = new VPWidenLoadRecipe(*Load, Ptr, Mask, Consecutive, *VPI,
Load->getDebugLoc());
if (Reverse) {
Builder.insert(LoadR);
return new VPInstruction(VPInstruction::Reverse, LoadR, {}, {},
LoadR->getDebugLoc());
}
return LoadR;
}
StoreInst *Store = cast<StoreInst>(I);
VPValue *StoredVal = VPI->getOperand(0);
if (Reverse)
StoredVal = Builder.createNaryOp(VPInstruction::Reverse, StoredVal,
Store->getDebugLoc());
return new VPWidenStoreRecipe(*Store, Ptr, StoredVal, Mask, Consecutive, *VPI,
Store->getDebugLoc());
}
VPWidenIntOrFpInductionRecipe *
VPRecipeBuilder::tryToOptimizeInductionTruncate(VPInstruction *VPI,
VFRange &Range) {
auto *I = cast<TruncInst>(VPI->getUnderlyingInstr());
// 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.
if (!LoopVectorizationPlanner::getDecisionAndClampRange(
bind_front(&LoopVectorizationCostModel::isOptimizableIVTruncate, CM,
I),
Range))
return nullptr;
auto *WidenIV = cast<VPWidenIntOrFpInductionRecipe>(
VPI->getOperand(0)->getDefiningRecipe());
PHINode *Phi = WidenIV->getPHINode();
VPIRValue *Start = WidenIV->getStartValue();
const InductionDescriptor &IndDesc = WidenIV->getInductionDescriptor();
// Wrap flags from the original induction do not apply to the truncated type,
// so do not propagate them.
VPIRFlags Flags = VPIRFlags::WrapFlagsTy(false, false);
VPValue *Step =
vputils::getOrCreateVPValueForSCEVExpr(Plan, IndDesc.getStep());
return new VPWidenIntOrFpInductionRecipe(
Phi, Start, Step, &Plan.getVF(), IndDesc, I, Flags, VPI->getDebugLoc());
}
VPSingleDefRecipe *VPRecipeBuilder::tryToWidenCall(VPInstruction *VPI,
VFRange &Range) {
CallInst *CI = cast<CallInst>(VPI->getUnderlyingInstr());
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 (VPCostContext::isFreeScalarIntrinsic(ID))
return nullptr;
SmallVector<VPValue *, 4> Ops(VPI->op_begin(),
VPI->op_begin() + CI->arg_size());
// Is it beneficial to perform intrinsic call compared to lib call?
bool ShouldUseVectorIntrinsic =
ID && LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) -> bool {
return CM.getCallWideningDecision(CI, VF).Kind ==
LoopVectorizationCostModel::CM_IntrinsicCall;
},
Range);
if (ShouldUseVectorIntrinsic)
return new VPWidenIntrinsicRecipe(*CI, ID, Ops, CI->getType(), *VPI, *VPI,
VPI->getDebugLoc());
Function *Variant = nullptr;
std::optional<unsigned> MaskPos;
// Is better to call a vectorized version of the function than to to scalarize
// the call?
auto ShouldUseVectorCall = LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) -> bool {
// The following case may be scalarized depending on the VF.
// The flag shows whether we can use a usual Call for vectorized
// version of the instruction.
// If we've found a variant at a previous VF, then stop looking. A
// vectorized variant of a function expects input in a certain shape
// -- basically the number of input registers, the number of lanes
// per register, and whether there's a mask required.
// We store a pointer to the variant in the VPWidenCallRecipe, so
// once we have an appropriate variant it's only valid for that VF.
// This will force a different vplan to be generated for each VF that
// finds a valid variant.
if (Variant)
return false;
LoopVectorizationCostModel::CallWideningDecision Decision =
CM.getCallWideningDecision(CI, VF);
if (Decision.Kind == LoopVectorizationCostModel::CM_VectorCall) {
Variant = Decision.Variant;
MaskPos = Decision.MaskPos;
return true;
}
return false;
},
Range);
if (ShouldUseVectorCall) {
if (MaskPos.has_value()) {
// We have 2 cases that would require a mask:
// 1) The call needs to be predicated, either due to a conditional
// in the scalar loop or use of an active lane mask with
// tail-folding, and we use the appropriate mask for the block.
// 2) No mask is required for the call instruction, but the only
// available vector variant at this VF requires a mask, so we
// synthesize an all-true mask.
VPValue *Mask = VPI->isMasked() ? VPI->getMask() : Plan.getTrue();
Ops.insert(Ops.begin() + *MaskPos, Mask);
}
Ops.push_back(VPI->getOperand(VPI->getNumOperandsWithoutMask() - 1));
return new VPWidenCallRecipe(CI, Variant, Ops, *VPI, *VPI,
VPI->getDebugLoc());
}
return nullptr;
}
bool VPRecipeBuilder::shouldWiden(Instruction *I, VFRange &Range) const {
assert((!isa<UncondBrInst, CondBrInst, PHINode, LoadInst, 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(VPInstruction *VPI) {
auto *I = VPI->getUnderlyingInstr();
switch (VPI->getOpcode()) {
default:
return nullptr;
case Instruction::SDiv:
case Instruction::UDiv:
case Instruction::SRem:
case Instruction::URem: {
// If not provably safe, use a select to form a safe divisor before widening the
// div/rem operation itself. Otherwise fall through to general handling below.
if (CM.isPredicatedInst(I)) {
SmallVector<VPValue *> Ops(VPI->operandsWithoutMask());
VPValue *Mask = VPI->getMask();
VPValue *One = Plan.getConstantInt(I->getType(), 1u);
auto *SafeRHS =
Builder.createSelect(Mask, Ops[1], One, VPI->getDebugLoc());
Ops[1] = SafeRHS;
return new VPWidenRecipe(*I, Ops, *VPI, *VPI, VPI->getDebugLoc());
}
[[fallthrough]];
}
case Instruction::Add:
case Instruction::And:
case Instruction::AShr:
case Instruction::FAdd:
case Instruction::FCmp:
case Instruction::FDiv:
case Instruction::FMul:
case Instruction::FNeg:
case Instruction::FRem:
case Instruction::FSub:
case Instruction::ICmp:
case Instruction::LShr:
case Instruction::Mul:
case Instruction::Or:
case Instruction::Select:
case Instruction::Shl:
case Instruction::Sub:
case Instruction::Xor:
case Instruction::Freeze:
return new VPWidenRecipe(*I, VPI->operandsWithoutMask(), *VPI, *VPI,
VPI->getDebugLoc());
case Instruction::ExtractValue: {
SmallVector<VPValue *> NewOps(VPI->operandsWithoutMask());
auto *EVI = cast<ExtractValueInst>(I);
assert(EVI->getNumIndices() == 1 && "Expected one extractvalue index");
unsigned Idx = EVI->getIndices()[0];
NewOps.push_back(Plan.getConstantInt(32, Idx));
return new VPWidenRecipe(*I, NewOps, *VPI, *VPI, VPI->getDebugLoc());
}
};
}
VPHistogramRecipe *VPRecipeBuilder::widenIfHistogram(VPInstruction *VPI) {
if (VPI->getOpcode() != Instruction::Store)
return nullptr;
auto HistInfo =
Legal->getHistogramInfo(cast<StoreInst>(VPI->getUnderlyingInstr()));
if (!HistInfo)
return nullptr;
const HistogramInfo *HI = *HistInfo;
// FIXME: Support other operations.
unsigned Opcode = HI->Update->getOpcode();
assert((Opcode == Instruction::Add || Opcode == Instruction::Sub) &&
"Histogram update operation must be an Add or Sub");
SmallVector<VPValue *, 3> HGramOps;
// Bucket address.
HGramOps.push_back(VPI->getOperand(1));
// Increment value.
HGramOps.push_back(Plan.getOrAddLiveIn(HI->Update->getOperand(1)));
// In case of predicated execution (due to tail-folding, or conditional
// execution, or both), pass the relevant mask.
if (CM.isMaskRequired(HI->Store))
HGramOps.push_back(VPI->getMask());
return new VPHistogramRecipe(Opcode, HGramOps, VPI->getDebugLoc());
}
bool VPRecipeBuilder::replaceWithFinalIfReductionStore(
VPInstruction *VPI, VPBuilder &FinalRedStoresBuilder) {
StoreInst *SI;
if ((SI = dyn_cast<StoreInst>(VPI->getUnderlyingInstr())) &&
Legal->isInvariantAddressOfReduction(SI->getPointerOperand())) {
// Only create recipe for the final invariant store of the reduction.
if (Legal->isInvariantStoreOfReduction(SI)) {
auto *Recipe = new VPReplicateRecipe(
SI, VPI->operandsWithoutMask(), true /* IsUniform */,
nullptr /*Mask*/, *VPI, *VPI, VPI->getDebugLoc());
FinalRedStoresBuilder.insert(Recipe);
}
VPI->eraseFromParent();
return true;
}
return false;
}
VPReplicateRecipe *VPRecipeBuilder::handleReplication(VPInstruction *VPI,
VFRange &Range) {
auto *I = VPI->getUnderlyingInstr();
bool IsUniform = LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) { return CM.isUniformAfterVectorization(I, VF); },
Range);
bool IsPredicated = CM.isPredicatedInst(I);
// Even if the instruction is not marked as uniform, there are certain
// intrinsic calls that can be effectively treated as such, so we check for
// them here. Conservatively, we only do this for scalable vectors, since
// for fixed-width VFs we can always fall back on full scalarization.
if (!IsUniform && Range.Start.isScalable() && isa<IntrinsicInst>(I)) {
switch (cast<IntrinsicInst>(I)->getIntrinsicID()) {
case Intrinsic::assume:
case Intrinsic::lifetime_start:
case Intrinsic::lifetime_end:
// For scalable vectors if one of the operands is variant then we still
// want to mark as uniform, which will generate one instruction for just
// the first lane of the vector. We can't scalarize the call in the same
// way as for fixed-width vectors because we don't know how many lanes
// there are.
//
// The reasons for doing it this way for scalable vectors are:
// 1. For the assume intrinsic generating the instruction for the first
// lane is still be better than not generating any at all. For
// example, the input may be a splat across all lanes.
// 2. For the lifetime start/end intrinsics the pointer operand only
// does anything useful when the input comes from a stack object,
// which suggests it should always be uniform. For non-stack objects
// the effect is to poison the object, which still allows us to
// remove the call.
IsUniform = true;
break;
default:
break;
}
}
VPValue *BlockInMask = nullptr;
if (!IsPredicated) {
// Finalize the recipe for Instr, first if it is not predicated.
LLVM_DEBUG(dbgs() << "LV: Scalarizing:" << *I << "\n");
} else {
LLVM_DEBUG(dbgs() << "LV: Scalarizing and predicating:" << *I << "\n");
// Instructions marked for predication are replicated and a mask operand is
// added initially. Masked replicate recipes will later be placed under an
// if-then construct to prevent side-effects. Generate recipes to compute
// the block mask for this region.
BlockInMask = VPI->getMask();
}
// Note that there is some custom logic to mark some intrinsics as uniform
// manually above for scalable vectors, which this assert needs to account for
// as well.
assert((Range.Start.isScalar() || !IsUniform || !IsPredicated ||
(Range.Start.isScalable() && isa<IntrinsicInst>(I))) &&
"Should not predicate a uniform recipe");
auto *Recipe =
new VPReplicateRecipe(I, VPI->operandsWithoutMask(), IsUniform,
BlockInMask, *VPI, *VPI, VPI->getDebugLoc());
return Recipe;
}
VPRecipeBase *
VPRecipeBuilder::tryToCreateWidenNonPhiRecipe(VPSingleDefRecipe *R,
VFRange &Range) {
assert(!R->isPhi() && "phis must be handled earlier");
// First, check for specific widening recipes that deal with optimizing
// truncates, calls and memory operations.
VPRecipeBase *Recipe;
auto *VPI = cast<VPInstruction>(R);
if (VPI->getOpcode() == Instruction::Trunc &&
(Recipe = tryToOptimizeInductionTruncate(VPI, Range)))
return Recipe;
// All widen recipes below deal only with VF > 1.
if (LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) { return VF.isScalar(); }, Range))
return nullptr;
if (VPI->getOpcode() == Instruction::Call)
return tryToWidenCall(VPI, Range);
Instruction *Instr = R->getUnderlyingInstr();
assert(!is_contained({Instruction::Load, Instruction::Store},
VPI->getOpcode()) &&
"Should have been handled prior to this!");
if (!shouldWiden(Instr, Range))
return nullptr;
if (VPI->getOpcode() == Instruction::GetElementPtr)
return new VPWidenGEPRecipe(cast<GetElementPtrInst>(Instr),
VPI->operandsWithoutMask(), *VPI,
VPI->getDebugLoc());
if (Instruction::isCast(VPI->getOpcode())) {
auto *CI = cast<CastInst>(Instr);
auto *CastR = cast<VPInstructionWithType>(VPI);
return new VPWidenCastRecipe(CI->getOpcode(), VPI->getOperand(0),
CastR->getResultType(), CI, *VPI, *VPI,
VPI->getDebugLoc());
}
return tryToWiden(VPI);
}
// To allow RUN_VPLAN_PASS to print the VPlan after VF/UF independent
// optimizations.
static void printOptimizedVPlan(VPlan &) {}
void LoopVectorizationPlanner::buildVPlansWithVPRecipes(ElementCount MinVF,
ElementCount MaxVF) {
if (ElementCount::isKnownGT(MinVF, MaxVF))
return;
assert(OrigLoop->isInnermost() && "Inner loop expected.");
const LoopAccessInfo *LAI = Legal->getLAI();
LoopVersioning LVer(*LAI, LAI->getRuntimePointerChecking()->getChecks(),
OrigLoop, LI, DT, PSE.getSE());
if (!LAI->getRuntimePointerChecking()->getChecks().empty() &&
!LAI->getRuntimePointerChecking()->getDiffChecks()) {
// Only use noalias metadata when using memory checks guaranteeing no
// overlap across all iterations.
LVer.prepareNoAliasMetadata();
}
// Create initial base VPlan0, to serve as common starting point for all
// candidates built later for specific VF ranges.
auto VPlan0 = VPlanTransforms::buildVPlan0(
OrigLoop, *LI, Legal->getWidestInductionType(),
getDebugLocFromInstOrOperands(Legal->getPrimaryInduction()), PSE, &LVer);
// Create recipes for header phis.
if (!RUN_VPLAN_PASS(VPlanTransforms::createHeaderPhiRecipes, *VPlan0, PSE,
*OrigLoop, Legal->getInductionVars(),
Legal->getReductionVars(),
Legal->getFixedOrderRecurrences(),
Config.getInLoopReductions(), Hints.allowReordering()))
return;
RUN_VPLAN_PASS(VPlanTransforms::simplifyRecipes, *VPlan0);
RUN_VPLAN_PASS(VPlanTransforms::removeDeadRecipes, *VPlan0);
// If we're vectorizing a loop with an uncountable exit, make sure that the
// recipes are safe to handle.
// TODO: Remove this once we can properly check the VPlan itself for both
// the presence of an uncountable exit and the presence of stores in
// the loop inside handleEarlyExits itself.
UncountableExitStyle EEStyle = UncountableExitStyle::NoUncountableExit;
if (Legal->hasUncountableEarlyExit())
EEStyle = Legal->hasUncountableExitWithSideEffects()
? UncountableExitStyle::MaskedHandleExitInScalarLoop
: UncountableExitStyle::ReadOnly;
if (!RUN_VPLAN_PASS(VPlanTransforms::handleEarlyExits, *VPlan0, EEStyle,
OrigLoop, PSE, *DT, Legal->getAssumptionCache()))
return;
RUN_VPLAN_PASS(VPlanTransforms::addMiddleCheck, *VPlan0,
CM.foldTailByMasking());
RUN_VPLAN_PASS(VPlanTransforms::createLoopRegions, *VPlan0);
if (CM.foldTailByMasking())
RUN_VPLAN_PASS(VPlanTransforms::foldTailByMasking, *VPlan0);
RUN_VPLAN_PASS(VPlanTransforms::introduceMasksAndLinearize, *VPlan0);
auto MaxVFTimes2 = MaxVF * 2;
for (ElementCount VF = MinVF; ElementCount::isKnownLT(VF, MaxVFTimes2);) {
VFRange SubRange = {VF, MaxVFTimes2};
auto Plan = tryToBuildVPlanWithVPRecipes(
std::unique_ptr<VPlan>(VPlan0->duplicate()), SubRange);
VF = SubRange.End;
if (!Plan)
continue;
// Now optimize the initial VPlan.
RUN_VPLAN_PASS(VPlanTransforms::hoistPredicatedLoads, *Plan, PSE, OrigLoop);
RUN_VPLAN_PASS(VPlanTransforms::sinkPredicatedStores, *Plan, PSE, OrigLoop);
RUN_VPLAN_PASS(VPlanTransforms::truncateToMinimalBitwidths, *Plan,
Config.getMinimalBitwidths());
RUN_VPLAN_PASS(VPlanTransforms::optimize, *Plan);
// TODO: try to put addExplicitVectorLength close to addActiveLaneMask
if (CM.foldTailWithEVL()) {
RUN_VPLAN_PASS(VPlanTransforms::addExplicitVectorLength, *Plan,
Config.getMaxSafeElements());
RUN_VPLAN_PASS(VPlanTransforms::optimizeEVLMasks, *Plan);
}
if (auto P = VPlanTransforms::narrowInterleaveGroups(*Plan, TTI))
VPlans.push_back(std::move(P));
RUN_VPLAN_PASS_NO_VERIFY(printOptimizedVPlan, *Plan);
assert(verifyVPlanIsValid(*Plan) && "VPlan is invalid");
VPlans.push_back(std::move(Plan));
}
}
VPlanPtr
LoopVectorizationPlanner::tryToBuildVPlanWithVPRecipes(VPlanPtr Plan,
VFRange &Range) {
using namespace llvm::VPlanPatternMatch;
SmallPtrSet<const InterleaveGroup<Instruction> *, 1> InterleaveGroups;
// ---------------------------------------------------------------------------
// 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.
// ---------------------------------------------------------------------------
bool RequiresScalarEpilogueCheck =
LoopVectorizationPlanner::getDecisionAndClampRange(
[this](ElementCount VF) {
return !CM.requiresScalarEpilogue(VF.isVector());
},
Range);
// Update the branch in the middle block if a scalar epilogue is required.
VPBasicBlock *MiddleVPBB = Plan->getMiddleBlock();
if (!RequiresScalarEpilogueCheck && MiddleVPBB->getNumSuccessors() == 2) {
auto *BranchOnCond = cast<VPInstruction>(MiddleVPBB->getTerminator());
assert(MiddleVPBB->getSuccessors()[1] == Plan->getScalarPreheader() &&
"second successor must be scalar preheader");
BranchOnCond->setOperand(0, Plan->getFalse());
}
// Don't use getDecisionAndClampRange here, because we don't know the UF
// so this function is better to be conservative, rather than to split
// it up into different VPlans.
// TODO: Consider using getDecisionAndClampRange here to split up VPlans.
bool IVUpdateMayOverflow = false;
for (ElementCount VF : Range)
IVUpdateMayOverflow |= !isIndvarOverflowCheckKnownFalse(&CM, VF);
TailFoldingStyle Style = CM.getTailFoldingStyle();
// Use NUW for the induction increment if we proved that it won't overflow in
// the vector loop or when not folding the tail. In the later case, we know
// that the canonical induction increment will not overflow as the vector trip
// count is >= increment and a multiple of the increment.
VPRegionBlock *LoopRegion = Plan->getVectorLoopRegion();
bool HasNUW = !IVUpdateMayOverflow || Style == TailFoldingStyle::None;
if (!HasNUW) {
auto *IVInc =
LoopRegion->getExitingBasicBlock()->getTerminator()->getOperand(0);
assert(match(IVInc,
m_VPInstruction<Instruction::Add>(
m_Specific(LoopRegion->getCanonicalIV()), m_VPValue())) &&
"Did not find the canonical IV increment");
LoopRegion->clearCanonicalIVNUW(cast<VPInstruction>(IVInc));
}
// ---------------------------------------------------------------------------
// Pre-construction: record ingredients whose recipes we'll need to further
// process after constructing the initial VPlan.
// ---------------------------------------------------------------------------
// 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 {
bool Result = (VF.isVector() && // Query is illegal for VF == 1
CM.getWideningDecision(IG->getInsertPos(), VF) ==
LoopVectorizationCostModel::CM_Interleave);
// For scalable vectors, the interleave factors must be <= 8 since we
// require the (de)interleaveN intrinsics instead of shufflevectors.
assert((!Result || !VF.isScalable() || IG->getFactor() <= 8) &&
"Unsupported interleave factor for scalable vectors");
return Result;
};
if (!getDecisionAndClampRange(ApplyIG, Range))
continue;
InterleaveGroups.insert(IG);
}
// ---------------------------------------------------------------------------
// Construct wide recipes and apply predication for original scalar
// VPInstructions in the loop.
// ---------------------------------------------------------------------------
VPRecipeBuilder RecipeBuilder(*Plan, TLI, Legal, CM, Builder);
// Scan the body of the loop in a topological order to visit each basic block
// after having visited its predecessor basic blocks.
VPBasicBlock *HeaderVPBB = LoopRegion->getEntryBasicBlock();
ReversePostOrderTraversal<VPBlockShallowTraversalWrapper<VPBlockBase *>> RPOT(
HeaderVPBB);
RUN_VPLAN_PASS(VPlanTransforms::createInLoopReductionRecipes, *Plan,
Range.Start);
VPCostContext CostCtx(CM.TTI, *CM.TLI, *Plan, CM, Config.CostKind, CM.PSE,
OrigLoop);
RUN_VPLAN_PASS(VPlanTransforms::makeMemOpWideningDecisions, *Plan, Range,
RecipeBuilder);
RUN_VPLAN_PASS(VPlanTransforms::makeScalarizationDecisions, *Plan, Range);
// Now process all other blocks and instructions.
for (VPBasicBlock *VPBB : VPBlockUtils::blocksOnly<VPBasicBlock>(RPOT)) {
// Convert input VPInstructions to widened recipes.
for (VPRecipeBase &R : make_early_inc_range(
make_range(VPBB->getFirstNonPhi(), VPBB->end()))) {
// Skip recipes that do not need transforming or have already been
// transformed.
if (isa<VPWidenCanonicalIVRecipe, VPBlendRecipe, VPReductionRecipe,
VPReplicateRecipe, VPWidenLoadRecipe, VPWidenStoreRecipe,
VPVectorPointerRecipe, VPVectorEndPointerRecipe,
VPHistogramRecipe>(&R))
continue;
auto *VPI = cast<VPInstruction>(&R);
if (!VPI->getUnderlyingValue())
continue;
// TODO: Gradually replace uses of underlying instruction by analyses on
// VPlan. Migrate code relying on the underlying instruction from VPlan0
// to construct recipes below to not use the underlying instruction.
Instruction *Instr = cast<Instruction>(VPI->getUnderlyingValue());
Builder.setInsertPoint(VPI);
VPRecipeBase *Recipe =
RecipeBuilder.tryToCreateWidenNonPhiRecipe(VPI, Range);
if (!Recipe)
Recipe =
RecipeBuilder.handleReplication(cast<VPInstruction>(VPI), Range);
if (isa<VPWidenIntOrFpInductionRecipe>(Recipe) && isa<TruncInst>(Instr)) {
// Optimized a truncate to VPWidenIntOrFpInductionRecipe. It needs to be
// moved to the phi section in the header.
Recipe->insertBefore(*HeaderVPBB, HeaderVPBB->getFirstNonPhi());
} else {
Builder.insert(Recipe);
}
if (Recipe->getNumDefinedValues() == 1) {
VPI->replaceAllUsesWith(Recipe->getVPSingleValue());
} else {
assert(Recipe->getNumDefinedValues() == 0 &&
"Unexpected multidef recipe");
}
R.eraseFromParent();
}
}
assert(isa<VPRegionBlock>(LoopRegion) &&
!LoopRegion->getEntryBasicBlock()->empty() &&
"entry block must be set to a VPRegionBlock having a non-empty entry "
"VPBasicBlock");
RUN_VPLAN_PASS(VPlanTransforms::adjustFirstOrderRecurrenceMiddleUsers, *Plan,
Range);
// ---------------------------------------------------------------------------
// Transform initial VPlan: Apply previously taken decisions, in order, to
// bring the VPlan to its final state.
// ---------------------------------------------------------------------------
addReductionResultComputation(Plan, RecipeBuilder, Range.Start);
// Optimize FindIV reductions to use sentinel-based approach when possible.
RUN_VPLAN_PASS(VPlanTransforms::optimizeFindIVReductions, *Plan, PSE,
*OrigLoop);
RUN_VPLAN_PASS(VPlanTransforms::optimizeInductionLiveOutUsers, *Plan, PSE,
CM.foldTailByMasking());
// Apply mandatory transformation to handle reductions with multiple in-loop
// uses if possible, bail out otherwise.
if (!RUN_VPLAN_PASS(VPlanTransforms::handleMultiUseReductions, *Plan, ORE,
OrigLoop))
return nullptr;
// Apply mandatory transformation to handle FP maxnum/minnum reduction with
// NaNs if possible, bail out otherwise.
if (!RUN_VPLAN_PASS(VPlanTransforms::handleMaxMinNumReductions, *Plan))
return nullptr;
// Create whole-vector selects for find-last recurrences.
if (!RUN_VPLAN_PASS(VPlanTransforms::handleFindLastReductions, *Plan))
return nullptr;
RUN_VPLAN_PASS(VPlanTransforms::removeBranchOnConst, *Plan, false);
// Create partial reduction recipes for scaled reductions and transform
// recipes to abstract recipes if it is legal and beneficial and clamp the
// range for better cost estimation.
// TODO: Enable following transform when the EVL-version of extended-reduction
// and mulacc-reduction are implemented.
if (!CM.foldTailWithEVL()) {
RUN_VPLAN_PASS(VPlanTransforms::createPartialReductions, *Plan, CostCtx,
Range);
RUN_VPLAN_PASS(VPlanTransforms::convertToAbstractRecipes, *Plan, CostCtx,
Range);
}
// Ensure scalar VF plans only contain VF=1, as required by hasScalarVFOnly.
if (Range.Start.isScalar())
Range.End = Range.Start * 2;
for (ElementCount VF : Range)
Plan->addVF(VF);
Plan->setName("Initial VPlan");
// 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.
RUN_VPLAN_PASS(VPlanTransforms::createInterleaveGroups, *Plan,
InterleaveGroups, CM.isEpilogueAllowed());
// Replace VPValues for known constant strides.
RUN_VPLAN_PASS(VPlanTransforms::replaceSymbolicStrides, *Plan, PSE,
Legal->getLAI()->getSymbolicStrides());
auto BlockNeedsPredication = [this](BasicBlock *BB) {
return Legal->blockNeedsPredication(BB);
};
RUN_VPLAN_PASS(VPlanTransforms::dropPoisonGeneratingRecipes, *Plan,
BlockNeedsPredication);
if (useActiveLaneMask(Style)) {
// TODO: Move checks to VPlanTransforms::addActiveLaneMask once
// TailFoldingStyle is visible there.
bool ForControlFlow = useActiveLaneMaskForControlFlow(Style);
RUN_VPLAN_PASS(VPlanTransforms::addActiveLaneMask, *Plan, ForControlFlow);
}
assert(verifyVPlanIsValid(*Plan) && "VPlan is invalid");
return Plan;
}
VPlanPtr LoopVectorizationPlanner::tryToBuildVPlan(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.");
auto Plan = VPlanTransforms::buildVPlan0(
OrigLoop, *LI, Legal->getWidestInductionType(),
getDebugLocFromInstOrOperands(Legal->getPrimaryInduction()), PSE);
if (!VPlanTransforms::createHeaderPhiRecipes(
*Plan, PSE, *OrigLoop, Legal->getInductionVars(),
MapVector<PHINode *, RecurrenceDescriptor>(),
SmallPtrSet<const PHINode *, 1>(), SmallPtrSet<PHINode *, 1>(),
/*AllowReordering=*/false))
return nullptr;
[[maybe_unused]] bool CanHandleExits = VPlanTransforms::handleEarlyExits(
*Plan, UncountableExitStyle::NoUncountableExit, OrigLoop, PSE, *DT,
Legal->getAssumptionCache());
assert(CanHandleExits &&
"early-exits are not supported in VPlan-native path");
VPlanTransforms::addMiddleCheck(*Plan, /*TailFolded*/ false);
VPlanTransforms::createLoopRegions(*Plan);
for (ElementCount VF : Range)
Plan->addVF(VF);
if (!VPlanTransforms::tryToConvertVPInstructionsToVPRecipes(*Plan, *TLI))
return nullptr;
// Optimize induction live-out users to use precomputed end values.
VPlanTransforms::optimizeInductionLiveOutUsers(*Plan, PSE,
/*FoldTail=*/false);
assert(verifyVPlanIsValid(*Plan) && "VPlan is invalid");
return Plan;
}
void LoopVectorizationPlanner::addReductionResultComputation(
VPlanPtr &Plan, VPRecipeBuilder &RecipeBuilder, ElementCount MinVF) {
using namespace VPlanPatternMatch;
VPTypeAnalysis TypeInfo(*Plan);
VPRegionBlock *VectorLoopRegion = Plan->getVectorLoopRegion();
VPBasicBlock *MiddleVPBB = Plan->getMiddleBlock();
SmallVector<VPRecipeBase *> ToDelete;
VPBasicBlock *LatchVPBB = VectorLoopRegion->getExitingBasicBlock();
Builder.setInsertPoint(&*std::prev(std::prev(LatchVPBB->end())));
VPBasicBlock::iterator IP = MiddleVPBB->getFirstNonPhi();
for (VPRecipeBase &R :
Plan->getVectorLoopRegion()->getEntryBasicBlock()->phis()) {
VPReductionPHIRecipe *PhiR = dyn_cast<VPReductionPHIRecipe>(&R);
if (!PhiR)
continue;
RecurKind RecurrenceKind = PhiR->getRecurrenceKind();
const RecurrenceDescriptor &RdxDesc = Legal->getRecurrenceDescriptor(
cast<PHINode>(PhiR->getUnderlyingInstr()));
Type *PhiTy = TypeInfo.inferScalarType(PhiR);
// If tail is folded by masking, introduce selects between the phi
// and the users outside the vector region of each reduction, at the
// beginning of the dedicated latch block.
auto *OrigExitingVPV = PhiR->getBackedgeValue();
auto *NewExitingVPV = PhiR->getBackedgeValue();
if (!PhiR->isInLoop() && CM.foldTailByMasking()) {
VPValue *Cond = vputils::findHeaderMask(*Plan);
NewExitingVPV =
Builder.createSelect(Cond, OrigExitingVPV, PhiR, {}, "", *PhiR);
OrigExitingVPV->replaceUsesWithIf(NewExitingVPV, [](VPUser &U, unsigned) {
return match(&U,
m_VPInstruction<VPInstruction::ComputeReductionResult>());
});
if (CM.usePredicatedReductionSelect(RecurrenceKind))
PhiR->setOperand(1, NewExitingVPV);
}
// We want code in the middle block to appear to execute on the location of
// the scalar loop's latch terminator 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.
DebugLoc ExitDL = OrigLoop->getLoopLatch()->getTerminator()->getDebugLoc();
// TODO: At the moment ComputeReductionResult also drives creation of the
// bc.merge.rdx phi nodes, hence it needs to be created unconditionally here
// even for in-loop reductions, until the reduction resume value handling is
// also modeled in VPlan.
VPInstruction *FinalReductionResult;
VPBuilder::InsertPointGuard Guard(Builder);
Builder.setInsertPoint(MiddleVPBB, IP);
// For AnyOf reductions, find the select among PhiR's users and convert
// the reduction phi to operate on bools before creating the final
// reduction result.
if (RecurrenceDescriptor::isAnyOfRecurrenceKind(RecurrenceKind)) {
auto *AnyOfSelect =
cast<VPSingleDefRecipe>(*find_if(PhiR->users(), [](VPUser *U) {
return match(U, m_Select(m_VPValue(), m_VPValue(), m_VPValue()));
}));
VPValue *Start = PhiR->getStartValue();
bool TrueValIsPhi = AnyOfSelect->getOperand(1) == PhiR;
// NewVal is the non-phi operand of the select.
VPValue *NewVal = TrueValIsPhi ? AnyOfSelect->getOperand(2)
: AnyOfSelect->getOperand(1);
// Adjust AnyOf reductions; replace the reduction phi for the selected
// value with a boolean reduction phi node to check if the condition is
// true in any iteration. The final value is selected by the final
// ComputeReductionResult.
VPValue *Cmp = AnyOfSelect->getOperand(0);
// If the compare is checking the reduction PHI node, adjust it to check
// the start value.
if (VPRecipeBase *CmpR = Cmp->getDefiningRecipe())
CmpR->replaceUsesOfWith(PhiR, PhiR->getStartValue());
Builder.setInsertPoint(AnyOfSelect);
// If the true value of the select is the reduction phi, the new value
// is selected if the negated condition is true in any iteration.
if (TrueValIsPhi)
Cmp = Builder.createNot(Cmp);
VPValue *Or = Builder.createOr(PhiR, Cmp);
// Only replace uses inside the vector region with Or. External uses
// (e.g. scalar preheader resume phis) must be replaced by the user
// update loop below with FinalReductionResult.
AnyOfSelect->replaceUsesWithIf(Or, [](VPUser &U, unsigned) {
return cast<VPRecipeBase>(&U)->getRegion();
});
ToDelete.push_back(AnyOfSelect);
// Convert the reduction phi to operate on bools.
PhiR->setOperand(0, Plan->getFalse());
// Update NewExitingVPV if it was pointing to the now-replaced select.
if (NewExitingVPV == AnyOfSelect)
NewExitingVPV = Or;
Builder.setInsertPoint(MiddleVPBB, IP);
FinalReductionResult =
Builder.createAnyOfReduction(NewExitingVPV, NewVal, Start, ExitDL);
} else {
VPIRFlags Flags(RecurrenceKind, PhiR->isOrdered(), PhiR->isInLoop(),
PhiR->getFastMathFlags());
FinalReductionResult =
Builder.createNaryOp(VPInstruction::ComputeReductionResult,
{NewExitingVPV}, Flags, ExitDL);
}
// 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 (MinVF.isVector() && PhiTy != RdxDesc.getRecurrenceType() &&
!RecurrenceDescriptor::isAnyOfRecurrenceKind(RecurrenceKind)) {
assert(!PhiR->isInLoop() && "Unexpected truncated inloop reduction!");
assert(!RecurrenceDescriptor::isMinMaxRecurrenceKind(RecurrenceKind) &&
"Unexpected truncated min-max recurrence!");
Type *RdxTy = RdxDesc.getRecurrenceType();
VPWidenCastRecipe *Trunc;
Instruction::CastOps ExtendOpc =
RdxDesc.isSigned() ? Instruction::SExt : Instruction::ZExt;
VPWidenCastRecipe *Extnd;
{
VPBuilder::InsertPointGuard Guard(Builder);
Builder.setInsertPoint(
NewExitingVPV->getDefiningRecipe()->getParent(),
std::next(NewExitingVPV->getDefiningRecipe()->getIterator()));
Trunc =
Builder.createWidenCast(Instruction::Trunc, NewExitingVPV, RdxTy);
Extnd = Builder.createWidenCast(ExtendOpc, Trunc, PhiTy);
}
if (PhiR->getOperand(1) == NewExitingVPV)
PhiR->setOperand(1, Extnd->getVPSingleValue());
// Update ComputeReductionResult with the truncated exiting value and
// extend its result. Operand 0 provides the values to be reduced.
FinalReductionResult->setOperand(0, Trunc);
FinalReductionResult =
Builder.createScalarCast(ExtendOpc, FinalReductionResult, PhiTy, {});
}
// Update all users outside the vector region. Also replace redundant
// extracts.
for (auto *U : to_vector(OrigExitingVPV->users())) {
auto *Parent = cast<VPRecipeBase>(U)->getParent();
if (FinalReductionResult == U || Parent->getParent())
continue;
// Skip ComputeReductionResult and FindIV reductions when they are not the
// final result.
if (match(U, m_VPInstruction<VPInstruction::ComputeReductionResult>()) ||
(RecurrenceDescriptor::isFindIVRecurrenceKind(RecurrenceKind) &&
match(U, m_VPInstruction<Instruction::ICmp>())))
continue;
U->replaceUsesOfWith(OrigExitingVPV, FinalReductionResult);
// Look through ExtractLastPart.
if (match(U, m_ExtractLastPart(m_VPValue())))
U = cast<VPInstruction>(U)->getSingleUser();
if (match(U, m_CombineOr(m_ExtractLane(m_VPValue(), m_VPValue()),
m_ExtractLastLane(m_VPValue()))))
cast<VPInstruction>(U)->replaceAllUsesWith(FinalReductionResult);
}
RecurKind RK = PhiR->getRecurrenceKind();
if ((!RecurrenceDescriptor::isAnyOfRecurrenceKind(RK) &&
!RecurrenceDescriptor::isFindIVRecurrenceKind(RK) &&
!RecurrenceDescriptor::isMinMaxRecurrenceKind(RK) &&
!RecurrenceDescriptor::isFindLastRecurrenceKind(RK))) {
VPBuilder PHBuilder(Plan->getVectorPreheader());
VPValue *Iden = Plan->getOrAddLiveIn(
getRecurrenceIdentity(RK, PhiTy, PhiR->getFastMathFlags()));
auto *ScaleFactorVPV = Plan->getConstantInt(32, 1);
VPValue *StartV = PHBuilder.createNaryOp(
VPInstruction::ReductionStartVector,
{PhiR->getStartValue(), Iden, ScaleFactorVPV}, *PhiR);
PhiR->setOperand(0, StartV);
}
}
for (VPRecipeBase *R : ToDelete)
R->eraseFromParent();
RUN_VPLAN_PASS(VPlanTransforms::clearReductionWrapFlags, *Plan);
}
void LoopVectorizationPlanner::attachRuntimeChecks(
VPlan &Plan, GeneratedRTChecks &RTChecks, bool HasBranchWeights) const {
const auto &[SCEVCheckCond, SCEVCheckBlock] = RTChecks.getSCEVChecks();
if (SCEVCheckBlock && SCEVCheckBlock->hasNPredecessors(0)) {
assert((!Config.OptForSize ||
CM.Hints->getForce() == LoopVectorizeHints::FK_Enabled) &&
"Cannot SCEV check stride or overflow when optimizing for size");
RUN_VPLAN_PASS(VPlanTransforms::attachCheckBlock, Plan, SCEVCheckCond,
SCEVCheckBlock, HasBranchWeights);
}
const auto &[MemCheckCond, MemCheckBlock] = RTChecks.getMemRuntimeChecks();
if (MemCheckBlock && MemCheckBlock->hasNPredecessors(0)) {
// VPlan-native path does not do any analysis for runtime checks
// currently.
assert((!EnableVPlanNativePath || OrigLoop->isInnermost()) &&
"Runtime checks are not supported for outer loops yet");
if (Config.OptForSize) {
assert(
CM.Hints->getForce() == LoopVectorizeHints::FK_Enabled &&
"Cannot emit memory checks when optimizing for size, unless forced "
"to vectorize.");
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationCodeSize",
OrigLoop->getStartLoc(),
OrigLoop->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').";
});
}
RUN_VPLAN_PASS(VPlanTransforms::attachCheckBlock, Plan, MemCheckCond,
MemCheckBlock, HasBranchWeights);
}
}
void LoopVectorizationPlanner::addMinimumIterationCheck(
VPlan &Plan, ElementCount VF, unsigned UF,
ElementCount MinProfitableTripCount) const {
const uint32_t *BranchWeights =
hasBranchWeightMD(*OrigLoop->getLoopLatch()->getTerminator())
? &MinItersBypassWeights[0]
: nullptr;
RUN_VPLAN_PASS(VPlanTransforms::addMinimumIterationCheck, Plan, VF, UF,
MinProfitableTripCount,
CM.requiresScalarEpilogue(VF.isVector()),
CM.foldTailByMasking(), OrigLoop, BranchWeights,
OrigLoop->getLoopPredecessor()->getTerminator()->getDebugLoc(),
PSE, /*CheckBlock=*/nullptr);
}
// Determine how to lower the epilogue, which depends on 1) optimising
// for minimum code-size, 2) tail-folding compiler options, 3) loop
// hints forcing tail-folding, and 4) a TTI hook that analyses whether the loop
// is suitable for tail-folding.
static EpilogueLowering
getEpilogueLowering(Function *F, Loop *L, LoopVectorizeHints &Hints,
bool OptForSize, TargetTransformInfo *TTI,
TargetLibraryInfo *TLI, LoopVectorizationLegality &LVL,
InterleavedAccessInfo *IAI) {
// 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 an epilogue.
if (F->hasOptSize() ||
(OptForSize && Hints.getForce() != LoopVectorizeHints::FK_Enabled))
return CM_EpilogueNotAllowedOptSize;
// 2) If set, obey the directives
if (TailFoldingPolicy.getNumOccurrences()) {
switch (TailFoldingPolicy) {
case TailFoldingPolicyTy::None:
return CM_EpilogueAllowed;
case TailFoldingPolicyTy::PreferFoldTail:
return CM_EpilogueNotNeededFoldTail;
case TailFoldingPolicyTy::MustFoldTail:
return CM_EpilogueNotAllowedFoldTail;
};
}
// 3) If set, obey the hints
switch (Hints.getPredicate()) {
case LoopVectorizeHints::FK_Enabled:
return CM_EpilogueNotNeededFoldTail;
case LoopVectorizeHints::FK_Disabled:
return CM_EpilogueAllowed;
};
// 4) if the TTI hook indicates this is profitable, request tail-folding.
TailFoldingInfo TFI(TLI, &LVL, IAI);
if (TTI->preferTailFoldingOverEpilogue(&TFI))
return CM_EpilogueNotNeededFoldTail;
return CM_EpilogueAllowed;
}
// 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,
std::function<BlockFrequencyInfo &()> GetBFI, bool OptForSize,
LoopVectorizeHints &Hints, LoopVectorizationRequirements &Requirements) {
if (isa<SCEVCouldNotCompute>(PSE.getBackedgeTakenCount())) {
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());
EpilogueLowering SEL =
getEpilogueLowering(F, L, Hints, OptForSize, TTI, TLI, *LVL, &IAI);
VFSelectionContext Config(*TTI, LVL, L, *F, PSE, DB, ORE, &Hints, OptForSize);
LoopVectorizationCostModel CM(SEL, L, PSE, LI, LVL, *TTI, TLI, AC, ORE,
GetBFI, F, &Hints, IAI, Config);
// 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, DT, TLI, *TTI, LVL, CM, Config, IAI, PSE,
Hints, ORE);
// Get user vectorization factor.
ElementCount UserVF = Hints.getWidth();
Config.collectElementTypesForWidening();
// Plan how to best vectorize, return the best VF and its cost.
const VectorizationFactor VF = LVP.planInVPlanNativePath(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 || VectorizationFactor::Disabled() == VF)
return false;
VPlan &BestPlan = LVP.getPlanFor(VF.Width);
{
GeneratedRTChecks Checks(PSE, DT, LI, TTI, Config.CostKind);
InnerLoopVectorizer LB(L, PSE, LI, DT, TTI, AC, VF.Width, /*UF=*/1, &CM,
Checks, BestPlan);
LLVM_DEBUG(dbgs() << "Vectorizing outer loop in \"" << F->getName()
<< "\"\n");
LVP.addMinimumIterationCheck(BestPlan, VF.Width, /*UF=*/1,
VF.MinProfitableTripCount);
bool HasBranchWeights =
hasBranchWeightMD(*L->getLoopLatch()->getTerminator());
LVP.attachRuntimeChecks(BestPlan, Checks, HasBranchWeights);
reportVectorization(ORE, L, VF, 1);
LVP.executePlan(VF.Width, /*UF=*/1, BestPlan, LB, DT);
}
assert(!verifyFunction(*F, &dbgs()));
return true;
}
// Emit a remark if there are stores to floats that required a floating point
// extension. If the vectorized loop was generated with floating point there
// will be a performance penalty from the conversion overhead and the change in
// the vector width.
static void checkMixedPrecision(Loop *L, OptimizationRemarkEmitter *ORE) {
SmallVector<Instruction *, 4> Worklist;
for (BasicBlock *BB : L->getBlocks()) {
for (Instruction &Inst : *BB) {
if (auto *S = dyn_cast<StoreInst>(&Inst)) {
if (S->getValueOperand()->getType()->isFloatTy())
Worklist.push_back(S);
}
}
}
// Traverse the floating point stores upwards searching, for floating point
// conversions.
SmallPtrSet<const Instruction *, 4> Visited;
SmallPtrSet<const Instruction *, 4> EmittedRemark;
while (!Worklist.empty()) {
auto *I = Worklist.pop_back_val();
if (!L->contains(I))
continue;
if (!Visited.insert(I).second)
continue;
// Emit a remark if the floating point store required a floating
// point conversion.
// TODO: More work could be done to identify the root cause such as a
// constant or a function return type and point the user to it.
if (isa<FPExtInst>(I) && EmittedRemark.insert(I).second)
ORE->emit([&]() {
return OptimizationRemarkAnalysis(LV_NAME, "VectorMixedPrecision",
I->getDebugLoc(), L->getHeader())
<< "floating point conversion changes vector width. "
<< "Mixed floating point precision requires an up/down "
<< "cast that will negatively impact performance.";
});
for (Use &Op : I->operands())
if (auto *OpI = dyn_cast<Instruction>(Op))
Worklist.push_back(OpI);
}
}
/// For loops with uncountable early exits, find the cost of doing work when
/// exiting the loop early, such as calculating the final exit values of
/// variables used outside the loop.
/// TODO: This is currently overly pessimistic because the loop may not take
/// the early exit, but better to keep this conservative for now. In future,
/// it might be possible to relax this by using branch probabilities.
static InstructionCost calculateEarlyExitCost(VPCostContext &CostCtx,
VPlan &Plan, ElementCount VF) {
InstructionCost Cost = 0;
for (auto *ExitVPBB : Plan.getExitBlocks()) {
for (auto *PredVPBB : ExitVPBB->getPredecessors()) {
// If the predecessor is not the middle.block, then it must be the
// vector.early.exit block, which may contain work to calculate the exit
// values of variables used outside the loop.
if (PredVPBB != Plan.getMiddleBlock()) {
LLVM_DEBUG(dbgs() << "Calculating cost of work in exit block "
<< PredVPBB->getName() << ":\n");
Cost += PredVPBB->cost(VF, CostCtx);
}
}
}
return Cost;
}
/// This function determines whether or not it's still profitable to vectorize
/// the loop given the extra work we have to do outside of the loop:
/// 1. Perform the runtime checks before entering the loop to ensure it's safe
/// to vectorize.
/// 2. In the case of loops with uncountable early exits, we may have to do
/// extra work when exiting the loop early, such as calculating the final
/// exit values of variables used outside the loop.
/// 3. The middle block.
static bool isOutsideLoopWorkProfitable(GeneratedRTChecks &Checks,
VectorizationFactor &VF, Loop *L,
PredicatedScalarEvolution &PSE,
VPCostContext &CostCtx, VPlan &Plan,
EpilogueLowering SEL,
std::optional<unsigned> VScale) {
InstructionCost RtC = Checks.getCost();
if (!RtC.isValid())
return false;
// When interleaving only scalar and vector cost will be equal, which in turn
// would lead to a divide by 0. Fall back to hard threshold.
if (VF.Width.isScalar()) {
// TODO: Should we rename VectorizeMemoryCheckThreshold?
if (RtC > VectorizeMemoryCheckThreshold) {
LLVM_DEBUG(
dbgs()
<< "LV: Interleaving only is not profitable due to runtime checks\n");
return false;
}
return true;
}
// The scalar cost should only be 0 when vectorizing with a user specified
// VF/IC. In those cases, runtime checks should always be generated.
uint64_t ScalarC = VF.ScalarCost.getValue();
if (ScalarC == 0)
return true;
InstructionCost TotalCost = RtC;
// Add on the cost of any work required in the vector early exit block, if
// one exists.
TotalCost += calculateEarlyExitCost(CostCtx, Plan, VF.Width);
TotalCost += Plan.getMiddleBlock()->cost(VF.Width, CostCtx);
// First, compute the minimum iteration count required so that the vector
// loop outperforms the scalar loop.
// The total cost of the scalar loop is
// ScalarC * TC
// where
// * TC is the actual trip count of the loop.
// * ScalarC is the cost of a single scalar iteration.
//
// The total cost of the vector loop is
// TotalCost + VecC * (TC / VF) + EpiC
// where
// * TotalCost is the sum of the costs cost of
// - the generated runtime checks, i.e. RtC
// - performing any additional work in the vector.early.exit block for
// loops with uncountable early exits.
// - the middle block, if ExpectedTC <= VF.Width.
// * VecC is the cost of a single vector iteration.
// * TC is the actual trip count of the loop
// * VF is the vectorization factor
// * EpiCost is the cost of the generated epilogue, including the cost
// of the remaining scalar operations.
//
// Vectorization is profitable once the total vector cost is less than the
// total scalar cost:
// TotalCost + VecC * (TC / VF) + EpiC < ScalarC * TC
//
// Now we can compute the minimum required trip count TC as
// VF * (TotalCost + EpiC) / (ScalarC * VF - VecC) < TC
//
// For now we assume the epilogue cost EpiC = 0 for simplicity. Note that
// the computations are performed on doubles, not integers and the result
// is rounded up, hence we get an upper estimate of the TC.
unsigned IntVF = estimateElementCount(VF.Width, VScale);
uint64_t Div = ScalarC * IntVF - VF.Cost.getValue();
uint64_t MinTC1 =
Div == 0 ? 0 : divideCeil(TotalCost.getValue() * IntVF, Div);
// Second, compute a minimum iteration count so that the cost of the
// runtime checks is only a fraction of the total scalar loop cost. This
// adds a loop-dependent bound on the overhead incurred if the runtime
// checks fail. In case the runtime checks fail, the cost is RtC + ScalarC
// * TC. To bound the runtime check to be a fraction 1/X of the scalar
// cost, compute
// RtC < ScalarC * TC * (1 / X) ==> RtC * X / ScalarC < TC
uint64_t MinTC2 = divideCeil(RtC.getValue() * 10, ScalarC);
// Now pick the larger minimum. If it is not a multiple of VF and an epilogue
// is allowed, choose the next closest multiple of VF. This should partly
// compensate for ignoring the epilogue cost.
uint64_t MinTC = std::max(MinTC1, MinTC2);
if (SEL == CM_EpilogueAllowed)
MinTC = alignTo(MinTC, IntVF);
VF.MinProfitableTripCount = ElementCount::getFixed(MinTC);
LLVM_DEBUG(
dbgs() << "LV: Minimum required TC for runtime checks to be profitable:"
<< VF.MinProfitableTripCount << "\n");
// Skip vectorization if the expected trip count is less than the minimum
// required trip count.
if (auto ExpectedTC = getSmallBestKnownTC(PSE, L)) {
if (ElementCount::isKnownLT(*ExpectedTC, VF.MinProfitableTripCount)) {
LLVM_DEBUG(dbgs() << "LV: Vectorization is not beneficial: expected "
"trip count < minimum profitable VF ("
<< *ExpectedTC << " < " << VF.MinProfitableTripCount
<< ")\n");
return false;
}
}
return true;
}
LoopVectorizePass::LoopVectorizePass(LoopVectorizeOptions Opts)
: InterleaveOnlyWhenForced(Opts.InterleaveOnlyWhenForced ||
!EnableLoopInterleaving),
VectorizeOnlyWhenForced(Opts.VectorizeOnlyWhenForced ||
!EnableLoopVectorization) {}
/// Prepare \p MainPlan for vectorizing the main vector loop during epilogue
/// vectorization.
static SmallVector<VPInstruction *>
preparePlanForMainVectorLoop(VPlan &MainPlan, VPlan &EpiPlan) {
using namespace VPlanPatternMatch;
// When vectorizing the epilogue, FindFirstIV & FindLastIV reductions can
// introduce multiple uses of undef/poison. If the reduction start value may
// be undef or poison it needs to be frozen and the frozen start has to be
// used when computing the reduction result. We also need to use the frozen
// value in the resume phi generated by the main vector loop, as this is also
// used to compute the reduction result after the epilogue vector loop.
auto AddFreezeForFindLastIVReductions = [](VPlan &Plan,
bool UpdateResumePhis) {
VPBuilder Builder(Plan.getEntry());
for (VPRecipeBase &R : *Plan.getMiddleBlock()) {
auto *VPI = dyn_cast<VPInstruction>(&R);
if (!VPI)
continue;
VPValue *OrigStart;
if (!matchFindIVResult(VPI, m_VPValue(), m_VPValue(OrigStart)))
continue;
if (isGuaranteedNotToBeUndefOrPoison(OrigStart->getLiveInIRValue()))
continue;
VPInstruction *Freeze =
Builder.createNaryOp(Instruction::Freeze, {OrigStart}, {}, "fr");
VPI->setOperand(2, Freeze);
if (UpdateResumePhis)
OrigStart->replaceUsesWithIf(Freeze, [Freeze](VPUser &U, unsigned) {
return Freeze != &U && isa<VPPhi>(&U);
});
}
};
AddFreezeForFindLastIVReductions(MainPlan, true);
AddFreezeForFindLastIVReductions(EpiPlan, false);
VPValue *VectorTC = nullptr;
auto *Term =
MainPlan.getVectorLoopRegion()->getExitingBasicBlock()->getTerminator();
[[maybe_unused]] bool MatchedTC =
match(Term, m_BranchOnCount(m_VPValue(), m_VPValue(VectorTC)));
assert(MatchedTC && "must match vector trip count");
// If there is a suitable resume value for the canonical induction in the
// scalar (which will become vector) epilogue loop, use it and move it to the
// beginning of the scalar preheader. Otherwise create it below.
VPBasicBlock *MainScalarPH = MainPlan.getScalarPreheader();
auto ResumePhiIter =
find_if(MainScalarPH->phis(), [VectorTC](VPRecipeBase &R) {
return match(&R, m_VPInstruction<Instruction::PHI>(m_Specific(VectorTC),
m_ZeroInt()));
});
VPPhi *ResumePhi = nullptr;
if (ResumePhiIter == MainScalarPH->phis().end()) {
Type *Ty = VPTypeAnalysis(MainPlan).inferScalarType(VectorTC);
VPBuilder ScalarPHBuilder(MainScalarPH, MainScalarPH->begin());
ResumePhi = ScalarPHBuilder.createScalarPhi(
{VectorTC, MainPlan.getZero(Ty)}, {}, "vec.epilog.resume.val");
} else {
ResumePhi = cast<VPPhi>(&*ResumePhiIter);
ResumePhi->setName("vec.epilog.resume.val");
if (&MainScalarPH->front() != ResumePhi)
ResumePhi->moveBefore(*MainScalarPH, MainScalarPH->begin());
}
// Create a ResumeForEpilogue for the canonical IV resume as the
// first non-phi, to keep it alive for the epilogue.
VPBuilder ResumeBuilder(MainScalarPH);
ResumeBuilder.createNaryOp(VPInstruction::ResumeForEpilogue, ResumePhi);
// Create ResumeForEpilogue instructions for the resume phis of the
// VPIRPhis in the scalar header of the main plan and return them so they can
// be used as resume values when vectorizing the epilogue.
return to_vector(
map_range(MainPlan.getScalarHeader()->phis(), [&](VPRecipeBase &R) {
assert(isa<VPIRPhi>(R) &&
"only VPIRPhis expected in the scalar header");
return ResumeBuilder.createNaryOp(VPInstruction::ResumeForEpilogue,
R.getOperand(0));
}));
}
/// Prepare \p Plan for vectorizing the epilogue loop. That is, re-use expanded
/// SCEVs from \p ExpandedSCEVs and set resume values for header recipes. Some
/// reductions require creating new instructions to compute the resume values.
/// They are collected in a vector and returned. They must be moved to the
/// preheader of the vector epilogue loop, after created by the execution of \p
/// Plan.
static SmallVector<Instruction *> preparePlanForEpilogueVectorLoop(
VPlan &Plan, Loop *L, const SCEV2ValueTy &ExpandedSCEVs,
EpilogueLoopVectorizationInfo &EPI, LoopVectorizationCostModel &CM,
VFSelectionContext &Config, ScalarEvolution &SE) {
VPRegionBlock *VectorLoop = Plan.getVectorLoopRegion();
VPBasicBlock *Header = VectorLoop->getEntryBasicBlock();
Header->setName("vec.epilog.vector.body");
VPValue *IV = VectorLoop->getCanonicalIV();
// When vectorizing the epilogue loop, the canonical induction needs to start
// at the resume value from the main vector loop. Find the resume value
// created during execution of the main VPlan. It must be the first phi in the
// loop preheader. Add this resume value as an offset to the canonical IV of
// the epilogue loop.
using namespace llvm::PatternMatch;
PHINode *EPResumeVal = &*L->getLoopPreheader()->phis().begin();
for (Value *Inc : EPResumeVal->incoming_values()) {
if (match(Inc, m_SpecificInt(0)))
continue;
assert(!EPI.VectorTripCount &&
"Must only have a single non-zero incoming value");
EPI.VectorTripCount = Inc;
}
// If we didn't find a non-zero vector trip count, all incoming values
// must be zero, which also means the vector trip count is zero. Pick the
// first zero as vector trip count.
// TODO: We should not choose VF * UF so the main vector loop is known to
// be dead.
if (!EPI.VectorTripCount) {
assert(EPResumeVal->getNumIncomingValues() > 0 &&
all_of(EPResumeVal->incoming_values(), match_fn(m_SpecificInt(0))) &&
"all incoming values must be 0");
EPI.VectorTripCount = EPResumeVal->getOperand(0);
}
VPValue *VPV = Plan.getOrAddLiveIn(EPResumeVal);
assert(all_of(IV->users(),
[](const VPUser *U) {
return isa<VPScalarIVStepsRecipe>(U) ||
isa<VPDerivedIVRecipe>(U) ||
cast<VPRecipeBase>(U)->isScalarCast() ||
cast<VPInstruction>(U)->getOpcode() ==
Instruction::Add;
}) &&
"the canonical IV should only be used by its increment or "
"ScalarIVSteps when resetting the start value");
VPBuilder Builder(Header, Header->getFirstNonPhi());
VPInstruction *Add = Builder.createAdd(IV, VPV);
// Replace all users of the canonical IV and its increment with the offset
// version, except for the Add itself and the canonical IV increment.
auto *Increment = vputils::findCanonicalIVIncrement(Plan);
assert(Increment && "Must have a canonical IV increment at this point");
IV->replaceUsesWithIf(Add, [Add, Increment](VPUser &U, unsigned) {
return &U != Add && &U != Increment;
});
VPInstruction *OffsetIVInc =
VPBuilder::getToInsertAfter(Increment).createAdd(Increment, VPV);
Increment->replaceAllUsesWith(OffsetIVInc);
OffsetIVInc->setOperand(0, Increment);
DenseMap<Value *, Value *> ToFrozen;
SmallVector<Instruction *> InstsToMove;
// Ensure that the start values for all header phi recipes are updated before
// vectorizing the epilogue loop.
for (VPRecipeBase &R : Header->phis()) {
Value *ResumeV = nullptr;
// TODO: Move setting of resume values to prepareToExecute.
if (auto *ReductionPhi = dyn_cast<VPReductionPHIRecipe>(&R)) {
// Find the reduction result by searching users of the phi or its backedge
// value.
auto IsReductionResult = [](VPRecipeBase *R) {
auto *VPI = dyn_cast<VPInstruction>(R);
return VPI && VPI->getOpcode() == VPInstruction::ComputeReductionResult;
};
auto *RdxResult = cast<VPInstruction>(
vputils::findRecipe(ReductionPhi->getBackedgeValue(), IsReductionResult));
assert(RdxResult && "expected to find reduction result");
ResumeV = cast<PHINode>(ReductionPhi->getUnderlyingInstr())
->getIncomingValueForBlock(L->getLoopPreheader());
// Check for FindIV pattern by looking for icmp user of RdxResult.
// The pattern is: select(icmp ne RdxResult, Sentinel), RdxResult, Start
using namespace VPlanPatternMatch;
VPValue *SentinelVPV = nullptr;
bool IsFindIV = any_of(RdxResult->users(), [&](VPUser *U) {
return match(U, VPlanPatternMatch::m_SpecificICmp(
ICmpInst::ICMP_NE, m_Specific(RdxResult),
m_VPValue(SentinelVPV)));
});
RecurKind RK = ReductionPhi->getRecurrenceKind();
if (RecurrenceDescriptor::isAnyOfRecurrenceKind(RK) || IsFindIV) {
auto *ResumePhi = cast<PHINode>(ResumeV);
Value *StartV = ResumePhi->getIncomingValueForBlock(
EPI.MainLoopIterationCountCheck);
IRBuilder<> Builder(ResumePhi->getParent(),
ResumePhi->getParent()->getFirstNonPHIIt());
if (RecurrenceDescriptor::isAnyOfRecurrenceKind(RK)) {
// VPReductionPHIRecipes for AnyOf reductions expect a boolean as
// start value; compare the final value from the main vector loop
// to the start value.
ResumeV = Builder.CreateICmpNE(ResumeV, StartV);
if (auto *I = dyn_cast<Instruction>(ResumeV))
InstsToMove.push_back(I);
} else {
assert(SentinelVPV && "expected to find icmp using RdxResult");
if (auto *FreezeI = dyn_cast<FreezeInst>(StartV))
ToFrozen[FreezeI->getOperand(0)] = StartV;
// Adjust resume: select(icmp eq ResumeV, StartV), Sentinel, ResumeV
Value *Cmp = Builder.CreateICmpEQ(ResumeV, StartV);
if (auto *I = dyn_cast<Instruction>(Cmp))
InstsToMove.push_back(I);
ResumeV = Builder.CreateSelect(Cmp, SentinelVPV->getLiveInIRValue(),
ResumeV);
if (auto *I = dyn_cast<Instruction>(ResumeV))
InstsToMove.push_back(I);
}
} else {
VPValue *StartVal = Plan.getOrAddLiveIn(ResumeV);
auto *PhiR = dyn_cast<VPReductionPHIRecipe>(&R);
if (auto *VPI = dyn_cast<VPInstruction>(PhiR->getStartValue())) {
assert(VPI->getOpcode() == VPInstruction::ReductionStartVector &&
"unexpected start value");
// Partial sub-reductions always start at 0 and account for the
// reduction start value in a final subtraction. Update it to use the
// resume value from the main vector loop.
if (PhiR->getVFScaleFactor() > 1 &&
PhiR->getRecurrenceKind() == RecurKind::Sub) {
auto *Sub = cast<VPInstruction>(RdxResult->getSingleUser());
assert(Sub->getOpcode() == Instruction::Sub && "Unexpected opcode");
assert(isa<VPIRValue>(Sub->getOperand(0)) &&
"Expected operand to match the original start value of the "
"reduction");
assert(VPlanPatternMatch::match(VPI->getOperand(0),
VPlanPatternMatch::m_ZeroInt()) &&
"Expected start value for partial sub-reduction to start at "
"zero");
Sub->setOperand(0, StartVal);
} else
VPI->setOperand(0, StartVal);
continue;
}
}
} else {
// Retrieve the induction resume values for wide inductions from
// their original phi nodes in the scalar loop.
PHINode *IndPhi = cast<VPWidenInductionRecipe>(&R)->getPHINode();
// Hook up to the PHINode generated by a ResumePhi recipe of main
// loop VPlan, which feeds the scalar loop.
ResumeV = IndPhi->getIncomingValueForBlock(L->getLoopPreheader());
}
assert(ResumeV && "Must have a resume value");
VPValue *StartVal = Plan.getOrAddLiveIn(ResumeV);
cast<VPHeaderPHIRecipe>(&R)->setStartValue(StartVal);
}
// For some VPValues in the epilogue plan we must re-use the generated IR
// values from the main plan. Replace them with live-in VPValues.
// TODO: This is a workaround needed for epilogue vectorization and it
// should be removed once induction resume value creation is done
// directly in VPlan.
for (auto &R : make_early_inc_range(*Plan.getEntry())) {
// Re-use frozen values from the main plan for Freeze VPInstructions in the
// epilogue plan. This ensures all users use the same frozen value.
auto *VPI = dyn_cast<VPInstruction>(&R);
if (VPI && VPI->getOpcode() == Instruction::Freeze) {
VPI->replaceAllUsesWith(Plan.getOrAddLiveIn(
ToFrozen.lookup(VPI->getOperand(0)->getLiveInIRValue())));
continue;
}
// Re-use the trip count and steps expanded for the main loop, as
// skeleton creation needs it as a value that dominates both the scalar
// and vector epilogue loops
auto *ExpandR = dyn_cast<VPExpandSCEVRecipe>(&R);
if (!ExpandR)
continue;
VPValue *ExpandedVal =
Plan.getOrAddLiveIn(ExpandedSCEVs.lookup(ExpandR->getSCEV()));
ExpandR->replaceAllUsesWith(ExpandedVal);
if (Plan.getTripCount() == ExpandR)
Plan.resetTripCount(ExpandedVal);
ExpandR->eraseFromParent();
}
auto VScale = Config.getVScaleForTuning();
unsigned MainLoopStep =
estimateElementCount(EPI.MainLoopVF * EPI.MainLoopUF, VScale);
unsigned EpilogueLoopStep =
estimateElementCount(EPI.EpilogueVF * EPI.EpilogueUF, VScale);
RUN_VPLAN_PASS(
VPlanTransforms::addMinimumVectorEpilogueIterationCheck, Plan,
EPI.VectorTripCount, CM.requiresScalarEpilogue(EPI.EpilogueVF.isVector()),
EPI.EpilogueVF, EPI.EpilogueUF, MainLoopStep, EpilogueLoopStep, SE);
return InstsToMove;
}
static void
fixScalarResumeValuesFromBypass(BasicBlock *BypassBlock, Loop *L,
VPlan &BestEpiPlan,
ArrayRef<VPInstruction *> ResumeValues) {
// Fix resume values from the additional bypass block.
BasicBlock *PH = L->getLoopPreheader();
for (auto *Pred : predecessors(PH)) {
for (PHINode &Phi : PH->phis()) {
if (Phi.getBasicBlockIndex(Pred) != -1)
continue;
Phi.addIncoming(Phi.getIncomingValueForBlock(BypassBlock), Pred);
}
}
auto *ScalarPH = cast<VPIRBasicBlock>(BestEpiPlan.getScalarPreheader());
if (ScalarPH->hasPredecessors()) {
// Fix resume values for inductions and reductions from the additional
// bypass block using the incoming values from the main loop's resume phis.
// ResumeValues correspond 1:1 with the scalar loop header phis.
for (auto [ResumeV, HeaderPhi] :
zip(ResumeValues, BestEpiPlan.getScalarHeader()->phis())) {
auto *HeaderPhiR = cast<VPIRPhi>(&HeaderPhi);
auto *EpiResumePhi =
cast<PHINode>(HeaderPhiR->getIRPhi().getIncomingValueForBlock(PH));
if (EpiResumePhi->getBasicBlockIndex(BypassBlock) == -1)
continue;
auto *MainResumePhi = cast<PHINode>(ResumeV->getUnderlyingValue());
EpiResumePhi->setIncomingValueForBlock(
BypassBlock, MainResumePhi->getIncomingValueForBlock(BypassBlock));
}
}
}
/// Connect the epilogue vector loop generated for \p EpiPlan to the main vector
/// loop, after both plans have executed, updating branches from the iteration
/// and runtime checks of the main loop, as well as updating various phis. \p
/// InstsToMove contains instructions that need to be moved to the preheader of
/// the epilogue vector loop.
static void connectEpilogueVectorLoop(VPlan &EpiPlan, Loop *L,
EpilogueLoopVectorizationInfo &EPI,
DominatorTree *DT,
GeneratedRTChecks &Checks,
ArrayRef<Instruction *> InstsToMove,
ArrayRef<VPInstruction *> ResumeValues) {
BasicBlock *VecEpilogueIterationCountCheck =
cast<VPIRBasicBlock>(EpiPlan.getEntry())->getIRBasicBlock();
BasicBlock *VecEpiloguePreHeader =
cast<CondBrInst>(VecEpilogueIterationCountCheck->getTerminator())
->getSuccessor(1);
// Adjust the control flow taking the state info from the main loop
// vectorization into account.
assert(EPI.MainLoopIterationCountCheck && EPI.EpilogueIterationCountCheck &&
"expected this to be saved from the previous pass.");
DomTreeUpdater DTU(DT, DomTreeUpdater::UpdateStrategy::Eager);
EPI.MainLoopIterationCountCheck->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, VecEpiloguePreHeader);
DTU.applyUpdates({{DominatorTree::Delete, EPI.MainLoopIterationCountCheck,
VecEpilogueIterationCountCheck},
{DominatorTree::Insert, EPI.MainLoopIterationCountCheck,
VecEpiloguePreHeader}});
BasicBlock *ScalarPH =
cast<VPIRBasicBlock>(EpiPlan.getScalarPreheader())->getIRBasicBlock();
EPI.EpilogueIterationCountCheck->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, ScalarPH);
DTU.applyUpdates(
{{DominatorTree::Delete, EPI.EpilogueIterationCountCheck,
VecEpilogueIterationCountCheck},
{DominatorTree::Insert, EPI.EpilogueIterationCountCheck, ScalarPH}});
// Adjust the terminators of runtime check blocks and phis using them.
BasicBlock *SCEVCheckBlock = Checks.getSCEVChecks().second;
BasicBlock *MemCheckBlock = Checks.getMemRuntimeChecks().second;
if (SCEVCheckBlock) {
SCEVCheckBlock->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, ScalarPH);
DTU.applyUpdates({{DominatorTree::Delete, SCEVCheckBlock,
VecEpilogueIterationCountCheck},
{DominatorTree::Insert, SCEVCheckBlock, ScalarPH}});
}
if (MemCheckBlock) {
MemCheckBlock->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, ScalarPH);
DTU.applyUpdates(
{{DominatorTree::Delete, MemCheckBlock, VecEpilogueIterationCountCheck},
{DominatorTree::Insert, MemCheckBlock, ScalarPH}});
}
// The vec.epilog.iter.check block may contain Phi nodes from inductions
// or reductions which merge control-flow from the latch block and the
// middle block. Update the incoming values here and move the Phi into the
// preheader.
SmallVector<PHINode *, 4> PhisInBlock(
llvm::make_pointer_range(VecEpilogueIterationCountCheck->phis()));
for (PHINode *Phi : PhisInBlock) {
Phi->moveBefore(VecEpiloguePreHeader->getFirstNonPHIIt());
Phi->replaceIncomingBlockWith(
VecEpilogueIterationCountCheck->getSinglePredecessor(),
VecEpilogueIterationCountCheck);
// If the phi doesn't have an incoming value from the
// EpilogueIterationCountCheck, we are done. Otherwise remove the
// incoming value and also those from other check blocks. This is needed
// for reduction phis only.
if (none_of(Phi->blocks(), [&](BasicBlock *IncB) {
return EPI.EpilogueIterationCountCheck == IncB;
}))
continue;
Phi->removeIncomingValue(EPI.EpilogueIterationCountCheck);
if (SCEVCheckBlock)
Phi->removeIncomingValue(SCEVCheckBlock);
if (MemCheckBlock)
Phi->removeIncomingValue(MemCheckBlock);
}
auto IP = VecEpiloguePreHeader->getFirstNonPHIIt();
for (auto *I : InstsToMove)
I->moveBefore(IP);
// VecEpilogueIterationCountCheck conditionally skips over the epilogue loop
// after executing the main loop. We need to update the resume values of
// inductions and reductions during epilogue vectorization.
fixScalarResumeValuesFromBypass(VecEpilogueIterationCountCheck, L, EpiPlan,
ResumeValues);
// Remove dead phis that were moved to the epilogue preheader but are unused
// (e.g., resume phis for inductions not widened in the epilogue vector loop).
for (PHINode &Phi : make_early_inc_range(VecEpiloguePreHeader->phis()))
if (Phi.use_empty())
Phi.eraseFromParent();
}
bool LoopVectorizePass::processLoop(Loop *L) {
assert((EnableVPlanNativePath || L->isInnermost()) &&
"VPlan-native path is not enabled. Only process inner loops.");
LLVM_DEBUG(dbgs() << "\nLV: Checking a loop in '"
<< L->getHeader()->getParent()->getName() << "' from "
<< L->getLocStr() << "\n");
LoopVectorizeHints Hints(L, InterleaveOnlyWhenForced, *ORE, TTI);
LLVM_DEBUG(
dbgs() << "LV: Loop hints:"
<< " force="
<< (Hints.getForce() == LoopVectorizeHints::FK_Disabled
? "disabled"
: (Hints.getForce() == LoopVectorizeHints::FK_Enabled
? "enabled"
: "?"))
<< " width=" << Hints.getWidth()
<< " interleave=" << 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);
// Query this against the original loop and save it here because the profile
// of the original loop header may change as the transformation happens.
bool OptForSize = llvm::shouldOptimizeForSize(
L->getHeader(), PSI,
PSI && PSI->hasProfileSummary() ? &GetBFI() : nullptr,
PGSOQueryType::IRPass);
// Check if it is legal to vectorize the loop.
LoopVectorizationRequirements Requirements;
LoopVectorizationLegality LVL(L, PSE, DT, TTI, TLI, F, *LAIs, LI, ORE,
&Requirements, &Hints, DB, AC,
/*AllowRuntimeSCEVChecks=*/!OptForSize, AA);
if (!LVL.canVectorize(EnableVPlanNativePath)) {
LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n");
Hints.emitRemarkWithHints();
return false;
}
if (LVL.hasUncountableEarlyExit()) {
if (!EnableEarlyExitVectorization) {
reportVectorizationFailure("Auto-vectorization of loops with uncountable "
"early exit is not enabled",
"UncountableEarlyExitLoopsDisabled", ORE, L);
return false;
}
}
// 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, GetBFI, OptForSize, Hints,
Requirements);
assert(L->isInnermost() && "Inner loop expected.");
InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL.getLAI());
bool UseInterleaved = TTI->enableInterleavedAccessVectorization();
// 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));
if (LVL.hasUncountableEarlyExit()) {
BasicBlock *LoopLatch = L->getLoopLatch();
if (IAI.requiresScalarEpilogue() ||
any_of(LVL.getCountableExitingBlocks(), not_equal_to(LoopLatch))) {
reportVectorizationFailure("Auto-vectorization of early exit loops "
"requiring a scalar epilogue is unsupported",
"UncountableEarlyExitUnsupported", ORE, L);
return false;
}
}
// Check the function attributes and profiles to find out if this function
// should be optimized for size.
EpilogueLowering SEL =
getEpilogueLowering(F, L, Hints, OptForSize, TTI, TLI, LVL, &IAI);
// 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(PSE, L);
if (ExpectedTC && ExpectedTC->isFixed() &&
ExpectedTC->getFixedValue() < 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");
// Tail-folded loops are efficient even when the loop
// iteration count is low. However, setting the epilogue policy to
// `CM_EpilogueNotAllowedLowTripLoop` prevents vectorizing loops
// with runtime checks. It's more effective to let
// `isOutsideLoopWorkProfitable` determine if vectorization is
// beneficial for the loop.
if (SEL != CM_EpilogueNotNeededFoldTail)
SEL = CM_EpilogueNotAllowedLowTripLoop;
}
}
// Check the function attributes to see if implicit floats or vectors are
// allowed.
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 AllowOrderedReductions;
// If the flag is set, use that instead and override the TTI behaviour.
if (ForceOrderedReductions.getNumOccurrences() > 0)
AllowOrderedReductions = ForceOrderedReductions;
else
AllowOrderedReductions = TTI->enableOrderedReductions();
if (!LVL.canVectorizeFPMath(AllowOrderedReductions)) {
ORE->emit([&]() {
auto *ExactFPMathInst = Requirements.getExactFPInst();
return OptimizationRemarkAnalysisFPCommute(DEBUG_TYPE, "CantReorderFPOps",
ExactFPMathInst->getDebugLoc(),
ExactFPMathInst->getParent())
<< "loop not vectorized: cannot prove it is safe to reorder "
"floating-point operations";
});
LLVM_DEBUG(dbgs() << "LV: loop not vectorized: cannot prove it is safe to "
"reorder floating-point operations\n");
Hints.emitRemarkWithHints();
return false;
}
// Use the cost model.
VFSelectionContext Config(*TTI, &LVL, L, *F, PSE, DB, ORE, &Hints,
OptForSize);
LoopVectorizationCostModel CM(SEL, L, PSE, LI, &LVL, *TTI, TLI, AC, ORE,
GetBFI, F, &Hints, IAI, Config);
// Use the planner for vectorization.
LoopVectorizationPlanner LVP(L, LI, DT, TLI, *TTI, &LVL, CM, Config, IAI, PSE,
Hints, ORE);
// Get user vectorization factor and interleave count.
ElementCount UserVF = Hints.getWidth();
unsigned UserIC = Hints.getInterleave();
if (UserIC > 1 && !LVL.isSafeForAnyVectorWidth())
UserIC = 1;
// Plan how to best vectorize.
LVP.plan(UserVF, UserIC);
auto [VF, BestPlanPtr] = LVP.computeBestVF();
unsigned IC = 1;
if (ORE->allowExtraAnalysis(LV_NAME))
LVP.emitInvalidCostRemarks(ORE);
GeneratedRTChecks Checks(PSE, DT, LI, TTI, Config.CostKind);
if (LVP.hasPlanWithVF(VF.Width)) {
// Select the interleave count.
IC = LVP.selectInterleaveCount(*BestPlanPtr, VF.Width, VF.Cost);
unsigned SelectedIC = std::max(IC, UserIC);
// Optimistically generate runtime checks if they are needed. Drop them if
// they turn out to not be profitable.
if (VF.Width.isVector() || SelectedIC > 1) {
Checks.create(L, *LVL.getLAI(), PSE.getPredicate(), VF.Width, SelectedIC,
*ORE);
// Bail out early if either the SCEV or memory runtime checks are known to
// fail. In that case, the vector loop would never execute.
using namespace llvm::PatternMatch;
if (Checks.getSCEVChecks().first &&
match(Checks.getSCEVChecks().first, m_One()))
return false;
if (Checks.getMemRuntimeChecks().first &&
match(Checks.getMemRuntimeChecks().first, m_One()))
return false;
}
// Check if it is profitable to vectorize with runtime checks.
bool ForceVectorization =
Hints.getForce() == LoopVectorizeHints::FK_Enabled;
VPCostContext CostCtx(CM.TTI, *CM.TLI, *BestPlanPtr, CM, Config.CostKind,
CM.PSE, L);
if (!ForceVectorization &&
!isOutsideLoopWorkProfitable(Checks, VF, L, PSE, CostCtx, *BestPlanPtr,
SEL, Config.getVScaleForTuning())) {
ORE->emit([&]() {
return OptimizationRemarkAnalysisAliasing(
DEBUG_TYPE, "CantReorderMemOps", L->getStartLoc(),
L->getHeader())
<< "loop not vectorized: cannot prove it is safe to reorder "
"memory operations";
});
LLVM_DEBUG(dbgs() << "LV: Too many memory checks needed.\n");
Hints.emitRemarkWithHints();
return false;
}
}
// Identify the diagnostic messages that should be produced.
std::pair<StringRef, std::string> VecDiagMsg, IntDiagMsg;
bool VectorizeLoop = true, InterleaveLoop = true;
if (VF.Width.isScalar()) {
LLVM_DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
VecDiagMsg = {
"VectorizationNotBeneficial",
"the cost-model indicates that vectorization is not beneficial"};
VectorizeLoop = false;
}
if (UserIC == 1 && Hints.getInterleave() > 1) {
assert(!LVL.isSafeForAnyVectorWidth() &&
"UserIC should only be ignored due to unsafe dependencies");
LLVM_DEBUG(dbgs() << "LV: Ignoring user-specified interleave count.\n");
IntDiagMsg = {"InterleavingUnsafe",
"Ignoring user-specified interleave count due to possibly "
"unsafe dependencies in the loop."};
InterleaveLoop = false;
} else if (!LVP.hasPlanWithVF(VF.Width) && 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 = {"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 = {
"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.\n");
IntDiagMsg = {"InterleavingBeneficialButDisabled",
"the cost-model indicates that interleaving is beneficial "
"but is explicitly disabled or interleave count is set to 1"};
InterleaveLoop = false;
}
// If there is a histogram in the loop, do not just interleave without
// vectorizing. The order of operations will be incorrect without the
// histogram intrinsics, which are only used for recipes with VF > 1.
if (!VectorizeLoop && InterleaveLoop && LVL.hasHistograms()) {
LLVM_DEBUG(dbgs() << "LV: Not interleaving without vectorization due "
<< "to histogram operations.\n");
IntDiagMsg = {
"HistogramPreventsScalarInterleaving",
"Unable to interleave without vectorization due to constraints on "
"the order of histogram operations"};
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;
}
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 " << L->getLocStr() << '\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 " << L->getLocStr() << '\n');
LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
}
// Report the vectorization decision.
if (VF.Width.isScalar()) {
using namespace ore;
assert(IC > 1);
ORE->emit([&]() {
return OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(),
L->getHeader())
<< "interleaved loop (interleaved count: "
<< NV("InterleaveCount", IC) << ")";
});
} else {
// Report the vectorization decision.
reportVectorization(ORE, L, VF, IC);
}
if (ORE->allowExtraAnalysis(LV_NAME))
checkMixedPrecision(L, ORE);
// If we decided that it is *legal* to interleave or vectorize the loop, then
// do it.
VPlan &BestPlan = *BestPlanPtr;
// Consider vectorizing the epilogue too if it's profitable.
std::unique_ptr<VPlan> EpiPlan =
LVP.selectBestEpiloguePlan(BestPlan, VF.Width, IC);
bool HasBranchWeights =
hasBranchWeightMD(*L->getLoopLatch()->getTerminator());
if (EpiPlan) {
VPlan &BestEpiPlan = *EpiPlan;
VPlan &BestMainPlan = BestPlan;
ElementCount EpilogueVF = BestEpiPlan.getSingleVF();
// The first pass vectorizes the main loop and creates a scalar epilogue
// to be vectorized by executing the plan (potentially with a different
// factor) again shortly afterwards.
BestEpiPlan.getMiddleBlock()->setName("vec.epilog.middle.block");
BestEpiPlan.getVectorPreheader()->setName("vec.epilog.ph");
SmallVector<VPInstruction *> ResumeValues =
preparePlanForMainVectorLoop(BestMainPlan, BestEpiPlan);
EpilogueLoopVectorizationInfo EPI(VF.Width, IC, EpilogueVF, 1, BestEpiPlan);
// Add minimum iteration check for the epilogue plan, followed by runtime
// checks for the main plan.
LVP.addMinimumIterationCheck(BestMainPlan, EPI.EpilogueVF, EPI.EpilogueUF,
ElementCount::getFixed(0));
LVP.attachRuntimeChecks(BestMainPlan, Checks, HasBranchWeights);
RUN_VPLAN_PASS(VPlanTransforms::addIterationCountCheckBlock, BestMainPlan,
EPI.MainLoopVF, EPI.MainLoopUF,
CM.requiresScalarEpilogue(EPI.MainLoopVF.isVector()), L,
HasBranchWeights ? MinItersBypassWeights : nullptr,
L->getLoopPredecessor()->getTerminator()->getDebugLoc(),
PSE);
EpilogueVectorizerMainLoop MainILV(L, PSE, LI, DT, TTI, AC, EPI, &CM,
Checks, BestMainPlan);
auto ExpandedSCEVs = LVP.executePlan(
EPI.MainLoopVF, EPI.MainLoopUF, BestMainPlan, MainILV, DT,
LoopVectorizationPlanner::EpilogueVectorizationKind::MainLoop);
++LoopsVectorized;
// Derive EPI fields from VPlan-generated IR.
BasicBlock *EntryBB =
cast<VPIRBasicBlock>(BestMainPlan.getEntry())->getIRBasicBlock();
EntryBB->setName("iter.check");
EPI.EpilogueIterationCountCheck = EntryBB;
// The check chain is: Entry -> [SCEV] -> [Mem] -> MainCheck -> VecPH.
// MainCheck is the non-bypass successor of the last runtime check block
// (or Entry if there are no runtime checks).
BasicBlock *LastCheck = EntryBB;
if (BasicBlock *MemBB = Checks.getMemRuntimeChecks().second)
LastCheck = MemBB;
else if (BasicBlock *SCEVBB = Checks.getSCEVChecks().second)
LastCheck = SCEVBB;
BasicBlock *ScalarPH = L->getLoopPreheader();
auto *BI = cast<CondBrInst>(LastCheck->getTerminator());
EPI.MainLoopIterationCountCheck =
BI->getSuccessor(BI->getSuccessor(0) == ScalarPH);
// Second pass vectorizes the epilogue and adjusts the control flow
// edges from the first pass.
EpilogueVectorizerEpilogueLoop EpilogILV(L, PSE, LI, DT, TTI, AC, EPI, &CM,
Checks, BestEpiPlan);
SmallVector<Instruction *> InstsToMove = preparePlanForEpilogueVectorLoop(
BestEpiPlan, L, ExpandedSCEVs, EPI, CM, Config, *PSE.getSE());
LVP.attachRuntimeChecks(BestEpiPlan, Checks, HasBranchWeights);
LVP.executePlan(
EPI.EpilogueVF, EPI.EpilogueUF, BestEpiPlan, EpilogILV, DT,
LoopVectorizationPlanner::EpilogueVectorizationKind::Epilogue);
connectEpilogueVectorLoop(BestEpiPlan, L, EPI, DT, Checks, InstsToMove,
ResumeValues);
++LoopsEpilogueVectorized;
} else {
InnerLoopVectorizer LB(L, PSE, LI, DT, TTI, AC, VF.Width, IC, &CM, Checks,
BestPlan);
LVP.addMinimumIterationCheck(BestPlan, VF.Width, IC,
VF.MinProfitableTripCount);
LVP.attachRuntimeChecks(BestPlan, Checks, HasBranchWeights);
LVP.executePlan(VF.Width, IC, BestPlan, LB, DT);
++LoopsVectorized;
}
assert(DT->verify(DominatorTree::VerificationLevel::Fast) &&
"DT not preserved correctly");
assert(!verifyFunction(*F, &dbgs()));
return true;
}
LoopVectorizeResult LoopVectorizePass::runImpl(Function &F) {
// 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(ElementCount::getFixed(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 (const 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);
if (Changed) {
LAIs->clear();
#ifndef NDEBUG
if (VerifySCEV)
SE->verify();
#endif
}
}
// Process each loop nest in the function.
return LoopVectorizeResult(Changed, CFGChanged);
}
PreservedAnalyses LoopVectorizePass::run(Function &F,
FunctionAnalysisManager &AM) {
LI = &AM.getResult<LoopAnalysis>(F);
// There are no loops in the function. Return before computing other
// expensive analyses.
if (LI->empty())
return PreservedAnalyses::all();
SE = &AM.getResult<ScalarEvolutionAnalysis>(F);
TTI = &AM.getResult<TargetIRAnalysis>(F);
DT = &AM.getResult<DominatorTreeAnalysis>(F);
TLI = &AM.getResult<TargetLibraryAnalysis>(F);
AC = &AM.getResult<AssumptionAnalysis>(F);
DB = &AM.getResult<DemandedBitsAnalysis>(F);
ORE = &AM.getResult<OptimizationRemarkEmitterAnalysis>(F);
LAIs = &AM.getResult<LoopAccessAnalysis>(F);
AA = &AM.getResult<AAManager>(F);
auto &MAMProxy = AM.getResult<ModuleAnalysisManagerFunctionProxy>(F);
PSI = MAMProxy.getCachedResult<ProfileSummaryAnalysis>(*F.getParent());
GetBFI = [&AM, &F]() -> BlockFrequencyInfo & {
return AM.getResult<BlockFrequencyAnalysis>(F);
};
LoopVectorizeResult Result = runImpl(F);
if (!Result.MadeAnyChange)
return PreservedAnalyses::all();
PreservedAnalyses PA;
if (isAssignmentTrackingEnabled(*F.getParent())) {
for (auto &BB : F)
RemoveRedundantDbgInstrs(&BB);
}
PA.preserve<LoopAnalysis>();
PA.preserve<DominatorTreeAnalysis>();
PA.preserve<ScalarEvolutionAnalysis>();
PA.preserve<LoopAccessAnalysis>();
if (Result.MadeCFGChange) {
// Making CFG changes likely means a loop got vectorized. Indicate that
// extra simplification passes should be run.
// TODO: MadeCFGChanges is not a prefect proxy. Extra passes should only
// be run if runtime checks have been added.
AM.getResult<ShouldRunExtraVectorPasses>(F);
PA.preserve<ShouldRunExtraVectorPasses>();
} else {
PA.preserveSet<CFGAnalyses>();
}
return PA;
}
void LoopVectorizePass::printPipeline(
raw_ostream &OS, function_ref<StringRef(StringRef)> MapClassName2PassName) {
static_cast<PassInfoMixin<LoopVectorizePass> *>(this)->printPipeline(
OS, MapClassName2PassName);
OS << '<';
OS << (InterleaveOnlyWhenForced ? "" : "no-") << "interleave-forced-only;";
OS << (VectorizeOnlyWhenForced ? "" : "no-") << "vectorize-forced-only;";
OS << '>';
}