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//===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===//
//
// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
// See https://llvm.org/LICENSE.txt for license information.
// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
//
//===----------------------------------------------------------------------===//
//
// This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops
// and generates target-independent LLVM-IR.
// The vectorizer uses the TargetTransformInfo analysis to estimate the costs
// of instructions in order to estimate the profitability of vectorization.
//
// The loop vectorizer combines consecutive loop iterations into a single
// 'wide' iteration. After this transformation the index is incremented
// by the SIMD vector width, and not by one.
//
// This pass has three parts:
// 1. The main loop pass that drives the different parts.
// 2. LoopVectorizationLegality - A unit that checks for the legality
// of the vectorization.
// 3. InnerLoopVectorizer - A unit that performs the actual
// widening of instructions.
// 4. LoopVectorizationCostModel - A unit that checks for the profitability
// of vectorization. It decides on the optimal vector width, which
// can be one, if vectorization is not profitable.
//
// There is a development effort going on to migrate loop vectorizer to the
// VPlan infrastructure and to introduce outer loop vectorization support (see
// docs/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 "VPlanHCFGBuilder.h"
#include "VPlanPatternMatch.h"
#include "VPlanTransforms.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/SmallSet.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/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/DebugInfoMetadata.h"
#include "llvm/IR/DebugLoc.h"
#include "llvm/IR/DerivedTypes.h"
#include "llvm/IR/DiagnosticInfo.h"
#include "llvm/IR/Dominators.h"
#include "llvm/IR/Function.h"
#include "llvm/IR/IRBuilder.h"
#include "llvm/IR/InstrTypes.h"
#include "llvm/IR/Instruction.h"
#include "llvm/IR/Instructions.h"
#include "llvm/IR/IntrinsicInst.h"
#include "llvm/IR/Intrinsics.h"
#include "llvm/IR/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/ValueHandle.h"
#include "llvm/IR/VectorBuilder.h"
#include "llvm/IR/Verifier.h"
#include "llvm/Support/Casting.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Compiler.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/ErrorHandling.h"
#include "llvm/Support/InstructionCost.h"
#include "llvm/Support/MathExtras.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/InjectTLIMappings.h"
#include "llvm/Transforms/Utils/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 <map>
#include <memory>
#include <string>
#include <tuple>
#include <utility>
using namespace llvm;
#define LV_NAME "loop-vectorize"
#define DEBUG_TYPE LV_NAME
#ifndef NDEBUG
const char VerboseDebug[] = DEBUG_TYPE "-verbose";
#endif
/// @{
/// Metadata attribute names
const char LLVMLoopVectorizeFollowupAll[] = "llvm.loop.vectorize.followup_all";
const char LLVMLoopVectorizeFollowupVectorized[] =
"llvm.loop.vectorize.followup_vectorized";
const char LLVMLoopVectorizeFollowupEpilogue[] =
"llvm.loop.vectorize.followup_epilogue";
/// @}
STATISTIC(LoopsVectorized, "Number of loops vectorized");
STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization");
STATISTIC(LoopsEpilogueVectorized, "Number of epilogues 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::init(16), 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 prefer-predicate-over-epilogue indicates that an epilogue is undesired,
// that predication is preferred, and this lists all options. I.e., the
// vectorizer will try to fold the tail-loop (epilogue) into the vector body
// and predicate the instructions accordingly. If tail-folding fails, there are
// different fallback strategies depending on these values:
namespace PreferPredicateTy {
enum Option {
ScalarEpilogue = 0,
PredicateElseScalarEpilogue,
PredicateOrDontVectorize
};
} // namespace PreferPredicateTy
static cl::opt<PreferPredicateTy::Option> PreferPredicateOverEpilogue(
"prefer-predicate-over-epilogue",
cl::init(PreferPredicateTy::ScalarEpilogue),
cl::Hidden,
cl::desc("Tail-folding and predication preferences over creating a scalar "
"epilogue loop."),
cl::values(clEnumValN(PreferPredicateTy::ScalarEpilogue,
"scalar-epilogue",
"Don't tail-predicate loops, create scalar epilogue"),
clEnumValN(PreferPredicateTy::PredicateElseScalarEpilogue,
"predicate-else-scalar-epilogue",
"prefer tail-folding, create scalar epilogue if tail "
"folding fails."),
clEnumValN(PreferPredicateTy::PredicateOrDontVectorize,
"predicate-dont-vectorize",
"prefers tail-folding, don't attempt vectorization if "
"tail-folding fails.")));
static cl::opt<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::DataAndControlFlowWithoutRuntimeCheck,
"data-and-control-without-rt-check",
"Similar to data-and-control, but remove the runtime check"),
clEnumValN(TailFoldingStyle::DataWithEVL, "data-with-evl",
"Use predicated EVL instructions for tail folding. If EVL "
"is unsupported, fallback to data-without-lane-mask.")));
static cl::opt<bool> MaximizeBandwidth(
"vectorizer-maximize-bandwidth", cl::init(false), cl::Hidden,
cl::desc("Maximize bandwidth when selecting vectorization factor which "
"will be determined by the smallest type in loop."));
static cl::opt<bool> EnableInterleavedMemAccesses(
"enable-interleaved-mem-accesses", cl::init(false), cl::Hidden,
cl::desc("Enable vectorization on interleaved memory accesses in a loop"));
/// An interleave-group may need masking if it resides in a block that needs
/// predication, or in order to mask away gaps.
static cl::opt<bool> EnableMaskedInterleavedMemAccesses(
"enable-masked-interleaved-mem-accesses", cl::init(false), cl::Hidden,
cl::desc("Enable vectorization on masked interleaved memory accesses in a loop"));
static cl::opt<unsigned> 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> 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<bool> ForceTargetSupportsScalableVectors(
"force-target-supports-scalable-vectors", cl::init(false), cl::Hidden,
cl::desc(
"Pretend that scalable vectors are supported, even if the target does "
"not support them. This flag should only be used for 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.
static cl::opt<unsigned> NumberOfStoresToPredicate(
"vectorize-num-stores-pred", cl::init(1), cl::Hidden,
cl::desc("Max number of stores to be predicated behind an if."));
static cl::opt<bool> EnableIndVarRegisterHeur(
"enable-ind-var-reg-heur", cl::init(true), cl::Hidden,
cl::desc("Count the induction variable only once when interleaving"));
static cl::opt<bool> EnableCondStoresVectorization(
"enable-cond-stores-vec", cl::init(true), cl::Hidden,
cl::desc("Enable if predication of stores during vectorization."));
static cl::opt<unsigned> MaxNestedScalarReductionIC(
"max-nested-scalar-reduction-interleave", cl::init(2), cl::Hidden,
cl::desc("The maximum interleave count to use when interleaving a scalar "
"reduction in a nested loop."));
static cl::opt<bool>
PreferInLoopReductions("prefer-inloop-reductions", cl::init(false),
cl::Hidden,
cl::desc("Prefer in-loop vector reductions, "
"overriding the targets preference."));
static cl::opt<bool> 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."));
namespace llvm {
cl::opt<bool> EnableVPlanNativePath(
"enable-vplan-native-path", cl::Hidden,
cl::desc("Enable VPlan-native vectorization path with "
"support for outer loop vectorization."));
}
// This flag enables the stress testing of the VPlan H-CFG construction in the
// VPlan-native vectorization path. It must be used in conjuction with
// -enable-vplan-native-path. -vplan-verify-hcfg can also be used to enable the
// verification of the H-CFGs built.
static cl::opt<bool> VPlanBuildStressTest(
"vplan-build-stress-test", cl::init(false), cl::Hidden,
cl::desc(
"Build VPlan for every supported loop nest in the function and bail "
"out right after the build (stress test the VPlan H-CFG construction "
"in the VPlan-native vectorization path)."));
cl::opt<bool> llvm::EnableLoopInterleaving(
"interleave-loops", cl::init(true), cl::Hidden,
cl::desc("Enable loop interleaving in Loop vectorization passes"));
cl::opt<bool> llvm::EnableLoopVectorization(
"vectorize-loops", cl::init(true), cl::Hidden,
cl::desc("Run the Loop vectorization passes"));
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> UseWiderVFIfCallVariantsPresent(
"vectorizer-maximize-bandwidth-for-vector-calls", cl::init(true),
cl::Hidden,
cl::desc("Try wider VFs if they enable the use of vector variants"));
// Likelyhood of bypassing the vectorized loop because assumptions about SCEV
// variables not overflowing do not hold. See `emitSCEVChecks`.
static constexpr uint32_t SCEVCheckBypassWeights[] = {1, 127};
// Likelyhood of bypassing the vectorized loop because pointers overlap. See
// `emitMemRuntimeChecks`.
static constexpr uint32_t MemCheckBypassWeights[] = {1, 127};
// 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);
}
/// Returns "best known" trip count for the specified loop \p L as defined by
/// the following procedure:
/// 1) Returns exact trip count if it is known.
/// 2) Returns expected trip count according to profile data if any.
/// 3) Returns upper bound estimate if it is known.
/// 4) Returns std::nullopt if all of the above failed.
static std::optional<unsigned> getSmallBestKnownTC(ScalarEvolution &SE,
Loop *L) {
// Check if exact trip count is known.
if (unsigned ExpectedTC = SE.getSmallConstantTripCount(L))
return ExpectedTC;
// Check if there is an expected trip count available from profile data.
if (LoopVectorizeWithBlockFrequency)
if (auto EstimatedTC = getLoopEstimatedTripCount(L))
return *EstimatedTC;
// Check if upper bound estimate is known.
if (unsigned ExpectedTC = SE.getSmallConstantMaxTripCount(L))
return ExpectedTC;
return 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 TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, ElementCount VecWidth,
ElementCount MinProfitableTripCount,
unsigned UnrollFactor, LoopVectorizationLegality *LVL,
LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI,
ProfileSummaryInfo *PSI, GeneratedRTChecks &RTChecks)
: OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI),
AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor),
Builder(PSE.getSE()->getContext()), Legal(LVL), Cost(CM), BFI(BFI),
PSI(PSI), RTChecks(RTChecks) {
// Query this against the original loop and save it here because the profile
// of the original loop header may change as the transformation happens.
OptForSizeBasedOnProfile = llvm::shouldOptimizeForSize(
OrigLoop->getHeader(), PSI, BFI, PGSOQueryType::IRPass);
if (MinProfitableTripCount.isZero())
this->MinProfitableTripCount = VecWidth;
else
this->MinProfitableTripCount = MinProfitableTripCount;
}
virtual ~InnerLoopVectorizer() = default;
/// Create a new empty loop that will contain vectorized instructions later
/// on, while the old loop will be used as the scalar remainder. Control flow
/// is generated around the vectorized (and scalar epilogue) loops consisting
/// of various checks and bypasses. Return the pre-header block of the new
/// loop and the start value for the canonical induction, if it is != 0. The
/// latter is the case when vectorizing the epilogue loop. In the case of
/// epilogue vectorization, this function is overriden to handle the more
/// complex control flow around the loops. \p ExpandedSCEVs is used to
/// look up SCEV expansions for expressions needed during skeleton creation.
virtual std::pair<BasicBlock *, Value *>
createVectorizedLoopSkeleton(const SCEV2ValueTy &ExpandedSCEVs);
/// Fix the vectorized code, taking care of header phi's, live-outs, and more.
void fixVectorizedLoop(VPTransformState &State, VPlan &Plan);
// Return true if any runtime check is added.
bool areSafetyChecksAdded() { return AddedSafetyChecks; }
/// A helper function to scalarize a single Instruction in the innermost loop.
/// Generates a sequence of scalar instances for each lane between \p MinLane
/// and \p MaxLane, times each part between \p MinPart and \p MaxPart,
/// inclusive. Uses the VPValue operands from \p RepRecipe instead of \p
/// Instr's operands.
void scalarizeInstruction(const Instruction *Instr,
VPReplicateRecipe *RepRecipe,
const VPIteration &Instance,
VPTransformState &State);
/// Fix the non-induction PHIs in \p Plan.
void fixNonInductionPHIs(VPlan &Plan, VPTransformState &State);
/// Create a new phi node for the induction variable \p OrigPhi to resume
/// iteration count in the scalar epilogue, from where the vectorized loop
/// left off. \p Step is the SCEV-expanded induction step to use. In cases
/// where the loop skeleton is more complicated (i.e., epilogue vectorization)
/// and the resume values can come from an additional bypass block, the \p
/// AdditionalBypass pair provides information about the bypass block and the
/// end value on the edge from bypass to this loop.
PHINode *createInductionResumeValue(
PHINode *OrigPhi, const InductionDescriptor &ID, Value *Step,
ArrayRef<BasicBlock *> BypassBlocks,
std::pair<BasicBlock *, Value *> AdditionalBypass = {nullptr, nullptr});
/// Returns the original loop trip count.
Value *getTripCount() const { return TripCount; }
/// Used to set the trip count after ILV's construction and after the
/// preheader block has been executed. Note that this always holds the trip
/// count of the original loop for both main loop and epilogue vectorization.
void setTripCount(Value *TC) { TripCount = TC; }
protected:
friend class LoopVectorizationPlanner;
/// A small list of PHINodes.
using PhiVector = SmallVector<PHINode *, 4>;
/// A type for scalarized values in the new loop. Each value from the
/// original loop, when scalarized, is represented by UF x VF scalar values
/// in the new unrolled loop, where UF is the unroll factor and VF is the
/// vectorization factor.
using ScalarParts = SmallVector<SmallVector<Value *, 4>, 2>;
/// Set up the values of the IVs correctly when exiting the vector loop.
void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II,
Value *VectorTripCount, Value *EndValue,
BasicBlock *MiddleBlock, BasicBlock *VectorHeader,
VPlan &Plan, VPTransformState &State);
/// Iteratively sink the scalarized operands of a predicated instruction into
/// the block that was created for it.
void sinkScalarOperands(Instruction *PredInst);
/// Returns (and creates if needed) the trip count of the widened loop.
Value *getOrCreateVectorTripCount(BasicBlock *InsertBlock);
/// Emit a bypass check to see if the vector trip count is zero, including if
/// it overflows.
void emitIterationCountCheck(BasicBlock *Bypass);
/// Emit a bypass check to see if all of the SCEV assumptions we've
/// had to make are correct. Returns the block containing the checks or
/// nullptr if no checks have been added.
BasicBlock *emitSCEVChecks(BasicBlock *Bypass);
/// Emit bypass checks to check any memory assumptions we may have made.
/// Returns the block containing the checks or nullptr if no checks have been
/// added.
BasicBlock *emitMemRuntimeChecks(BasicBlock *Bypass);
/// Emit basic blocks (prefixed with \p Prefix) for the iteration check,
/// vector loop preheader, middle block and scalar preheader.
void createVectorLoopSkeleton(StringRef Prefix);
/// Create new phi nodes for the induction variables to resume iteration count
/// in the scalar epilogue, from where the vectorized loop left off.
/// In cases where the loop skeleton is more complicated (eg. epilogue
/// vectorization) and the resume values can come from an additional bypass
/// block, the \p AdditionalBypass pair provides information about the bypass
/// block and the end value on the edge from bypass to this loop.
void createInductionResumeValues(
const SCEV2ValueTy &ExpandedSCEVs,
std::pair<BasicBlock *, Value *> AdditionalBypass = {nullptr, nullptr});
/// Complete the loop skeleton by adding debug MDs, creating appropriate
/// conditional branches in the middle block, preparing the builder and
/// running the verifier. Return the preheader of the completed vector loop.
BasicBlock *completeLoopSkeleton();
/// 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 Library Info.
const TargetLibraryInfo *TLI;
/// Target Transform Info.
const TargetTransformInfo *TTI;
/// Assumption Cache.
AssumptionCache *AC;
/// Interface to emit optimization remarks.
OptimizationRemarkEmitter *ORE;
/// The vectorization SIMD factor to use. Each vector will have this many
/// vector elements.
ElementCount VF;
ElementCount MinProfitableTripCount;
/// The vectorization unroll factor to use. Each scalar is vectorized to this
/// many different vector instructions.
unsigned UF;
/// The builder that we use
IRBuilder<> Builder;
// --- Vectorization state ---
/// The vector-loop preheader.
BasicBlock *LoopVectorPreHeader;
/// The scalar-loop preheader.
BasicBlock *LoopScalarPreHeader;
/// Middle Block between the vector and the scalar.
BasicBlock *LoopMiddleBlock;
/// The unique ExitBlock of the scalar loop if one exists. Note that
/// there can be multiple exiting edges reaching this block.
BasicBlock *LoopExitBlock;
/// The scalar loop body.
BasicBlock *LoopScalarBody;
/// A list of all bypass blocks. The first block is the entry of the loop.
SmallVector<BasicBlock *, 4> LoopBypassBlocks;
/// Store instructions that were predicated.
SmallVector<Instruction *, 4> PredicatedInstructions;
/// Trip count of the original loop.
Value *TripCount = nullptr;
/// Trip count of the widened loop (TripCount - TripCount % (VF*UF))
Value *VectorTripCount = nullptr;
/// The legality analysis.
LoopVectorizationLegality *Legal;
/// The profitablity analysis.
LoopVectorizationCostModel *Cost;
// Record whether runtime checks are added.
bool AddedSafetyChecks = false;
// Holds the end values for each induction variable. We save the end values
// so we can later fix-up the external users of the induction variables.
DenseMap<PHINode *, Value *> IVEndValues;
/// BFI and PSI are used to check for profile guided size optimizations.
BlockFrequencyInfo *BFI;
ProfileSummaryInfo *PSI;
// Whether this loop should be optimized for size based on profile guided size
// optimizatios.
bool OptForSizeBasedOnProfile;
/// Structure to hold information about generated runtime checks, responsible
/// for cleaning the checks, if vectorization turns out unprofitable.
GeneratedRTChecks &RTChecks;
// Holds the resume values for reductions in the loops, used to set the
// correct start value of reduction PHIs when vectorizing the epilogue.
SmallMapVector<const RecurrenceDescriptor *, PHINode *, 4>
ReductionResumeValues;
};
class InnerLoopUnroller : public InnerLoopVectorizer {
public:
InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
LoopInfo *LI, DominatorTree *DT,
const TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, unsigned UnrollFactor,
LoopVectorizationLegality *LVL,
LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI,
ProfileSummaryInfo *PSI, GeneratedRTChecks &Check)
: InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE,
ElementCount::getFixed(1),
ElementCount::getFixed(1), UnrollFactor, LVL, CM,
BFI, PSI, Check) {}
};
/// 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;
BasicBlock *SCEVSafetyCheck = nullptr;
BasicBlock *MemSafetyCheck = nullptr;
Value *TripCount = nullptr;
Value *VectorTripCount = nullptr;
EpilogueLoopVectorizationInfo(ElementCount MVF, unsigned MUF,
ElementCount EVF, unsigned EUF)
: MainLoopVF(MVF), MainLoopUF(MUF), EpilogueVF(EVF), EpilogueUF(EUF) {
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 TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI,
LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM,
BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI,
GeneratedRTChecks &Checks)
: InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE,
EPI.MainLoopVF, EPI.MainLoopVF, EPI.MainLoopUF, LVL,
CM, BFI, PSI, Checks),
EPI(EPI) {}
// Override this function to handle the more complex control flow around the
// three loops.
std::pair<BasicBlock *, Value *> createVectorizedLoopSkeleton(
const SCEV2ValueTy &ExpandedSCEVs) final {
return createEpilogueVectorizedLoopSkeleton(ExpandedSCEVs);
}
/// The interface for creating a vectorized skeleton using one of two
/// different strategies, each corresponding to one execution of the vplan
/// as described above.
virtual std::pair<BasicBlock *, Value *>
createEpilogueVectorizedLoopSkeleton(const SCEV2ValueTy &ExpandedSCEVs) = 0;
/// 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;
};
/// 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 TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI,
LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM,
BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI,
GeneratedRTChecks &Check)
: InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE,
EPI, LVL, CM, BFI, PSI, Check) {}
/// Implements the interface for creating a vectorized skeleton using the
/// *main loop* strategy (ie the first pass of vplan execution).
std::pair<BasicBlock *, Value *>
createEpilogueVectorizedLoopSkeleton(const SCEV2ValueTy &ExpandedSCEVs) final;
protected:
/// Emits an iteration count bypass check once for the main loop (when \p
/// ForEpilogue is false) and once for the epilogue loop (when \p
/// ForEpilogue is true).
BasicBlock *emitIterationCountCheck(BasicBlock *Bypass, bool ForEpilogue);
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 TargetLibraryInfo *TLI,
const TargetTransformInfo *TTI, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI,
LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM,
BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI,
GeneratedRTChecks &Checks)
: InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE,
EPI, LVL, CM, BFI, PSI, Checks) {
TripCount = EPI.TripCount;
}
/// Implements the interface for creating a vectorized skeleton using the
/// *epilogue loop* strategy (ie the second pass of vplan execution).
std::pair<BasicBlock *, Value *>
createEpilogueVectorizedLoopSkeleton(const SCEV2ValueTy &ExpandedSCEVs) final;
protected:
/// Emits an iteration count bypass check after the main vector loop has
/// finished to see if there are any iterations left to execute by either
/// the vector epilogue or the scalar epilogue.
BasicBlock *emitMinimumVectorEpilogueIterCountCheck(
BasicBlock *Bypass,
BasicBlock *Insert);
void printDebugTracesAtStart() override;
void printDebugTracesAtEnd() override;
};
} // end namespace llvm
/// Look for a meaningful debug location on the instruction or it's
/// operands.
static DebugLoc getDebugLocFromInstOrOperands(Instruction *I) {
if (!I)
return DebugLoc();
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, Loop *TheLoop,
Instruction *I, DebugLoc DL = {}) {
Value *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 a value for Step multiplied by VF.
Value *createStepForVF(IRBuilderBase &B, Type *Ty, ElementCount VF,
int64_t Step) {
assert(Ty->isIntegerTy() && "Expected an integer step");
return B.CreateElementCount(Ty, VF.multiplyCoefficientBy(Step));
}
/// Return the runtime value for VF.
Value *getRuntimeVF(IRBuilderBase &B, Type *Ty, ElementCount VF) {
return B.CreateElementCount(Ty, VF);
}
const SCEV *createTripCountSCEV(Type *IdxTy, PredicatedScalarEvolution &PSE,
Loop *OrigLoop) {
const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount();
assert(!isa<SCEVCouldNotCompute>(BackedgeTakenCount) && "Invalid loop count");
ScalarEvolution &SE = *PSE.getSE();
return SE.getTripCountFromExitCount(BackedgeTakenCount, IdxTy, OrigLoop);
}
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, true /* doesn't matter */, *ORE);
ORE->emit(
createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop, I)
<< "loop not vectorized: " << OREMsg);
}
/// Reports an informative message: print \p Msg for debugging purposes as well
/// as an optimization remark. Uses either \p I as location of the remark, or
/// otherwise \p TheLoop. If \p DL is passed, use it as debug location for the
/// remark. If \p DL is passed, use it as debug location for the remark.
static void reportVectorizationInfo(const StringRef Msg, const StringRef ORETag,
OptimizationRemarkEmitter *ORE,
Loop *TheLoop, Instruction *I = nullptr,
DebugLoc DL = {}) {
LLVM_DEBUG(debugVectorizationMessage("", Msg, I));
LoopVectorizeHints Hints(TheLoop, true /* 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 scalar epilogue loop should be
// lowered.
enum ScalarEpilogueLowering {
// The default: allowing scalar epilogues.
CM_ScalarEpilogueAllowed,
// Vectorization with OptForSize: don't allow epilogues.
CM_ScalarEpilogueNotAllowedOptSize,
// A special case of vectorisation with OptForSize: loops with a very small
// trip count are considered for vectorization under OptForSize, thereby
// making sure the cost of their loop body is dominant, free of runtime
// guards and scalar iteration overheads.
CM_ScalarEpilogueNotAllowedLowTripLoop,
// Loop hint predicate indicating an epilogue is undesired.
CM_ScalarEpilogueNotNeededUsePredicate,
// Directive indicating we must either tail fold or not vectorize
CM_ScalarEpilogueNotAllowedUsePredicate
};
using InstructionVFPair = std::pair<Instruction *, ElementCount>;
/// LoopVectorizationCostModel - estimates the expected speedups due to
/// vectorization.
/// In many cases vectorization is not profitable. This can happen because of
/// a number of reasons. In this class we mainly attempt to predict the
/// expected speedup/slowdowns due to the supported instruction set. We use the
/// TargetTransformInfo to query the different backends for the cost of
/// different operations.
class LoopVectorizationCostModel {
public:
LoopVectorizationCostModel(ScalarEpilogueLowering SEL, Loop *L,
PredicatedScalarEvolution &PSE, LoopInfo *LI,
LoopVectorizationLegality *Legal,
const TargetTransformInfo &TTI,
const TargetLibraryInfo *TLI, DemandedBits *DB,
AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, const Function *F,
const LoopVectorizeHints *Hints,
InterleavedAccessInfo &IAI)
: ScalarEpilogueStatus(SEL), TheLoop(L), PSE(PSE), LI(LI), Legal(Legal),
TTI(TTI), TLI(TLI), DB(DB), AC(AC), ORE(ORE), TheFunction(F),
Hints(Hints), InterleaveInfo(IAI) {}
/// \return An upper bound for the vectorization factors (both fixed and
/// scalable). If the factors are 0, vectorization and interleaving should be
/// avoided up front.
FixedScalableVFPair computeMaxVF(ElementCount UserVF, unsigned UserIC);
/// \return True if runtime checks are required for vectorization, and false
/// otherwise.
bool runtimeChecksRequired();
/// Setup cost-based decisions for user vectorization factor.
/// \return true if the UserVF is a feasible VF to be chosen.
bool selectUserVectorizationFactor(ElementCount UserVF) {
collectUniformsAndScalars(UserVF);
collectInstsToScalarize(UserVF);
return expectedCost(UserVF).isValid();
}
/// \return The size (in bits) of the smallest and widest types in the code
/// that needs to be vectorized. We ignore values that remain scalar such as
/// 64 bit loop indices.
std::pair<unsigned, unsigned> getSmallestAndWidestTypes();
/// \return The desired interleave count.
/// If interleave count has been specified by metadata it will be returned.
/// Otherwise, the interleave count is computed and returned. VF and LoopCost
/// are the selected vectorization factor and the cost of the selected VF.
unsigned selectInterleaveCount(ElementCount VF, InstructionCost LoopCost);
/// Memory access instruction may be vectorized in more than one way.
/// Form of instruction after vectorization depends on cost.
/// This function takes cost-based decisions for Load/Store instructions
/// and collects them in a map. This decisions map is used for building
/// the lists of loop-uniform and loop-scalar instructions.
/// The calculated cost is saved with widening decision in order to
/// avoid redundant calculations.
void setCostBasedWideningDecision(ElementCount VF);
/// A 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);
/// A struct that represents some properties of the register usage
/// of a loop.
struct RegisterUsage {
/// Holds the number of loop invariant values that are used in the loop.
/// The key is ClassID of target-provided register class.
SmallMapVector<unsigned, unsigned, 4> LoopInvariantRegs;
/// Holds the maximum number of concurrent live intervals in the loop.
/// The key is ClassID of target-provided register class.
SmallMapVector<unsigned, unsigned, 4> MaxLocalUsers;
};
/// \return Returns information about the register usages of the loop for the
/// given vectorization factors.
SmallVector<RegisterUsage, 8>
calculateRegisterUsage(ArrayRef<ElementCount> VFs);
/// Collect values we want to ignore in the cost model.
void collectValuesToIgnore();
/// Collect all element types in the loop for which widening is needed.
void collectElementTypesForWidening();
/// Split reductions into those that happen in the loop, and those that happen
/// outside. In loop reductions are collected into InLoopReductions.
void collectInLoopReductions();
/// Returns true if we should use strict in-order reductions for the given
/// RdxDesc. This is true if the -enable-strict-reductions flag is passed,
/// the IsOrdered flag of RdxDesc is set and we do not allow reordering
/// of FP operations.
bool useOrderedReductions(const RecurrenceDescriptor &RdxDesc) const {
return !Hints->allowReordering() && RdxDesc.isOrdered();
}
/// \returns The smallest bitwidth each instruction can be represented with.
/// The vector equivalents of these instructions should be truncated to this
/// type.
const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const {
return MinBWs;
}
/// \returns True if it is more profitable to scalarize instruction \p I for
/// vectorization factor \p VF.
bool isProfitableToScalarize(Instruction *I, ElementCount VF) const {
assert(VF.isVector() &&
"Profitable to scalarize relevant only for VF > 1.");
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)");
// Pseudo probe needs to be duplicated for each unrolled iteration and
// vector lane so that profiled loop trip count can be accurately
// accumulated instead of being under counted.
if (isa<PseudoProbeInst>(I))
return false;
if (VF.isScalar())
return true;
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 {
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[std::make_pair(I, VF)] = std::make_pair(W, Cost);
}
/// Save vectorization decision \p W and \p Cost taken by the cost model for
/// interleaving group \p Grp and vector width \p VF.
void setWideningDecision(const InterleaveGroup<Instruction> *Grp,
ElementCount VF, InstWidening W,
InstructionCost Cost) {
assert(VF.isVector() && "Expected VF >=2");
/// Broadcast this decicion to all instructions inside the group.
/// But the cost will be assigned to one instruction only.
for (unsigned i = 0; i < Grp->getFactor(); ++i) {
if (auto *I = Grp->getMember(i)) {
if (Grp->getInsertPos() == I)
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
else
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0);
}
}
}
/// Return the cost model decision for the given instruction \p I and vector
/// width \p VF. Return CM_Unknown if this instruction did not pass
/// through the cost modeling.
InstWidening getWideningDecision(Instruction *I, ElementCount VF) 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 = std::make_pair(I, VF);
auto Itr = WideningDecisions.find(InstOnVF);
if (Itr == WideningDecisions.end())
return CM_Unknown;
return Itr->second.first;
}
/// Return the vectorization cost for the given instruction \p I and vector
/// width \p VF.
InstructionCost getWideningCost(Instruction *I, ElementCount VF) {
assert(VF.isVector() && "Expected VF >=2");
std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(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[std::make_pair(CI, VF)] = {Kind, Variant, IID,
MaskPos, Cost};
}
CallWideningDecision getCallWideningDecision(CallInst *CI,
ElementCount VF) const {
assert(!VF.isScalar() && "Expected vector VF");
return CallWideningDecisions.at(std::make_pair(CI, VF));
}
/// Return True if instruction \p I is an optimizable truncate whose operand
/// is an induction variable. Such a truncate will be removed by adding a new
/// induction variable with the destination type.
bool isOptimizableIVTruncate(Instruction *I, ElementCount VF) {
// If the instruction is not a truncate, return false.
auto *Trunc = dyn_cast<TruncInst>(I);
if (!Trunc)
return false;
// Get the source and destination types of the truncate.
Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF);
Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF);
// If the truncate is free for the given types, return false. Replacing a
// free truncate with an induction variable would add an induction variable
// update instruction to each iteration of the loop. We exclude from this
// check the primary induction variable since it will need an update
// instruction regardless.
Value *Op = Trunc->getOperand(0);
if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy))
return false;
// If the truncated value is not an induction variable, return false.
return Legal->isInductionPhi(Op);
}
/// Collects the instructions to scalarize for each predicated instruction in
/// the loop.
void collectInstsToScalarize(ElementCount VF);
/// Collect Uniform and Scalar values for the given \p VF.
/// The sets depend on CM decision for Load/Store instructions
/// that may be vectorized as interleave, gather-scatter or scalarized.
/// 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 collectUniformsAndScalars(ElementCount VF) {
// Do the analysis once.
if (VF.isScalar() || Uniforms.contains(VF))
return;
setCostBasedWideningDecision(VF);
setVectorizedCallDecision(VF);
collectLoopUniforms(VF);
collectLoopScalars(VF);
}
/// Returns true if the target machine supports masked store operation
/// for the given \p DataType and kind of access to \p Ptr.
bool isLegalMaskedStore(Type *DataType, Value *Ptr, Align Alignment) const {
return Legal->isConsecutivePtr(DataType, Ptr) &&
TTI.isLegalMaskedStore(DataType, Alignment);
}
/// Returns true if the target machine supports masked load operation
/// for the given \p DataType and kind of access to \p Ptr.
bool isLegalMaskedLoad(Type *DataType, Value *Ptr, Align Alignment) const {
return Legal->isConsecutivePtr(DataType, Ptr) &&
TTI.isLegalMaskedLoad(DataType, Alignment);
}
/// Returns true if the target machine can represent \p V as a masked gather
/// or scatter operation.
bool isLegalGatherOrScatter(Value *V, ElementCount VF) {
bool LI = isa<LoadInst>(V);
bool SI = isa<StoreInst>(V);
if (!LI && !SI)
return false;
auto *Ty = getLoadStoreType(V);
Align Align = getLoadStoreAlignment(V);
if (VF.isVector())
Ty = VectorType::get(Ty, VF);
return (LI && TTI.isLegalMaskedGather(Ty, Align)) ||
(SI && TTI.isLegalMaskedScatter(Ty, Align));
}
/// Returns true if the target machine supports all of the reduction
/// variables found for the given VF.
bool canVectorizeReductions(ElementCount VF) const {
return (all_of(Legal->getReductionVars(), [&](auto &Reduction) -> bool {
const RecurrenceDescriptor &RdxDesc = Reduction.second;
return TTI.isLegalToVectorizeReduction(RdxDesc, 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) 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;
/// 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) const;
/// 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 (!isScalarEpilogueAllowed()) {
LLVM_DEBUG(dbgs() << "LV: Loop does not require scalar epilogue\n");
return false;
}
// If we might exit from anywhere but the latch, must run the exiting
// iteration in scalar form.
if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) {
LLVM_DEBUG(
dbgs() << "LV: Loop requires scalar epilogue: multiple exits\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 we're required to use a scalar epilogue for at least
/// the final iteration of the original loop for all VFs in \p Range.
/// A scalar epilogue must either be required for all VFs in \p Range or for
/// none.
bool requiresScalarEpilogue(VFRange Range) const {
auto RequiresScalarEpilogue = [this](ElementCount VF) {
return requiresScalarEpilogue(VF.isVector());
};
bool IsRequired = all_of(Range, RequiresScalarEpilogue);
assert(
(IsRequired || none_of(Range, RequiresScalarEpilogue)) &&
"all VFs in range must agree on whether a scalar epilogue is required");
return IsRequired;
}
/// Returns true if a scalar epilogue is not allowed due to optsize or a
/// loop hint annotation.
bool isScalarEpilogueAllowed() const {
return ScalarEpilogueStatus == CM_ScalarEpilogueAllowed;
}
/// Returns the TailFoldingStyle that is best for the current loop.
TailFoldingStyle getTailFoldingStyle(bool IVUpdateMayOverflow = true) const {
if (!ChosenTailFoldingStyle)
return TailFoldingStyle::None;
return IVUpdateMayOverflow ? ChosenTailFoldingStyle->first
: ChosenTailFoldingStyle->second;
}
/// Selects and saves TailFoldingStyle for 2 options - if IV update may
/// overflow or not.
/// \param IsScalableVF true if scalable vector factors enabled.
/// \param UserIC User specific interleave count.
void setTailFoldingStyles(bool IsScalableVF, unsigned UserIC) {
assert(!ChosenTailFoldingStyle && "Tail folding must not be selected yet.");
if (!Legal->canFoldTailByMasking()) {
ChosenTailFoldingStyle =
std::make_pair(TailFoldingStyle::None, TailFoldingStyle::None);
return;
}
if (!ForceTailFoldingStyle.getNumOccurrences()) {
ChosenTailFoldingStyle = std::make_pair(
TTI.getPreferredTailFoldingStyle(/*IVUpdateMayOverflow=*/true),
TTI.getPreferredTailFoldingStyle(/*IVUpdateMayOverflow=*/false));
return;
}
// Set styles when forced.
ChosenTailFoldingStyle = std::make_pair(ForceTailFoldingStyle.getValue(),
ForceTailFoldingStyle.getValue());
if (ForceTailFoldingStyle != TailFoldingStyle::DataWithEVL)
return;
// Override forced styles if needed.
// FIXME: use actual opcode/data type for analysis here.
// FIXME: Investigate opportunity for fixed vector factor.
bool EVLIsLegal =
IsScalableVF && UserIC <= 1 &&
TTI.hasActiveVectorLength(0, nullptr, Align()) &&
!EnableVPlanNativePath &&
// FIXME: implement support for max safe dependency distance.
Legal->isSafeForAnyVectorWidth();
if (!EVLIsLegal) {
// If for some reason EVL mode is unsupported, fallback to
// DataWithoutLaneMask to try to vectorize the loop with folded tail
// in a generic way.
ChosenTailFoldingStyle =
std::make_pair(TailFoldingStyle::DataWithoutLaneMask,
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 {
// TODO: check if it is possible to check for None style independent of
// IVUpdateMayOverflow flag in getTailFoldingStyle.
return getTailFoldingStyle() != TailFoldingStyle::None;
}
/// 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 Phi is part of an inloop reduction.
bool isInLoopReduction(PHINode *Phi) const {
return InLoopReductions.contains(Phi);
}
/// Estimate cost of an intrinsic call instruction CI if it were vectorized
/// with factor VF. Return the cost of the instruction, including
/// scalarization overhead if it's needed.
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);
bool hasPredStores() const { return NumPredStores > 0; }
/// Returns true if epilogue vectorization is considered profitable, and
/// false otherwise.
/// \p VF is the vectorization factor chosen for the original loop.
bool isEpilogueVectorizationProfitable(const ElementCount VF) 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,
TTI::TargetCostKind CostKind) const;
private:
unsigned NumPredStores = 0;
/// \return An upper bound for the vectorization factors for both
/// fixed and scalable vectorization, where the minimum-known number of
/// elements is a power-of-2 larger than zero. If scalable vectorization is
/// disabled or unsupported, then the scalable part will be equal to
/// ElementCount::getScalable(0).
FixedScalableVFPair computeFeasibleMaxVF(unsigned MaxTripCount,
ElementCount UserVF,
bool FoldTailByMasking);
/// \return the maximized element count based on the targets vector
/// registers and the loop trip-count, but limited to a maximum safe VF.
/// This is a helper function of computeFeasibleMaxVF.
ElementCount getMaximizedVFForTarget(unsigned MaxTripCount,
unsigned SmallestType,
unsigned WidestType,
ElementCount MaxSafeVF,
bool FoldTailByMasking);
/// Checks if scalable vectorization is supported and enabled. Caches the
/// result to avoid repeated debug dumps for repeated queries.
bool isScalableVectorizationAllowed();
/// \return the maximum legal scalable VF, based on the safe max number
/// of elements.
ElementCount getMaxLegalScalableVF(unsigned MaxSafeElements);
/// 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,
TTI::TargetCostKind CostKind) const;
/// Returns true if an artificially high cost for emulated masked memrefs
/// should be used.
bool useEmulatedMaskMemRefHack(Instruction *I, ElementCount VF);
/// Map of scalar integer values to the smallest bitwidth they can be legally
/// represented as. The vector equivalents of these values should be truncated
/// to this type.
MapVector<Instruction *, uint64_t> MinBWs;
/// A type representing the costs for instructions if they were to be
/// scalarized rather than vectorized. The entries are Instruction-Cost
/// pairs.
using ScalarCostsTy = DenseMap<Instruction *, 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.
ScalarEpilogueLowering ScalarEpilogueStatus = CM_ScalarEpilogueAllowed;
/// Control finally chosen tail folding style. The first element is used if
/// the IV update may overflow, the second element - if it does not.
std::optional<std::pair<TailFoldingStyle, TailFoldingStyle>>
ChosenTailFoldingStyle;
/// true if scalable vectorization is supported and enabled.
std::optional<bool> IsScalableVectorizationAllowed;
/// A map holding scalar costs for different vectorization factors. The
/// presence of a cost for an instruction in the mapping indicates that the
/// instruction will be scalarized when vectorizing with the associated
/// vectorization factor. The entries are VF-ScalarCostTy pairs.
DenseMap<ElementCount, ScalarCostsTy> InstsToScalarize;
/// Holds the instructions known to be uniform after vectorization.
/// The data is collected per VF.
DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Uniforms;
/// Holds the instructions known to be scalar after vectorization.
/// The data is collected per VF.
DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Scalars;
/// Holds the instructions (address computations) that are forced to be
/// scalarized.
DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> ForcedScalars;
/// PHINodes of the reductions that should be expanded in-loop.
SmallPtrSet<PHINode *, 4> InLoopReductions;
/// A Map of inloop reduction operations and their immediate chain operand.
/// FIXME: This can be removed once reductions can be costed correctly in
/// VPlan. This was added to allow quick lookup of the inloop operations.
DenseMap<Instruction *, Instruction *> InLoopReductionImmediateChains;
/// 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))
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 {
return SmallVector<Value *, 4>(make_filter_range(
Ops, [this, VF](Value *V) { return this->needsExtract(V, VF); }));
}
public:
/// The loop that we evaluate.
Loop *TheLoop;
/// Predicated scalar evolution analysis.
PredicatedScalarEvolution &PSE;
/// Loop Info analysis.
LoopInfo *LI;
/// Vectorization legality.
LoopVectorizationLegality *Legal;
/// Vector target information.
const TargetTransformInfo &TTI;
/// Target Library Info.
const TargetLibraryInfo *TLI;
/// Demanded bits analysis.
DemandedBits *DB;
/// Assumption cache.
AssumptionCache *AC;
/// Interface to emit optimization remarks.
OptimizationRemarkEmitter *ORE;
const Function *TheFunction;
/// Loop Vectorize Hint.
const LoopVectorizeHints *Hints;
/// The interleave access information contains groups of interleaved accesses
/// with the same stride and close to each other.
InterleavedAccessInfo &InterleaveInfo;
/// Values to ignore in the cost model.
SmallPtrSet<const Value *, 16> ValuesToIgnore;
/// Values to ignore in the cost model when VF > 1.
SmallPtrSet<const Value *, 16> VecValuesToIgnore;
/// All element types found in the loop.
SmallPtrSet<Type *, 16> ElementTypesInLoop;
};
} // 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, either no SCEV checks have been generated or they have been used.
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, either no memory runtime checks have been generated or
/// they have been used.
Value *MemRuntimeCheckCond = nullptr;
DominatorTree *DT;
LoopInfo *LI;
TargetTransformInfo *TTI;
SCEVExpander SCEVExp;
SCEVExpander MemCheckExp;
bool CostTooHigh = false;
const bool AddBranchWeights;
Loop *OuterLoop = nullptr;
public:
GeneratedRTChecks(ScalarEvolution &SE, DominatorTree *DT, LoopInfo *LI,
TargetTransformInfo *TTI, const DataLayout &DL,
bool AddBranchWeights)
: DT(DT), LI(LI), TTI(TTI), SCEVExp(SE, DL, "scev.check"),
MemCheckExp(SE, DL, "scev.check"), AddBranchWeights(AddBranchWeights) {}
/// 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 is 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) {
// 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)
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());
}
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");
}
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());
new UnreachableInst(Preheader->getContext(), SCEVCheckBlock);
Preheader->getTerminator()->eraseFromParent();
}
if (MemCheckBlock) {
MemCheckBlock->getTerminator()->moveBefore(Preheader->getTerminator());
new UnreachableInst(Preheader->getContext(), MemCheckBlock);
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, TTI::TCK_RecipThroughput);
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, TTI::TCK_RecipThroughput);
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;
// If exact trip count is known use that.
if (unsigned SmallTC = SE->getSmallConstantTripCount(OuterLoop))
BestTripCount = SmallTC;
else if (LoopVectorizeWithBlockFrequency) {
// Else use profile data if available.
if (auto EstimatedTC = getLoopEstimatedTripCount(OuterLoop))
BestTripCount = *EstimatedTC;
}
BestTripCount = std::max(BestTripCount, 1U);
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);
if (!SCEVCheckCond)
SCEVCleaner.markResultUsed();
if (!MemRuntimeCheckCond)
MemCheckCleaner.markResultUsed();
if (MemRuntimeCheckCond) {
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 (SCEVCheckCond)
SCEVCheckBlock->eraseFromParent();
if (MemRuntimeCheckCond)
MemCheckBlock->eraseFromParent();
}
/// Adds the generated SCEVCheckBlock before \p LoopVectorPreHeader and
/// adjusts the branches to branch to the vector preheader or \p Bypass,
/// depending on the generated condition.
BasicBlock *emitSCEVChecks(BasicBlock *Bypass,
BasicBlock *LoopVectorPreHeader,
BasicBlock *LoopExitBlock) {
if (!SCEVCheckCond)
return nullptr;
Value *Cond = SCEVCheckCond;
// Mark the check as used, to prevent it from being removed during cleanup.
SCEVCheckCond = nullptr;
if (auto *C = dyn_cast<ConstantInt>(Cond))
if (C->isZero())
return nullptr;
auto *Pred = LoopVectorPreHeader->getSinglePredecessor();
BranchInst::Create(LoopVectorPreHeader, SCEVCheckBlock);
// Create new preheader for vector loop.
if (OuterLoop)
OuterLoop->addBasicBlockToLoop(SCEVCheckBlock, *LI);
SCEVCheckBlock->getTerminator()->eraseFromParent();
SCEVCheckBlock->moveBefore(LoopVectorPreHeader);
Pred->getTerminator()->replaceSuccessorWith(LoopVectorPreHeader,
SCEVCheckBlock);
DT->addNewBlock(SCEVCheckBlock, Pred);
DT->changeImmediateDominator(LoopVectorPreHeader, SCEVCheckBlock);
BranchInst &BI = *BranchInst::Create(Bypass, LoopVectorPreHeader, Cond);
if (AddBranchWeights)
setBranchWeights(BI, SCEVCheckBypassWeights, /*IsExpected=*/false);
ReplaceInstWithInst(SCEVCheckBlock->getTerminator(), &BI);
return SCEVCheckBlock;
}
/// Adds the generated MemCheckBlock before \p LoopVectorPreHeader and adjusts
/// the branches to branch to the vector preheader or \p Bypass, depending on
/// the generated condition.
BasicBlock *emitMemRuntimeChecks(BasicBlock *Bypass,
BasicBlock *LoopVectorPreHeader) {
// Check if we generated code that checks in runtime if arrays overlap.
if (!MemRuntimeCheckCond)
return nullptr;
auto *Pred = LoopVectorPreHeader->getSinglePredecessor();
Pred->getTerminator()->replaceSuccessorWith(LoopVectorPreHeader,
MemCheckBlock);
DT->addNewBlock(MemCheckBlock, Pred);
DT->changeImmediateDominator(LoopVectorPreHeader, MemCheckBlock);
MemCheckBlock->moveBefore(LoopVectorPreHeader);
if (OuterLoop)
OuterLoop->addBasicBlockToLoop(MemCheckBlock, *LI);
BranchInst &BI =
*BranchInst::Create(Bypass, LoopVectorPreHeader, MemRuntimeCheckCond);
if (AddBranchWeights) {
setBranchWeights(BI, MemCheckBypassWeights, /*IsExpected=*/false);
}
ReplaceInstWithInst(MemCheckBlock->getTerminator(), &BI);
MemCheckBlock->getTerminator()->setDebugLoc(
Pred->getTerminator()->getDebugLoc());
// Mark the check as used, to prevent it from being removed during cleanup.
MemRuntimeCheckCond = nullptr;
return MemCheckBlock;
}
};
} // namespace
static bool useActiveLaneMask(TailFoldingStyle Style) {
return Style == TailFoldingStyle::Data ||
Style == TailFoldingStyle::DataAndControlFlow ||
Style == TailFoldingStyle::DataAndControlFlowWithoutRuntimeCheck;
}
static bool useActiveLaneMaskForControlFlow(TailFoldingStyle Style) {
return Style == TailFoldingStyle::DataAndControlFlow ||
Style == TailFoldingStyle::DataAndControlFlowWithoutRuntimeCheck;
}
// 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.
//===----------------------------------------------------------------------===//
/// Compute the transformed value of Index at offset StartValue using step
/// StepValue.
/// For integer induction, returns StartValue + Index * StepValue.
/// For pointer induction, returns StartValue[Index * StepValue].
/// FIXME: The newly created binary instructions should contain nsw/nuw
/// flags, which can be found from the original scalar operations.
static Value *
emitTransformedIndex(IRBuilderBase &B, Value *Index, Value *StartValue,
Value *Step,
InductionDescriptor::InductionKind InductionKind,
const BinaryOperator *InductionBinOp) {
Type *StepTy = Step->getType();
Value *CastedIndex = StepTy->isIntegerTy()
? B.CreateSExtOrTrunc(Index, StepTy)
: B.CreateCast(Instruction::SIToFP, Index, StepTy);
if (CastedIndex != Index) {
CastedIndex->setName(CastedIndex->getName() + ".cast");
Index = CastedIndex;
}
// Note: the IR at this point is broken. We cannot use SE to create any new
// SCEV and then expand it, hoping that SCEV's simplification will give us
// a more optimal code. Unfortunately, attempt of doing so on invalid IR may
// lead to various SCEV crashes. So all we can do is to use builder and rely
// on InstCombine for future simplifications. Here we handle some trivial
// cases only.
auto CreateAdd = [&B](Value *X, Value *Y) {
assert(X->getType() == Y->getType() && "Types don't match!");
if (auto *CX = dyn_cast<ConstantInt>(X))
if (CX->isZero())
return Y;
if (auto *CY = dyn_cast<ConstantInt>(Y))
if (CY->isZero())
return X;
return B.CreateAdd(X, Y);
};
// We allow X to be a vector type, in which case Y will potentially be
// splatted into a vector with the same element count.
auto CreateMul = [&B](Value *X, Value *Y) {
assert(X->getType()->getScalarType() == Y->getType() &&
"Types don't match!");
if (auto *CX = dyn_cast<ConstantInt>(X))
if (CX->isOne())
return Y;
if (auto *CY = dyn_cast<ConstantInt>(Y))
if (CY->isOne())
return X;
VectorType *XVTy = dyn_cast<VectorType>(X->getType());
if (XVTy && !isa<VectorType>(Y->getType()))
Y = B.CreateVectorSplat(XVTy->getElementCount(), Y);
return B.CreateMul(X, Y);
};
switch (InductionKind) {
case InductionDescriptor::IK_IntInduction: {
assert(!isa<VectorType>(Index->getType()) &&
"Vector indices not supported for integer inductions yet");
assert(Index->getType() == StartValue->getType() &&
"Index type does not match StartValue type");
if (isa<ConstantInt>(Step) && cast<ConstantInt>(Step)->isMinusOne())
return B.CreateSub(StartValue, Index);
auto *Offset = CreateMul(Index, Step);
return CreateAdd(StartValue, Offset);
}
case InductionDescriptor::IK_PtrInduction:
return B.CreatePtrAdd(StartValue, CreateMul(Index, Step));
case InductionDescriptor::IK_FpInduction: {
assert(!isa<VectorType>(Index->getType()) &&
"Vector indices not supported for FP inductions yet");
assert(Step->getType()->isFloatingPointTy() && "Expected FP Step value");
assert(InductionBinOp &&
(InductionBinOp->getOpcode() == Instruction::FAdd ||
InductionBinOp->getOpcode() == Instruction::FSub) &&
"Original bin op should be defined for FP induction");
Value *MulExp = B.CreateFMul(Step, Index);
return B.CreateBinOp(InductionBinOp->getOpcode(), StartValue, MulExp,
"induction");
}
case InductionDescriptor::IK_NoInduction:
return nullptr;
}
llvm_unreachable("invalid enum");
}
std::optional<unsigned> getMaxVScale(const Function &F,
const TargetTransformInfo &TTI) {
if (std::optional<unsigned> MaxVScale = TTI.getMaxVScale())
return MaxVScale;
if (F.hasFnAttribute(Attribute::VScaleRange))
return F.getFnAttribute(Attribute::VScaleRange).getVScaleRangeMax();
return std::nullopt;
}
/// 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);
Type *IdxTy = Cost->Legal->getWidestInductionType();
APInt MaxUIntTripCount = cast<IntegerType>(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.getSE()->getSmallConstantMaxTripCount(Cost->TheLoop)) {
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();
}
void InnerLoopVectorizer::scalarizeInstruction(const Instruction *Instr,
VPReplicateRecipe *RepRecipe,
const VPIteration &Instance,
VPTransformState &State) {
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
// llvm.experimental.noalias.scope.decl intrinsics must only be duplicated for
// the first lane and part.
if (isa<NoAliasScopeDeclInst>(Instr))
if (!Instance.isFirstIteration())
return;
// Does this instruction return a value ?
bool IsVoidRetTy = Instr->getType()->isVoidTy();
Instruction *Cloned = Instr->clone();
if (!IsVoidRetTy) {
Cloned->setName(Instr->getName() + ".cloned");
#if !defined(NDEBUG)
// Verify that VPlan type inference results agree with the type of the
// generated values.
assert(State.TypeAnalysis.inferScalarType(RepRecipe) == Cloned->getType() &&
"inferred type and type from generated instructions do not match");
#endif
}
RepRecipe->setFlags(Cloned);
if (auto DL = Instr->getDebugLoc())
State.setDebugLocFrom(DL);
// Replace the operands of the cloned instructions with their scalar
// equivalents in the new loop.
for (const auto &I : enumerate(RepRecipe->operands())) {
auto InputInstance = Instance;
VPValue *Operand = I.value();
if (vputils::isUniformAfterVectorization(Operand))
InputInstance.Lane = VPLane::getFirstLane();
Cloned->setOperand(I.index(), State.get(Operand, InputInstance));
}
State.addNewMetadata(Cloned, Instr);
// Place the cloned scalar in the new loop.
State.Builder.Insert(Cloned);
State.set(RepRecipe, Cloned, Instance);
// If we just cloned a new assumption, add it the assumption cache.
if (auto *II = dyn_cast<AssumeInst>(Cloned))
AC->registerAssumption(II);
// End if-block.
bool IfPredicateInstr = RepRecipe->getParent()->getParent()->isReplicator();
if (IfPredicateInstr)
PredicatedInstructions.push_back(Cloned);
}
Value *
InnerLoopVectorizer::getOrCreateVectorTripCount(BasicBlock *InsertBlock) {
if (VectorTripCount)
return VectorTripCount;
Value *TC = getTripCount();
IRBuilder<> Builder(InsertBlock->getTerminator());
Type *Ty = TC->getType();
// This is where we can make the step a runtime constant.
Value *Step = createStepForVF(Builder, Ty, VF, UF);
// If the tail is to be folded by masking, round the number of iterations N
// up to a multiple of Step instead of rounding down. This is done by first
// adding Step-1 and then rounding down. Note that it's ok if this addition
// overflows: the vector induction variable will eventually wrap to zero given
// that it starts at zero and its Step is a power of two; the loop will then
// exit, with the last early-exit vector comparison also producing all-true.
// For scalable vectors the VF is not guaranteed to be a power of 2, but this
// is accounted for in emitIterationCountCheck that adds an overflow check.
if (Cost->foldTailByMasking()) {
assert(isPowerOf2_32(VF.getKnownMinValue() * UF) &&
"VF*UF must be a power of 2 when folding tail by masking");
TC = Builder.CreateAdd(TC, Builder.CreateSub(Step, ConstantInt::get(Ty, 1)),
"n.rnd.up");
}
// Now we need to generate the expression for the part of the loop that the
// vectorized body will execute. This is equal to N - (N % Step) if scalar
// iterations are not required for correctness, or N - Step, otherwise. Step
// is equal to the vectorization factor (number of SIMD elements) times the
// unroll factor (number of SIMD instructions).
Value *R = Builder.CreateURem(TC, Step, "n.mod.vf");
// There are cases where we *must* run at least one iteration in the remainder
// loop. See the cost model for when this can happen. If the step evenly
// divides the trip count, we set the remainder to be equal to the step. If
// the step does not evenly divide the trip count, no adjustment is necessary
// since there will already be scalar iterations. Note that the minimum
// iterations check ensures that N >= Step.
if (Cost->requiresScalarEpilogue(VF.isVector())) {
auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0));
R = Builder.CreateSelect(IsZero, Step, R);
}
VectorTripCount = Builder.CreateSub(TC, R, "n.vec");
return VectorTripCount;
}
void InnerLoopVectorizer::emitIterationCountCheck(BasicBlock *Bypass) {
Value *Count = getTripCount();
// Reuse existing vector loop preheader for TC checks.
// Note that new preheader block is generated for vector loop.
BasicBlock *const TCCheckBlock = LoopVectorPreHeader;
IRBuilder<> Builder(TCCheckBlock->getTerminator());
// Generate code to check if the loop's trip count is less than VF * UF, or
// equal to it in case a scalar epilogue is required; this implies that the
// vector trip count is zero. This check also covers the case where adding one
// to the backedge-taken count overflowed leading to an incorrect trip count
// of zero. In this case we will also jump to the scalar loop.
auto P = Cost->requiresScalarEpilogue(VF.isVector()) ? ICmpInst::ICMP_ULE
: ICmpInst::ICMP_ULT;
// If tail is to be folded, vector loop takes care of all iterations.
Type *CountTy = Count->getType();
Value *CheckMinIters = Builder.getFalse();
auto CreateStep = [&]() -> Value * {
// Create step with max(MinProTripCount, UF * VF).
if (UF * VF.getKnownMinValue() >= MinProfitableTripCount.getKnownMinValue())
return createStepForVF(Builder, CountTy, VF, UF);
Value *MinProfTC =
createStepForVF(Builder, CountTy, MinProfitableTripCount, 1);
if (!VF.isScalable())
return MinProfTC;
return Builder.CreateBinaryIntrinsic(
Intrinsic::umax, MinProfTC, createStepForVF(Builder, CountTy, VF, UF));
};
TailFoldingStyle Style = Cost->getTailFoldingStyle();
if (Style == TailFoldingStyle::None)
CheckMinIters =
Builder.CreateICmp(P, Count, CreateStep(), "min.iters.check");
else if (VF.isScalable() &&
!isIndvarOverflowCheckKnownFalse(Cost, VF, UF) &&
Style != TailFoldingStyle::DataAndControlFlowWithoutRuntimeCheck) {
// vscale is not necessarily a power-of-2, which means we cannot guarantee
// an overflow to zero when updating induction variables and so an
// additional overflow check is required before entering the vector loop.
// Get the maximum unsigned value for the type.
Value *MaxUIntTripCount =
ConstantInt::get(CountTy, cast<IntegerType>(CountTy)->getMask());
Value *LHS = Builder.CreateSub(MaxUIntTripCount, Count);
// Don't execute the vector loop if (UMax - n) < (VF * UF).
CheckMinIters = Builder.CreateICmp(ICmpInst::ICMP_ULT, LHS, CreateStep());
}
// Create new preheader for vector loop.
LoopVectorPreHeader =
SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), DT, LI, nullptr,
"vector.ph");
assert(DT->properlyDominates(DT->getNode(TCCheckBlock),
DT->getNode(Bypass)->getIDom()) &&
"TC check is expected to dominate Bypass");
// Update dominator for Bypass & LoopExit (if needed).
DT->changeImmediateDominator(Bypass, TCCheckBlock);
BranchInst &BI =
*BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters);
if (hasBranchWeightMD(*OrigLoop->getLoopLatch()->getTerminator()))
setBranchWeights(BI, MinItersBypassWeights, /*IsExpected=*/false);
ReplaceInstWithInst(TCCheckBlock->getTerminator(), &BI);
LoopBypassBlocks.push_back(TCCheckBlock);
}
BasicBlock *InnerLoopVectorizer::emitSCEVChecks(BasicBlock *Bypass) {
BasicBlock *const SCEVCheckBlock =
RTChecks.emitSCEVChecks(Bypass, LoopVectorPreHeader, LoopExitBlock);
if (!SCEVCheckBlock)
return nullptr;
assert(!(SCEVCheckBlock->getParent()->hasOptSize() ||
(OptForSizeBasedOnProfile &&
Cost->Hints->getForce() != LoopVectorizeHints::FK_Enabled)) &&
"Cannot SCEV check stride or overflow when optimizing for size");
// Update dominator only if this is first RT check.
if (LoopBypassBlocks.empty()) {
DT->changeImmediateDominator(Bypass, SCEVCheckBlock);
if (!Cost->requiresScalarEpilogue(VF.isVector()))
// If there is an epilogue which must run, there's no edge from the
// middle block to exit blocks and thus no need to update the immediate
// dominator of the exit blocks.
DT->changeImmediateDominator(LoopExitBlock, SCEVCheckBlock);
}
LoopBypassBlocks.push_back(SCEVCheckBlock);
AddedSafetyChecks = true;
return SCEVCheckBlock;
}
BasicBlock *InnerLoopVectorizer::emitMemRuntimeChecks(BasicBlock *Bypass) {
// VPlan-native path does not do any analysis for runtime checks currently.
if (EnableVPlanNativePath)
return nullptr;
BasicBlock *const MemCheckBlock =
RTChecks.emitMemRuntimeChecks(Bypass, LoopVectorPreHeader);
// Check if we generated code that checks in runtime if arrays overlap. We put
// the checks into a separate block to make the more common case of few
// elements faster.
if (!MemCheckBlock)
return nullptr;
if (MemCheckBlock->getParent()->hasOptSize() || OptForSizeBasedOnProfile) {
assert(Cost->Hints->getForce() == LoopVectorizeHints::FK_Enabled &&
"Cannot emit memory checks when optimizing for size, unless forced "
"to vectorize.");
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationCodeSize",
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').";
});
}
LoopBypassBlocks.push_back(MemCheckBlock);
AddedSafetyChecks = true;
return MemCheckBlock;
}
void InnerLoopVectorizer::createVectorLoopSkeleton(StringRef Prefix) {
LoopScalarBody = OrigLoop->getHeader();
LoopVectorPreHeader = OrigLoop->getLoopPreheader();
assert(LoopVectorPreHeader && "Invalid loop structure");
LoopExitBlock = OrigLoop->getUniqueExitBlock(); // may be nullptr
assert((LoopExitBlock || Cost->requiresScalarEpilogue(VF.isVector())) &&
"multiple exit loop without required epilogue?");
LoopMiddleBlock =
SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT,
LI, nullptr, Twine(Prefix) + "middle.block");
LoopScalarPreHeader =
SplitBlock(LoopMiddleBlock, LoopMiddleBlock->getTerminator(), DT, LI,
nullptr, Twine(Prefix) + "scalar.ph");
}
PHINode *InnerLoopVectorizer::createInductionResumeValue(
PHINode *OrigPhi, const InductionDescriptor &II, Value *Step,
ArrayRef<BasicBlock *> BypassBlocks,
std::pair<BasicBlock *, Value *> AdditionalBypass) {
Value *VectorTripCount = getOrCreateVectorTripCount(LoopVectorPreHeader);
assert(VectorTripCount && "Expected valid arguments");
Instruction *OldInduction = Legal->getPrimaryInduction();
Value *&EndValue = IVEndValues[OrigPhi];
Value *EndValueFromAdditionalBypass = AdditionalBypass.second;
if (OrigPhi == OldInduction) {
// We know what the end value is.
EndValue = VectorTripCount;
} else {
IRBuilder<> B(LoopVectorPreHeader->getTerminator());
// Fast-math-flags propagate from the original induction instruction.
if (II.getInductionBinOp() && isa<FPMathOperator>(II.getInductionBinOp()))
B.setFastMathFlags(II.getInductionBinOp()->getFastMathFlags());
EndValue = emitTransformedIndex(B, VectorTripCount, II.getStartValue(),
Step, II.getKind(), II.getInductionBinOp());
EndValue->setName("ind.end");
// Compute the end value for the additional bypass (if applicable).
if (AdditionalBypass.first) {
B.SetInsertPoint(AdditionalBypass.first,
AdditionalBypass.first->getFirstInsertionPt());
EndValueFromAdditionalBypass =
emitTransformedIndex(B, AdditionalBypass.second, II.getStartValue(),
Step, II.getKind(), II.getInductionBinOp());
EndValueFromAdditionalBypass->setName("ind.end");
}
}
// Create phi nodes to merge from the backedge-taken check block.
PHINode *BCResumeVal =
PHINode::Create(OrigPhi->getType(), 3, "bc.resume.val",
LoopScalarPreHeader->getFirstNonPHIIt());
// Copy original phi DL over to the new one.
BCResumeVal->setDebugLoc(OrigPhi->getDebugLoc());
// The new PHI merges the original incoming value, in case of a bypass,
// or the value at the end of the vectorized loop.
BCResumeVal->addIncoming(EndValue, LoopMiddleBlock);
// Fix the scalar body counter (PHI node).
// The old induction's phi node in the scalar body needs the truncated
// value.
for (BasicBlock *BB : BypassBlocks)
BCResumeVal->addIncoming(II.getStartValue(), BB);
if (AdditionalBypass.first)
BCResumeVal->setIncomingValueForBlock(AdditionalBypass.first,
EndValueFromAdditionalBypass);
return BCResumeVal;
}
/// Return the expanded step for \p ID using \p ExpandedSCEVs to look up SCEV
/// expansion results.
static Value *getExpandedStep(const InductionDescriptor &ID,
const SCEV2ValueTy &ExpandedSCEVs) {
const SCEV *Step = ID.getStep();
if (auto *C = dyn_cast<SCEVConstant>(Step))
return C->getValue();
if (auto *U = dyn_cast<SCEVUnknown>(Step))
return U->getValue();
auto I = ExpandedSCEVs.find(Step);
assert(I != ExpandedSCEVs.end() && "SCEV must be expanded at this point");
return I->second;
}
void InnerLoopVectorizer::createInductionResumeValues(
const SCEV2ValueTy &ExpandedSCEVs,
std::pair<BasicBlock *, Value *> AdditionalBypass) {
assert(((AdditionalBypass.first && AdditionalBypass.second) ||
(!AdditionalBypass.first && !AdditionalBypass.second)) &&
"Inconsistent information about additional bypass.");
// We are going to resume the execution of the scalar loop.
// Go over all of the induction variables that we found and fix the
// PHIs that are left in the scalar version of the loop.
// The starting values of PHI nodes depend on the counter of the last
// iteration in the vectorized loop.
// If we come from a bypass edge then we need to start from the original
// start value.
for (const auto &InductionEntry : Legal->getInductionVars()) {
PHINode *OrigPhi = InductionEntry.first;
const InductionDescriptor &II = InductionEntry.second;
PHINode *BCResumeVal = createInductionResumeValue(
OrigPhi, II, getExpandedStep(II, ExpandedSCEVs), LoopBypassBlocks,
AdditionalBypass);
OrigPhi->setIncomingValueForBlock(LoopScalarPreHeader, BCResumeVal);
}
}
std::pair<BasicBlock *, Value *>
InnerLoopVectorizer::createVectorizedLoopSkeleton(
const SCEV2ValueTy &ExpandedSCEVs) {
/*
In this function we generate a new loop. The new loop will contain
the vectorized instructions while the old loop will continue to run the
scalar remainder.
[ ] <-- old preheader - loop iteration number check and SCEVs in Plan's
/ | preheader are expanded here. Eventually all required SCEV
/ | expansion should happen here.
/ v
| [ ] <-- vector loop bypass (may consist of multiple blocks).
| / |
| / v
|| [ ] <-- vector pre header.
|/ |
| v
| [ ] \
| [ ]_| <-- vector loop (created during VPlan execution).
| |
| v
\ -[ ] <--- middle-block (wrapped in VPIRBasicBlock with the branch to
| | successors created during VPlan execution)
\/ |
/\ v
| ->[ ] <--- new preheader (wrapped in VPIRBasicBlock).
| |
(opt) v <-- edge from middle to exit iff epilogue is not required.
| [ ] \
| [ ]_| <-- old scalar loop to handle remainder (scalar epilogue).
\ |
\ v
>[ ] <-- exit block(s). (wrapped in VPIRBasicBlock)
...
*/
// Create an empty vector loop, and prepare basic blocks for the runtime
// checks.
createVectorLoopSkeleton("");
// Now, compare the new count to zero. If it is zero skip the vector loop and
// jump to the scalar loop. This check also covers the case where the
// backedge-taken count is uint##_max: adding one to it will overflow leading
// to an incorrect trip count of zero. In this (rare) case we will also jump
// to the scalar loop.
emitIterationCountCheck(LoopScalarPreHeader);
// Generate the code to check any assumptions that we've made for SCEV
// expressions.
emitSCEVChecks(LoopScalarPreHeader);
// Generate the code that checks in runtime if arrays overlap. We put the
// checks into a separate block to make the more common case of few elements
// faster.
emitMemRuntimeChecks(LoopScalarPreHeader);
// Emit phis for the new starting index of the scalar loop.
createInductionResumeValues(ExpandedSCEVs);
return {LoopVectorPreHeader, nullptr};
}
// Fix up external users of the induction variable. At this point, we are
// in LCSSA form, with all external PHIs that use the IV having one input value,
// coming from the remainder loop. We need those PHIs to also have a correct
// value for the IV when arriving directly from the middle block.
void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi,
const InductionDescriptor &II,
Value *VectorTripCount, Value *EndValue,
BasicBlock *MiddleBlock,
BasicBlock *VectorHeader, VPlan &Plan,
VPTransformState &State) {
// There are two kinds of external IV usages - those that use the value
// computed in the last iteration (the PHI) and those that use the penultimate
// value (the value that feeds into the phi from the loop latch).
// We allow both, but they, obviously, have different values.
assert(OrigLoop->getUniqueExitBlock() && "Expected a single exit block");
DenseMap<Value *, Value *> MissingVals;
// An external user of the last iteration's value should see the value that
// the remainder loop uses to initialize its own IV.
Value *PostInc = OrigPhi->getIncomingValueForBlock(OrigLoop->getLoopLatch());
for (User *U : PostInc->users()) {
Instruction *UI = cast<Instruction>(U);
if (!OrigLoop->contains(UI)) {
assert(isa<PHINode>(UI) && "Expected LCSSA form");
MissingVals[UI] = EndValue;
}
}
// An external user of the penultimate value need to see EndValue - Step.
// The simplest way to get this is to recompute it from the constituent SCEVs,
// that is Start + (Step * (CRD - 1)).
for (User *U : OrigPhi->users()) {
auto *UI = cast<Instruction>(U);
if (!OrigLoop->contains(UI)) {
assert(isa<PHINode>(UI) && "Expected LCSSA form");
IRBuilder<> B(MiddleBlock->getTerminator());
// Fast-math-flags propagate from the original induction instruction.
if (II.getInductionBinOp() && isa<FPMathOperator>(II.getInductionBinOp()))
B.setFastMathFlags(II.getInductionBinOp()->getFastMathFlags());
Value *CountMinusOne = B.CreateSub(
VectorTripCount, ConstantInt::get(VectorTripCount->getType(), 1));
CountMinusOne->setName("cmo");
VPValue *StepVPV = Plan.getSCEVExpansion(II.getStep());
assert(StepVPV && "step must have been expanded during VPlan execution");
Value *Step = StepVPV->isLiveIn() ? StepVPV->getLiveInIRValue()
: State.get(StepVPV, {0, 0});
Value *Escape =
emitTransformedIndex(B, CountMinusOne, II.getStartValue(), Step,
II.getKind(), II.getInductionBinOp());
Escape->setName("ind.escape");
MissingVals[UI] = Escape;
}
}
for (auto &I : MissingVals) {
PHINode *PHI = cast<PHINode>(I.first);
// One corner case we have to handle is two IVs "chasing" each-other,
// that is %IV2 = phi [...], [ %IV1, %latch ]
// In this case, if IV1 has an external use, we need to avoid adding both
// "last value of IV1" and "penultimate value of IV2". So, verify that we
// don't already have an incoming value for the middle block.
if (PHI->getBasicBlockIndex(MiddleBlock) == -1)
PHI->addIncoming(I.second, MiddleBlock);
}
}
namespace {
struct CSEDenseMapInfo {
static bool canHandle(const Instruction *I) {
return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) ||
isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I);
}
static inline Instruction *getEmptyKey() {
return DenseMapInfo<Instruction *>::getEmptyKey();
}
static inline Instruction *getTombstoneKey() {
return DenseMapInfo<Instruction *>::getTombstoneKey();
}
static unsigned getHashValue(const Instruction *I) {
assert(canHandle(I) && "Unknown instruction!");
return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(),
I->value_op_end()));
}
static bool isEqual(const Instruction *LHS, const Instruction *RHS) {
if (LHS == getEmptyKey() || RHS == getEmptyKey() ||
LHS == getTombstoneKey() || RHS == getTombstoneKey())
return LHS == RHS;
return LHS->isIdenticalTo(RHS);
}
};
} // end anonymous namespace
///Perform cse of induction variable instructions.
static void cse(BasicBlock *BB) {
// Perform simple cse.
SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap;
for (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;
}
}
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 CallWideningDecisions.at(std::make_pair(CI, VF)).Cost;
TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
Type *RetTy = CI->getType();
if (RecurrenceDescriptor::isFMulAddIntrinsic(CI))
if (auto RedCost = getReductionPatternCost(CI, VF, RetTy, CostKind))
return *RedCost;
SmallVector<Type *, 4> Tys;
for (auto &ArgOp : CI->args())
Tys.push_back(ArgOp->getType());
InstructionCost ScalarCallCost =
TTI.getCallInstrCost(CI->getCalledFunction(), RetTy, Tys, 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 *Elt, ElementCount VF) {
if (VF.isScalar() || (!Elt->isIntOrPtrTy() && !Elt->isFloatingPointTy()))
return Elt;
return VectorType::get(Elt, 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));
return TTI.getIntrinsicInstrCost(CostAttrs,
TargetTransformInfo::TCK_RecipThroughput);
}
void InnerLoopVectorizer::fixVectorizedLoop(VPTransformState &State,
VPlan &Plan) {
// Fix widened non-induction PHIs by setting up the PHI operands.
if (EnableVPlanNativePath)
fixNonInductionPHIs(Plan, State);
// Forget the original basic block.
PSE.getSE()->forgetLoop(OrigLoop);
PSE.getSE()->forgetBlockAndLoopDispositions();
// 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.
SmallVector<BasicBlock *> ExitBlocks;
OrigLoop->getExitBlocks(ExitBlocks);
for (BasicBlock *Exit : ExitBlocks)
for (PHINode &PN : Exit->phis())
PSE.getSE()->forgetLcssaPhiWithNewPredecessor(OrigLoop, &PN);
VPRegionBlock *VectorRegion = State.Plan->getVectorLoopRegion();
VPBasicBlock *LatchVPBB = VectorRegion->getExitingBasicBlock();
Loop *VectorLoop = LI->getLoopFor(State.CFG.VPBB2IRBB[LatchVPBB]);
if (Cost->requiresScalarEpilogue(VF.isVector())) {
// No edge from the middle block to the unique exit block has been inserted
// and there is nothing to fix from vector loop; phis should have incoming
// from scalar loop only.
} else {
// TODO: Check VPLiveOuts to see if IV users need fixing instead of checking
// the cost model.
// If we inserted an edge from the middle block to the unique exit block,
// update uses outside the loop (phis) to account for the newly inserted
// edge.
// Fix-up external users of the induction variables.
for (const auto &Entry : Legal->getInductionVars())
fixupIVUsers(Entry.first, Entry.second,
getOrCreateVectorTripCount(VectorLoop->getLoopPreheader()),
IVEndValues[Entry.first], LoopMiddleBlock,
VectorLoop->getHeader(), Plan, State);
}
// Fix live-out phis not already fixed earlier.
for (const auto &KV : Plan.getLiveOuts())
KV.second->fixPhi(Plan, State);
for (Instruction *PI : PredicatedInstructions)
sinkScalarOperands(&*PI);
// Remove redundant induction instructions.
cse(VectorLoop->getHeader());
// Set/update profile weights for the vector and remainder loops as original
// loop iterations are now distributed among them. Note that original loop
// represented by LoopScalarBody becomes remainder loop after vectorization.
//
// For cases like foldTailByMasking() and requiresScalarEpiloque() we may
// end up getting slightly roughened result but that should be OK since
// profile is not inherently precise anyway. Note also possible bypass of
// vector code caused by legality checks is ignored, assigning all the weight
// to the vector loop, optimistically.
//
// For scalable vectorization we can't know at compile time how many iterations
// of the loop are handled in one vector iteration, so instead assume a pessimistic
// vscale of '1'.
setProfileInfoAfterUnrolling(LI->getLoopFor(LoopScalarBody), VectorLoop,
LI->getLoopFor(LoopScalarBody),
VF.getKnownMinValue() * UF);
}
void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) {
// The basic block and loop containing the predicated instruction.
auto *PredBB = PredInst->getParent();
auto *VectorLoop = LI->getLoopFor(PredBB);
// Initialize a worklist with the operands of the predicated instruction.
SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end());
// Holds instructions that we need to analyze again. An instruction may be
// reanalyzed if we don't yet know if we can sink it or not.
SmallVector<Instruction *, 8> InstsToReanalyze;
// Returns true if a given use occurs in the predicated block. Phi nodes use
// their operands in their corresponding predecessor blocks.
auto isBlockOfUsePredicated = [&](Use &U) -> bool {
auto *I = cast<Instruction>(U.getUser());
BasicBlock *BB = I->getParent();
if (auto *Phi = dyn_cast<PHINode>(I))
BB = Phi->getIncomingBlock(
PHINode::getIncomingValueNumForOperand(U.getOperandNo()));
return BB == PredBB;
};
// Iteratively sink the scalarized operands of the predicated instruction
// into the block we created for it. When an instruction is sunk, it's
// operands are then added to the worklist. The algorithm ends after one pass
// through the worklist doesn't sink a single instruction.
bool Changed;
do {
// Add the instructions that need to be reanalyzed to the worklist, and
// reset the changed indicator.
Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end());
InstsToReanalyze.clear();
Changed = false;
while (!Worklist.empty()) {
auto *I = dyn_cast<Instruction>(Worklist.pop_back_val());
// We can't sink an instruction if it is a phi node, is not in the loop,
// may have side effects or may read from memory.
// TODO Could dor more granular checking to allow sinking a load past non-store instructions.
if (!I || isa<PHINode>(I) || !VectorLoop->contains(I) ||
I->mayHaveSideEffects() || I->mayReadFromMemory())
continue;
// If the instruction is already in PredBB, check if we can sink its
// operands. In that case, VPlan's sinkScalarOperands() succeeded in
// sinking the scalar instruction I, hence it appears in PredBB; but it
// may have failed to sink I's operands (recursively), which we try
// (again) here.
if (I->getParent() == PredBB) {
Worklist.insert(I->op_begin(), I->op_end());
continue;
}
// It's legal to sink the instruction if all its uses occur in the
// predicated block. Otherwise, there's nothing to do yet, and we may
// need to reanalyze the instruction.
if (!llvm::all_of(I->uses(), isBlockOfUsePredicated)) {
InstsToReanalyze.push_back(I);
continue;
}
// Move the instruction to the beginning of the predicated block, and add
// it's operands to the worklist.
I->moveBefore(&*PredBB->getFirstInsertionPt());
Worklist.insert(I->op_begin(), I->op_end());
// The sinking may have enabled other instructions to be sunk, so we will
// need to iterate.
Changed = true;
}
} while (Changed);
}
void InnerLoopVectorizer::fixNonInductionPHIs(VPlan &Plan,
VPTransformState &State) {
auto Iter = vp_depth_first_deep(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, 0));
// Make sure the builder has a valid insert point.
Builder.SetInsertPoint(NewPhi);
for (unsigned i = 0; i < VPPhi->getNumOperands(); ++i) {
VPValue *Inc = VPPhi->getIncomingValue(i);
VPBasicBlock *VPBB = VPPhi->getIncomingBlock(i);
NewPhi->addIncoming(State.get(Inc, 0), 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(Uniforms[VF].begin(), Uniforms[VF].end());
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) && llvm::all_of(I->users(), [&](User *U) {
return isa<LoadInst>(U) || isa<StoreInst>(U);
}))
ScalarPtrs.insert(I);
else
PossibleNonScalarPtrs.insert(I);
};
// We seed the scalars analysis with three classes of instructions: (1)
// instructions marked uniform-after-vectorization and (2) bitcast,
// getelementptr and (pointer) phi instructions used by memory accesses
// requiring a scalar use.
//
// (1) Add to the worklist all instructions that have been identified as
// uniform-after-vectorization.
Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end());
// (2) Add to the worklist all bitcast and getelementptr instructions used by
// memory accesses requiring a scalar use. The pointer operands of loads and
// stores will be scalar as long as the memory accesses is not a gather or
// scatter operation. The value operand of a store will remain scalar if the
// store is scalarized.
for (auto *BB : TheLoop->blocks())
for (auto &I : *BB) {
if (auto *Load = dyn_cast<LoadInst>(&I)) {
evaluatePtrUse(Load, Load->getPointerOperand());
} else if (auto *Store = dyn_cast<StoreInst>(&I)) {
evaluatePtrUse(Store, Store->getPointerOperand());
evaluatePtrUse(Store, Store->getValueOperand());
}
}
for (auto *I : ScalarPtrs)
if (!PossibleNonScalarPtrs.count(I)) {
LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n");
Worklist.insert(I);
}
// Insert the forced scalars.
// FIXME: Currently 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.
auto ScalarInd = llvm::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.
auto ScalarIndUpdate =
llvm::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(Worklist.begin(), Worklist.end());
}
bool LoopVectorizationCostModel::isScalarWithPredication(
Instruction *I, ElementCount VF) const {
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 CallWideningDecisions.at(std::make_pair(cast<CallInst>(I), VF))
.Kind == CM_Scalarize;
case Instruction::Load:
case Instruction::Store: {
auto *Ptr = getLoadStorePointerOperand(I);
auto *Ty = getLoadStoreType(I);
Type *VTy = Ty;
if (VF.isVector())
VTy = VectorType::get(Ty, VF);
const Align Alignment = getLoadStoreAlignment(I);
return isa<LoadInst>(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) ||
TTI.isLegalMaskedGather(VTy, Alignment))
: !(isLegalMaskedStore(Ty, Ptr, Alignment) ||
TTI.isLegalMaskedScatter(VTy, Alignment));
}
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);
}
}
}
// TODO: Fold into LoopVectorizationLegality::isMaskRequired.
bool LoopVectorizationCostModel::isPredicatedInst(Instruction *I) const {
// If predication is not needed, avoid it.
// TODO: We can use the loop-preheader as context point here and get
// context sensitive reasoning for isSafeToSpeculativelyExecute.
if (!blockNeedsPredicationForAnyReason(I->getParent()) ||
isSafeToSpeculativelyExecute(I) ||
(isa<LoadInst, StoreInst, CallInst>(I) && !Legal->isMaskRequired(I)) ||
isa<BranchInst, 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;
// 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(Legal->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::SDiv:
case Instruction::SRem:
case Instruction::URem:
// If the divisor is loop-invariant no predication is needed.
return !TheLoop->isLoopInvariant(I->getOperand(1));
}
}
std::pair<InstructionCost, InstructionCost>
LoopVectorizationCostModel::getDivRemSpeculationCost(Instruction *I,
ElementCount VF) const {
assert(I->getOpcode() == Instruction::UDiv ||
I->getOpcode() == Instruction::SDiv ||
I->getOpcode() == Instruction::SRem ||
I->getOpcode() == Instruction::URem);
assert(!isSafeToSpeculativelyExecute(I));
const TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
// 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.getKnownMinValue() *
TTI.getCFInstrCost(Instruction::PHI, CostKind);
// The cost of the non-predicated instruction.
ScalarizationCost += VF.getKnownMinValue() *
TTI.getArithmeticInstrCost(I->getOpcode(), I->getType(), CostKind);
// The cost of insertelement and extractelement instructions needed for
// scalarization.
ScalarizationCost += getScalarizationOverhead(I, VF, CostKind);
// 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 / getReciprocalPredBlockProb();
}
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, CostKind);
// 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);
auto Op2Info = TTI.getOperandInfo(Op2);
if (Op2Info.Kind == TargetTransformInfo::OK_AnyValue &&
Legal->isInvariant(Op2))
Op2Info.Kind = TargetTransformInfo::OK_UniformValue;
SmallVector<const Value *, 4> Operands(I->operand_values());
SafeDivisorCost += TTI.getArithmeticInstrCost(
I->getOpcode(), VecTy, CostKind,
{TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None},
Op2Info, 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.");
// If the instruction's allocated size doesn't equal it's type size, it
// requires padding and will be scalarized.
auto &DL = I->getDataLayout();
auto *ScalarTy = getLoadStoreType(I);
if (hasIrregularType(ScalarTy, DL))
return false;
// If the group involves a non-integral pointer, we may not be able to
// losslessly cast all values to a common type.
unsigned InterleaveFactor = Group->getFactor();
bool ScalarNI = DL.isNonIntegralPointerType(ScalarTy);
for (unsigned i = 0; i < InterleaveFactor; i++) {
Instruction *Member = Group->getMember(i);
if (!Member)
continue;
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;
} else 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()) &&
Legal->isMaskRequired(I);
bool LoadAccessWithGapsRequiresEpilogMasking =
isa<LoadInst>(I) && Group->requiresScalarEpilogue() &&
!isScalarEpilogueAllowed();
bool StoreAccessWithGapsRequiresMasking =
isa<StoreInst>(I) && (Group->getNumMembers() < Group->getFactor());
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;
auto *Ty = getLoadStoreType(I);
const Align Alignment = getLoadStoreAlignment(I);
return isa<LoadInst>(I) ? TTI.isLegalMaskedLoad(Ty, Alignment)
: TTI.isLegalMaskedStore(Ty, Alignment);
}
bool LoopVectorizationCostModel::memoryInstructionCanBeWidened(
Instruction *I, ElementCount VF) {
// Get and ensure we have a valid memory instruction.
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'll not find any uniform value, we'll
// not analyze again. Uniforms.count(VF) will return 1.
Uniforms[VF].clear();
// We now know that the loop is vectorizable!
// Collect instructions inside the loop that will remain uniform after
// vectorization.
// Global values, params and instructions outside of current loop are out of
// scope.
auto isOutOfScope = [&](Value *V) -> bool {
Instruction *I = dyn_cast<Instruction>(V);
return (!I || !TheLoop->contains(I));
};
// 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.
SmallVector<BasicBlock *> Exiting;
TheLoop->getExitingBlocks(Exiting);
for (BasicBlock *E : Exiting) {
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 chose 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;
}
}
// ExtractValue instructions must be uniform, because the operands are
// known to be loop-invariant.
if (auto *EVI = dyn_cast<ExtractValueInst>(&I)) {
assert(isOutOfScope(EVI->getAggregateOperand()) &&
"Expected aggregate value to be loop invariant");
addToWorklistIfAllowed(EVI);
continue;
}
// If there's no pointer operand, there's nothing to do.
auto *Ptr = getLoadStorePointerOperand(&I);
if (!Ptr)
continue;
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);
auto UsersAreMemAccesses =
llvm::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.
auto UniformInd = llvm::all_of(Ind->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) ||
isVectorizedMemAccessUse(I, Ind);
});
if (!UniformInd)
continue;
// Determine if all users of the induction variable update instruction are
// uniform after vectorization.
auto UniformIndUpdate =
llvm::all_of(IndUpdate->users(), [&](User *U) -> bool {
auto *I = cast<Instruction>(U);
return I == Ind || !TheLoop->contains(I) || Worklist.count(I) ||
isVectorizedMemAccessUse(I, IndUpdate);
});
if (!UniformIndUpdate)
continue;
// The induction variable and its update instruction will remain uniform.
addToWorklistIfAllowed(Ind);
addToWorklistIfAllowed(IndUpdate);
}
Uniforms[VF].insert(Worklist.begin(), Worklist.end());
}
bool LoopVectorizationCostModel::runtimeChecksRequired() {
LLVM_DEBUG(dbgs() << "LV: Performing code size checks.\n");
if (Legal->getRuntimePointerChecking()->Need) {
reportVectorizationFailure("Runtime ptr check is required with -Os/-Oz",
"runtime pointer checks needed. Enable vectorization of this "
"loop with '#pragma clang loop vectorize(enable)' when "
"compiling with -Os/-Oz",
"CantVersionLoopWithOptForSize", ORE, TheLoop);
return true;
}
if (!PSE.getPredicate().isAlwaysTrue()) {
reportVectorizationFailure("Runtime SCEV check is required with -Os/-Oz",
"runtime SCEV checks needed. Enable vectorization of this "
"loop with '#pragma clang loop vectorize(enable)' when "
"compiling with -Os/-Oz",
"CantVersionLoopWithOptForSize", ORE, TheLoop);
return true;
}
// FIXME: Avoid specializing for stride==1 instead of bailing out.
if (!Legal->getLAI()->getSymbolicStrides().empty()) {
reportVectorizationFailure("Runtime stride check for small trip count",
"runtime stride == 1 checks needed. Enable vectorization of "
"this loop without such check by compiling with -Os/-Oz",
"CantVersionLoopWithOptForSize", ORE, TheLoop);
return true;
}
return false;
}
bool LoopVectorizationCostModel::isScalableVectorizationAllowed() {
if (IsScalableVectorizationAllowed)
return *IsScalableVectorizationAllowed;
IsScalableVectorizationAllowed = false;
if (!TTI.supportsScalableVectors() && !ForceTargetSupportsScalableVectors)
return false;
if (Hints->isScalableVectorizationDisabled()) {
reportVectorizationInfo("Scalable vectorization is explicitly disabled",
"ScalableVectorizationDisabled", ORE, TheLoop);
return false;
}
LLVM_DEBUG(dbgs() << "LV: Scalable vectorization is available\n");
auto MaxScalableVF = ElementCount::getScalable(
std::numeric_limits<ElementCount::ScalarTy>::max());
// Test that the loop-vectorizer can legalize all operations for this MaxVF.
// FIXME: While for scalable vectors this is currently sufficient, this should
// be replaced by a more detailed mechanism that filters out specific VFs,
// instead of invalidating vectorization for a whole set of VFs based on the
// MaxVF.
// Disable scalable vectorization if the loop contains unsupported reductions.
if (!canVectorizeReductions(MaxScalableVF)) {
reportVectorizationInfo(
"Scalable vectorization not supported for the reduction "
"operations found in this loop.",
"ScalableVFUnfeasible", ORE, TheLoop);
return false;
}
// Disable scalable vectorization if the loop contains any instructions
// with element types not supported for scalable vectors.
if (any_of(ElementTypesInLoop, [&](Type *Ty) {
return !Ty->isVoidTy() &&
!this->TTI.isElementTypeLegalForScalableVector(Ty);
})) {
reportVectorizationInfo("Scalable vectorization is not supported "
"for all element types found in this loop.",
"ScalableVFUnfeasible", ORE, TheLoop);
return false;
}
if (!Legal->isSafeForAnyVectorWidth() && !getMaxVScale(*TheFunction, TTI)) {
reportVectorizationInfo("The target does not provide maximum vscale value "
"for safe distance analysis.",
"ScalableVFUnfeasible", ORE, TheLoop);
return false;
}
IsScalableVectorizationAllowed = true;
return true;
}
ElementCount
LoopVectorizationCostModel::getMaxLegalScalableVF(unsigned MaxSafeElements) {
if (!isScalableVectorizationAllowed())
return ElementCount::getScalable(0);
auto MaxScalableVF = ElementCount::getScalable(
std::numeric_limits<ElementCount::ScalarTy>::max());
if (Legal->isSafeForAnyVectorWidth())
return MaxScalableVF;
std::optional<unsigned> MaxVScale = getMaxVScale(*TheFunction, TTI);
// Limit MaxScalableVF by the maximum safe dependence distance.
MaxScalableVF = ElementCount::getScalable(MaxSafeElements / *MaxVScale);
if (!MaxScalableVF)
reportVectorizationInfo(
"Max legal vector width too small, scalable vectorization "
"unfeasible.",
"ScalableVFUnfeasible", ORE, TheLoop);
return MaxScalableVF;
}
FixedScalableVFPair LoopVectorizationCostModel::computeFeasibleMaxVF(
unsigned MaxTripCount, ElementCount UserVF, bool FoldTailByMasking) {
MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI);
unsigned SmallestType, WidestType;
std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes();
// Get the maximum safe dependence distance in bits computed by LAA.
// It is computed by MaxVF * sizeOf(type) * 8, where type is taken from
// the memory accesses that is most restrictive (involved in the smallest
// dependence distance).
unsigned MaxSafeElements =
llvm::bit_floor(Legal->getMaxSafeVectorWidthInBits() / WidestType);
auto MaxSafeFixedVF = ElementCount::getFixed(MaxSafeElements);
auto MaxSafeScalableVF = getMaxLegalScalableVF(MaxSafeElements);
LLVM_DEBUG(dbgs() << "LV: The max safe fixed VF is: " << MaxSafeFixedVF
<< ".\n");
LLVM_DEBUG(dbgs() << "LV: The max safe scalable VF is: " << MaxSafeScalableVF
<< ".\n");
// First analyze the UserVF, fall back if the UserVF should be ignored.
if (UserVF) {
auto MaxSafeUserVF =
UserVF.isScalable() ? MaxSafeScalableVF : MaxSafeFixedVF;
if (ElementCount::isKnownLE(UserVF, MaxSafeUserVF)) {
// If `VF=vscale x N` is safe, then so is `VF=N`
if (UserVF.isScalable())
return FixedScalableVFPair(
ElementCount::getFixed(UserVF.getKnownMinValue()), UserVF);
else
return UserVF;
}
assert(ElementCount::isKnownGT(UserVF, MaxSafeUserVF));
// Only clamp if the UserVF is not scalable. If the UserVF is scalable, it
// is better to ignore the hint and let the compiler choose a suitable VF.
if (!UserVF.isScalable()) {
LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVF
<< " is unsafe, clamping to max safe VF="
<< MaxSafeFixedVF << ".\n");
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationFactor",
TheLoop->getStartLoc(),
TheLoop->getHeader())
<< "User-specified vectorization factor "
<< ore::NV("UserVectorizationFactor", UserVF)
<< " is unsafe, clamping to maximum safe vectorization factor "
<< ore::NV("VectorizationFactor", MaxSafeFixedVF);
});
return MaxSafeFixedVF;
}
if (!TTI.supportsScalableVectors() && !ForceTargetSupportsScalableVectors) {
LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVF
<< " is ignored because scalable vectors are not "
"available.\n");
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationFactor",
TheLoop->getStartLoc(),
TheLoop->getHeader())
<< "User-specified vectorization factor "
<< ore::NV("UserVectorizationFactor", UserVF)
<< " is ignored because the target does not support scalable "
"vectors. The compiler will pick a more suitable value.";
});
} else {
LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVF
<< " is unsafe. Ignoring scalable UserVF.\n");
ORE->emit([&]() {
return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationFactor",
TheLoop->getStartLoc(),
TheLoop->getHeader())
<< "User-specified vectorization factor "
<< ore::NV("UserVectorizationFactor", UserVF)
<< " is unsafe. Ignoring the hint to let the compiler pick a "
"more suitable value.";
});
}
}
LLVM_DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestType
<< " / " << WidestType << " bits.\n");
FixedScalableVFPair Result(ElementCount::getFixed(1),
ElementCount::getScalable(0));
if (auto MaxVF =
getMaximizedVFForTarget(MaxTripCount, SmallestType, WidestType,
MaxSafeFixedVF, FoldTailByMasking))
Result.FixedVF = MaxVF;
if (auto MaxVF =
getMaximizedVFForTarget(MaxTripCount, SmallestType, WidestType,
MaxSafeScalableVF, FoldTailByMasking))
if (MaxVF.isScalable()) {
Result.ScalableVF = MaxVF;
LLVM_DEBUG(dbgs() << "LV: Found feasible scalable VF = " << MaxVF
<< "\n");
}
return Result;
}
FixedScalableVFPair
LoopVectorizationCostModel::computeMaxVF(ElementCount UserVF, unsigned UserIC) {
if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) {
// TODO: It may by useful to do since it's still likely to be dynamically
// uniform if the target can skip.
reportVectorizationFailure(
"Not inserting runtime ptr check for divergent target",
"runtime pointer checks needed. Not enabled for divergent target",
"CantVersionLoopWithDivergentTarget", ORE, TheLoop);
return FixedScalableVFPair::getNone();
}
unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
unsigned MaxTC = PSE.getSE()->getSmallConstantMaxTripCount(TheLoop);
LLVM_DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n');
if (TC == 1) {
reportVectorizationFailure("Single iteration (non) loop",
"loop trip count is one, irrelevant for vectorization",
"SingleIterationLoop", ORE, TheLoop);
return FixedScalableVFPair::getNone();
}
switch (ScalarEpilogueStatus) {
case CM_ScalarEpilogueAllowed:
return computeFeasibleMaxVF(MaxTC, UserVF, false);
case CM_ScalarEpilogueNotAllowedUsePredicate:
[[fallthrough]];
case CM_ScalarEpilogueNotNeededUsePredicate:
LLVM_DEBUG(
dbgs() << "LV: vector predicate hint/switch found.\n"
<< "LV: Not allowing scalar epilogue, creating predicated "
<< "vector loop.\n");
break;
case CM_ScalarEpilogueNotAllowedLowTripLoop:
// fallthrough as a special case of OptForSize
case CM_ScalarEpilogueNotAllowedOptSize:
if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedOptSize)
LLVM_DEBUG(
dbgs() << "LV: Not allowing scalar epilogue due to -Os/-Oz.\n");
else
LLVM_DEBUG(dbgs() << "LV: Not allowing scalar epilogue due to low trip "
<< "count.\n");
// Bail if runtime checks are required, which are not good when optimising
// for size.
if (runtimeChecksRequired())
return FixedScalableVFPair::getNone();
break;
}
// The only loops we can vectorize without a scalar epilogue, are loops with
// a bottom-test and a single exiting block. We'd have to handle the fact
// that not every instruction executes on the last iteration. This will
// require a lane mask which varies through the vector loop body. (TODO)
if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) {
// If there was a tail-folding hint/switch, but we can't fold the tail by
// masking, fallback to a vectorization with a scalar epilogue.
if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) {
LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a "
"scalar epilogue instead.\n");
ScalarEpilogueStatus = CM_ScalarEpilogueAllowed;
return computeFeasibleMaxVF(MaxTC, UserVF, false);
}
return FixedScalableVFPair::getNone();
}
// Now try the tail folding
// Invalidate interleave groups that require an epilogue if we can't mask
// the interleave-group.
if (!useMaskedInterleavedAccesses(TTI)) {
assert(WideningDecisions.empty() && Uniforms.empty() && Scalars.empty() &&
"No decisions should have been taken at this point");
// Note: There is no need to invalidate any cost modeling decisions here, as
// non where taken so far.
InterleaveInfo.invalidateGroupsRequiringScalarEpilogue();
}
FixedScalableVFPair MaxFactors = computeFeasibleMaxVF(MaxTC, UserVF, 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 && TTI.isVScaleKnownToBeAPowerOfTwo()) {
MaxPowerOf2RuntimeVF = std::max<unsigned>(
*MaxPowerOf2RuntimeVF,
*MaxVScale * MaxFactors.ScalableVF.getKnownMinValue());
} else
MaxPowerOf2RuntimeVF = std::nullopt; // Stick with tail-folding for now.
}
if (MaxPowerOf2RuntimeVF && *MaxPowerOf2RuntimeVF > 0) {
assert((UserVF.isNonZero() || isPowerOf2_32(*MaxPowerOf2RuntimeVF)) &&
"MaxFixedVF must be a power of 2");
unsigned MaxVFtimesIC =
UserIC ? *MaxPowerOf2RuntimeVF * UserIC : *MaxPowerOf2RuntimeVF;
ScalarEvolution *SE = PSE.getSE();
const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount();
const SCEV *ExitCount = SE->getAddExpr(
BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType()));
const SCEV *Rem = SE->getURemExpr(
SE->applyLoopGuards(ExitCount, TheLoop),
SE->getConstant(BackedgeTakenCount->getType(), MaxVFtimesIC));
if (Rem->isZero()) {
// Accept MaxFixedVF if we do not have a tail.
LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n");
return MaxFactors;
}
}
// 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.
setTailFoldingStyles(MaxFactors.ScalableVF.isScalable(), UserIC);
if (foldTailByMasking()) {
if (getTailFoldingStyle() == TailFoldingStyle::DataWithEVL) {
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(MaxFactors.ScalableVF.isScalable() &&
"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 a scalar epilogue.
if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) {
LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a "
"scalar epilogue instead.\n");
ScalarEpilogueStatus = CM_ScalarEpilogueAllowed;
return MaxFactors;
}
if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedUsePredicate) {
LLVM_DEBUG(dbgs() << "LV: Can't fold tail by masking: don't vectorize\n");
return FixedScalableVFPair::getNone();
}
if (TC == 0) {
reportVectorizationFailure(
"Unable to calculate the loop count due to complex control flow",
"unable to calculate the loop count due to complex control flow",
"UnknownLoopCountComplexCFG", ORE, TheLoop);
return 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();
}
ElementCount LoopVectorizationCostModel::getMaximizedVFForTarget(
unsigned MaxTripCount, unsigned SmallestType, unsigned WidestType,
ElementCount MaxSafeVF, bool FoldTailByMasking) {
bool ComputeScalableMaxVF = MaxSafeVF.isScalable();
const TypeSize WidestRegister = TTI.getRegisterBitWidth(
ComputeScalableMaxVF ? TargetTransformInfo::RGK_ScalableVector
: TargetTransformInfo::RGK_FixedWidthVector);
// Convenience function to return the minimum of two ElementCounts.
auto MinVF = [](const ElementCount &LHS, const ElementCount &RHS) {
assert((LHS.isScalable() == RHS.isScalable()) &&
"Scalable flags must match");
return ElementCount::isKnownLT(LHS, RHS) ? LHS : RHS;
};
// Ensure MaxVF is a power of 2; the dependence distance bound may not be.
// Note that both WidestRegister and WidestType may not be a powers of 2.
auto MaxVectorElementCount = ElementCount::get(
llvm::bit_floor(WidestRegister.getKnownMinValue() / WidestType),
ComputeScalableMaxVF);
MaxVectorElementCount = MinVF(MaxVectorElementCount, MaxSafeVF);
LLVM_DEBUG(dbgs() << "LV: The Widest register safe to use is: "
<< (MaxVectorElementCount * WidestType) << " bits.\n");
if (!MaxVectorElementCount) {
LLVM_DEBUG(dbgs() << "LV: The target has no "
<< (ComputeScalableMaxVF ? "scalable" : "fixed")
<< " vector registers.\n");
return ElementCount::getFixed(1);
}
unsigned WidestRegisterMinEC = MaxVectorElementCount.getKnownMinValue();
if (MaxVectorElementCount.isScalable() &&
TheFunction->hasFnAttribute(Attribute::VScaleRange)) {
auto Attr = TheFunction->getFnAttribute(Attribute::VScaleRange);
auto Min = Attr.getVScaleRangeMin();
WidestRegisterMinEC *= Min;
}
// When a scalar epilogue is required, at least one iteration of the scalar
// loop has to execute. Adjust MaxTripCount accordingly to avoid picking a
// max VF that results in a dead vector loop.
if (MaxTripCount > 0 && requiresScalarEpilogue(true))
MaxTripCount -= 1;
if (MaxTripCount && MaxTripCount <= WidestRegisterMinEC &&
(!FoldTailByMasking || isPowerOf2_32(MaxTripCount))) {
// If upper bound loop trip count (TC) is known at compile time there is no
// point in choosing VF greater than TC (as done in the loop below). Select
// maximum power of two which doesn't exceed TC. If MaxVectorElementCount is
// scalable, we only fall back on a fixed VF when the TC is less than or
// equal to the known number of lanes.
auto ClampedUpperTripCount = llvm::bit_floor(MaxTripCount);
LLVM_DEBUG(dbgs() << "LV: Clamping the MaxVF to maximum power of two not "
"exceeding the constant trip count: "
<< ClampedUpperTripCount << "\n");
return ElementCount::get(
ClampedUpperTripCount,
FoldTailByMasking ? MaxVectorElementCount.isScalable() : false);
}
TargetTransformInfo::RegisterKind RegKind =
ComputeScalableMaxVF ? TargetTransformInfo::RGK_ScalableVector
: TargetTransformInfo::RGK_FixedWidthVector;
ElementCount MaxVF = MaxVectorElementCount;
if (MaximizeBandwidth ||
(MaximizeBandwidth.getNumOccurrences() == 0 &&
(TTI.shouldMaximizeVectorBandwidth(RegKind) ||
(UseWiderVFIfCallVariantsPresent && Legal->hasVectorCallVariants())))) {
auto MaxVectorElementCountMaxBW = ElementCount::get(
llvm::bit_floor(WidestRegister.getKnownMinValue() / SmallestType),
ComputeScalableMaxVF);
MaxVectorElementCountMaxBW = MinVF(MaxVectorElementCountMaxBW, MaxSafeVF);
// Collect all viable vectorization factors larger than the default MaxVF
// (i.e. MaxVectorElementCount).
SmallVector<ElementCount, 8> VFs;
for (ElementCount VS = MaxVectorElementCount * 2;
ElementCount::isKnownLE(VS, MaxVectorElementCountMaxBW); VS *= 2)
VFs.push_back(VS);
// For each VF calculate its register usage.
auto RUs = calculateRegisterUsage(VFs);
// Select the largest VF which doesn't require more registers than existing
// ones.
for (int I = RUs.size() - 1; I >= 0; --I) {
const auto &MLU = RUs[I].MaxLocalUsers;
if (all_of(MLU, [&](decltype(MLU.front()) &LU) {
return LU.second <= TTI.getNumberOfRegisters(LU.first);
})) {
MaxVF = VFs[I];
break;
}
}
if (ElementCount MinVF =
TTI.getMinimumVF(SmallestType, ComputeScalableMaxVF)) {
if (ElementCount::isKnownLT(MaxVF, MinVF)) {
LLVM_DEBUG(dbgs() << "LV: Overriding calculated MaxVF(" << MaxVF
<< ") with target's minimum: " << MinVF << '\n');
MaxVF = MinVF;
}
}
// Invalidate any widening decisions we might have made, in case the loop
// requires prediction (decided later), but we have already made some
// load/store widening decisions.
invalidateCostModelingDecisions();
}
return MaxVF;
}
/// Convenience function that returns the value of vscale_range iff
/// vscale_range.min == vscale_range.max or otherwise returns the value
/// returned by the corresponding TTI method.
static std::optional<unsigned>
getVScaleForTuning(const Loop *L, const TargetTransformInfo &TTI) {
const Function *Fn = L->getHeader()->getParent();
if (Fn->hasFnAttribute(Attribute::VScaleRange)) {
auto Attr = Fn->getFnAttribute(Attribute::VScaleRange);
auto Min = Attr.getVScaleRangeMin();
auto Max = Attr.getVScaleRangeMax();
if (Max && Min == Max)
return Max;
}
return TTI.getVScaleForTuning();
}
bool LoopVectorizationPlanner::isMoreProfitable(
const VectorizationFactor &A, const VectorizationFactor &B) const {
InstructionCost CostA = A.Cost;
InstructionCost CostB = B.Cost;
unsigned MaxTripCount = PSE.getSE()->getSmallConstantMaxTripCount(OrigLoop);
// 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 = getVScaleForTuning(OrigLoop, TTI)) {
if (A.Width.isScalable())
EstimatedWidthA *= *VScale;
if (B.Width.isScalable())
EstimatedWidthB *= *VScale;
}
// 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() &&
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)
if (!MaxTripCount)
return CmpFn(CostA * EstimatedWidthB, CostB * EstimatedWidthA);
auto GetCostForTC = [MaxTripCount, this](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 (CM.foldTailByMasking())
return VectorCost * divideCeil(MaxTripCount, VF);
return VectorCost * (MaxTripCount / VF) + ScalarCost * (MaxTripCount % VF);
};
auto RTCostA = GetCostForTC(EstimatedWidthA, CostA, A.ScalarCost);
auto RTCostB = GetCostForTC(EstimatedWidthB, CostB, B.ScalarCost);
return CmpFn(RTCostA, RTCostB);
}
void LoopVectorizationPlanner::emitInvalidCostRemarks(
OptimizationRemarkEmitter *ORE) {
using RecipeVFPair = std::pair<VPRecipeBase *, ElementCount>;
LLVMContext &LLVMCtx = OrigLoop->getHeader()->getContext();
SmallVector<RecipeVFPair> InvalidCosts;
for (const auto &Plan : VPlans) {
for (ElementCount VF : Plan->vectorFactors()) {
VPCostContext CostCtx(CM.TTI, *CM.TLI, Legal->getWidestInductionType(),
LLVMCtx, CM);
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.count(Pair.first))
Numbering[Pair.first] = I++;
// Sort the list, first on recipe(number) then on VF.
sort(InvalidCosts, [&Numbering](RecipeVFPair &A, RecipeVFPair &B) {
if (Numbering[A.first] != Numbering[B.first])
return Numbering[A.first] < Numbering[B.first];
const auto &LHS = A.second;
const auto &RHS = B.second;
return std::make_tuple(LHS.isScalable(), LHS.getKnownMinValue()) <
std::make_tuple(RHS.isScalable(), RHS.getKnownMinValue());
});
// 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<VPHeaderPHIRecipe>(
[](const auto *R) { return Instruction::PHI; })
.Case<VPWidenSelectRecipe>(
[](const auto *R) { return Instruction::Select; })
.Case<VPWidenStoreRecipe>(
[](const auto *R) { return Instruction::Store; })
.Case<VPWidenLoadRecipe>(
[](const auto *R) { return Instruction::Load; })
.Case<VPWidenCallRecipe>(
[](const auto *R) { return Instruction::Call; })
.Case<VPInstruction, VPWidenRecipe, VPReplicateRecipe,
VPWidenCastRecipe>(
[](const auto *R) { return R->getOpcode(); })
.Case<VPInterleaveRecipe>([](const VPInterleaveRecipe *R) {
return R->getStoredValues().empty() ? Instruction::Load
: Instruction::Store;
});
// 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) {
auto *WidenCall = dyn_cast<VPWidenCallRecipe>(R);
Function *CalledFn =
WidenCall ? WidenCall->getCalledScalarFunction()
: cast<Function>(R->getOperand(R->getNumOperands() - 1)
->getLiveInIRValue());
OS << " call to " << CalledFn->getName();
} else
OS << " " << Instruction::getOpcodeName(Opcode);
OS.flush();
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.getCanonicalIV()->getScalarType(),
Plan.getCanonicalIV()->getScalarType()->getContext());
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.getVPDefID()) {
case VPDef::VPDerivedIVSC:
case VPDef::VPScalarIVStepsSC:
case VPDef::VPScalarCastSC:
case VPDef::VPReplicateSC:
case VPDef::VPInstructionSC:
case VPDef::VPCanonicalIVPHISC:
case VPDef::VPVectorPointerSC:
case VPDef::VPExpandSCEVSC:
case VPDef::VPEVLBasedIVPHISC:
case VPDef::VPPredInstPHISC:
case VPDef::VPBranchOnMaskSC:
continue;
case VPDef::VPReductionSC:
case VPDef::VPActiveLaneMaskPHISC:
case VPDef::VPWidenCallSC:
case VPDef::VPWidenCanonicalIVSC:
case VPDef::VPWidenCastSC:
case VPDef::VPWidenGEPSC:
case VPDef::VPWidenSC:
case VPDef::VPWidenSelectSC:
case VPDef::VPBlendSC:
case VPDef::VPFirstOrderRecurrencePHISC:
case VPDef::VPWidenPHISC:
case VPDef::VPWidenIntOrFpInductionSC:
case VPDef::VPWidenPointerInductionSC:
case VPDef::VPReductionPHISC:
case VPDef::VPInterleaveSC:
case VPDef::VPWidenLoadEVLSC:
case VPDef::VPWidenLoadSC:
case VPDef::VPWidenStoreEVLSC:
case VPDef::VPWidenStoreSC:
break;
default:
llvm_unreachable("unhandled recipe");
}
auto WillWiden = [&TTI, VF](Type *ScalarTy) {
Type *VectorTy = ToVectorTy(ScalarTy, VF);
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 parts that share a register - are vectorized.
return NumLegalParts < VF.getKnownMinValue();
};
// If no def nor is a store, e.g., branches, continue - no value to check.
if (R.getNumDefinedValues() == 0 &&
!isa<VPWidenStoreRecipe, VPWidenStoreEVLRecipe, VPInterleaveRecipe>(
&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;
if (WillWiden(ScalarTy))
return true;
}
}
return false;
}
#ifndef NDEBUG
VectorizationFactor LoopVectorizationPlanner::selectVectorizationFactor() {
InstructionCost ExpectedCost = CM.expectedCost(ElementCount::getFixed(1));
LLVM_DEBUG(dbgs() << "LV: Scalar loop costs: " << ExpectedCost << ".\n");
assert(ExpectedCost.isValid() && "Unexpected invalid cost for scalar loop");
assert(any_of(VPlans,
[](std::unique_ptr<VPlan> &P) {
return P->hasVF(ElementCount::getFixed(1));
}) &&
"Expected Scalar VF to be a candidate");
const VectorizationFactor ScalarCost(ElementCount::getFixed(1), ExpectedCost,
ExpectedCost);
VectorizationFactor ChosenFactor = ScalarCost;
bool ForceVectorization = Hints.getForce() == LoopVectorizeHints::FK_Enabled;
if (ForceVectorization &&
(VPlans.size() > 1 || !VPlans[0]->hasScalarVFOnly())) {
// Ignore scalar width, because the user explicitly wants vectorization.
// Initialize cost to max so that VF = 2 is, at least, chosen during cost
// evaluation.
ChosenFactor.Cost = InstructionCost::getMax();
}
for (auto &P : VPlans) {
for (ElementCount VF : P->vectorFactors()) {
// The cost for scalar VF=1 is already calculated, so ignore it.
if (VF.isScalar())
continue;
InstructionCost C = CM.expectedCost(VF);
VectorizationFactor Candidate(VF, C, ScalarCost.ScalarCost);
unsigned AssumedMinimumVscale =
getVScaleForTuning(OrigLoop, TTI).value_or(1);
unsigned Width =
Candidate.Width.isScalable()
? Candidate.Width.getKnownMinValue() * AssumedMinimumVscale
: Candidate.Width.getFixedValue();
LLVM_DEBUG(dbgs() << "LV: Vector loop of width " << VF
<< " costs: " << (Candidate.Cost / Width));
if (VF.isScalable())
LLVM_DEBUG(dbgs() << " (assuming a minimum vscale of "
<< AssumedMinimumVscale << ")");
LLVM_DEBUG(dbgs() << ".\n");
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 (isMoreProfitable(Candidate, ChosenFactor))
ChosenFactor = Candidate;
}
}
if (!EnableCondStoresVectorization && CM.hasPredStores()) {
reportVectorizationFailure(
"There are conditional stores.",
"store that is conditionally executed prevents vectorization",
"ConditionalStore", ORE, OrigLoop);
ChosenFactor = ScalarCost;
}
LLVM_DEBUG(if (ForceVectorization && !ChosenFactor.Width.isScalar() &&
!isMoreProfitable(ChosenFactor, ScalarCost)) dbgs()
<< "LV: Vectorization seems to be not beneficial, "
<< "but was forced by a user.\n");
LLVM_DEBUG(dbgs() << "LV: Selecting VF: " << ChosenFactor.Width << ".\n");
return ChosenFactor;
}
#endif
bool LoopVectorizationPlanner::isCandidateForEpilogueVectorization(
ElementCount VF) const {
// Cross iteration phis such as reductions need special handling and are
// currently unsupported.
if (any_of(OrigLoop->getHeader()->phis(),
[&](PHINode &Phi) { return Legal->isFixedOrderRecurrence(&Phi); }))
return false;
// Phis with uses outside of the loop require special handling and are
// currently unsupported.
for (const auto &Entry : Legal->getInductionVars()) {
// Look for uses of the value of the induction at the last iteration.
Value *PostInc =
Entry.first->getIncomingValueForBlock(OrigLoop->getLoopLatch());
for (User *U : PostInc->users())
if (!OrigLoop->contains(cast<Instruction>(U)))
return false;
// Look for uses of penultimate value of the induction.
for (User *U : Entry.first->users())
if (!OrigLoop->contains(cast<Instruction>(U)))
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.
if (OrigLoop->getExitingBlock() != OrigLoop->getLoopLatch())
return false;
return true;
}
bool LoopVectorizationCostModel::isEpilogueVectorizationProfitable(
const ElementCount VF) 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 entirely.
if (!TTI.preferEpilogueVectorization())
return false;
// We also consider epilogue vectorization unprofitable for targets that don't
// consider interleaving beneficial (eg. MVE).
if (TTI.getMaxInterleaveFactor(VF) <= 1)
return false;
unsigned Multiplier = 1;
if (VF.isScalable())
Multiplier = getVScaleForTuning(TheLoop, TTI).value_or(1);
if ((Multiplier * VF.getKnownMinValue()) >= EpilogueVectorizationMinVF)
return true;
return false;
}
VectorizationFactor LoopVectorizationPlanner::selectEpilogueVectorizationFactor(
const ElementCount MainLoopVF, unsigned IC) {
VectorizationFactor Result = VectorizationFactor::Disabled();
if (!EnableEpilogueVectorization) {
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is disabled.\n");
return Result;
}
if (!CM.isScalarEpilogueAllowed()) {
LLVM_DEBUG(dbgs() << "LEV: Unable to vectorize epilogue because no "
"epilogue is allowed.\n");
return Result;
}
// Not really a cost consideration, but check for unsupported cases here to
// simplify the logic.
if (!isCandidateForEpilogueVectorization(MainLoopVF)) {
LLVM_DEBUG(dbgs() << "LEV: Unable to vectorize epilogue because the loop "
"is not a supported candidate.\n");
return Result;
}
if (EpilogueVectorizationForceVF > 1) {
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization factor is forced.\n");
ElementCount ForcedEC = ElementCount::getFixed(EpilogueVectorizationForceVF);
if (hasPlanWithVF(ForcedEC))
return {ForcedEC, 0, 0};
else {
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization forced factor is not "
"viable.\n");
return Result;
}
}
if (OrigLoop->getHeader()->getParent()->hasOptSize() ||
OrigLoop->getHeader()->getParent()->hasMinSize()) {
LLVM_DEBUG(
dbgs() << "LEV: Epilogue vectorization skipped due to opt for size.\n");
return Result;
}
if (!CM.isEpilogueVectorizationProfitable(MainLoopVF)) {
LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is not profitable for "
"this loop\n");
return Result;
}
// 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 = MainLoopVF;
if (MainLoopVF.isScalable()) {
EstimatedRuntimeVF = ElementCount::getFixed(MainLoopVF.getKnownMinValue());
if (std::optional<unsigned> VScale = getVScaleForTuning(OrigLoop, TTI))
EstimatedRuntimeVF *= *VScale;
}
ScalarEvolution &SE = *PSE.getSE();
Type *TCType = Legal->getWidestInductionType();
const SCEV *RemainingIterations = nullptr;
for (auto &NextVF : ProfitableVFs) {
// Skip candidate VFs without a corresponding VPlan.
if (!hasPlanWithVF(NextVF.Width))
continue;
// Skip candidate VFs with widths >= the estimate runtime VF (scalable
// vectors) or the VF of the main loop (fixed vectors).
if ((!NextVF.Width.isScalable() && MainLoopVF.isScalable() &&
ElementCount::isKnownGE(NextVF.Width, EstimatedRuntimeVF)) ||
ElementCount::isKnownGE(NextVF.Width, MainLoopVF))
continue;
// If NextVF is greater than the number of remaining iterations, the
// epilogue loop would be dead. Skip such factors.
if (!MainLoopVF.isScalable() && !NextVF.Width.isScalable()) {
// TODO: extend to support scalable VFs.
if (!RemainingIterations) {
const SCEV *TC = createTripCountSCEV(TCType, PSE, OrigLoop);
RemainingIterations = SE.getURemExpr(
TC, SE.getConstant(TCType, MainLoopVF.getKnownMinValue() * IC));
}
if (SE.isKnownPredicate(
CmpInst::ICMP_UGT,
SE.getConstant(TCType, NextVF.Width.getKnownMinValue()),
RemainingIterations))
continue;
}
if (Result.Width.isScalar() || isMoreProfitable(NextVF, Result))
Result = NextVF;
}
if (Result != VectorizationFactor::Disabled())
LLVM_DEBUG(dbgs() << "LEV: Vectorizing epilogue loop with VF = "
<< Result.Width << "\n");
return Result;
}
std::pair<unsigned, unsigned>
LoopVectorizationCostModel::getSmallestAndWidestTypes() {
unsigned MinWidth = -1U;
unsigned MaxWidth = 8;
const DataLayout &DL = TheFunction->getDataLayout();
// For in-loop reductions, no element types are added to ElementTypesInLoop
// if there are no loads/stores in the loop. In this case, check through the
// reduction variables to determine the maximum width.
if (ElementTypesInLoop.empty() && !Legal->getReductionVars().empty()) {
// Reset MaxWidth so that we can find the smallest type used by recurrences
// in the loop.
MaxWidth = -1U;
for (const auto &PhiDescriptorPair : Legal->getReductionVars()) {
const RecurrenceDescriptor &RdxDesc = PhiDescriptorPair.second;
// When finding the min width used by the recurrence we need to account
// for casts on the input operands of the recurrence.
MaxWidth = std::min<unsigned>(
MaxWidth, std::min<unsigned>(
RdxDesc.getMinWidthCastToRecurrenceTypeInBits(),
RdxDesc.getRecurrenceType()->getScalarSizeInBits()));
}
} else {
for (Type *T : ElementTypesInLoop) {
MinWidth = std::min<unsigned>(
MinWidth, DL.getTypeSizeInBits(T->getScalarType()).getFixedValue());
MaxWidth = std::max<unsigned>(
MaxWidth, DL.getTypeSizeInBits(T->getScalarType()).getFixedValue());
}
}
return {MinWidth, MaxWidth};
}
void LoopVectorizationCostModel::collectElementTypesForWidening() {
ElementTypesInLoop.clear();
// For each block.
for (BasicBlock *BB : TheLoop->blocks()) {
// For each instruction in the loop.
for (Instruction &I : BB->instructionsWithoutDebug()) {
Type *T = I.getType();
// Skip ignored values.
if (ValuesToIgnore.count(&I))
continue;
// Only examine Loads, Stores and PHINodes.
if (!isa<LoadInst>(I) && !isa<StoreInst>(I) && !isa<PHINode>(I))
continue;
// Examine PHI nodes that are reduction variables. Update the type to
// account for the recurrence type.
if (auto *PN = dyn_cast<PHINode>(&I)) {
if (!Legal->isReductionVariable(PN))
continue;
const RecurrenceDescriptor &RdxDesc =
Legal->getReductionVars().find(PN)->second;
if (PreferInLoopReductions || useOrderedReductions(RdxDesc) ||
TTI.preferInLoopReduction(RdxDesc.getOpcode(),
RdxDesc.getRecurrenceType(),
TargetTransformInfo::ReductionFlags()))
continue;
T = RdxDesc.getRecurrenceType();
}
// Examine the stored values.
if (auto *ST = dyn_cast<StoreInst>(&I))
T = ST->getValueOperand()->getType();
assert(T->isSized() &&
"Expected the load/store/recurrence type to be sized");
ElementTypesInLoop.insert(T);
}
}
}
unsigned
LoopVectorizationCostModel::selectInterleaveCount(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.
if (!isScalarEpilogueAllowed())
return 1;
// Do not interleave if EVL is preferred and no User IC is specified.
if (foldTailWithEVL()) {
LLVM_DEBUG(dbgs() << "LV: Preference for VP intrinsics indicated. "
"Unroll factor forced to be 1.\n");
return 1;
}
// We used the distance for the interleave count.
if (!Legal->isSafeForAnyVectorWidth())
return 1;
auto BestKnownTC = getSmallBestKnownTC(*PSE.getSE(), TheLoop);
const bool HasReductions = !Legal->getReductionVars().empty();
// If we did not calculate the cost for VF (because the user selected the VF)
// then we calculate the cost of VF here.
if (LoopCost == 0) {
LoopCost = expectedCost(VF);
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;
}
RegisterUsage R = calculateRegisterUsage({VF})[0];
// We divide by these constants so assume that we have at least one
// instruction that uses at least one register.
for (auto& pair : R.MaxLocalUsers) {
pair.second = std::max(pair.second, 1U);
}
// We calculate the interleave count using the following formula.
// Subtract the number of loop invariants from the number of available
// registers. These registers are used by all of the interleaved instances.
// Next, divide the remaining registers by the number of registers that is
// required by the loop, in order to estimate how many parallel instances
// fit without causing spills. All of this is rounded down if necessary to be
// a power of two. We want power of two interleave count to simplify any
// addressing operations or alignment considerations.
// We also want power of two interleave counts to ensure that the induction
// variable of the vector loop wraps to zero, when tail is folded by masking;
// this currently happens when OptForSize, in which case IC is set to 1 above.
unsigned IC = UINT_MAX;
for (auto& pair : R.MaxLocalUsers) {
unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first);
LLVM_DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters
<< " registers of "
<< TTI.getRegisterClassName(pair.first) << " register class\n");
if (VF.isScalar()) {
if (ForceTargetNumScalarRegs.getNumOccurrences() > 0)
TargetNumRegisters = ForceTargetNumScalarRegs;
} else {
if (ForceTargetNumVectorRegs.getNumOccurrences() > 0)
TargetNumRegisters = ForceTargetNumVectorRegs;
}
unsigned MaxLocalUsers = pair.second;
unsigned LoopInvariantRegs = 0;
if (R.LoopInvariantRegs.find(pair.first) != R.LoopInvariantRegs.end())
LoopInvariantRegs = R.LoopInvariantRegs[pair.first];
unsigned TmpIC = 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);
// Check if the user has overridden the max.
if (VF.isScalar()) {
if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor;
} else {
if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor;
}
unsigned EstimatedVF = VF.getKnownMinValue();
if (VF.isScalable()) {
if (std::optional<unsigned> VScale = getVScaleForTuning(TheLoop, TTI))
EstimatedVF *= *VScale;
}
assert(EstimatedVF >= 1 && "Estimated VF shouldn't be less than 1");
unsigned KnownTC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
if (KnownTC > 0) {
// At least one iteration must be scalar when this constraint holds. So the
// maximum available iterations for interleaving is one less.
unsigned AvailableTC =
requiresScalarEpilogue(VF.isVector()) ? KnownTC - 1 : KnownTC;
// If trip count is known 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)));
unsigned InterleaveCountLB = bit_floor(std::max(
1u, std::min(AvailableTC / (EstimatedVF * 2), 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 (BestKnownTC && *BestKnownTC > 0) {
// At least one iteration must be scalar when this constraint holds. So the
// maximum available iterations for interleaving is one less.
unsigned AvailableTC = requiresScalarEpilogue(VF.isVector())
? (*BestKnownTC) - 1
: *BestKnownTC;
// 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 = bit_floor(std::max(
1u, std::min(AvailableTC / (EstimatedVF * 2), MaxInterleaveCount)));
}
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 predication 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(TheLoop->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 AggressivelyInterleaveReductions =
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 = Legal->getNumStores();
unsigned NumLoads = Legal->getNumLoads();
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(Legal->getReductionVars(), [&](auto &Reduction) -> bool {
const RecurrenceDescriptor &RdxDesc = Reduction.second;
return RecurrenceDescriptor::isAnyOfRecurrenceKind(
RdxDesc.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 && TheLoop->getLoopDepth() > 1) {
bool HasOrderedReductions =
any_of(Legal->getReductionVars(), [&](auto &Reduction) -> bool {
const RecurrenceDescriptor &RdxDesc = Reduction.second;
return RdxDesc.isOrdered();
});
if (HasOrderedReductions) {
LLVM_DEBUG(
dbgs() << "LV: Not interleaving scalar ordered reductions.\n");
return 1;
}
unsigned F = static_cast<unsigned>(MaxNestedScalarReductionIC);
SmallIC = std::min(SmallIC, F);
StoresIC = std::min(StoresIC, F);
LoadsIC = std::min(LoadsIC, F);
}
if (EnableLoadStoreRuntimeInterleave &&
std::max(StoresIC, LoadsIC) > SmallIC) {
LLVM_DEBUG(
dbgs() << "LV: Interleaving to saturate store or load ports.\n");
return std::max(StoresIC, LoadsIC);
}
// If there are scalar reductions and TTI has enabled aggressive
// interleaving for reductions, we will interleave to expose ILP.
if (VF.isScalar() && AggressivelyInterleaveReductions) {
LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
// Interleave no less than SmallIC but not as aggressive as the normal IC
// to satisfy the rare situation when resources are too limited.
return std::max(IC / 2, SmallIC);
} else {
LLVM_DEBUG(dbgs() << "LV: Interleaving to reduce branch cost.\n");
return SmallIC;
}
}
// Interleave if this is a large loop (small loops are already dealt with by
// this point) that could benefit from interleaving.
if (AggressivelyInterleaveReductions) {
LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
return IC;
}
LLVM_DEBUG(dbgs() << "LV: Not Interleaving.\n");
return 1;
}
SmallVector<LoopVectorizationCostModel::RegisterUsage, 8>
LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<ElementCount> VFs) {
// This function calculates the register usage by measuring the highest number
// of values that are alive at a single location. Obviously, this is a very
// rough estimation. We scan the loop in a topological order in order and
// assign a number to each instruction. We use RPO to ensure that defs are
// met before their users. We assume that each instruction that has in-loop
// users starts an interval. We record every time that an in-loop value is
// used, so we have a list of the first and last occurrences of each
// instruction. Next, we transpose this data structure into a multi map that
// holds the list of intervals that *end* at a specific location. This multi
// map allows us to perform a linear search. We scan the instructions linearly
// and record each time that a new interval starts, by placing it in a set.
// If we find this value in the multi-map then we remove it from the set.
// The max register usage is the maximum size of the set.
// We also search for instructions that are defined outside the loop, but are
// used inside the loop. We need this number separately from the max-interval
// usage number because when we unroll, loop-invariant values do not take
// more register.
LoopBlocksDFS DFS(TheLoop);
DFS.perform(LI);
RegisterUsage RU;
// Each 'key' in the map opens a new interval. The values
// of the map are the index of the 'last seen' usage of the
// instruction that is the key.
using IntervalMap = DenseMap<Instruction *, unsigned>;
// Maps instruction to its index.
SmallVector<Instruction *, 64> IdxToInstr;
// Marks the end of each interval.
IntervalMap EndPoint;
// Saves the list of instruction indices that are used in the loop.
SmallPtrSet<Instruction *, 8> Ends;
// Saves the list of values that are used in the loop but are defined outside
// the loop (not including non-instruction values such as arguments and
// constants).
SmallSetVector<Instruction *, 8> LoopInvariants;
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
for (Instruction &I : BB->instructionsWithoutDebug()) {
IdxToInstr.push_back(&I);
// Save the end location of each USE.
for (Value *U : I.operands()) {
auto *Instr = dyn_cast<Instruction>(U);
// Ignore non-instruction values such as arguments, constants, etc.
// FIXME: Might need some motivation why these values are ignored. If
// for example an argument is used inside the loop it will increase the
// register pressure (so shouldn't we add it to LoopInvariants).
if (!Instr)
continue;
// If this instruction is outside the loop then record it and continue.
if (!TheLoop->contains(Instr)) {
LoopInvariants.insert(Instr);
continue;
}
// Overwrite previous end points.
EndPoint[Instr] = IdxToInstr.size();
Ends.insert(Instr);
}
}
}
// Saves the list of intervals that end with the index in 'key'.
using InstrList = SmallVector<Instruction *, 2>;
DenseMap<unsigned, InstrList> TransposeEnds;
// Transpose the EndPoints to a list of values that end at each index.
for (auto &Interval : EndPoint)
TransposeEnds[Interval.second].push_back(Interval.first);
SmallPtrSet<Instruction *, 8> OpenIntervals;
SmallVector<RegisterUsage, 8> RUs(VFs.size());
SmallVector<SmallMapVector<unsigned, unsigned, 4>, 8> MaxUsages(VFs.size());
LLVM_DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
const auto &TTICapture = TTI;
auto GetRegUsage = [&TTICapture](Type *Ty, ElementCount VF) -> unsigned {
if (Ty->isTokenTy() || !VectorType::isValidElementType(Ty))
return 0;
return TTICapture.getRegUsageForType(VectorType::get(Ty, VF));
};
for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) {
Instruction *I = IdxToInstr[i];
// Remove all of the instructions that end at this location.
InstrList &List = TransposeEnds[i];
for (Instruction *ToRemove : List)
OpenIntervals.erase(ToRemove);
// Ignore instructions that are never used within the loop.
if (!Ends.count(I))
continue;
// Skip ignored values.
if (ValuesToIgnore.count(I))
continue;
collectInLoopReductions();
// For each VF find the maximum usage of registers.
for (unsigned j = 0, e = VFs.size(); j < e; ++j) {
// Count the number of registers used, per register class, given all open
// intervals.
// Note that elements in this SmallMapVector will be default constructed
// as 0. So we can use "RegUsage[ClassID] += n" in the code below even if
// there is no previous entry for ClassID.
SmallMapVector<unsigned, unsigned, 4> RegUsage;
if (VFs[j].isScalar()) {
for (auto *Inst : OpenIntervals) {
unsigned ClassID =
TTI.getRegisterClassForType(false, Inst->getType());
// FIXME: The target might use more than one register for the type
// even in the scalar case.
RegUsage[ClassID] += 1;
}
} else {
collectUniformsAndScalars(VFs[j]);
for (auto *Inst : OpenIntervals) {
// Skip ignored values for VF > 1.
if (VecValuesToIgnore.count(Inst))
continue;
if (isScalarAfterVectorization(Inst, VFs[j])) {
unsigned ClassID =
TTI.getRegisterClassForType(false, Inst->getType());
// FIXME: The target might use more than one register for the type
// even in the scalar case.
RegUsage[ClassID] += 1;
} else {
unsigned ClassID =
TTI.getRegisterClassForType(true, Inst->getType());
RegUsage[ClassID] += GetRegUsage(Inst->getType(), VFs[j]);
}
}
}
for (auto& pair : RegUsage) {
auto &Entry = MaxUsages[j][pair.first];
Entry = std::max(Entry, pair.second);
}
}
LLVM_DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # "
<< OpenIntervals.size() << '\n');
// Add the current instruction to the list of open intervals.
OpenIntervals.insert(I);
}
for (unsigned i = 0, e = VFs.size(); i < e; ++i) {
// Note that elements in this SmallMapVector will be default constructed
// as 0. So we can use "Invariant[ClassID] += n" in the code below even if
// there is no previous entry for ClassID.
SmallMapVector<unsigned, unsigned, 4> Invariant;
for (auto *Inst : LoopInvariants) {
// FIXME: The target might use more than one register for the type
// even in the scalar case.
bool IsScalar = all_of(Inst->users(), [&](User *U) {
auto *I = cast<Instruction>(U);
return TheLoop != LI->getLoopFor(I->getParent()) ||
isScalarAfterVectorization(I, VFs[i]);
});
ElementCount VF = IsScalar ? ElementCount::getFixed(1) : VFs[i];
unsigned ClassID =
TTI.getRegisterClassForType(VF.isVector(), Inst->getType());
Invariant[ClassID] += GetRegUsage(Inst->getType(), VF);
}
LLVM_DEBUG({
dbgs() << "LV(REG): VF = " << VFs[i] << '\n';
dbgs() << "LV(REG): Found max usage: " << MaxUsages[i].size()
<< " item\n";
for (const auto &pair : MaxUsages[i]) {
dbgs() << "LV(REG): RegisterClass: "
<< TTI.getRegisterClassName(pair.first) << ", " << pair.second
<< " registers\n";
}
dbgs() << "LV(REG): Found invariant usage: " << Invariant.size()
<< " item\n";
for (const auto &pair : Invariant) {
dbgs() << "LV(REG): RegisterClass: "
<< TTI.getRegisterClassName(pair.first) << ", " << pair.second
<< " registers\n";
}
});
RU.LoopInvariantRegs = Invariant;
RU.MaxLocalUsers = MaxUsages[i];
RUs[i] = RU;
}
return RUs;
}
bool LoopVectorizationCostModel::useEmulatedMaskMemRefHack(Instruction *I,
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) {
// If we aren't vectorizing the loop, or if we've already collected the
// instructions to scalarize, there's nothing to do. Collection may already
// have occurred if we have a user-selected VF and are now computing the
// expected cost for interleaving.
if (VF.isScalar() || VF.isZero() || InstsToScalarize.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];
PredicatedBBsAfterVectorization[VF].clear();
// 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)
ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end());
// 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;
// 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.
TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
if (isScalarWithPredication(I, VF) && !I->getType()->isVoidTy()) {
ScalarCost += TTI.getScalarizationOverhead(
cast<VectorType>(ToVectorTy(I->getType(), VF)),
APInt::getAllOnes(VF.getFixedValue()), /*Insert*/ true,
/*Extract*/ false, CostKind);
ScalarCost +=
VF.getFixedValue() * TTI.getCFInstrCost(Instruction::PHI, 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(VectorType::isValidElementType(J->getType()) &&
"Instruction has non-scalar type");
if (canBeScalarized(J))
Worklist.push_back(J);
else if (needsExtract(J, VF)) {
ScalarCost += TTI.getScalarizationOverhead(
cast<VectorType>(ToVectorTy(J->getType(), VF)),
APInt::getAllOnes(VF.getFixedValue()), /*Insert*/ false,
/*Extract*/ true, CostKind);
}
}
// Scale the total scalar cost by block probability.
ScalarCost /= getReciprocalPredBlockProb();
// Compute the discount. A non-negative discount means the vector version
// of the instruction costs more, and scalarizing would be beneficial.
Discount += VectorCost - ScalarCost;
ScalarCosts[I] = ScalarCost;
}
return Discount;
}
InstructionCost LoopVectorizationCostModel::expectedCost(ElementCount VF) {
InstructionCost Cost;
// For each block.
for (BasicBlock *BB : TheLoop->blocks()) {
InstructionCost BlockCost;
// For each instruction in the old loop.
for (Instruction &I : BB->instructionsWithoutDebug()) {
// Skip ignored values.
if (ValuesToIgnore.count(&I) ||
(VF.isVector() && VecValuesToIgnore.count(&I)))
continue;
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');
}
// If we are vectorizing a predicated block, it will have been
// if-converted. This means that the block's instructions (aside from
// stores and instructions that may divide by zero) will now be
// unconditionally executed. For the scalar case, we may not always execute
// the predicated block, if it is an if-else block. Thus, scale the block's
// cost by the probability of executing it. blockNeedsPredication from
// Legal is used so as to not include all blocks in tail folded loops.
if (VF.isScalar() && Legal->blockNeedsPredication(BB))
BlockCost /= getReciprocalPredBlockProb();
Cost += BlockCost;
}
return Cost;
}
/// Gets Address Access SCEV after verifying that the access pattern
/// is loop invariant except the induction variable dependence.
///
/// This SCEV can be sent to the Target in order to estimate the address
/// calculation cost.
static const SCEV *getAddressAccessSCEV(
Value *Ptr,
LoopVectorizationLegality *Legal,
PredicatedScalarEvolution &PSE,
const Loop *TheLoop) {
auto *Gep = dyn_cast<GetElementPtrInst>(Ptr);
if (!Gep)
return nullptr;
// We are looking for a gep with all loop invariant indices except for one
// which should be an induction variable.
auto SE = PSE.getSE();
unsigned NumOperands = Gep->getNumOperands();
for (unsigned i = 1; i < NumOperands; ++i) {
Value *Opd = Gep->getOperand(i);
if (!SE->isLoopInvariant(SE->getSCEV(Opd), TheLoop) &&
!Legal->isInductionVariable(Opd))
return nullptr;
}
// Now we know we have a GEP ptr, %inv, %ind, %inv. return the Ptr SCEV.
return PSE.getSCEV(Ptr);
}
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, Legal, PSE, TheLoop);
// Get the cost of the scalar memory instruction and address computation.
InstructionCost Cost =
VF.getKnownMinValue() * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV);
// Don't pass *I here, since it is scalar but will actually be part of a
// vectorized loop where the user of it is a vectorized instruction.
TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
const Align Alignment = getLoadStoreAlignment(I);
Cost += VF.getKnownMinValue() * TTI.getMemoryOpCost(I->getOpcode(),
ValTy->getScalarType(),
Alignment, AS, CostKind);
// Get the overhead of the extractelement and insertelement instructions
// we might create due to scalarization.
Cost += getScalarizationOverhead(I, VF, CostKind);
// 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 /= getReciprocalPredBlockProb();
// Add the cost of an i1 extract and a branch
auto *Vec_i1Ty =
VectorType::get(IntegerType::getInt1Ty(ValTy->getContext()), VF);
Cost += TTI.getScalarizationOverhead(
Vec_i1Ty, APInt::getAllOnes(VF.getKnownMinValue()),
/*Insert=*/false, /*Extract=*/true, CostKind);
Cost += TTI.getCFInstrCost(Instruction::Br, 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);
enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
"Stride should be 1 or -1 for consecutive memory access");
const Align Alignment = getLoadStoreAlignment(I);
InstructionCost Cost = 0;
if (Legal->isMaskRequired(I)) {
Cost += TTI.getMaskedMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS,
CostKind);
} else {
TTI::OperandValueInfo OpInfo = TTI::getOperandInfo(I->getOperand(0));
Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS,
CostKind, OpInfo, I);
}
bool Reverse = ConsecutiveStride < 0;
if (Reverse)
Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy,
std::nullopt, CostKind, 0);
return Cost;
}
InstructionCost
LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I,
ElementCount VF) {
assert(Legal->isUniformMemOp(*I, VF));
Type *ValTy = getLoadStoreType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
if (isa<LoadInst>(I)) {
return TTI.getAddressComputationCost(ValTy) +
TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS,
CostKind) +
TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy);
}
StoreInst *SI = cast<StoreInst>(I);
bool isLoopInvariantStoreValue = Legal->isInvariant(SI->getValueOperand());
return TTI.getAddressComputationCost(ValTy) +
TTI.getMemoryOpCost(Instruction::Store, ValTy, Alignment, AS,
CostKind) +
(isLoopInvariantStoreValue
? 0
: TTI.getVectorInstrCost(Instruction::ExtractElement, VectorTy,
CostKind, VF.getKnownMinValue() - 1));
}
InstructionCost
LoopVectorizationCostModel::getGatherScatterCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getLoadStoreType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
const Align Alignment = getLoadStoreAlignment(I);
const Value *Ptr = getLoadStorePointerOperand(I);
return TTI.getAddressComputationCost(VectorTy) +
TTI.getGatherScatterOpCost(
I->getOpcode(), VectorTy, Ptr, Legal->isMaskRequired(I), Alignment,
TargetTransformInfo::TCK_RecipThroughput, I);
}
InstructionCost
LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I,
ElementCount VF) {
Type *ValTy = getLoadStoreType(I);
auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF));
unsigned AS = getLoadStoreAddressSpace(I);
enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
auto Group = getInterleavedAccessGroup(I);
assert(Group && "Fail to get an interleaved access group.");
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() && !isScalarEpilogueAllowed()) ||
(isa<StoreInst>(I) && (Group->getNumMembers() < Group->getFactor()));
InstructionCost Cost = TTI.getInterleavedMemoryOpCost(
I->getOpcode(), WideVecTy, Group->getFactor(), Indices, Group->getAlign(),
AS, CostKind, Legal->isMaskRequired(I), UseMaskForGaps);
if (Group->isReverse()) {
// TODO: Add support for reversed masked interleaved access.
assert(!Legal->isMaskRequired(I) &&
"Reverse masked interleaved access not supported.");
Cost += Group->getNumMembers() *
TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy,
std::nullopt, CostKind, 0);
}
return Cost;
}
std::optional<InstructionCost>
LoopVectorizationCostModel::getReductionPatternCost(
Instruction *I, ElementCount VF, Type *Ty,
TTI::TargetCostKind CostKind) const {
using namespace llvm::PatternMatch;
// Early exit for no inloop reductions
if (InLoopReductions.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.
if (!InLoopReductionImmediateChains.count(RetI))
return std::nullopt;
// Find the reduction this chain is a part of and calculate the basic cost of
// the reduction on its own.
Instruction *LastChain = InLoopReductionImmediateChains.at(RetI);
Instruction *ReductionPhi = LastChain;
while (!isa<PHINode>(ReductionPhi))
ReductionPhi = InLoopReductionImmediateChains.at(ReductionPhi);
const RecurrenceDescriptor &RdxDesc =
Legal->getReductionVars().find(cast<PHINode>(ReductionPhi))->second;
InstructionCost BaseCost;
RecurKind RK = RdxDesc.getRecurrenceKind();
if (RecurrenceDescriptor::isMinMaxRecurrenceKind(RK)) {
Intrinsic::ID MinMaxID = getMinMaxReductionIntrinsicOp(RK);
BaseCost = TTI.getMinMaxReductionCost(MinMaxID, VectorTy,
RdxDesc.getFastMathFlags(), CostKind);
} else {
BaseCost = TTI.getArithmeticReductionCost(
RdxDesc.getOpcode(), VectorTy, RdxDesc.getFastMathFlags(), 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, 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 (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, CostKind, Op0);
InstructionCost MulCost =
TTI.getArithmeticInstrCost(Instruction::Mul, MulType, CostKind);
InstructionCost Ext2Cost =
TTI.getCastInstrCost(RedOp->getOpcode(), VectorTy, MulType,
TTI::CastContextHint::None, CostKind, RedOp);
InstructionCost RedCost = TTI.getMulAccReductionCost(
IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, 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(), CostKind);
InstructionCost ExtCost =
TTI.getCastInstrCost(RedOp->getOpcode(), VectorTy, ExtType,
TTI::CastContextHint::None, 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, CostKind, Op0);
InstructionCost ExtCost1 = TTI.getCastInstrCost(
Op1->getOpcode(), VectorTy, VectorType::get(Op1Ty, VectorTy),
TTI::CastContextHint::None, CostKind, Op1);
InstructionCost MulCost =
TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy, CostKind);
InstructionCost RedCost = TTI.getMulAccReductionCost(
IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, 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, 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, CostKind);
InstructionCost RedCost = TTI.getMulAccReductionCost(
true, RdxDesc.getRecurrenceType(), VectorTy, 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);
const Align Alignment = getLoadStoreAlignment(I);
unsigned AS = getLoadStoreAddressSpace(I);
TTI::OperandValueInfo OpInfo = TTI::getOperandInfo(I->getOperand(0));
return TTI.getAddressComputationCost(ValTy) +
TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS,
TTI::TCK_RecipThroughput, OpInfo, I);
}
return getWideningCost(I, VF);
}
InstructionCost LoopVectorizationCostModel::getScalarizationOverhead(
Instruction *I, ElementCount VF, TTI::TargetCostKind CostKind) 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 = ToVectorTy(I->getType(), VF);
if (!RetTy->isVoidTy() &&
(!isa<LoadInst>(I) || !TTI.supportsEfficientVectorElementLoadStore()))
Cost += TTI.getScalarizationOverhead(
cast<VectorType>(RetTy), APInt::getAllOnes(VF.getKnownMinValue()),
/*Insert*/ true,
/*Extract*/ false, CostKind);
// 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));
return Cost + TTI.getOperandsScalarizationOverhead(
filterExtractingOperands(Ops, VF), Tys, CostKind);
}
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 =
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)) {
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 =
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 (auto Group = getInterleavedAccessGroup(&I))
setWideningDecision(Group, VF, Decision, Cost);
else
setWideningDecision(&I, VF, Decision, Cost);
}
}
// Make sure that any load of address and any other address computation
// remains scalar unless there is gather/scatter support. This avoids
// inevitable extracts into address registers, and also has the benefit of
// activating LSR more, since that pass can't optimize vectorized
// addresses.
if (TTI.prefersVectorizedAddressing())
return;
// Start with all scalar pointer uses.
SmallPtrSet<Instruction *, 8> AddrDefs;
for (BasicBlock *BB : TheLoop->blocks())
for (Instruction &I : *BB) {
Instruction *PtrDef =
dyn_cast_or_null<Instruction>(getLoadStorePointerOperand(&I));
if (PtrDef && TheLoop->contains(PtrDef) &&
getWideningDecision(&I, VF) != CM_GatherScatter)
AddrDefs.insert(PtrDef);
}
// Add all instructions used to generate the addresses.
SmallVector<Instruction *, 4> Worklist;
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 ((InstOp->getParent() == I->getParent()) && !isa<PHINode>(InstOp) &&
AddrDefs.insert(InstOp).second)
Worklist.push_back(InstOp);
}
for (auto *I : AddrDefs) {
if (isa<LoadInst>(I)) {
// Setting the desired widening decision should ideally be handled in
// by cost functions, but since this involves the task of finding out
// if the loaded register is involved in an address computation, it is
// instead changed here when we know this is the case.
InstWidening Decision = getWideningDecision(I, VF);
if (Decision == CM_Widen || Decision == CM_Widen_Reverse)
// Scalarize a widened load of address.
setWideningDecision(
I, VF, CM_Scalarize,
(VF.getKnownMinValue() *
getMemoryInstructionCost(I, ElementCount::getFixed(1))));
else if (auto Group = getInterleavedAccessGroup(I)) {
// Scalarize an interleave group of address loads.
for (unsigned I = 0; I < Group->getFactor(); ++I) {
if (Instruction *Member = Group->getMember(I))
setWideningDecision(
Member, VF, CM_Scalarize,
(VF.getKnownMinValue() *
getMemoryInstructionCost(Member, ElementCount::getFixed(1))));
}
}
} else
// Make sure I gets scalarized and a cost estimate without
// scalarization overhead.
ForcedScalars[VF].insert(I);
}
}
void LoopVectorizationCostModel::setVectorizedCallDecision(ElementCount VF) {
assert(!VF.isScalar() &&
"Trying to set a vectorization decision for a scalar 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();
TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
Function *ScalarFunc = CI->getCalledFunction();
Type *ScalarRetTy = CI->getType();
SmallVector<Type *, 4> Tys, ScalarTys;
bool MaskRequired = Legal->isMaskRequired(CI);
for (auto &ArgOp : CI->args())
ScalarTys.push_back(ArgOp->getType());
// Compute corresponding vector type for return value and arguments.
Type *RetTy = ToVectorTy(ScalarRetTy, VF);
for (Type *ScalarTy : ScalarTys)
Tys.push_back(ToVectorTy(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, CostKind)) {
setCallWideningDecision(CI, VF, CM_IntrinsicCall, nullptr,
getVectorIntrinsicIDForCall(CI, TLI),
std::nullopt, *RedCost);
continue;
}
// 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.
InstructionCost ScalarCallCost =
TTI.getCallInstrCost(ScalarFunc, ScalarRetTy, ScalarTys, CostKind);
// Compute costs of unpacking argument values for the scalar calls and
// packing the return values to a vector.
InstructionCost ScalarizationCost =
getScalarizationOverhead(CI, VF, CostKind);
ScalarCost = ScalarCallCost * VF.getKnownMinValue() + ScalarizationCost;
// Find the cost of vectorizing the call, if we can find a suitable
// vector variant of the function.
bool UsesMask = false;
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();
const auto *SAR =
dyn_cast<SCEVAddRecExpr>(SE->getSCEV(ScalarParam));
if (!SAR || SAR->getLoop() != TheLoop) {
ParamsOk = false;
break;
}
const SCEVConstant *Step =
dyn_cast<SCEVConstant>(SAR->getStepRecurrence(*SE));
if (!Step ||
Step->getAPInt().getSExtValue() != Param.LinearStepOrPos)
ParamsOk = false;
break;
}
case VFParamKind::GlobalPredicate:
UsesMask = true;
break;
default:
ParamsOk = false;
break;
}
}
if (!ParamsOk)
continue;
// Found a suitable candidate, stop here.
VecFunc = CI->getModule()->getFunction(Info.VectorName);
FuncInfo = Info;
break;
}
// Add in the cost of synthesizing a mask if one wasn't required.
InstructionCost MaskCost = 0;
if (VecFunc && UsesMask && !MaskRequired)
MaskCost = TTI.getShuffleCost(
TargetTransformInfo::SK_Broadcast,
VectorType::get(IntegerType::getInt1Ty(
VecFunc->getFunctionType()->getContext()),
VF));
if (TLI && VecFunc && !CI->isNoBuiltin())
VectorCost =
TTI.getCallInstrCost(nullptr, RetTy, Tys, CostKind) + MaskCost;
// 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 <= Cost) {
Cost = VectorCost;
Decision = CM_VectorCall;
}
if (IntrinsicCost <= Cost) {
Cost = IntrinsicCost;
Decision = CM_IntrinsicCall;
}
setCallWideningDecision(CI, VF, Decision, VecFunc, IID,
FuncInfo.getParamIndexForOptionalMask(), Cost);
}
}
}
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();
}
Type *RetTy = I->getType();
if (canTruncateToMinimalBitwidth(I, VF))
RetTy = IntegerType::get(RetTy->getContext(), MinBWs[I]);
auto SE = PSE.getSE();
TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput;
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);
});
};
(void) hasSingleCopyAfterVectorization;
Type *VectorTy;
if (isScalarAfterVectorization(I, VF)) {
// 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 = ToVectorTy(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::Br: {
// In cases of scalarized and predicated instructions, there will be VF
// predicated blocks in the vectorized loop. Each branch around these
// blocks requires also an extract of its vector compare i1 element.
// 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;
BranchInst *BI = cast<BranchInst>(I);
if (VF.isVector() && BI->isConditional() &&
(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 *Vec_i1Ty =
VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
return (
TTI.getScalarizationOverhead(
Vec_i1Ty, APInt::getAllOnes(VF.getFixedValue()),
/*Insert*/ false, /*Extract*/ true, CostKind) +
(TTI.getCFInstrCost(Instruction::Br, CostKind) * VF.getFixedValue()));
} else if (I->getParent() == TheLoop->getLoopLatch() || VF.isScalar())
// The back-edge branch will remain, as will all scalar branches.
return TTI.getCFInstrCost(Instruction::Br, CostKind);
else
// This branch will be eliminated by if-conversion.
return 0;
// Note: We currently assume zero cost for an unconditional branch inside
// a predicated block since it will become a fall-through, although we
// may decide in the future to call TTI for all branches.
}
case Instruction::Switch: {
if (VF.isScalar())
return TTI.getCFInstrCost(Instruction::Switch, 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, 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)) {
// For <vscale x 1 x i64>, if vscale = 1 we are unable to extract the
// penultimate value of the recurrence.
// TODO: Consider vscale_range info.
if (VF.isScalable() && VF.getKnownMinValue() == 1)
return InstructionCost::getInvalid();
SmallVector<int> Mask(VF.getKnownMinValue());
std::iota(Mask.begin(), Mask.end(), VF.getKnownMinValue() - 1);
return TTI.getShuffleCost(TargetTransformInfo::SK_Splice,
cast<VectorType>(VectorTy), Mask, CostKind,
VF.getKnownMinValue() - 1);
}
// Phi nodes in non-header blocks (not inductions, reductions, etc.) are
// converted into select instructions. We require N - 1 selects per phi
// node, where N is the number of incoming values.
if (VF.isVector() && Phi->getParent() != TheLoop->getHeader())
return (Phi->getNumIncomingValues() - 1) *
TTI.getCmpSelInstrCost(
Instruction::Select, ToVectorTy(Phi->getType(), VF),
ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF),
CmpInst::BAD_ICMP_PREDICATE, CostKind);
return TTI.getCFInstrCost(Instruction::PHI, 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::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::FDiv:
case Instruction::FRem:
case Instruction::Shl:
case Instruction::LShr:
case Instruction::AShr:
case Instruction::And:
case Instruction::Or:
case Instruction::Xor: {
// 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 &&
(PSE.getSCEV(I->getOperand(0))->isOne() ||
PSE.getSCEV(I->getOperand(1))->isOne()))
return 0;
// Detect reduction patterns
if (auto RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind))
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);
auto Op2Info = TTI.getOperandInfo(Op2);
if (Op2Info.Kind == TargetTransformInfo::OK_AnyValue &&
Legal->isInvariant(Op2))
Op2Info.Kind = TargetTransformInfo::OK_UniformValue;
SmallVector<const Value *, 4> Operands(I->operand_values());
return TTI.getArithmeticInstrCost(
I->getOpcode(), VectorTy, CostKind,
{TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None},
Op2Info, Operands, I, TLI);
}
case Instruction::FNeg: {
return TTI.getArithmeticInstrCost(
I->getOpcode(), VectorTy, 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);
SmallVector<const Value *, 2> Operands{Op0, Op1};
return TTI.getArithmeticInstrCost(
match(I, m_LogicalOr()) ? Instruction::Or : Instruction::And, VectorTy,
CostKind, {Op1VK, Op1VP}, {Op2VK, Op2VP}, Operands, 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,
CostKind, I);
}
case Instruction::ICmp:
case Instruction::FCmp: {
Type *ValTy = I->getOperand(0)->getType();
Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0));
if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF))
ValTy = IntegerType::get(ValTy->getContext(), MinBWs[Op0AsInstruction]);
VectorTy = ToVectorTy(ValTy, VF);
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, nullptr,
cast<CmpInst>(I)->getPredicate(), CostKind,
I);
}
case Instruction::Store:
case Instruction::Load: {
ElementCount Width = VF;
if (Width.isVector()) {
InstWidening Decision = getWideningDecision(I, Width);
assert(Decision != CM_Unknown &&
"CM decision should be taken at this point");
if (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 Legal->isMaskRequired(I) ? TTI::CastContextHint::Masked
: TTI::CastContextHint::Normal;
case LoopVectorizationCostModel::CM_Widen_Reverse:
return TTI::CastContextHint::Reversed;
case LoopVectorizationCostModel::CM_Unknown:
llvm_unreachable("Instr did not go through cost modelling?");
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, CostKind, Trunc);
}
// Detect reduction patterns
if (auto RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind))
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[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, CostKind, I);
}
case Instruction::Call:
return getVectorCallCost(cast<CallInst>(I), VF);
case Instruction::ExtractValue:
return TTI.getInstructionCost(I, TTI::TCK_RecipThroughput);
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();
[[fallthrough]];
default:
// This opcode is unknown. Assume that it is the same as 'mul'.
return TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy, 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);
MapVector<Value *, SmallVector<Value *>> DeadInvariantStoreOps;
for (BasicBlock *BB : reverse(make_range(DFS.beginRPO(), DFS.endRPO())))
for (Instruction &I : reverse(*BB)) {
// Find all stores to invariant variables. Since they are going to sink
// outside the loop we do not need calculate cost for them.
StoreInst *SI;
if ((SI = dyn_cast<StoreInst>(&I)) &&
Legal->isInvariantAddressOfReduction(SI->getPointerOperand())) {
ValuesToIgnore.insert(&I);
auto I = DeadInvariantStoreOps.insert({SI->getPointerOperand(), {}});
I.first->second.push_back(SI->getValueOperand());
}
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 (auto *Br = dyn_cast<BranchInst>(&I)) {
if (Br->isConditional())
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);
DeadInterleavePointerOps.append(Op->op_begin(), Op->op_end());
}
for (const auto &[_, Ops] : DeadInvariantStoreOps) {
for (Value *Op : ArrayRef(Ops).drop_back())
DeadOps.push_back(Op);
}
// 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<BranchInst>(&I) && !cast<BranchInst>(&I)->isConditional());
});
};
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<BranchInst>(Op)) {
BasicBlock *ThenBB = Br->getSuccessor(0);
BasicBlock *ElseBB = Br->getSuccessor(1);
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 (!TheLoop->contains(Op->getParent()))
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);
DeadOps.append(Op->op_begin(), Op->op_end());
}
// 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(Casts.begin(), Casts.end());
}
// Ignore type-casting instructions we identified during induction
// detection.
for (const auto &Induction : Legal->getInductionVars()) {
const InductionDescriptor &IndDes = Induction.second;
const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts();
VecValuesToIgnore.insert(Casts.begin(), Casts.end());
}
}
void LoopVectorizationCostModel::collectInLoopReductions() {
for (const auto &Reduction : Legal->getReductionVars()) {
PHINode *Phi = Reduction.first;
const RecurrenceDescriptor &RdxDesc = Reduction.second;
// We don't collect reductions that are type promoted (yet).
if (RdxDesc.getRecurrenceType() != Phi->getType())
continue;
// If the target would prefer this reduction to happen "in-loop", then we
// want to record it as such.
unsigned Opcode = RdxDesc.getOpcode();
if (!PreferInLoopReductions && !useOrderedReductions(RdxDesc) &&
!TTI.preferInLoopReduction(Opcode, Phi->getType(),
TargetTransformInfo::ReductionFlags()))
continue;
// Check that we can correctly put the reductions into the loop, by
// finding the chain of operations that leads from the phi to the loop
// exit value.
SmallVector<Instruction *, 4> ReductionOperations =
RdxDesc.getReductionOpChain(Phi, TheLoop);
bool InLoop = !ReductionOperations.empty();
if (InLoop) {
InLoopReductions.insert(Phi);
// Add the elements to InLoopReductionImmediateChains for cost modelling.
Instruction *LastChain = Phi;
for (auto *I : ReductionOperations) {
InLoopReductionImmediateChains[I] = LastChain;
LastChain = I;
}
}
LLVM_DEBUG(dbgs() << "LV: Using " << (InLoop ? "inloop" : "out of loop")
<< " reduction for phi: " << *Phi << "\n");
}
}
VPValue *VPBuilder::createICmp(CmpInst::Predicate Pred, VPValue *A, VPValue *B,
DebugLoc DL, const Twine &Name) {
assert(Pred >= CmpInst::FIRST_ICMP_PREDICATE &&
Pred <= CmpInst::LAST_ICMP_PREDICATE && "invalid predicate");
return tryInsertInstruction(
new VPInstruction(Instruction::ICmp, Pred, A, B, DL, Name));
}
// 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,
LoopVectorizationCostModel &CM) {
unsigned WidestType;
std::tie(std::ignore, WidestType) = CM.getSmallestAndWidestTypes();
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, CM);
LLVM_DEBUG(dbgs() << "LV: VPlan computed VF " << VF << ".\n");
// Make sure we have a VF > 1 for stress testing.
if (VPlanBuildStressTest && (VF.isScalar() || VF.isZero())) {
LLVM_DEBUG(dbgs() << "LV: VPlan stress testing: "
<< "overriding computed VF.\n");
VF = ElementCount::getFixed(4);
}
} else if (UserVF.isScalable() && !TTI.supportsScalableVectors() &&
!ForceTargetSupportsScalableVectors) {
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);
// 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();
CM.collectElementTypesForWidening();
FixedScalableVFPair MaxFactors = CM.computeMaxVF(UserVF, UserIC);
if (!MaxFactors) // Cases that should not to be vectorized nor interleaved.
return;
// 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.
CM.collectInLoopReductions();
if (CM.selectUserVectorizationFactor(UserVF)) {
LLVM_DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
buildVPlansWithVPRecipes(UserVF, UserVF);
LLVM_DEBUG(printPlans(dbgs()));
return;
} else
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);
CM.collectInLoopReductions();
for (const auto &VF : VFCandidates) {
// Collect Uniform and Scalar instructions after vectorization with VF.
CM.collectUniformsAndScalars(VF);
// Collect the instructions (and their associated costs) that will be more
// profitable to scalarize.
if (VF.isVector())
CM.collectInstsToScalarize(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 {
return CM.getInstructionCost(UI, VF);
}
bool VPCostContext::skipCostComputation(Instruction *UI, bool IsVector) const {
return CM.ValuesToIgnore.contains(UI) ||
(IsVector && CM.VecValuesToIgnore.contains(UI)) ||
SkipCostComputation.contains(UI);
}
InstructionCost LoopVectorizationPlanner::cost(VPlan &Plan,
ElementCount VF) const {
InstructionCost Cost = 0;
LLVMContext &LLVMCtx = OrigLoop->getHeader()->getContext();
VPCostContext CostCtx(CM.TTI, *CM.TLI, Legal->getWidestInductionType(),
LLVMCtx, CM);
// 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);
}
for (Instruction *IVInst : IVInsts) {
if (!CostCtx.SkipCostComputation.insert(IVInst).second)
continue;
InstructionCost InductionCost = CostCtx.getLegacyCost(IVInst, VF);
LLVM_DEBUG({
dbgs() << "Cost of " << InductionCost << " for VF " << VF
<< ": induction instruction " << *IVInst << "\n";
});
Cost += InductionCost;
}
}
/// 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<BranchInst>(EB->getTerminator());
if (!Term)
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;
Cost += CostCtx.getLegacyCost(CondI, VF);
for (Value *Op : CondI->operands()) {
auto *OpI = dyn_cast<Instruction>(Op);
if (!OpI || any_of(OpI->users(), [&ExitInstrs, this](User *U) {
return OrigLoop->contains(cast<Instruction>(U)->getParent()) &&
!ExitInstrs.contains(cast<Instruction>(U));
}))
continue;
ExitInstrs.insert(OpI);
}
}
// The legacy cost model has special logic to compute the cost of in-loop
// reductions, which may be smaller than the sum of all instructions involved
// in the reduction. For AnyOf reductions, VPlan codegen may remove the select
// which the legacy cost model uses to assign cost. Pre-compute their costs
// for now.
// TODO: Switch to costing based on VPlan once the logic has been ported.
for (const auto &[RedPhi, RdxDesc] : Legal->getReductionVars()) {
if (!CM.isInLoopReduction(RedPhi) &&
!RecurrenceDescriptor::isAnyOfRecurrenceKind(
RdxDesc.getRecurrenceKind()))
continue;
// AnyOf reduction codegen may remove the select. To match the legacy cost
// model, pre-compute the cost for AnyOf reductions here.
if (RecurrenceDescriptor::isAnyOfRecurrenceKind(
RdxDesc.getRecurrenceKind())) {
auto *Select = cast<SelectInst>(*find_if(
RedPhi->users(), [](User *U) { return isa<SelectInst>(U); }));
assert(!CostCtx.SkipCostComputation.contains(Select) &&
"reduction op visited multiple times");
CostCtx.SkipCostComputation.insert(Select);
auto ReductionCost = CostCtx.getLegacyCost(Select, VF);
LLVM_DEBUG(dbgs() << "Cost of " << ReductionCost << " for VF " << VF
<< ":\n any-of reduction " << *Select << "\n");
Cost += ReductionCost;
continue;
}
const auto &ChainOps = RdxDesc.getReductionOpChain(RedPhi, OrigLoop);
SetVector<Instruction *> ChainOpsAndOperands(ChainOps.begin(),
ChainOps.end());
// Also include the operands of instructions in the chain, as the cost-model
// may mark extends as free.
for (auto *ChainOp : ChainOps) {
for (Value *Op : ChainOp->operands()) {
if (auto *I = dyn_cast<Instruction>(Op))
ChainOpsAndOperands.insert(I);
}
}
// Pre-compute the cost for I, if it has a reduction pattern cost.
for (Instruction *I : ChainOpsAndOperands) {
auto ReductionCost = CM.getReductionPatternCost(
I, VF, ToVectorTy(I->getType(), VF), TTI::TCK_RecipThroughput);
if (!ReductionCost)
continue;
assert(!CostCtx.SkipCostComputation.contains(I) &&
"reduction op visited multiple times");
CostCtx.SkipCostComputation.insert(I);
LLVM_DEBUG(dbgs() << "Cost of " << ReductionCost << " for VF " << VF
<< ":\n in-loop reduction " << *I << "\n");
Cost += *ReductionCost;
}
}
// 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 (BB == OrigLoop->getLoopLatch())
continue;
CostCtx.SkipCostComputation.insert(BB->getTerminator());
auto BranchCost = CostCtx.getLegacyCost(BB->getTerminator(), VF);
Cost += BranchCost;
}
// Now compute and add the VPlan-based cost.
Cost += Plan.cost(VF, CostCtx);
LLVM_DEBUG(dbgs() << "Cost for VF " << VF << ": " << Cost << "\n");
return Cost;
}
VectorizationFactor LoopVectorizationPlanner::computeBestVF() {
if (VPlans.empty())
return VectorizationFactor::Disabled();
// If there is a single VPlan with a single VF, return it directly.
VPlan &FirstPlan = *VPlans[0];
if (VPlans.size() == 1 && size(FirstPlan.vectorFactors()) == 1)
return {*FirstPlan.vectorFactors().begin(), 0, 0};
ElementCount ScalarVF = ElementCount::getFixed(1);
assert(hasPlanWithVF(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();
}
for (auto &P : VPlans) {
for (ElementCount VF : P->vectorFactors()) {
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;
}
InstructionCost Cost = cost(*P, VF);
VectorizationFactor CurrentFactor(VF, Cost, ScalarCost);
if (isMoreProfitable(CurrentFactor, BestFactor))
BestFactor = CurrentFactor;
// If profitable add it to ProfitableVF list.
if (isMoreProfitable(CurrentFactor, ScalarFactor))
ProfitableVFs.push_back(CurrentFactor);
}
}
#ifndef NDEBUG
// Select the optimal vectorization factor according to the legacy cost-model.
// This is now only used to verify the decisions by the new VPlan-based
// cost-model and will be retired once the VPlan-based cost-model is
// stabilized.
VectorizationFactor LegacyVF = selectVectorizationFactor();
assert(BestFactor.Width == LegacyVF.Width &&
" VPlan cost model and legacy cost model disagreed");
assert((BestFactor.Width.isScalar() || BestFactor.ScalarCost > 0) &&
"when vectorizing, the scalar cost must be computed.");
#endif
return BestFactor;
}
static void AddRuntimeUnrollDisableMetaData(Loop *L) {
SmallVector<Metadata *, 4> MDs;
// Reserve first location for self reference to the LoopID metadata node.
MDs.push_back(nullptr);
bool IsUnrollMetadata = false;
MDNode *LoopID = L->getLoopID();
if (LoopID) {
// First find existing loop unrolling disable metadata.
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
auto *MD = dyn_cast<MDNode>(LoopID->getOperand(i));
if (MD) {
const auto *S = dyn_cast<MDString>(MD->getOperand(0));
IsUnrollMetadata =
S && S->getString().starts_with("llvm.loop.unroll.disable");
}
MDs.push_back(LoopID->getOperand(i));
}
}
if (!IsUnrollMetadata) {
// Add runtime unroll disable metadata.
LLVMContext &Context = L->getHeader()->getContext();
SmallVector<Metadata *, 1> DisableOperands;
DisableOperands.push_back(
MDString::get(Context, "llvm.loop.unroll.runtime.disable"));
MDNode *DisableNode = MDNode::get(Context, DisableOperands);
MDs.push_back(DisableNode);
MDNode *NewLoopID = MDNode::get(Context, MDs);
// Set operand 0 to refer to the loop id itself.
NewLoopID->replaceOperandWith(0, NewLoopID);
L->setLoopID(NewLoopID);
}
}
// Check if \p RedResult is a ComputeReductionResult instruction, and if it is
// create a merge phi node for it and add it to \p ReductionResumeValues.
static void createAndCollectMergePhiForReduction(
VPInstruction *RedResult,
DenseMap<const RecurrenceDescriptor *, Value *> &ReductionResumeValues,
VPTransformState &State, Loop *OrigLoop, BasicBlock *LoopMiddleBlock,
bool VectorizingEpilogue) {
if (!RedResult ||
RedResult->getOpcode() != VPInstruction::ComputeReductionResult)
return;
auto *PhiR = cast<VPReductionPHIRecipe>(RedResult->getOperand(0));
const RecurrenceDescriptor &RdxDesc = PhiR->getRecurrenceDescriptor();
Value *FinalValue =
State.get(RedResult, VPIteration(State.UF - 1, VPLane::getFirstLane()));
auto *ResumePhi =
dyn_cast<PHINode>(PhiR->getStartValue()->getUnderlyingValue());
if (VectorizingEpilogue && RecurrenceDescriptor::isAnyOfRecurrenceKind(
RdxDesc.getRecurrenceKind())) {
auto *Cmp = cast<ICmpInst>(PhiR->getStartValue()->getUnderlyingValue());
assert(Cmp->getPredicate() == CmpInst::ICMP_NE);
assert(Cmp->getOperand(1) == RdxDesc.getRecurrenceStartValue());
ResumePhi = cast<PHINode>(Cmp->getOperand(0));
}
assert((!VectorizingEpilogue || ResumePhi) &&
"when vectorizing the epilogue loop, we need a resume phi from main "
"vector loop");
// TODO: bc.merge.rdx should not be created here, instead it should be
// modeled in VPlan.
BasicBlock *LoopScalarPreHeader = OrigLoop->getLoopPreheader();
// Create a phi node that merges control-flow from the backedge-taken check
// block and the middle block.
auto *BCBlockPhi =
PHINode::Create(FinalValue->getType(), 2, "bc.merge.rdx",
LoopScalarPreHeader->getTerminator()->getIterator());
// If we are fixing reductions in the epilogue loop then we should already
// have created a bc.merge.rdx Phi after the main vector body. Ensure that
// we carry over the incoming values correctly.
for (auto *Incoming : predecessors(LoopScalarPreHeader)) {
if (Incoming == LoopMiddleBlock)
BCBlockPhi->addIncoming(FinalValue, Incoming);
else if (ResumePhi && is_contained(ResumePhi->blocks(), Incoming))
BCBlockPhi->addIncoming(ResumePhi->getIncomingValueForBlock(Incoming),
Incoming);
else
BCBlockPhi->addIncoming(RdxDesc.getRecurrenceStartValue(), Incoming);
}
auto *OrigPhi = cast<PHINode>(PhiR->getUnderlyingValue());
// TODO: This fixup should instead be modeled in VPlan.
// Fix the scalar loop reduction variable with the incoming reduction sum
// from the vector body and from the backedge value.
int IncomingEdgeBlockIdx =
OrigPhi->getBasicBlockIndex(OrigLoop->getLoopLatch());
assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
// Pick the other block.
int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
OrigPhi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi);
Instruction *LoopExitInst = RdxDesc.getLoopExitInstr();
OrigPhi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst);
ReductionResumeValues[&RdxDesc] = BCBlockPhi;
}
std::pair<DenseMap<const SCEV *, Value *>,
DenseMap<const RecurrenceDescriptor *, Value *>>
LoopVectorizationPlanner::executePlan(
ElementCount BestVF, unsigned BestUF, VPlan &BestVPlan,
InnerLoopVectorizer &ILV, DominatorTree *DT, bool IsEpilogueVectorization,
const DenseMap<const SCEV *, Value *> *ExpandedSCEVs) {
assert(BestVPlan.hasVF(BestVF) &&
"Trying to execute plan with unsupported VF");
assert(BestVPlan.hasUF(BestUF) &&
"Trying to execute plan with unsupported UF");
assert(
(IsEpilogueVectorization || !ExpandedSCEVs) &&
"expanded SCEVs to reuse can only be used during epilogue vectorization");
(void)IsEpilogueVectorization;
VPlanTransforms::optimizeForVFAndUF(BestVPlan, BestVF, BestUF, PSE);
LLVM_DEBUG(dbgs() << "Executing best plan with VF=" << BestVF
<< ", UF=" << BestUF << '\n');
BestVPlan.setName("Final VPlan");
LLVM_DEBUG(BestVPlan.dump());
// Perform the actual loop transformation.
VPTransformState State(BestVF, BestUF, LI, DT, ILV.Builder, &ILV, &BestVPlan,
OrigLoop->getHeader()->getContext());
// 0. Generate SCEV-dependent code into the preheader, including TripCount,
// before making any changes to the CFG.
if (!BestVPlan.getPreheader()->empty()) {
State.CFG.PrevBB = OrigLoop->getLoopPreheader();
State.Builder.SetInsertPoint(OrigLoop->getLoopPreheader()->getTerminator());
BestVPlan.getPreheader()->execute(&State);
}
if (!ILV.getTripCount())
ILV.setTripCount(State.get(BestVPlan.getTripCount(), {0, 0}));
else
assert(IsEpilogueVectorization && "should only re-use the existing trip "
"count during epilogue vectorization");
// 1. Set up the skeleton for vectorization, including vector pre-header and
// middle block. The vector loop is created during VPlan execution.
Value *CanonicalIVStartValue;
std::tie(State.CFG.PrevBB, CanonicalIVStartValue) =
ILV.createVectorizedLoopSkeleton(ExpandedSCEVs ? *ExpandedSCEVs
: State.ExpandedSCEVs);
#ifdef EXPENSIVE_CHECKS
assert(DT->verify(DominatorTree::VerificationLevel::Fast));
#endif
// Only use noalias metadata when using memory checks guaranteeing no overlap
// across all iterations.
const LoopAccessInfo *LAI = ILV.Legal->getLAI();
std::unique_ptr<LoopVersioning> LVer = nullptr;
if (LAI && !LAI->getRuntimePointerChecking()->getChecks().empty() &&
!LAI->getRuntimePointerChecking()->getDiffChecks()) {
// We currently don't use LoopVersioning for the actual loop cloning but we
// still use it to add the noalias metadata.
// TODO: Find a better way to re-use LoopVersioning functionality to add
// metadata.
LVer = std::make_unique<LoopVersioning>(
*LAI, LAI->getRuntimePointerChecking()->getChecks(), OrigLoop, LI, DT,
PSE.getSE());
State.LVer = &*LVer;
State.LVer->prepareNoAliasMetadata();
}
ILV.printDebugTracesAtStart();
//===------------------------------------------------===//
//
// Notice: any optimization or new instruction that go
// into the code below should also be implemented in
// the cost-model.
//
//===------------------------------------------------===//
// 2. Copy and widen instructions from the old loop into the new loop.
BestVPlan.prepareToExecute(ILV.getTripCount(),
ILV.getOrCreateVectorTripCount(nullptr),
CanonicalIVStartValue, State);
BestVPlan.execute(&State);
// 2.5 Collect reduction resume values.
DenseMap<const RecurrenceDescriptor *, Value *> ReductionResumeValues;
auto *ExitVPBB =
cast<VPBasicBlock>(BestVPlan.getVectorLoopRegion()->getSingleSuccessor());
for (VPRecipeBase &R : *ExitVPBB) {
createAndCollectMergePhiForReduction(
dyn_cast<VPInstruction>(&R), ReductionResumeValues, State, OrigLoop,
State.CFG.VPBB2IRBB[ExitVPBB], ExpandedSCEVs);
}
// 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).
MDNode *OrigLoopID = OrigLoop->getLoopID();
std::optional<MDNode *> VectorizedLoopID =
makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll,
LLVMLoopVectorizeFollowupVectorized});
VPBasicBlock *HeaderVPBB =
BestVPlan.getVectorLoopRegion()->getEntryBasicBlock();
Loop *L = LI->getLoopFor(State.CFG.VPBB2IRBB[HeaderVPBB]);
if (VectorizedLoopID)
L->setLoopID(*VectorizedLoopID);
else {
// Keep all loop hints from the original loop on the vector loop (we'll
// replace the vectorizer-specific hints below).
if (MDNode *LID = OrigLoop->getLoopID())
L->setLoopID(LID);
LoopVectorizeHints Hints(L, true, *ORE);
Hints.setAlreadyVectorized();
}
TargetTransformInfo::UnrollingPreferences UP;
TTI.getUnrollingPreferences(L, *PSE.getSE(), UP, ORE);
if (!UP.UnrollVectorizedLoop || CanonicalIVStartValue)
AddRuntimeUnrollDisableMetaData(L);
// 3. Fix the vectorized code: take care of header phi's, live-outs,
// predication, updating analyses.
ILV.fixVectorizedLoop(State, BestVPlan);
ILV.printDebugTracesAtEnd();
// 4. Adjust branch weight of the branch in the middle block.
auto *MiddleTerm =
cast<BranchInst>(State.CFG.VPBB2IRBB[ExitVPBB]->getTerminator());
if (MiddleTerm->isConditional() &&
hasBranchWeightMD(*OrigLoop->getLoopLatch()->getTerminator())) {
// Assume that `Count % VectorTripCount` is equally distributed.
unsigned TripCount = State.UF * State.VF.getKnownMinValue();
assert(TripCount > 0 && "trip count should not be zero");
const uint32_t Weights[] = {1, TripCount - 1};
setBranchWeights(*MiddleTerm, Weights, /*IsExpected=*/false);
}
return {State.ExpandedSCEVs, ReductionResumeValues};
}
//===--------------------------------------------------------------------===//
// EpilogueVectorizerMainLoop
//===--------------------------------------------------------------------===//
/// This function is partially responsible for generating the control flow
/// depicted in https://llvm.org/docs/Vectorizers.html#epilogue-vectorization.
std::pair<BasicBlock *, Value *>
EpilogueVectorizerMainLoop::createEpilogueVectorizedLoopSkeleton(
const SCEV2ValueTy &ExpandedSCEVs) {
createVectorLoopSkeleton("");
// Generate the code to check the minimum iteration count of the vector
// epilogue (see below).
EPI.EpilogueIterationCountCheck =
emitIterationCountCheck(LoopScalarPreHeader, true);
EPI.EpilogueIterationCountCheck->setName("iter.check");
// Generate the code to check any assumptions that we've made for SCEV
// expressions.
EPI.SCEVSafetyCheck = emitSCEVChecks(LoopScalarPreHeader);
// Generate the code that checks at runtime if arrays overlap. We put the
// checks into a separate block to make the more common case of few elements
// faster.
EPI.MemSafetyCheck = emitMemRuntimeChecks(LoopScalarPreHeader);
// Generate the iteration count check for the main loop, *after* the check
// for the epilogue loop, so that the path-length is shorter for the case
// that goes directly through the vector epilogue. The longer-path length for
// the main loop is compensated for, by the gain from vectorizing the larger
// trip count. Note: the branch will get updated later on when we vectorize
// the epilogue.
EPI.MainLoopIterationCountCheck =
emitIterationCountCheck(LoopScalarPreHeader, false);
// Generate the induction variable.
EPI.VectorTripCount = getOrCreateVectorTripCount(LoopVectorPreHeader);
// Skip induction resume value creation here because they will be created in
// the second pass for the scalar loop. The induction resume values for the
// inductions in the epilogue loop are created before executing the plan for
// the epilogue loop.
return {LoopVectorPreHeader, nullptr};
}
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";
});
}
BasicBlock *
EpilogueVectorizerMainLoop::emitIterationCountCheck(BasicBlock *Bypass,
bool ForEpilogue) {
assert(Bypass && "Expected valid bypass basic block.");
ElementCount VFactor = ForEpilogue ? EPI.EpilogueVF : VF;
unsigned UFactor = ForEpilogue ? EPI.EpilogueUF : UF;
Value *Count = getTripCount();
// Reuse existing vector loop preheader for TC checks.
// Note that new preheader block is generated for vector loop.
BasicBlock *const TCCheckBlock = LoopVectorPreHeader;
IRBuilder<> Builder(TCCheckBlock->getTerminator());
// Generate code to check if the loop's trip count is less than VF * UF of the
// main vector loop.
auto P = Cost->requiresScalarEpilogue(ForEpilogue ? EPI.EpilogueVF.isVector()
: VF.isVector())
? ICmpInst::ICMP_ULE
: ICmpInst::ICMP_ULT;
Value *CheckMinIters = Builder.CreateICmp(
P, Count, createStepForVF(Builder, Count->getType(), VFactor, UFactor),
"min.iters.check");
if (!ForEpilogue)
TCCheckBlock->setName("vector.main.loop.iter.check");
// Create new preheader for vector loop.
LoopVectorPreHeader = SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(),
DT, LI, nullptr, "vector.ph");
if (ForEpilogue) {
assert(DT->properlyDominates(DT->getNode(TCCheckBlock),
DT->getNode(Bypass)->getIDom()) &&
"TC check is expected to dominate Bypass");
// Update dominator for Bypass.
DT->changeImmediateDominator(Bypass, TCCheckBlock);
LoopBypassBlocks.push_back(TCCheckBlock);
// Save the trip count so we don't have to regenerate it in the
// vec.epilog.iter.check. This is safe to do because the trip count
// generated here dominates the vector epilog iter check.
EPI.TripCount = Count;
}
BranchInst &BI =
*BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters);
if (hasBranchWeightMD(*OrigLoop->getLoopLatch()->getTerminator()))
setBranchWeights(BI, MinItersBypassWeights, /*IsExpected=*/false);
ReplaceInstWithInst(TCCheckBlock->getTerminator(), &BI);
return TCCheckBlock;
}
//===--------------------------------------------------------------------===//
// EpilogueVectorizerEpilogueLoop
//===--------------------------------------------------------------------===//
/// This function is partially responsible for generating the control flow
/// depicted in https://llvm.org/docs/Vectorizers.html#epilogue-vectorization.
std::pair<BasicBlock *, Value *>
EpilogueVectorizerEpilogueLoop::createEpilogueVectorizedLoopSkeleton(
const SCEV2ValueTy &ExpandedSCEVs) {
createVectorLoopSkeleton("vec.epilog.");
// Now, compare the remaining count and if there aren't enough iterations to
// execute the vectorized epilogue skip to the scalar part.
LoopVectorPreHeader->setName("vec.epilog.ph");
BasicBlock *VecEpilogueIterationCountCheck =
SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->begin(), DT, LI,
nullptr, "vec.epilog.iter.check", true);
emitMinimumVectorEpilogueIterCountCheck(LoopScalarPreHeader,
VecEpilogueIterationCountCheck);
// 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.");
EPI.MainLoopIterationCountCheck->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, LoopVectorPreHeader);
DT->changeImmediateDominator(LoopVectorPreHeader,
EPI.MainLoopIterationCountCheck);
EPI.EpilogueIterationCountCheck->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, LoopScalarPreHeader);
if (EPI.SCEVSafetyCheck)
EPI.SCEVSafetyCheck->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, LoopScalarPreHeader);
if (EPI.MemSafetyCheck)
EPI.MemSafetyCheck->getTerminator()->replaceUsesOfWith(
VecEpilogueIterationCountCheck, LoopScalarPreHeader);
DT->changeImmediateDominator(
VecEpilogueIterationCountCheck,
VecEpilogueIterationCountCheck->getSinglePredecessor());
DT->changeImmediateDominator(LoopScalarPreHeader,
EPI.EpilogueIterationCountCheck);
if (!Cost->requiresScalarEpilogue(EPI.EpilogueVF.isVector()))
// If there is an epilogue which must run, there's no edge from the
// middle block to exit blocks and thus no need to update the immediate
// dominator of the exit blocks.
DT->changeImmediateDominator(LoopExitBlock,
EPI.EpilogueIterationCountCheck);
// Keep track of bypass blocks, as they feed start values to the induction and
// reduction phis in the scalar loop preheader.
if (EPI.SCEVSafetyCheck)
LoopBypassBlocks.push_back(EPI.SCEVSafetyCheck);
if (EPI.MemSafetyCheck)
LoopBypassBlocks.push_back(EPI.MemSafetyCheck);
LoopBypassBlocks.push_back(EPI.EpilogueIterationCountCheck);
// 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;
for (PHINode &Phi : VecEpilogueIterationCountCheck->phis())
PhisInBlock.push_back(&Phi);
for (PHINode *Phi : PhisInBlock) {
Phi->moveBefore(LoopVectorPreHeader->getFirstNonPHI());
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 (EPI.SCEVSafetyCheck)
Phi->removeIncomingValue(EPI.SCEVSafetyCheck);
if (EPI.MemSafetyCheck)
Phi->removeIncomingValue(EPI.MemSafetyCheck);
}
// Generate a resume induction for the vector epilogue and put it in the
// vector epilogue preheader
Type *IdxTy = Legal->getWidestInductionType();
PHINode *EPResumeVal = PHINode::Create(IdxTy, 2, "vec.epilog.resume.val");
EPResumeVal->insertBefore(LoopVectorPreHeader->getFirstNonPHIIt());
EPResumeVal->addIncoming(EPI.VectorTripCount, VecEpilogueIterationCountCheck);
EPResumeVal->addIncoming(ConstantInt::get(IdxTy, 0),
EPI.MainLoopIterationCountCheck);
// Generate induction resume values. These variables save the new starting
// indexes for the scalar loop. They are used to test if there are any tail
// iterations left once the vector loop has completed.
// Note that when the vectorized epilogue is skipped due to iteration count
// check, then the resume value for the induction variable comes from
// the trip count of the main vector loop, hence passing the AdditionalBypass
// argument.
createInductionResumeValues(ExpandedSCEVs,
{VecEpilogueIterationCountCheck,
EPI.VectorTripCount} /* AdditionalBypass */);
return {LoopVectorPreHeader, EPResumeVal};
}
BasicBlock *
EpilogueVectorizerEpilogueLoop::emitMinimumVectorEpilogueIterCountCheck(
BasicBlock *Bypass, BasicBlock *Insert) {
assert(EPI.TripCount &&
"Expected trip count to have been safed in the first pass.");
assert(
(!isa<Instruction>(EPI.TripCount) ||
DT->dominates(cast<Instruction>(EPI.TripCount)->getParent(), Insert)) &&
"saved trip count does not dominate insertion point.");
Value *TC = EPI.TripCount;
IRBuilder<> Builder(Insert->getTerminator());
Value *Count = Builder.CreateSub(TC, EPI.VectorTripCount, "n.vec.remaining");
// Generate code to check if the loop's trip count is less than VF * UF of the
// vector epilogue loop.
auto P = Cost->requiresScalarEpilogue(EPI.EpilogueVF.isVector())
? ICmpInst::ICMP_ULE
: ICmpInst::ICMP_ULT;
Value *CheckMinIters =
Builder.CreateICmp(P, Count,
createStepForVF(Builder, Count->getType(),
EPI.EpilogueVF, EPI.EpilogueUF),
"min.epilog.iters.check");
BranchInst &BI =
*BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters);
if (hasBranchWeightMD(*OrigLoop->getLoopLatch()->getTerminator())) {
unsigned MainLoopStep = UF * VF.getKnownMinValue();
unsigned EpilogueLoopStep =
EPI.EpilogueUF * EPI.EpilogueVF.getKnownMinValue();
// We assume the remaining `Count` is equally distributed in
// [0, MainLoopStep)
// So the probability for `Count < EpilogueLoopStep` should be
// min(MainLoopStep, EpilogueLoopStep) / MainLoopStep
unsigned EstimatedSkipCount = std::min(MainLoopStep, EpilogueLoopStep);
const uint32_t Weights[] = {EstimatedSkipCount,
MainLoopStep - EstimatedSkipCount};
setBranchWeights(BI, Weights, /*IsExpected=*/false);
}
ReplaceInstWithInst(Insert->getTerminator(), &BI);
LoopBypassBlocks.push_back(Insert);
return Insert;
}
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";
});
}
iterator_range<mapped_iterator<Use *, std::function<VPValue *(Value *)>>>
VPRecipeBuilder::mapToVPValues(User::op_range Operands) {
std::function<VPValue *(Value *)> Fn = [this](Value *Op) {
if (auto *I = dyn_cast<Instruction>(Op)) {
if (auto *R = Ingredient2Recipe.lookup(I))
return R->getVPSingleValue();
}
return Plan.getOrAddLiveIn(Op);
};
return map_range(Operands, Fn);
}
void VPRecipeBuilder::createSwitchEdgeMasks(SwitchInst *SI) {
BasicBlock *Src = SI->getParent();
assert(!OrigLoop->isLoopExiting(Src) &&
all_of(successors(Src),
[this](BasicBlock *Succ) {
return OrigLoop->getHeader() != Succ;
}) &&
"unsupported switch either exiting loop or continuing to header");
// Create masks where the terminator in Src is a switch. We create mask for
// all edges at the same time. This is more efficient, as we can create and
// collect compares for all cases once.
VPValue *Cond = getVPValueOrAddLiveIn(SI->getCondition(), Plan);
BasicBlock *DefaultDst = SI->getDefaultDest();
MapVector<BasicBlock *, SmallVector<VPValue *>> Dst2Compares;
for (auto &C : SI->cases()) {
BasicBlock *Dst = C.getCaseSuccessor();
assert(!EdgeMaskCache.contains({Src, Dst}) && "Edge masks already created");
// Cases whose destination is the same as default are redundant and can be
// ignored - they will get there anyhow.
if (Dst == DefaultDst)
continue;
auto I = Dst2Compares.insert({Dst, {}});
VPValue *V = getVPValueOrAddLiveIn(C.getCaseValue(), Plan);
I.first->second.push_back(Builder.createICmp(CmpInst::ICMP_EQ, Cond, V));
}
// We need to handle 2 separate cases below for all entries in Dst2Compares,
// which excludes destinations matching the default destination.
VPValue *SrcMask = getBlockInMask(Src);
VPValue *DefaultMask = nullptr;
for (const auto &[Dst, Conds] : Dst2Compares) {
// 1. Dst is not the default destination. Dst is reached if any of the cases
// with destination == Dst are taken. Join the conditions for each case
// whose destination == Dst using an OR.
VPValue *Mask = Conds[0];
for (VPValue *V : ArrayRef<VPValue *>(Conds).drop_front())
Mask = Builder.createOr(Mask, V);
if (SrcMask)
Mask = Builder.createLogicalAnd(SrcMask, Mask);
EdgeMaskCache[{Src, Dst}] = Mask;
// 2. Create the mask for the default destination, which is reached if none
// of the cases with destination != default destination are taken. Join the
// conditions for each case where the destination is != Dst using an OR and
// negate it.
DefaultMask = DefaultMask ? Builder.createOr(DefaultMask, Mask) : Mask;
}
if (DefaultMask) {
DefaultMask = Builder.createNot(DefaultMask);
if (SrcMask)
DefaultMask = Builder.createLogicalAnd(SrcMask, DefaultMask);
}
EdgeMaskCache[{Src, DefaultDst}] = DefaultMask;
}
VPValue *VPRecipeBuilder::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) {
assert(is_contained(predecessors(Dst), Src) && "Invalid edge");
// Look for cached value.
std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst);
EdgeMaskCacheTy::iterator ECEntryIt = EdgeMaskCache.find(Edge);
if (ECEntryIt != EdgeMaskCache.end())
return ECEntryIt->second;
if (auto *SI = dyn_cast<SwitchInst>(Src->getTerminator())) {
createSwitchEdgeMasks(SI);
assert(EdgeMaskCache.contains(Edge) && "Mask for Edge not created?");
return EdgeMaskCache[Edge];
}
VPValue *SrcMask = getBlockInMask(Src);
// The terminator has to be a branch inst!
BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator());
assert(BI && "Unexpected terminator found");
if (!BI->isConditional() || BI->getSuccessor(0) == BI->getSuccessor(1))
return EdgeMaskCache[Edge] = SrcMask;
// If source is an exiting block, we know the exit edge is dynamically dead
// in the vector loop, and thus we don't need to restrict the mask. Avoid
// adding uses of an otherwise potentially dead instruction.
if (OrigLoop->isLoopExiting(Src))
return EdgeMaskCache[Edge] = SrcMask;
VPValue *EdgeMask = getVPValueOrAddLiveIn(BI->getCondition(), Plan);
assert(EdgeMask && "No Edge Mask found for condition");
if (BI->getSuccessor(0) != Dst)
EdgeMask = Builder.createNot(EdgeMask, BI->getDebugLoc());
if (SrcMask) { // Otherwise block in-mask is all-one, no need to AND.
// The bitwise 'And' of SrcMask and EdgeMask introduces new UB if SrcMask
// is false and EdgeMask is poison. Avoid that by using 'LogicalAnd'
// instead which generates 'select i1 SrcMask, i1 EdgeMask, i1 false'.
EdgeMask = Builder.createLogicalAnd(SrcMask, EdgeMask, BI->getDebugLoc());
}
return EdgeMaskCache[Edge] = EdgeMask;
}
VPValue *VPRecipeBuilder::getEdgeMask(BasicBlock *Src, BasicBlock *Dst) const {
assert(is_contained(predecessors(Dst), Src) && "Invalid edge");
// Look for cached value.
std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst);
EdgeMaskCacheTy::const_iterator ECEntryIt = EdgeMaskCache.find(Edge);
assert(ECEntryIt != EdgeMaskCache.end() &&
"looking up mask for edge which has not been created");
return ECEntryIt->second;
}
void VPRecipeBuilder::createHeaderMask() {
BasicBlock *Header = OrigLoop->getHeader();
// When not folding the tail, use nullptr to model all-true mask.
if (!CM.foldTailByMasking()) {
BlockMaskCache[Header] = nullptr;
return;
}
// Introduce the early-exit compare IV <= BTC to form header block mask.
// This is used instead of IV < TC because TC may wrap, unlike BTC. Start by
// constructing the desired canonical IV in the header block as its first
// non-phi instructions.
VPBasicBlock *HeaderVPBB = Plan.getVectorLoopRegion()->getEntryBasicBlock();
auto NewInsertionPoint = HeaderVPBB->getFirstNonPhi();
auto *IV = new VPWidenCanonicalIVRecipe(Plan.getCanonicalIV());
HeaderVPBB->insert(IV, NewInsertionPoint);
VPBuilder::InsertPointGuard Guard(Builder);
Builder.setInsertPoint(HeaderVPBB, NewInsertionPoint);
VPValue *BlockMask = nullptr;
VPValue *BTC = Plan.getOrCreateBackedgeTakenCount();
BlockMask = Builder.createICmp(CmpInst::ICMP_ULE, IV, BTC);
BlockMaskCache[Header] = BlockMask;
}
VPValue *VPRecipeBuilder::getBlockInMask(BasicBlock *BB) const {
// Return the cached value.
BlockMaskCacheTy::const_iterator BCEntryIt = BlockMaskCache.find(BB);
assert(BCEntryIt != BlockMaskCache.end() &&
"Trying to access mask for block without one.");
return BCEntryIt->second;
}
void VPRecipeBuilder::createBlockInMask(BasicBlock *BB) {
assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
assert(BlockMaskCache.count(BB) == 0 && "Mask for block already computed");
assert(OrigLoop->getHeader() != BB &&
"Loop header must have cached block mask");
// All-one mask is modelled as no-mask following the convention for masked
// load/store/gather/scatter. Initialize BlockMask to no-mask.
VPValue *BlockMask = nullptr;
// This is the block mask. We OR all unique incoming edges.
for (auto *Predecessor :
SetVector<BasicBlock *>(pred_begin(BB), pred_end(BB))) {
VPValue *EdgeMask = createEdgeMask(Predecessor, BB);
if (!EdgeMask) { // Mask of predecessor is all-one so mask of block is too.
BlockMaskCache[BB] = EdgeMask;
return;
}
if (!BlockMask) { // BlockMask has its initialized nullptr value.
BlockMask = EdgeMask;
continue;
}
BlockMask = Builder.createOr(BlockMask, EdgeMask, {});
}
BlockMaskCache[BB] = BlockMask;
}
VPWidenMemoryRecipe *
VPRecipeBuilder::tryToWidenMemory(Instruction *I, ArrayRef<VPValue *> Operands,
VFRange &Range) {
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
"Must be called with either a load or store");
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;
VPValue *Mask = nullptr;
if (Legal->isMaskRequired(I))
Mask = getBlockInMask(I->getParent());
// 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 = isa<LoadInst>(I) ? Operands[0] : Operands[1];
if (Consecutive) {
auto *GEP = dyn_cast<GetElementPtrInst>(
Ptr->getUnderlyingValue()->stripPointerCasts());
auto *VectorPtr = new VPVectorPointerRecipe(
Ptr, getLoadStoreType(I), Reverse, GEP ? GEP->isInBounds() : false,
I->getDebugLoc());
Builder.getInsertBlock()->appendRecipe(VectorPtr);
Ptr = VectorPtr;
}
if (LoadInst *Load = dyn_cast<LoadInst>(I))
return new VPWidenLoadRecipe(*Load, Ptr, Mask, Consecutive, Reverse,
I->getDebugLoc());
StoreInst *Store = cast<StoreInst>(I);
return new VPWidenStoreRecipe(*Store, Ptr, Operands[0], Mask, Consecutive,
Reverse, I->getDebugLoc());
}
/// Creates a VPWidenIntOrFpInductionRecpipe for \p Phi. If needed, it will also
/// insert a recipe to expand the step for the induction recipe.
static VPWidenIntOrFpInductionRecipe *
createWidenInductionRecipes(PHINode *Phi, Instruction *PhiOrTrunc,
VPValue *Start, const InductionDescriptor &IndDesc,
VPlan &Plan, ScalarEvolution &SE, Loop &OrigLoop) {
assert(IndDesc.getStartValue() ==
Phi->getIncomingValueForBlock(OrigLoop.getLoopPreheader()));
assert(SE.isLoopInvariant(IndDesc.getStep(), &OrigLoop) &&
"step must be loop invariant");
VPValue *Step =
vputils::getOrCreateVPValueForSCEVExpr(Plan, IndDesc.getStep(), SE);
if (auto *TruncI = dyn_cast<TruncInst>(PhiOrTrunc)) {
return new VPWidenIntOrFpInductionRecipe(Phi, Start, Step, IndDesc, TruncI);
}
assert(isa<PHINode>(PhiOrTrunc) && "must be a phi node here");
return new VPWidenIntOrFpInductionRecipe(Phi, Start, Step, IndDesc);
}
VPHeaderPHIRecipe *VPRecipeBuilder::tryToOptimizeInductionPHI(
PHINode *Phi, ArrayRef<VPValue *> Operands, VFRange &Range) {
// Check if this is an integer or fp induction. If so, build the recipe that
// produces its scalar and vector values.
if (auto *II = Legal->getIntOrFpInductionDescriptor(Phi))
return createWidenInductionRecipes(Phi, Phi, Operands[0], *II, Plan,
*PSE.getSE(), *OrigLoop);
// Check if this is pointer induction. If so, build the recipe for it.
if (auto *II = Legal->getPointerInductionDescriptor(Phi)) {
VPValue *Step = vputils::getOrCreateVPValueForSCEVExpr(Plan, II->getStep(),
*PSE.getSE());
return new VPWidenPointerInductionRecipe(
Phi, Operands[0], Step, *II,
LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) {
return CM.isScalarAfterVectorization(Phi, VF);
},
Range));
}
return nullptr;
}
VPWidenIntOrFpInductionRecipe *VPRecipeBuilder::tryToOptimizeInductionTruncate(
TruncInst *I, ArrayRef<VPValue *> Operands, VFRange &Range) {
// Optimize the special case where the source is a constant integer
// induction variable. Notice that we can only optimize the 'trunc' case
// because (a) FP conversions lose precision, (b) sext/zext may wrap, and
// (c) other casts depend on pointer size.
// Determine whether \p K is a truncation based on an induction variable that
// can be optimized.
auto isOptimizableIVTruncate =
[&](Instruction *K) -> std::function<bool(ElementCount)> {
return [=](ElementCount VF) -> bool {
return CM.isOptimizableIVTruncate(K, VF);
};
};
if (LoopVectorizationPlanner::getDecisionAndClampRange(
isOptimizableIVTruncate(I), Range)) {
auto *Phi = cast<PHINode>(I->getOperand(0));
const InductionDescriptor &II = *Legal->getIntOrFpInductionDescriptor(Phi);
VPValue *Start = Plan.getOrAddLiveIn(II.getStartValue());
return createWidenInductionRecipes(Phi, I, Start, II, Plan, *PSE.getSE(),
*OrigLoop);
}
return nullptr;
}
VPBlendRecipe *VPRecipeBuilder::tryToBlend(PHINode *Phi,
ArrayRef<VPValue *> Operands) {
unsigned NumIncoming = Phi->getNumIncomingValues();
// We know that all PHIs in non-header blocks are converted into selects, so
// we don't have to worry about the insertion order and we can just use the
// builder. At this point we generate the predication tree. There may be
// duplications since this is a simple recursive scan, but future
// optimizations will clean it up.
SmallVector<VPValue *, 2> OperandsWithMask;
for (unsigned In = 0; In < NumIncoming; In++) {
OperandsWithMask.push_back(Operands[In]);
VPValue *EdgeMask =
getEdgeMask(Phi->getIncomingBlock(In), Phi->getParent());
if (!EdgeMask) {
assert(In == 0 && "Both null and non-null edge masks found");
assert(all_equal(Operands) &&
"Distinct incoming values with one having a full mask");
break;
}
OperandsWithMask.push_back(EdgeMask);
}
return new VPBlendRecipe(Phi, OperandsWithMask);
}
VPWidenCallRecipe *VPRecipeBuilder::tryToWidenCall(CallInst *CI,
ArrayRef<VPValue *> Operands,
VFRange &Range) {
bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange(
[this, CI](ElementCount VF) {
return CM.isScalarWithPredication(CI, VF);
},
Range);
if (IsPredicated)
return nullptr;
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end ||
ID == Intrinsic::lifetime_start || ID == Intrinsic::sideeffect ||
ID == Intrinsic::pseudoprobe ||
ID == Intrinsic::experimental_noalias_scope_decl))
return nullptr;
SmallVector<VPValue *, 4> Ops(Operands.take_front(CI->arg_size()));
Ops.push_back(Operands.back());
// 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 VPWidenCallRecipe(CI, make_range(Ops.begin(), Ops.end()), ID,
CI->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 block 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 block, but the only available
// vector variant at this VF requires a mask, so we synthesize an
// all-true mask.
VPValue *Mask = nullptr;
if (Legal->isMaskRequired(CI))
Mask = getBlockInMask(CI->getParent());
else
Mask = Plan.getOrAddLiveIn(ConstantInt::getTrue(
IntegerType::getInt1Ty(Variant->getFunctionType()->getContext())));
Ops.insert(Ops.begin() + *MaskPos, Mask);
}
return new VPWidenCallRecipe(CI, make_range(Ops.begin(), Ops.end()),
Intrinsic::not_intrinsic, CI->getDebugLoc(),
Variant);
}
return nullptr;
}
bool VPRecipeBuilder::shouldWiden(Instruction *I, VFRange &Range) const {
assert(!isa<BranchInst>(I) && !isa<PHINode>(I) && !isa<LoadInst>(I) &&
!isa<StoreInst>(I) && "Instruction should have been handled earlier");
// Instruction should be widened, unless it is scalar after vectorization,
// scalarization is profitable or it is predicated.
auto WillScalarize = [this, I](ElementCount VF) -> bool {
return CM.isScalarAfterVectorization(I, VF) ||
CM.isProfitableToScalarize(I, VF) ||
CM.isScalarWithPredication(I, VF);
};
return !LoopVectorizationPlanner::getDecisionAndClampRange(WillScalarize,
Range);
}
VPWidenRecipe *VPRecipeBuilder::tryToWiden(Instruction *I,
ArrayRef<VPValue *> Operands,
VPBasicBlock *VPBB) {
switch (I->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(Operands);
VPValue *Mask = getBlockInMask(I->getParent());
VPValue *One =
Plan.getOrAddLiveIn(ConstantInt::get(I->getType(), 1u, false));
auto *SafeRHS = Builder.createSelect(Mask, Ops[1], One, I->getDebugLoc());
Ops[1] = SafeRHS;
return new VPWidenRecipe(*I, make_range(Ops.begin(), Ops.end()));
}
[[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, make_range(Operands.begin(), Operands.end()));
};
}
void VPRecipeBuilder::fixHeaderPhis() {
BasicBlock *OrigLatch = OrigLoop->getLoopLatch();
for (VPHeaderPHIRecipe *R : PhisToFix) {
auto *PN = cast<PHINode>(R->getUnderlyingValue());
VPRecipeBase *IncR =
getRecipe(cast<Instruction>(PN->getIncomingValueForBlock(OrigLatch)));
R->addOperand(IncR->getVPSingleValue());
}
}
VPReplicateRecipe *VPRecipeBuilder::handleReplication(Instruction *I,
VFRange &Range) {
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 = getBlockInMask(I->getParent());
}
// 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, mapToVPValues(I->operands()),
IsUniform, BlockInMask);
return Recipe;
}
VPRecipeBase *
VPRecipeBuilder::tryToCreateWidenRecipe(Instruction *Instr,
ArrayRef<VPValue *> Operands,
VFRange &Range, VPBasicBlock *VPBB) {
// First, check for specific widening recipes that deal with inductions, Phi
// nodes, calls and memory operations.
VPRecipeBase *Recipe;
if (auto Phi = dyn_cast<PHINode>(Instr)) {
if (Phi->getParent() != OrigLoop->getHeader())
return tryToBlend(Phi, Operands);
if ((Recipe = tryToOptimizeInductionPHI(Phi, Operands, Range)))
return Recipe;
VPHeaderPHIRecipe *PhiRecipe = nullptr;
assert((Legal->isReductionVariable(Phi) ||
Legal->isFixedOrderRecurrence(Phi)) &&
"can only widen reductions and fixed-order recurrences here");
VPValue *StartV = Operands[0];
if (Legal->isReductionVariable(Phi)) {
const RecurrenceDescriptor &RdxDesc =
Legal->getReductionVars().find(Phi)->second;
assert(RdxDesc.getRecurrenceStartValue() ==
Phi->getIncomingValueForBlock(OrigLoop->getLoopPreheader()));
PhiRecipe = new VPReductionPHIRecipe(Phi, RdxDesc, *StartV,
CM.isInLoopReduction(Phi),
CM.useOrderedReductions(RdxDesc));
} else {
// TODO: Currently fixed-order recurrences are modeled as chains of
// first-order recurrences. If there are no users of the intermediate
// recurrences in the chain, the fixed order recurrence should be modeled
// directly, enabling more efficient codegen.
PhiRecipe = new VPFirstOrderRecurrencePHIRecipe(Phi, *StartV);
}
PhisToFix.push_back(PhiRecipe);
return PhiRecipe;
}
if (isa<TruncInst>(Instr) && (Recipe = tryToOptimizeInductionTruncate(
cast<TruncInst>(Instr), Operands, Range)))
return Recipe;
// All widen recipes below deal only with VF > 1.
if (LoopVectorizationPlanner::getDecisionAndClampRange(
[&](ElementCount VF) { return VF.isScalar(); }, Range))
return nullptr;
if (auto *CI = dyn_cast<CallInst>(Instr))
return tryToWidenCall(CI, Operands, Range);
if (isa<LoadInst>(Instr) || isa<StoreInst>(Instr))
return tryToWidenMemory(Instr, Operands, Range);
if (!shouldWiden(Instr, Range))
return nullptr;
if (auto GEP = dyn_cast<GetElementPtrInst>(Instr))
return new VPWidenGEPRecipe(GEP,
make_range(Operands.begin(), Operands.end()));
if (auto *SI = dyn_cast<SelectInst>(Instr)) {
return new VPWidenSelectRecipe(
*SI, make_range(Operands.begin(), Operands.end()));
}
if (auto *CI = dyn_cast<CastInst>(Instr)) {
return new VPWidenCastRecipe(CI->getOpcode(), Operands[0], CI->getType(),
*CI);
}
return tryToWiden(Instr, Operands, VPBB);
}
void LoopVectorizationPlanner::buildVPlansWithVPRecipes(ElementCount MinVF,
ElementCount MaxVF) {
assert(OrigLoop->isInnermost() && "Inner loop expected.");
auto MaxVFTimes2 = MaxVF * 2;
for (ElementCount VF = MinVF; ElementCount::isKnownLT(VF, MaxVFTimes2);) {
VFRange SubRange = {VF, MaxVFTimes2};
if (auto Plan = tryToBuildVPlanWithVPRecipes(SubRange)) {
// Now optimize the initial VPlan.
if (!Plan->hasVF(ElementCount::getFixed(1)))
VPlanTransforms::truncateToMinimalBitwidths(
*Plan, CM.getMinimalBitwidths(), PSE.getSE()->getContext());
VPlanTransforms::optimize(*Plan, *PSE.getSE());
// TODO: try to put it close to addActiveLaneMask().
// Discard the plan if it is not EVL-compatible
if (CM.foldTailWithEVL() &&
!VPlanTransforms::tryAddExplicitVectorLength(*Plan))
break;
assert(verifyVPlanIsValid(*Plan) && "VPlan is invalid");
VPlans.push_back(std::move(Plan));
}
VF = SubRange.End;
}
}
// Add the necessary canonical IV and branch recipes required to control the
// loop.
static void addCanonicalIVRecipes(VPlan &Plan, Type *IdxTy, bool HasNUW,
DebugLoc DL) {
Value *StartIdx = ConstantInt::get(IdxTy, 0);
auto *StartV = Plan.getOrAddLiveIn(StartIdx);
// Add a VPCanonicalIVPHIRecipe starting at 0 to the header.
auto *CanonicalIVPHI = new VPCanonicalIVPHIRecipe(StartV, DL);
VPRegionBlock *TopRegion = Plan.getVectorLoopRegion();
VPBasicBlock *Header = TopRegion->getEntryBasicBlock();
Header->insert(CanonicalIVPHI, Header->begin());
VPBuilder Builder(TopRegion->getExitingBasicBlock());
// Add a VPInstruction to increment the scalar canonical IV by VF * UF.
auto *CanonicalIVIncrement = Builder.createOverflowingOp(
Instruction::Add, {CanonicalIVPHI, &Plan.getVFxUF()}, {HasNUW, false}, DL,
"index.next");
CanonicalIVPHI->addOperand(CanonicalIVIncrement);
// Add the BranchOnCount VPInstruction to the latch.
Builder.createNaryOp(VPInstruction::BranchOnCount,
{CanonicalIVIncrement, &Plan.getVectorTripCount()}, DL);
}
// Collect (ExitPhi, ExitingValue) pairs phis in the original exit block that
// are modeled in VPlan. Some exiting values are not modeled explicitly yet and
// won't be included. Those are un-truncated VPWidenIntOrFpInductionRecipe,
// VPWidenPointerInductionRecipe and induction increments.
static MapVector<PHINode *, VPValue *> collectUsersInExitBlock(
Loop *OrigLoop, VPRecipeBuilder &Builder, VPlan &Plan,
const MapVector<PHINode *, InductionDescriptor> &Inductions) {
auto MiddleVPBB =
cast<VPBasicBlock>(Plan.getVectorLoopRegion()->getSingleSuccessor());
// No edge from the middle block to the unique exit block has been inserted
// and there is nothing to fix from vector loop; phis should have incoming
// from scalar loop only.
if (MiddleVPBB->getNumSuccessors() != 2)
return {};
MapVector<PHINode *, VPValue *> ExitingValuesToFix;
BasicBlock *ExitBB =
cast<VPIRBasicBlock>(MiddleVPBB->getSuccessors()[0])->getIRBasicBlock();
BasicBlock *ExitingBB = OrigLoop->getExitingBlock();
for (PHINode &ExitPhi : ExitBB->phis()) {
Value *IncomingValue =
ExitPhi.getIncomingValueForBlock(ExitingBB);
VPValue *V = Builder.getVPValueOrAddLiveIn(IncomingValue, Plan);
// Exit values for inductions are computed and updated outside of VPlan and
// independent of induction recipes.
// TODO: Compute induction exit values in VPlan, use VPLiveOuts to update
// live-outs.
if ((isa<VPWidenIntOrFpInductionRecipe>(V) &&
!cast<VPWidenIntOrFpInductionRecipe>(V)->getTruncInst()) ||
isa<VPWidenPointerInductionRecipe>(V) ||
(isa<Instruction>(IncomingValue) &&
any_of(IncomingValue->users(), [&Inductions](User *U) {
auto *P = dyn_cast<PHINode>(U);
return P && Inductions.contains(P);
})))
continue;
ExitingValuesToFix.insert({&ExitPhi, V});
}
return ExitingValuesToFix;
}
// Add exit values to \p Plan. Extracts and VPLiveOuts are added for each entry
// in \p ExitingValuesToFix.
static void
addUsersInExitBlock(VPlan &Plan,
MapVector<PHINode *, VPValue *> &ExitingValuesToFix) {
if (ExitingValuesToFix.empty())
return;
auto MiddleVPBB =
cast<VPBasicBlock>(Plan.getVectorLoopRegion()->getSingleSuccessor());
BasicBlock *ExitBB =
cast<VPIRBasicBlock>(MiddleVPBB->getSuccessors()[0])->getIRBasicBlock();
// TODO: set B to MiddleVPBB->getFirstNonPhi(), taking care of affected tests.
VPBuilder B(MiddleVPBB);
if (auto *Terminator = MiddleVPBB->getTerminator()) {
auto *Condition = dyn_cast<VPInstruction>(Terminator->getOperand(0));
assert((!Condition || Condition->getParent() == MiddleVPBB) &&
"Condition expected in MiddleVPBB");
B.setInsertPoint(Condition ? Condition : Terminator);
}
// Introduce VPUsers modeling the exit values.
for (const auto &[ExitPhi, V] : ExitingValuesToFix) {
VPValue *Ext = B.createNaryOp(
VPInstruction::ExtractFromEnd,
{V, Plan.getOrAddLiveIn(ConstantInt::get(
IntegerType::get(ExitBB->getContext(), 32), 1))});
Plan.addLiveOut(ExitPhi, Ext);
}
}
/// Handle live-outs for first order reductions, both in the scalar preheader
/// and the original exit block:
/// 1. Feed a resume value for every FOR from the vector loop to the scalar
/// loop, if middle block branches to scalar preheader, by introducing
/// ExtractFromEnd and ResumePhi recipes in each, respectively, and a
/// VPLiveOut which uses the latter and corresponds to the scalar header.
/// 2. Feed the penultimate value of recurrences to their LCSSA phi users in
/// the original exit block using a VPLiveOut.
static void addLiveOutsForFirstOrderRecurrences(
VPlan &Plan, MapVector<PHINode *, VPValue *> &ExitingValuesToFix) {
VPRegionBlock *VectorRegion = Plan.getVectorLoopRegion();
// Start by finding out if middle block branches to scalar preheader, which is
// not a VPIRBasicBlock, unlike Exit block - the other possible successor of
// middle block.
// TODO: Should be replaced by
// Plan->getScalarLoopRegion()->getSinglePredecessor() in the future once the
// scalar region is modeled as well.
auto *MiddleVPBB = cast<VPBasicBlock>(VectorRegion->getSingleSuccessor());
BasicBlock *ExitBB = nullptr;
VPBasicBlock *ScalarPHVPBB = nullptr;
if (MiddleVPBB->getNumSuccessors() == 2) {
// Order is strict: first is the exit block, second is the scalar preheader.
ExitBB =
cast<VPIRBasicBlock>(MiddleVPBB->getSuccessors()[0])->getIRBasicBlock();
ScalarPHVPBB = cast<VPBasicBlock>(MiddleVPBB->getSuccessors()[1]);
} else if (ExitingValuesToFix.empty()) {
ScalarPHVPBB = cast<VPBasicBlock>(MiddleVPBB->getSingleSuccessor());
} else {
ExitBB = cast<VPIRBasicBlock>(MiddleVPBB->getSingleSuccessor())
->getIRBasicBlock();
}
if (!ScalarPHVPBB) {
assert(ExitingValuesToFix.empty() &&
"missed inserting extracts for exiting values");
return;
}
VPBuilder ScalarPHBuilder(ScalarPHVPBB);
VPBuilder MiddleBuilder(MiddleVPBB);
// Reset insert point so new recipes are inserted before terminator and
// condition, if there is either the former or both.
// TODO: set MiddleBuilder to MiddleVPBB->getFirstNonPhi().
if (auto *Terminator = MiddleVPBB->getTerminator()) {
auto *Condition = dyn_cast<VPInstruction>(Terminator->getOperand(0));
assert((!Condition || Condition->getParent() == MiddleVPBB) &&
"Condition expected in MiddleVPBB");
MiddleBuilder.setInsertPoint(Condition ? Condition : Terminator);
}
VPValue *OneVPV = Plan.getOrAddLiveIn(
ConstantInt::get(Plan.getCanonicalIV()->getScalarType(), 1));
VPValue *TwoVPV = Plan.getOrAddLiveIn(
ConstantInt::get(Plan.getCanonicalIV()->getScalarType(), 2));
for (auto &HeaderPhi : VectorRegion->getEntryBasicBlock()->phis()) {
auto *FOR = dyn_cast<VPFirstOrderRecurrencePHIRecipe>(&HeaderPhi);
if (!FOR)
continue;
// This is the second phase of vectorizing first-order recurrences, creating
// extract for users outside the loop. An overview of the transformation is
// described below. Suppose we have the following loop with some use after
// the loop of the last a[i-1],
//
// for (int i = 0; i < n; ++i) {
// t = a[i - 1];
// b[i] = a[i] - t;
// }
// use t;
//
// There is a first-order recurrence on "a". For this loop, the shorthand
// scalar IR looks like:
//
// scalar.ph:
// s.init = a[-1]
// br scalar.body
//
// scalar.body:
// i = phi [0, scalar.ph], [i+1, scalar.body]
// s1 = phi [s.init, scalar.ph], [s2, scalar.body]
// s2 = a[i]
// b[i] = s2 - s1
// br cond, scalar.body, exit.block
//
// exit.block:
// use = lcssa.phi [s1, scalar.body]
//
// In this example, s1 is a recurrence because it's value depends on the
// previous iteration. In the first phase of vectorization, we created a
// VPFirstOrderRecurrencePHIRecipe v1 for s1. Now we create the extracts
// for users in the scalar preheader and exit block.
//
// vector.ph:
// v_init = vector(..., ..., ..., a[-1])
// br vector.body
//
// vector.body
// i = phi [0, vector.ph], [i+4, vector.body]
// v1 = phi [v_init, vector.ph], [v2, vector.body]
// v2 = a[i, i+1, i+2, i+3]
// b[i] = v2 - v1
// // Next, third phase will introduce v1' = splice(v1(3), v2(0, 1, 2))
// b[i, i+1, i+2, i+3] = v2 - v1
// br cond, vector.body, middle.block
//
// middle.block:
// vector.recur.extract.for.phi = v2(2)
// vector.recur.extract = v2(3)
// br cond, scalar.ph, exit.block
//
// scalar.ph:
// scalar.recur.init = phi [vector.recur.extract, middle.block],
// [s.init, otherwise]
// br scalar.body
//
// scalar.body:
// i = phi [0, scalar.ph], [i+1, scalar.body]
// s1 = phi [scalar.recur.init, scalar.ph], [s2, scalar.body]
// s2 = a[i]
// b[i] = s2 - s1
// br cond, scalar.body, exit.block
//
// exit.block:
// lo = lcssa.phi [s1, scalar.body],
// [vector.recur.extract.for.phi, middle.block]
//
// Extract the resume value and create a new VPLiveOut for it.
auto *Resume = MiddleBuilder.createNaryOp(VPInstruction::ExtractFromEnd,
{FOR->getBackedgeValue(), OneVPV},
{}, "vector.recur.extract");
auto *ResumePhiRecipe = ScalarPHBuilder.createNaryOp(
VPInstruction::ResumePhi, {Resume, FOR->getStartValue()}, {},
"scalar.recur.init");
auto *FORPhi = cast<PHINode>(FOR->getUnderlyingInstr());
Plan.addLiveOut(FORPhi, ResumePhiRecipe);
// Now create VPLiveOuts for users in the exit block.
// Extract the penultimate value of the recurrence and add VPLiveOut
// users of the recurrence splice.
// No edge from the middle block to the unique exit block has been inserted
// and there is nothing to fix from vector loop; phis should have incoming
// from scalar loop only.
if (ExitingValuesToFix.empty())
continue;
for (User *U : FORPhi->users()) {
auto *UI = cast<Instruction>(U);
if (UI->getParent() != ExitBB)
continue;
VPValue *Ext = MiddleBuilder.createNaryOp(
VPInstruction::ExtractFromEnd, {FOR->getBackedgeValue(), TwoVPV}, {},
"vector.recur.extract.for.phi");
Plan.addLiveOut(cast<PHINode>(UI), Ext);
ExitingValuesToFix.erase(cast<PHINode>(UI));
}
}
}
VPlanPtr
LoopVectorizationPlanner::tryToBuildVPlanWithVPRecipes(VFRange &Range) {
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.
// ---------------------------------------------------------------------------
// Create initial VPlan skeleton, having a basic block for the pre-header
// which contains SCEV expansions that need to happen before the CFG is
// modified; a basic block for the vector pre-header, followed by a region for
// the vector loop, followed by the middle basic block. The skeleton vector
// loop region contains a header and latch basic blocks.
bool RequiresScalarEpilogueCheck =
LoopVectorizationPlanner::getDecisionAndClampRange(
[this](ElementCount VF) {
return !CM.requiresScalarEpilogue(VF.isVector());
},
Range);
VPlanPtr Plan = VPlan::createInitialVPlan(
createTripCountSCEV(Legal->getWidestInductionType(), PSE, OrigLoop),
*PSE.getSE(), RequiresScalarEpilogueCheck, CM.foldTailByMasking(),
OrigLoop);
// 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);
DebugLoc DL = getDebugLocFromInstOrOperands(Legal->getPrimaryInduction());
TailFoldingStyle Style = CM.getTailFoldingStyle(IVUpdateMayOverflow);
// When not folding the tail, we know that the induction increment will not
// overflow.
bool HasNUW = Style == TailFoldingStyle::None;
addCanonicalIVRecipes(*Plan, Legal->getWidestInductionType(), HasNUW, DL);
VPRecipeBuilder RecipeBuilder(*Plan, OrigLoop, TLI, Legal, CM, PSE, Builder);
// ---------------------------------------------------------------------------
// 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 only interleave factor currently supported
// is 2 since we require the (de)interleave2 intrinsics instead of
// shufflevectors.
assert((!Result || !VF.isScalable() || IG->getFactor() == 2) &&
"Unsupported interleave factor for scalable vectors");
return Result;
};
if (!getDecisionAndClampRange(applyIG, Range))
continue;
InterleaveGroups.insert(IG);
};
// ---------------------------------------------------------------------------
// Construct recipes for the instructions in the loop
// ---------------------------------------------------------------------------
// Scan the body of the loop in a topological order to visit each basic block
// after having visited its predecessor basic blocks.
LoopBlocksDFS DFS(OrigLoop);
DFS.perform(LI);
VPBasicBlock *HeaderVPBB = Plan->getVectorLoopRegion()->getEntryBasicBlock();
VPBasicBlock *VPBB = HeaderVPBB;
BasicBlock *HeaderBB = OrigLoop->getHeader();
bool NeedsMasks =
CM.foldTailByMasking() ||
any_of(OrigLoop->blocks(), [this, HeaderBB](BasicBlock *BB) {
bool NeedsBlends = BB != HeaderBB && !BB->phis().empty();
return Legal->blockNeedsPredication(BB) || NeedsBlends;
});
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
// Relevant instructions from basic block BB will be grouped into VPRecipe
// ingredients and fill a new VPBasicBlock.
if (VPBB != HeaderVPBB)
VPBB->setName(BB->getName());
Builder.setInsertPoint(VPBB);
if (VPBB == HeaderVPBB)
RecipeBuilder.createHeaderMask();
else if (NeedsMasks)
RecipeBuilder.createBlockInMask(BB);
// Introduce each ingredient into VPlan.
// TODO: Model and preserve debug intrinsics in VPlan.
for (Instruction &I : drop_end(BB->instructionsWithoutDebug(false))) {
Instruction *Instr = &I;
SmallVector<VPValue *, 4> Operands;
auto *Phi = dyn_cast<PHINode>(Instr);
if (Phi && Phi->getParent() == HeaderBB) {
Operands.push_back(Plan->getOrAddLiveIn(
Phi->getIncomingValueForBlock(OrigLoop->getLoopPreheader())));
} else {
auto OpRange = RecipeBuilder.mapToVPValues(Instr->operands());
Operands = {OpRange.begin(), OpRange.end()};
}
// Invariant stores inside loop will be deleted and a single store
// with the final reduction value will be added to the exit block
StoreInst *SI;
if ((SI = dyn_cast<StoreInst>(&I)) &&
Legal->isInvariantAddressOfReduction(SI->getPointerOperand()))
continue;
VPRecipeBase *Recipe =
RecipeBuilder.tryToCreateWidenRecipe(Instr, Operands, Range, VPBB);
if (!Recipe)
Recipe = RecipeBuilder.handleReplication(Instr, Range);
RecipeBuilder.setRecipe(Instr, Recipe);
if (isa<VPHeaderPHIRecipe>(Recipe)) {
// VPHeaderPHIRecipes must be kept in the phi section of HeaderVPBB. In
// the following cases, VPHeaderPHIRecipes may be created after non-phi
// recipes and need to be moved to the phi section of HeaderVPBB:
// * tail-folding (non-phi recipes computing the header mask are
// introduced earlier than regular header phi recipes, and should appear
// after them)
// * Optimizing truncates to VPWidenIntOrFpInductionRecipe.
assert((HeaderVPBB->getFirstNonPhi() == VPBB->end() ||
CM.foldTailByMasking() || isa<TruncInst>(Instr)) &&
"unexpected recipe needs moving");
Recipe->insertBefore(*HeaderVPBB, HeaderVPBB->getFirstNonPhi());
} else
VPBB->appendRecipe(Recipe);
}
VPBlockUtils::insertBlockAfter(new VPBasicBlock(), VPBB);
VPBB = cast<VPBasicBlock>(VPBB->getSingleSuccessor());
}
// After here, VPBB should not be used.
VPBB = nullptr;
assert(isa<VPRegionBlock>(Plan->getVectorLoopRegion()) &&
!Plan->getVectorLoopRegion()->getEntryBasicBlock()->empty() &&
"entry block must be set to a VPRegionBlock having a non-empty entry "
"VPBasicBlock");
RecipeBuilder.fixHeaderPhis();
MapVector<PHINode *, VPValue *> ExitingValuesToFix = collectUsersInExitBlock(
OrigLoop, RecipeBuilder, *Plan, Legal->getInductionVars());
addLiveOutsForFirstOrderRecurrences(*Plan, ExitingValuesToFix);
addUsersInExitBlock(*Plan, ExitingValuesToFix);
// ---------------------------------------------------------------------------
// Transform initial VPlan: Apply previously taken decisions, in order, to
// bring the VPlan to its final state.
// ---------------------------------------------------------------------------
// Adjust the recipes for any inloop reductions.
adjustRecipesForReductions(Plan, RecipeBuilder, Range.Start);
// Interleave memory: for each Interleave Group we marked earlier as relevant
// for this VPlan, replace the Recipes widening its memory instructions with a
// single VPInterleaveRecipe at its insertion point.
for (const auto *IG : InterleaveGroups) {
auto *Recipe =
cast<VPWidenMemoryRecipe>(RecipeBuilder.getRecipe(IG->getInsertPos()));
SmallVector<VPValue *, 4> StoredValues;
for (unsigned i = 0; i < IG->getFactor(); ++i)
if (auto *SI = dyn_cast_or_null<StoreInst>(IG->getMember(i))) {
auto *StoreR = cast<VPWidenStoreRecipe>(RecipeBuilder.getRecipe(SI));
StoredValues.push_back(StoreR->getStoredValue());
}
bool NeedsMaskForGaps =
IG->requiresScalarEpilogue() && !CM.isScalarEpilogueAllowed();
assert((!NeedsMaskForGaps || useMaskedInterleavedAccesses(CM.TTI)) &&
"masked interleaved groups are not allowed.");
auto *VPIG = new VPInterleaveRecipe(IG, Recipe->getAddr(), StoredValues,
Recipe->getMask(), NeedsMaskForGaps);
VPIG->insertBefore(Recipe);
unsigned J = 0;
for (unsigned i = 0; i < IG->getFactor(); ++i)
if (Instruction *Member = IG->getMember(i)) {
VPRecipeBase *MemberR = RecipeBuilder.getRecipe(Member);
if (!Member->getType()->isVoidTy()) {
VPValue *OriginalV = MemberR->getVPSingleValue();
OriginalV->replaceAllUsesWith(VPIG->getVPValue(J));
J++;
}
MemberR->eraseFromParent();
}
}
for (ElementCount VF : Range)
Plan->addVF(VF);
Plan->setName("Initial VPlan");
// Replace VPValues for known constant strides guaranteed by predicate scalar
// evolution.
for (auto [_, Stride] : Legal->getLAI()->getSymbolicStrides()) {
auto *StrideV = cast<SCEVUnknown>(Stride)->getValue();
auto *ScevStride = dyn_cast<SCEVConstant>(PSE.getSCEV(StrideV));
// Only handle constant strides for now.
if (!ScevStride)
continue;
auto *CI = Plan->getOrAddLiveIn(
ConstantInt::get(Stride->getType(), ScevStride->getAPInt()));
if (VPValue *StrideVPV = Plan->getLiveIn(StrideV))
StrideVPV->replaceAllUsesWith(CI);
// The versioned value may not be used in the loop directly but through a
// sext/zext. Add new live-ins in those cases.
for (Value *U : StrideV->users()) {
if (!isa<SExtInst, ZExtInst>(U))
continue;
VPValue *StrideVPV = Plan->getLiveIn(U);
if (!StrideVPV)
continue;
unsigned BW = U->getType()->getScalarSizeInBits();
APInt C = isa<SExtInst>(U) ? ScevStride->getAPInt().sext(BW)
: ScevStride->getAPInt().zext(BW);
VPValue *CI = Plan->getOrAddLiveIn(ConstantInt::get(U->getType(), C));
StrideVPV->replaceAllUsesWith(CI);
}
}
VPlanTransforms::dropPoisonGeneratingRecipes(*Plan, [this](BasicBlock *BB) {
return Legal->blockNeedsPredication(BB);
});
// Sink users of fixed-order recurrence past the recipe defining the previous
// value and introduce FirstOrderRecurrenceSplice VPInstructions.
if (!VPlanTransforms::adjustFixedOrderRecurrences(*Plan, Builder))
return nullptr;
if (useActiveLaneMask(Style)) {
// TODO: Move checks to VPlanTransforms::addActiveLaneMask once
// TailFoldingStyle is visible there.
bool ForControlFlow = useActiveLaneMaskForControlFlow(Style);
bool WithoutRuntimeCheck =
Style == TailFoldingStyle::DataAndControlFlowWithoutRuntimeCheck;
VPlanTransforms::addActiveLaneMask(*Plan, ForControlFlow,
WithoutRuntimeCheck);
}
return Plan;
}
VPlanPtr LoopVectorizationPlanner::buildVPlan(VFRange &Range) {
// Outer loop handling: They may require CFG and instruction level
// transformations before even evaluating whether vectorization is profitable.
// Since we cannot modify the incoming IR, we need to build VPlan upfront in
// the vectorization pipeline.
assert(!OrigLoop->isInnermost());
assert(EnableVPlanNativePath && "VPlan-native path is not enabled.");
// Create new empty VPlan
auto Plan = VPlan::createInitialVPlan(
createTripCountSCEV(Legal->getWidestInductionType(), PSE, OrigLoop),
*PSE.getSE(), true, false, OrigLoop);
// Build hierarchical CFG
VPlanHCFGBuilder HCFGBuilder(OrigLoop, LI, *Plan);
HCFGBuilder.buildHierarchicalCFG();
for (ElementCount VF : Range)
Plan->addVF(VF);
VPlanTransforms::VPInstructionsToVPRecipes(
Plan,
[this](PHINode *P) { return Legal->getIntOrFpInductionDescriptor(P); },
*PSE.getSE(), *TLI);
// Remove the existing terminator of the exiting block of the top-most region.
// A BranchOnCount will be added instead when adding the canonical IV recipes.
auto *Term =
Plan->getVectorLoopRegion()->getExitingBasicBlock()->getTerminator();
Term->eraseFromParent();
// Tail folding is not supported for outer loops, so the induction increment
// is guaranteed to not wrap.
bool HasNUW = true;
addCanonicalIVRecipes(*Plan, Legal->getWidestInductionType(), HasNUW,
DebugLoc());
assert(verifyVPlanIsValid(*Plan) && "VPlan is invalid");
return Plan;
}
// Adjust the recipes for reductions. For in-loop reductions the chain of
// instructions leading from the loop exit instr to the phi need to be converted
// to reductions, with one operand being vector and the other being the scalar
// reduction chain. For other reductions, a select is introduced between the phi
// and live-out recipes when folding the tail.
//
// A ComputeReductionResult recipe is added to the middle block, also for
// in-loop reductions which compute their result in-loop, because generating
// the subsequent bc.merge.rdx phi is driven by ComputeReductionResult recipes.
//
// 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.
void LoopVectorizationPlanner::adjustRecipesForReductions(
VPlanPtr &Plan, VPRecipeBuilder &RecipeBuilder, ElementCount MinVF) {
using namespace VPlanPatternMatch;
VPRegionBlock *VectorLoopRegion = Plan->getVectorLoopRegion();
VPBasicBlock *Header = VectorLoopRegion->getEntryBasicBlock();
// Gather all VPReductionPHIRecipe and sort them so that Intermediate stores
// sank outside of the loop would keep the same order as they had in the
// original loop.
SmallVector<VPReductionPHIRecipe *> ReductionPHIList;
for (VPRecipeBase &R : Header->phis()) {
if (auto *ReductionPhi = dyn_cast<VPReductionPHIRecipe>(&R))
ReductionPHIList.emplace_back(ReductionPhi);
}
bool HasIntermediateStore = false;
stable_sort(ReductionPHIList,
[this, &HasIntermediateStore](const VPReductionPHIRecipe *R1,
const VPReductionPHIRecipe *R2) {
auto *IS1 = R1->getRecurrenceDescriptor().IntermediateStore;
auto *IS2 = R2->getRecurrenceDescriptor().IntermediateStore;
HasIntermediateStore |= IS1 || IS2;
// If neither of the recipes has an intermediate store, keep the
// order the same.
if (!IS1 && !IS2)
return false;
// If only one of the recipes has an intermediate store, then
// move it towards the beginning of the list.
if (IS1 && !IS2)
return true;
if (!IS1 && IS2)
return false;
// If both recipes have an intermediate store, then the recipe
// with the later store should be processed earlier. So it
// should go to the beginning of the list.
return DT->dominates(IS2, IS1);
});
if (HasIntermediateStore && ReductionPHIList.size() > 1)
for (VPRecipeBase *R : ReductionPHIList)
R->moveBefore(*Header, Header->getFirstNonPhi());
for (VPRecipeBase &R : Header->phis()) {
auto *PhiR = dyn_cast<VPReductionPHIRecipe>(&R);
if (!PhiR || !PhiR->isInLoop() || (MinVF.isScalar() && !PhiR->isOrdered()))
continue;
const RecurrenceDescriptor &RdxDesc = PhiR->getRecurrenceDescriptor();
RecurKind Kind = RdxDesc.getRecurrenceKind();
assert(!RecurrenceDescriptor::isAnyOfRecurrenceKind(Kind) &&
"AnyOf reductions are not allowed for in-loop reductions");
// Collect the chain of "link" recipes for the reduction starting at PhiR.
SetVector<VPSingleDefRecipe *> Worklist;
Worklist.insert(PhiR);
for (unsigned I = 0; I != Worklist.size(); ++I) {
VPSingleDefRecipe *Cur = Worklist[I];
for (VPUser *U : Cur->users()) {
auto *UserRecipe = cast<VPSingleDefRecipe>(U);
if (!UserRecipe->getParent()->getEnclosingLoopRegion()) {
assert(match(U, m_Binary<VPInstruction::ExtractFromEnd>(
m_VPValue(), m_VPValue())) &&
"U must be an ExtractFromEnd VPInstruction");
continue;
}
Worklist.insert(UserRecipe);
}
}
// Visit operation "Links" along the reduction chain top-down starting from
// the phi until LoopExitValue. We keep track of the previous item
// (PreviousLink) to tell which of the two operands of a Link will remain
// scalar and which will be reduced. For minmax by select(cmp), Link will be
// the select instructions. Blend recipes of in-loop reduction phi's will
// get folded to their non-phi operand, as the reduction recipe handles the
// condition directly.
VPSingleDefRecipe *PreviousLink = PhiR; // Aka Worklist[0].
for (VPSingleDefRecipe *CurrentLink : Worklist.getArrayRef().drop_front()) {
Instruction *CurrentLinkI = CurrentLink->getUnderlyingInstr();
// Index of the first operand which holds a non-mask vector operand.
unsigned IndexOfFirstOperand;
// Recognize a call to the llvm.fmuladd intrinsic.
bool IsFMulAdd = (Kind == RecurKind::FMulAdd);
VPValue *VecOp;
VPBasicBlock *LinkVPBB = CurrentLink->getParent();
if (IsFMulAdd) {
assert(
RecurrenceDescriptor::isFMulAddIntrinsic(CurrentLinkI) &&
"Expected instruction to be a call to the llvm.fmuladd intrinsic");
assert(((MinVF.isScalar() && isa<VPReplicateRecipe>(CurrentLink)) ||
isa<VPWidenCallRecipe>(CurrentLink)) &&
CurrentLink->getOperand(2) == PreviousLink &&
"expected a call where the previous link is the added operand");
// If the instruction is a call to the llvm.fmuladd intrinsic then we
// need to create an fmul recipe (multiplying the first two operands of
// the fmuladd together) to use as the vector operand for the fadd
// reduction.
VPInstruction *FMulRecipe = new VPInstruction(
Instruction::FMul,
{CurrentLink->getOperand(0), CurrentLink->getOperand(1)},
CurrentLinkI->getFastMathFlags());
LinkVPBB->insert(FMulRecipe, CurrentLink->getIterator());
VecOp = FMulRecipe;
} else {
auto *Blend = dyn_cast<VPBlendRecipe>(CurrentLink);
if (PhiR->isInLoop() && Blend) {
assert(Blend->getNumIncomingValues() == 2 &&
"Blend must have 2 incoming values");
if (Blend->getIncomingValue(0) == PhiR)
Blend->replaceAllUsesWith(Blend->getIncomingValue(1));
else {
assert(Blend->getIncomingValue(1) == PhiR &&
"PhiR must be an operand of the blend");
Blend->replaceAllUsesWith(Blend->getIncomingValue(0));
}
continue;
}
if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) {
if (isa<VPWidenRecipe>(CurrentLink)) {
assert(isa<CmpInst>(CurrentLinkI) &&
"need to have the compare of the select");
continue;
}
assert(isa<VPWidenSelectRecipe>(CurrentLink) &&
"must be a select recipe");
IndexOfFirstOperand = 1;
} else {
assert((MinVF.isScalar() || isa<VPWidenRecipe>(CurrentLink)) &&
"Expected to replace a VPWidenSC");
IndexOfFirstOperand = 0;
}
// Note that for non-commutable operands (cmp-selects), the semantics of
// the cmp-select are captured in the recurrence kind.
unsigned VecOpId =
CurrentLink->getOperand(IndexOfFirstOperand) == PreviousLink
? IndexOfFirstOperand + 1
: IndexOfFirstOperand;
VecOp = CurrentLink->getOperand(VecOpId);
assert(VecOp != PreviousLink &&
CurrentLink->getOperand(CurrentLink->getNumOperands() - 1 -
(VecOpId - IndexOfFirstOperand)) ==
PreviousLink &&
"PreviousLink must be the operand other than VecOp");
}
BasicBlock *BB = CurrentLinkI->getParent();
VPValue *CondOp = nullptr;
if (CM.blockNeedsPredicationForAnyReason(BB))
CondOp = RecipeBuilder.getBlockInMask(BB);
VPReductionRecipe *RedRecipe =
new VPReductionRecipe(RdxDesc, CurrentLinkI, PreviousLink, VecOp,
CondOp, CM.useOrderedReductions(RdxDesc));
// Append the recipe to the end of the VPBasicBlock because we need to
// ensure that it comes after all of it's inputs, including CondOp.
// Note that this transformation may leave over dead recipes (including
// CurrentLink), which will be cleaned by a later VPlan transform.
LinkVPBB->appendRecipe(RedRecipe);
CurrentLink->replaceAllUsesWith(RedRecipe);
PreviousLink = RedRecipe;
}
}
VPBasicBlock *LatchVPBB = VectorLoopRegion->getExitingBasicBlock();
Builder.setInsertPoint(&*LatchVPBB->begin());
VPBasicBlock *MiddleVPBB =
cast<VPBasicBlock>(VectorLoopRegion->getSingleSuccessor());
VPBasicBlock::iterator IP = MiddleVPBB->getFirstNonPhi();
for (VPRecipeBase &R :
Plan->getVectorLoopRegion()->getEntryBasicBlock()->phis()) {
VPReductionPHIRecipe *PhiR = dyn_cast<VPReductionPHIRecipe>(&R);
if (!PhiR)
continue;
const RecurrenceDescriptor &RdxDesc = PhiR->getRecurrenceDescriptor();
// 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.
if (RecurrenceDescriptor::isAnyOfRecurrenceKind(
RdxDesc.getRecurrenceKind())) {
auto *Select = cast<VPRecipeBase>(*find_if(PhiR->users(), [](VPUser *U) {
return isa<VPWidenSelectRecipe>(U) ||
(isa<VPReplicateRecipe>(U) &&
cast<VPReplicateRecipe>(U)->getUnderlyingInstr()->getOpcode() ==
Instruction::Select);
}));
VPValue *Cmp = Select->getOperand(0);
// If the compare is checking the reduction PHI node, adjust it to check
// the start value.
if (VPRecipeBase *CmpR = Cmp->getDefiningRecipe()) {
for (unsigned I = 0; I != CmpR->getNumOperands(); ++I)
if (CmpR->getOperand(I) == PhiR)
CmpR->setOperand(I, PhiR->getStartValue());
}
VPBuilder::InsertPointGuard Guard(Builder);
Builder.setInsertPoint(Select);
// 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 (Select->getOperand(1) == PhiR)
Cmp = Builder.createNot(Cmp);
VPValue *Or = Builder.createOr(PhiR, Cmp);
Select->getVPSingleValue()->replaceAllUsesWith(Or);
// Convert the reduction phi to operate on bools.
PhiR->setOperand(0, Plan->getOrAddLiveIn(ConstantInt::getFalse(
OrigLoop->getHeader()->getContext())));
}
// If tail is folded by masking, introduce selects between the phi
// and the live-out instruction 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 = RecipeBuilder.getBlockInMask(OrigLoop->getHeader());
assert(OrigExitingVPV->getDefiningRecipe()->getParent() != LatchVPBB &&
"reduction recipe must be defined before latch");
Type *PhiTy = PhiR->getOperand(0)->getLiveInIRValue()->getType();
std::optional<FastMathFlags> FMFs =
PhiTy->isFloatingPointTy()
? std::make_optional(RdxDesc.getFastMathFlags())
: std::nullopt;
NewExitingVPV =
Builder.createSelect(Cond, OrigExitingVPV, PhiR, {}, "", FMFs);
OrigExitingVPV->replaceUsesWithIf(NewExitingVPV, [](VPUser &U, unsigned) {
return isa<VPInstruction>(&U) &&
cast<VPInstruction>(&U)->getOpcode() ==
VPInstruction::ComputeReductionResult;
});
if (PreferPredicatedReductionSelect ||
TTI.preferPredicatedReductionSelect(
PhiR->getRecurrenceDescriptor().getOpcode(), PhiTy,
TargetTransformInfo::ReductionFlags()))
PhiR->setOperand(1, NewExitingVPV);
}
// 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.
Type *PhiTy = PhiR->getStartValue()->getLiveInIRValue()->getType();
if (MinVF.isVector() && PhiTy != RdxDesc.getRecurrenceType() &&
!RecurrenceDescriptor::isAnyOfRecurrenceKind(
RdxDesc.getRecurrenceKind())) {
assert(!PhiR->isInLoop() && "Unexpected truncated inloop reduction!");
Type *RdxTy = RdxDesc.getRecurrenceType();
auto *Trunc =
new VPWidenCastRecipe(Instruction::Trunc, NewExitingVPV, RdxTy);
auto *Extnd =
RdxDesc.isSigned()
? new VPWidenCastRecipe(Instruction::SExt, Trunc, PhiTy)
: new VPWidenCastRecipe(Instruction::ZExt, Trunc, PhiTy);
Trunc->insertAfter(NewExitingVPV->getDefiningRecipe());
Extnd->insertAfter(Trunc);
if (PhiR->getOperand(1) == NewExitingVPV)
PhiR->setOperand(1, Extnd->getVPSingleValue());
NewExitingVPV = Extnd;
}
// 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.
auto *FinalReductionResult = new VPInstruction(
VPInstruction::ComputeReductionResult, {PhiR, NewExitingVPV}, ExitDL);
FinalReductionResult->insertBefore(*MiddleVPBB, IP);
OrigExitingVPV->replaceUsesWithIf(FinalReductionResult, [](VPUser &User,
unsigned) {
return match(&User, m_Binary<VPInstruction::ExtractFromEnd>(m_VPValue(),
m_VPValue()));
});
}
VPlanTransforms::clearReductionWrapFlags(*Plan);
}
void VPDerivedIVRecipe::execute(VPTransformState &State) {
assert(!State.Instance && "VPDerivedIVRecipe being replicated.");
// Fast-math-flags propagate from the original induction instruction.
IRBuilder<>::FastMathFlagGuard FMFG(State.Builder);
if (FPBinOp)
State.Builder.setFastMathFlags(FPBinOp->getFastMathFlags());
Value *Step = State.get(getStepValue(), VPIteration(0, 0));
Value *CanonicalIV = State.get(getOperand(1), VPIteration(0, 0));
Value *DerivedIV = emitTransformedIndex(
State.Builder, CanonicalIV, getStartValue()->getLiveInIRValue(), Step,
Kind, cast_if_present<BinaryOperator>(FPBinOp));
DerivedIV->setName("offset.idx");
assert(DerivedIV != CanonicalIV && "IV didn't need transforming?");
State.set(this, DerivedIV, VPIteration(0, 0));
}
void VPReplicateRecipe::execute(VPTransformState &State) {
Instruction *UI = getUnderlyingInstr();
if (State.Instance) { // Generate a single instance.
assert((State.VF.isScalar() || !isUniform()) &&
"uniform recipe shouldn't be predicated");
assert(!State.VF.isScalable() && "Can't scalarize a scalable vector");
State.ILV->scalarizeInstruction(UI, this, *State.Instance, State);
// Insert scalar instance packing it into a vector.
if (State.VF.isVector() && shouldPack()) {
// If we're constructing lane 0, initialize to start from poison.
if (State.Instance->Lane.isFirstLane()) {
assert(!State.VF.isScalable() && "VF is assumed to be non scalable.");
Value *Poison = PoisonValue::get(
VectorType::get(UI->getType(), State.VF));
State.set(this, Poison, State.Instance->Part);
}
State.packScalarIntoVectorValue(this, *State.Instance);
}
return;
}
if (IsUniform) {
// If the recipe is uniform across all parts (instead of just per VF), only
// generate a single instance.
if ((isa<LoadInst>(UI) || isa<StoreInst>(UI)) &&
all_of(operands(), [](VPValue *Op) {
return Op->isDefinedOutsideVectorRegions();
})) {
State.ILV->scalarizeInstruction(UI, this, VPIteration(0, 0), State);
if (user_begin() != user_end()) {
for (unsigned Part = 1; Part < State.UF; ++Part)
State.set(this, State.get(this, VPIteration(0, 0)),
VPIteration(Part, 0));
}
return;
}
// Uniform within VL means we need to generate lane 0 only for each
// unrolled copy.
for (unsigned Part = 0; Part < State.UF; ++Part)
State.ILV->scalarizeInstruction(UI, this, VPIteration(Part, 0), State);
return;
}
// A store of a loop varying value to a uniform address only needs the last
// copy of the store.
if (isa<StoreInst>(UI) &&
vputils::isUniformAfterVectorization(getOperand(1))) {
auto Lane = VPLane::getLastLaneForVF(State.VF);
State.ILV->scalarizeInstruction(UI, this, VPIteration(State.UF - 1, Lane),
State);
return;
}
// Generate scalar instances for all VF lanes of all UF parts.
assert(!State.VF.isScalable() && "Can't scalarize a scalable vector");
const unsigned EndLane = State.VF.getKnownMinValue();
for (unsigned Part = 0; Part < State.UF; ++Part)
for (unsigned Lane = 0; Lane < EndLane; ++Lane)
State.ILV->scalarizeInstruction(UI, this, VPIteration(Part, Lane), State);
}
/// Use all-true mask for reverse rather than actual mask, as it avoids a
/// dependence w/o affecting the result.
static Instruction *createReverseEVL(IRBuilderBase &Builder, Value *Operand,
Value *EVL, const Twine &Name) {
VectorType *ValTy = cast<VectorType>(Operand->getType());
Value *AllTrueMask =
Builder.CreateVectorSplat(ValTy->getElementCount(), Builder.getTrue());
return Builder.CreateIntrinsic(ValTy, Intrinsic::experimental_vp_reverse,
{Operand, AllTrueMask, EVL}, nullptr, Name);
}
void VPWidenLoadEVLRecipe::execute(VPTransformState &State) {
assert(State.UF == 1 && "Expected only UF == 1 when vectorizing with "
"explicit vector length.");
auto *LI = cast<LoadInst>(&Ingredient);
Type *ScalarDataTy = getLoadStoreType(&Ingredient);
auto *DataTy = VectorType::get(ScalarDataTy, State.VF);
const Align Alignment = getLoadStoreAlignment(&Ingredient);
bool CreateGather = !isConsecutive();
auto &Builder = State.Builder;
State.setDebugLocFrom(getDebugLoc());
CallInst *NewLI;
Value *EVL = State.get(getEVL(), VPIteration(0, 0));
Value *Addr = State.get(getAddr(), 0, !CreateGather);
Value *Mask = nullptr;
if (VPValue *VPMask = getMask()) {
Mask = State.get(VPMask, 0);
if (isReverse())
Mask = createReverseEVL(Builder, Mask, EVL, "vp.reverse.mask");
} else {
Mask = Builder.CreateVectorSplat(State.VF, Builder.getTrue());
}
if (CreateGather) {
NewLI =
Builder.CreateIntrinsic(DataTy, Intrinsic::vp_gather, {Addr, Mask, EVL},
nullptr, "wide.masked.gather");
} else {
VectorBuilder VBuilder(Builder);
VBuilder.setEVL(EVL).setMask(Mask);
NewLI = cast<CallInst>(VBuilder.createVectorInstruction(
Instruction::Load, DataTy, Addr, "vp.op.load"));
}
NewLI->addParamAttr(
0, Attribute::getWithAlignment(NewLI->getContext(), Alignment));
State.addMetadata(NewLI, LI);
Instruction *Res = NewLI;
if (isReverse())
Res = createReverseEVL(Builder, Res, EVL, "vp.reverse");
State.set(this, Res, 0);
}
void VPWidenStoreEVLRecipe::execute(VPTransformState &State) {
assert(State.UF == 1 && "Expected only UF == 1 when vectorizing with "
"explicit vector length.");
auto *SI = cast<StoreInst>(&Ingredient);
VPValue *StoredValue = getStoredValue();
bool CreateScatter = !isConsecutive();
const Align Alignment = getLoadStoreAlignment(&Ingredient);
auto &Builder = State.Builder;
State.setDebugLocFrom(getDebugLoc());
CallInst *NewSI = nullptr;
Value *StoredVal = State.get(StoredValue, 0);
Value *EVL = State.get(getEVL(), VPIteration(0, 0));
if (isReverse())
StoredVal = createReverseEVL(Builder, StoredVal, EVL, "vp.reverse");
Value *Mask = nullptr;
if (VPValue *VPMask = getMask()) {
Mask = State.get(VPMask, 0);
if (isReverse())
Mask = createReverseEVL(Builder, Mask, EVL, "vp.reverse.mask");
} else {
Mask = Builder.CreateVectorSplat(State.VF, Builder.getTrue());
}
Value *Addr = State.get(getAddr(), 0, !CreateScatter);
if (CreateScatter) {
NewSI = Builder.CreateIntrinsic(Type::getVoidTy(EVL->getContext()),
Intrinsic::vp_scatter,
{StoredVal, Addr, Mask, EVL});
} else {
VectorBuilder VBuilder(Builder);
VBuilder.setEVL(EVL).setMask(Mask);
NewSI = cast<CallInst>(VBuilder.createVectorInstruction(
Instruction::Store, Type::getVoidTy(EVL->getContext()),
{StoredVal, Addr}));
}
NewSI->addParamAttr(
1, Attribute::getWithAlignment(NewSI->getContext(), Alignment));
State.addMetadata(NewSI, SI);
}
// Determine how to lower the scalar epilogue, which depends on 1) optimising
// for minimum code-size, 2) predicate compiler options, 3) loop hints forcing
// predication, and 4) a TTI hook that analyses whether the loop is suitable
// for predication.
static ScalarEpilogueLowering getScalarEpilogueLowering(
Function *F, Loop *L, LoopVectorizeHints &Hints, ProfileSummaryInfo *PSI,
BlockFrequencyInfo *BFI, TargetTransformInfo *TTI, TargetLibraryInfo *TLI,
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 a scalar epilogue.
// (For PGSO, as shouldOptimizeForSize isn't currently accessible from
// LoopAccessInfo (due to code dependency and not being able to reliably get
// PSI/BFI from a loop analysis under NPM), we cannot suppress the collection
// of strides in LoopAccessInfo::analyzeLoop() and vectorize without
// versioning when the vectorization is forced, unlike hasOptSize. So revert
// back to the old way and vectorize with versioning when forced. See D81345.)
if (F->hasOptSize() || (llvm::shouldOptimizeForSize(L->getHeader(), PSI, BFI,
PGSOQueryType::IRPass) &&
Hints.getForce() != LoopVectorizeHints::FK_Enabled))
return CM_ScalarEpilogueNotAllowedOptSize;
// 2) If set, obey the directives
if (PreferPredicateOverEpilogue.getNumOccurrences()) {
switch (PreferPredicateOverEpilogue) {
case PreferPredicateTy::ScalarEpilogue:
return CM_ScalarEpilogueAllowed;
case PreferPredicateTy::PredicateElseScalarEpilogue:
return CM_ScalarEpilogueNotNeededUsePredicate;
case PreferPredicateTy::PredicateOrDontVectorize:
return CM_ScalarEpilogueNotAllowedUsePredicate;
};
}
// 3) If set, obey the hints
switch (Hints.getPredicate()) {
case LoopVectorizeHints::FK_Enabled:
return CM_ScalarEpilogueNotNeededUsePredicate;
case LoopVectorizeHints::FK_Disabled:
return CM_ScalarEpilogueAllowed;
};
// 4) if the TTI hook indicates this is profitable, request predication.
TailFoldingInfo TFI(TLI, &LVL, IAI);
if (TTI->preferPredicateOverEpilogue(&TFI))
return CM_ScalarEpilogueNotNeededUsePredicate;
return CM_ScalarEpilogueAllowed;
}
// Process the loop in the VPlan-native vectorization path. This path builds
// VPlan upfront in the vectorization pipeline, which allows to apply
// VPlan-to-VPlan transformations from the very beginning without modifying the
// input LLVM IR.
static bool processLoopInVPlanNativePath(
Loop *L, PredicatedScalarEvolution &PSE, LoopInfo *LI, DominatorTree *DT,
LoopVectorizationLegality *LVL, TargetTransformInfo *TTI,
TargetLibraryInfo *TLI, DemandedBits *DB, AssumptionCache *AC,
OptimizationRemarkEmitter *ORE, BlockFrequencyInfo *BFI,
ProfileSummaryInfo *PSI, LoopVectorizeHints &Hints,
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());
ScalarEpilogueLowering SEL =
getScalarEpilogueLowering(F, L, Hints, PSI, BFI, TTI, TLI, *LVL, &IAI);
LoopVectorizationCostModel CM(SEL, L, PSE, LI, LVL, *TTI, TLI, DB, AC, ORE, F,
&Hints, IAI);
// Use the planner for outer loop vectorization.
// TODO: CM is not used at this point inside the planner. Turn CM into an
// optional argument if we don't need it in the future.
LoopVectorizationPlanner LVP(L, LI, DT, TLI, *TTI, LVL, CM, IAI, PSE, Hints,
ORE);
// Get user vectorization factor.
ElementCount UserVF = Hints.getWidth();
CM.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);
{
bool AddBranchWeights =
hasBranchWeightMD(*L->getLoopLatch()->getTerminator());
GeneratedRTChecks Checks(*PSE.getSE(), DT, LI, TTI,
F->getDataLayout(), AddBranchWeights);
InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width,
VF.Width, 1, LVL, &CM, BFI, PSI, Checks);
LLVM_DEBUG(dbgs() << "Vectorizing outer loop in \""
<< L->getHeader()->getParent()->getName() << "\"\n");
LVP.executePlan(VF.Width, 1, BestPlan, LB, DT, false);
}
reportVectorization(ORE, L, VF, 1);
// Mark the loop as already vectorized to avoid vectorizing again.
Hints.setAlreadyVectorized();
assert(!verifyFunction(*L->getHeader()->getParent(), &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);
}
}
static bool areRuntimeChecksProfitable(GeneratedRTChecks &Checks,
VectorizationFactor &VF,
std::optional<unsigned> VScale, Loop *L,
ScalarEvolution &SE,
ScalarEpilogueLowering SEL) {
InstructionCost CheckCost = Checks.getCost();
if (!CheckCost.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()) {
if (CheckCost > 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;
// 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
// RtC + VecC * (TC / VF) + EpiC
// where
// * RtC is the cost of the generated runtime checks
// * 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:
// RtC + VecC * (TC / VF) + EpiC < ScalarC * TC
//
// Now we can compute the minimum required trip count TC as
// VF * (RtC + 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 = VF.Width.getKnownMinValue();
if (VF.Width.isScalable()) {
unsigned AssumedMinimumVscale = 1;
if (VScale)
AssumedMinimumVscale = *VScale;
IntVF *= AssumedMinimumVscale;
}
uint64_t RtC = *CheckCost.getValue();
uint64_t Div = ScalarC * IntVF - *VF.Cost.getValue();
uint64_t MinTC1 = Div == 0 ? 0 : divideCeil(RtC * 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 * 10, ScalarC);
// Now pick the larger minimum. If it is not a multiple of VF and a scalar
// 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_ScalarEpilogueAllowed)
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(SE, L)) {
if (ElementCount::isKnownLT(ElementCount::getFixed(*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) {}
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);
// 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, BFI, PSI);
if (!LVL.canVectorize(EnableVPlanNativePath)) {
LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n");
Hints.emitRemarkWithHints();
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, BFI, PSI, 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));
// Check the function attributes and profiles to find out if this function
// should be optimized for size.
ScalarEpilogueLowering SEL =
getScalarEpilogueLowering(F, L, Hints, PSI, BFI, TTI, TLI, 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(*SE, L);
if (ExpectedTC && *ExpectedTC < TinyTripCountVectorThreshold) {
LLVM_DEBUG(dbgs() << "LV: Found a loop with a very small trip count. "
<< "This loop is worth vectorizing only if no scalar "
<< "iteration overheads are incurred.");
if (Hints.getForce() == LoopVectorizeHints::FK_Enabled)
LLVM_DEBUG(dbgs() << " But vectorizing was explicitly forced.\n");
else {
if (*ExpectedTC > TTI->getMinTripCountTailFoldingThreshold()) {
LLVM_DEBUG(dbgs() << "\n");
// Predicate tail-folded loops are efficient even when the loop
// iteration count is low. However, setting the epilogue policy to
// `CM_ScalarEpilogueNotAllowedLowTripLoop` prevents vectorizing loops
// with runtime checks. It's more effective to let
// `areRuntimeChecksProfitable` determine if vectorization is beneficial
// for the loop.
if (SEL != CM_ScalarEpilogueNotNeededUsePredicate)
SEL = CM_ScalarEpilogueNotAllowedLowTripLoop;
} else {
LLVM_DEBUG(dbgs() << " But the target considers the trip count too "
"small to consider vectorizing.\n");
reportVectorizationFailure(
"The trip count is below the minial threshold value.",
"loop trip count is too low, avoiding vectorization",
"LowTripCount", ORE, L);
Hints.emitRemarkWithHints();
return false;
}
}
}
// 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.
LoopVectorizationCostModel CM(SEL, L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE,
F, &Hints, IAI);
// Use the planner for vectorization.
LoopVectorizationPlanner LVP(L, LI, DT, TLI, *TTI, &LVL, CM, IAI, PSE, Hints,
ORE);
// Get user vectorization factor and interleave count.
ElementCount UserVF = Hints.getWidth();
unsigned UserIC = Hints.getInterleave();
// Plan how to best vectorize.
LVP.plan(UserVF, UserIC);
VectorizationFactor VF = LVP.computeBestVF();
unsigned IC = 1;
if (ORE->allowExtraAnalysis(LV_NAME))
LVP.emitInvalidCostRemarks(ORE);
bool AddBranchWeights =
hasBranchWeightMD(*L->getLoopLatch()->getTerminator());
GeneratedRTChecks Checks(*PSE.getSE(), DT, LI, TTI,
F->getDataLayout(), AddBranchWeights);
if (LVP.hasPlanWithVF(VF.Width)) {
// Select the interleave count.
IC = CM.selectInterleaveCount(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);
// Check if it is profitable to vectorize with runtime checks.
bool ForceVectorization =
Hints.getForce() == LoopVectorizeHints::FK_Enabled;
if (!ForceVectorization &&
!areRuntimeChecksProfitable(Checks, VF, getVScaleForTuning(L, *TTI), L,
*PSE.getSE(), SEL)) {
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 = std::make_pair(
"VectorizationNotBeneficial",
"the cost-model indicates that vectorization is not beneficial");
VectorizeLoop = false;
}
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 = std::make_pair(
"InterleavingAvoided",
"Ignoring UserIC, because interleaving was avoided up front");
InterleaveLoop = false;
} else if (IC == 1 && UserIC <= 1) {
// Tell the user interleaving is not beneficial.
LLVM_DEBUG(dbgs() << "LV: Interleaving is not beneficial.\n");
IntDiagMsg = std::make_pair(
"InterleavingNotBeneficial",
"the cost-model indicates that interleaving is not beneficial");
InterleaveLoop = false;
if (UserIC == 1) {
IntDiagMsg.first = "InterleavingNotBeneficialAndDisabled";
IntDiagMsg.second +=
" and is explicitly disabled or interleave count is set to 1";
}
} else if (IC > 1 && UserIC == 1) {
// Tell the user interleaving is beneficial, but it explicitly disabled.
LLVM_DEBUG(
dbgs() << "LV: Interleaving is beneficial but is explicitly disabled.");
IntDiagMsg = std::make_pair(
"InterleavingBeneficialButDisabled",
"the cost-model indicates that interleaving is beneficial "
"but is explicitly disabled or interleave count is set to 1");
InterleaveLoop = false;
}
// Override IC if user provided an interleave count.
IC = UserIC > 0 ? UserIC : IC;
// Emit diagnostic messages, if any.
const char *VAPassName = Hints.vectorizeAnalysisPassName();
if (!VectorizeLoop && !InterleaveLoop) {
// Do not vectorize or interleaving the loop.
ORE->emit([&]() {
return OptimizationRemarkMissed(VAPassName, VecDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< VecDiagMsg.second;
});
ORE->emit([&]() {
return OptimizationRemarkMissed(LV_NAME, IntDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< IntDiagMsg.second;
});
return false;
} else if (!VectorizeLoop && InterleaveLoop) {
LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
ORE->emit([&]() {
return OptimizationRemarkAnalysis(VAPassName, VecDiagMsg.first,
L->getStartLoc(), L->getHeader())
<< VecDiagMsg.second;
});
} else if (VectorizeLoop && !InterleaveLoop) {
LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width
<< ") in " << 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');
}
bool DisableRuntimeUnroll = false;
MDNode *OrigLoopID = L->getLoopID();
{
using namespace ore;
if (!VectorizeLoop) {
assert(IC > 1 && "interleave count should not be 1 or 0");
// If we decided that it is not legal to vectorize the loop, then
// interleave it.
InnerLoopUnroller Unroller(L, PSE, LI, DT, TLI, TTI, AC, ORE, IC, &LVL,
&CM, BFI, PSI, Checks);
VPlan &BestPlan = LVP.getPlanFor(VF.Width);
LVP.executePlan(VF.Width, IC, BestPlan, Unroller, DT, false);
ORE->emit([&]() {
return OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(),
L->getHeader())
<< "interleaved loop (interleaved count: "
<< NV("InterleaveCount", IC) << ")";
});
} else {
// If we decided that it is *legal* to vectorize the loop, then do it.
VPlan &BestPlan = LVP.getPlanFor(VF.Width);
// Consider vectorizing the epilogue too if it's profitable.
VectorizationFactor EpilogueVF =
LVP.selectEpilogueVectorizationFactor(VF.Width, IC);
if (EpilogueVF.Width.isVector()) {
// 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.
EpilogueLoopVectorizationInfo EPI(VF.Width, IC, EpilogueVF.Width, 1);
EpilogueVectorizerMainLoop MainILV(L, PSE, LI, DT, TLI, TTI, AC, ORE,
EPI, &LVL, &CM, BFI, PSI, Checks);
std::unique_ptr<VPlan> BestMainPlan(BestPlan.duplicate());
const auto &[ExpandedSCEVs, ReductionResumeValues] = LVP.executePlan(
EPI.MainLoopVF, EPI.MainLoopUF, *BestMainPlan, MainILV, DT, true);
++LoopsVectorized;
// Second pass vectorizes the epilogue and adjusts the control flow
// edges from the first pass.
EPI.MainLoopVF = EPI.EpilogueVF;
EPI.MainLoopUF = EPI.EpilogueUF;
EpilogueVectorizerEpilogueLoop EpilogILV(L, PSE, LI, DT, TLI, TTI, AC,
ORE, EPI, &LVL, &CM, BFI, PSI,
Checks);
VPlan &BestEpiPlan = LVP.getPlanFor(EPI.EpilogueVF);
VPRegionBlock *VectorLoop = BestEpiPlan.getVectorLoopRegion();
VPBasicBlock *Header = VectorLoop->getEntryBasicBlock();
Header->setName("vec.epilog.vector.body");
// 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
// TODO: This is a workaround needed for epilogue vectorization and it
// should be removed once induction resume value creation is done
// directly in VPlan.
EpilogILV.setTripCount(MainILV.getTripCount());
for (auto &R : make_early_inc_range(*BestEpiPlan.getPreheader())) {
auto *ExpandR = cast<VPExpandSCEVRecipe>(&R);
auto *ExpandedVal = BestEpiPlan.getOrAddLiveIn(
ExpandedSCEVs.find(ExpandR->getSCEV())->second);
ExpandR->replaceAllUsesWith(ExpandedVal);
if (BestEpiPlan.getTripCount() == ExpandR)
BestEpiPlan.resetTripCount(ExpandedVal);
ExpandR->eraseFromParent();
}
// Ensure that the start values for any VPWidenIntOrFpInductionRecipe,
// VPWidenPointerInductionRecipe and VPReductionPHIRecipes are updated
// before vectorizing the epilogue loop.
for (VPRecipeBase &R : Header->phis()) {
if (isa<VPCanonicalIVPHIRecipe>(&R))
continue;
Value *ResumeV = nullptr;
// TODO: Move setting of resume values to prepareToExecute.
if (auto *ReductionPhi = dyn_cast<VPReductionPHIRecipe>(&R)) {
const RecurrenceDescriptor &RdxDesc =
ReductionPhi->getRecurrenceDescriptor();
RecurKind RK = RdxDesc.getRecurrenceKind();
ResumeV = ReductionResumeValues.find(&RdxDesc)->second;
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.
IRBuilder<> Builder(
cast<Instruction>(ResumeV)->getParent()->getFirstNonPHI());
ResumeV = Builder.CreateICmpNE(ResumeV,
RdxDesc.getRecurrenceStartValue());
}
} else {
// Create induction resume values for both widened pointer and
// integer/fp inductions and update the start value of the induction
// recipes to use the resume value.
PHINode *IndPhi = nullptr;
const InductionDescriptor *ID;
if (auto *Ind = dyn_cast<VPWidenPointerInductionRecipe>(&R)) {
IndPhi = cast<PHINode>(Ind->getUnderlyingValue());
ID = &Ind->getInductionDescriptor();
} else {
auto *WidenInd = cast<VPWidenIntOrFpInductionRecipe>(&R);
IndPhi = WidenInd->getPHINode();
ID = &WidenInd->getInductionDescriptor();
}
ResumeV = MainILV.createInductionResumeValue(
IndPhi, *ID, getExpandedStep(*ID, ExpandedSCEVs),
{EPI.MainLoopIterationCountCheck});
}
assert(ResumeV && "Must have a resume value");
VPValue *StartVal = BestEpiPlan.getOrAddLiveIn(ResumeV);
cast<VPHeaderPHIRecipe>(&R)->setStartValue(StartVal);
}
assert(DT->verify(DominatorTree::VerificationLevel::Fast) &&
"DT not preserved correctly");
LVP.executePlan(EPI.EpilogueVF, EPI.EpilogueUF, BestEpiPlan, EpilogILV,
DT, true, &ExpandedSCEVs);
++LoopsEpilogueVectorized;
if (!MainILV.areSafetyChecksAdded())
DisableRuntimeUnroll = true;
} else {
InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width,
VF.MinProfitableTripCount, IC, &LVL, &CM, BFI,
PSI, Checks);
LVP.executePlan(VF.Width, IC, BestPlan, LB, DT, false);
++LoopsVectorized;
// Add metadata to disable runtime unrolling a scalar loop when there
// are no runtime checks about strides and memory. A scalar loop that is
// rarely used is not worth unrolling.
if (!LB.areSafetyChecksAdded())
DisableRuntimeUnroll = true;
}
// Report the vectorization decision.
reportVectorization(ORE, L, VF, IC);
}
if (ORE->allowExtraAnalysis(LV_NAME))
checkMixedPrecision(L, ORE);
}
std::optional<MDNode *> RemainderLoopID =
makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll,
LLVMLoopVectorizeFollowupEpilogue});
if (RemainderLoopID) {
L->setLoopID(*RemainderLoopID);
} else {
if (DisableRuntimeUnroll)
AddRuntimeUnrollDisableMetaData(L);
// Mark the loop as already vectorized to avoid vectorizing again.
Hints.setAlreadyVectorized();
}
assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs()));
return true;
}
LoopVectorizeResult LoopVectorizePass::runImpl(Function &F) {
// 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);
auto &MAMProxy = AM.getResult<ModuleAnalysisManagerFunctionProxy>(F);
PSI = MAMProxy.getCachedResult<ProfileSummaryAnalysis>(*F.getParent());
BFI = nullptr;
if (PSI && PSI->hasProfileSummary())
BFI = &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 << '>';
}