| //===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===// |
| // |
| // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| // See https://llvm.org/LICENSE.txt for license information. |
| // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| // |
| //===----------------------------------------------------------------------===// |
| // |
| // This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops |
| // and generates target-independent LLVM-IR. |
| // The vectorizer uses the TargetTransformInfo analysis to estimate the costs |
| // of instructions in order to estimate the profitability of vectorization. |
| // |
| // The loop vectorizer combines consecutive loop iterations into a single |
| // 'wide' iteration. After this transformation the index is incremented |
| // by the SIMD vector width, and not by one. |
| // |
| // This pass has three parts: |
| // 1. The main loop pass that drives the different parts. |
| // 2. LoopVectorizationLegality - A unit that checks for the legality |
| // of the vectorization. |
| // 3. InnerLoopVectorizer - A unit that performs the actual |
| // widening of instructions. |
| // 4. LoopVectorizationCostModel - A unit that checks for the profitability |
| // of vectorization. It decides on the optimal vector width, which |
| // can be one, if vectorization is not profitable. |
| // |
| // There is a development effort going on to migrate loop vectorizer to the |
| // VPlan infrastructure and to introduce outer loop vectorization support (see |
| // docs/Proposal/VectorizationPlan.rst and |
| // http://lists.llvm.org/pipermail/llvm-dev/2017-December/119523.html). For this |
| // purpose, we temporarily introduced the VPlan-native vectorization path: an |
| // alternative vectorization path that is natively implemented on top of the |
| // VPlan infrastructure. See EnableVPlanNativePath for enabling. |
| // |
| //===----------------------------------------------------------------------===// |
| // |
| // The reduction-variable vectorization is based on the paper: |
| // D. Nuzman and R. Henderson. Multi-platform Auto-vectorization. |
| // |
| // Variable uniformity checks are inspired by: |
| // Karrenberg, R. and Hack, S. Whole Function Vectorization. |
| // |
| // The interleaved access vectorization is based on the paper: |
| // Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved |
| // Data for SIMD |
| // |
| // Other ideas/concepts are from: |
| // A. Zaks and D. Nuzman. Autovectorization in GCC-two years later. |
| // |
| // S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of |
| // Vectorizing Compilers. |
| // |
| //===----------------------------------------------------------------------===// |
| |
| #include "llvm/Transforms/Vectorize/LoopVectorize.h" |
| #include "LoopVectorizationPlanner.h" |
| #include "VPRecipeBuilder.h" |
| #include "VPlan.h" |
| #include "VPlanHCFGBuilder.h" |
| #include "VPlanPredicator.h" |
| #include "VPlanTransforms.h" |
| #include "llvm/ADT/APInt.h" |
| #include "llvm/ADT/ArrayRef.h" |
| #include "llvm/ADT/DenseMap.h" |
| #include "llvm/ADT/DenseMapInfo.h" |
| #include "llvm/ADT/Hashing.h" |
| #include "llvm/ADT/MapVector.h" |
| #include "llvm/ADT/None.h" |
| #include "llvm/ADT/Optional.h" |
| #include "llvm/ADT/STLExtras.h" |
| #include "llvm/ADT/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/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/VectorUtils.h" |
| #include "llvm/IR/Attributes.h" |
| #include "llvm/IR/BasicBlock.h" |
| #include "llvm/IR/CFG.h" |
| #include "llvm/IR/Constant.h" |
| #include "llvm/IR/Constants.h" |
| #include "llvm/IR/DataLayout.h" |
| #include "llvm/IR/DebugInfoMetadata.h" |
| #include "llvm/IR/DebugLoc.h" |
| #include "llvm/IR/DerivedTypes.h" |
| #include "llvm/IR/DiagnosticInfo.h" |
| #include "llvm/IR/Dominators.h" |
| #include "llvm/IR/Function.h" |
| #include "llvm/IR/IRBuilder.h" |
| #include "llvm/IR/InstrTypes.h" |
| #include "llvm/IR/Instruction.h" |
| #include "llvm/IR/Instructions.h" |
| #include "llvm/IR/IntrinsicInst.h" |
| #include "llvm/IR/Intrinsics.h" |
| #include "llvm/IR/LLVMContext.h" |
| #include "llvm/IR/Metadata.h" |
| #include "llvm/IR/Module.h" |
| #include "llvm/IR/Operator.h" |
| #include "llvm/IR/PatternMatch.h" |
| #include "llvm/IR/Type.h" |
| #include "llvm/IR/Use.h" |
| #include "llvm/IR/User.h" |
| #include "llvm/IR/Value.h" |
| #include "llvm/IR/ValueHandle.h" |
| #include "llvm/IR/Verifier.h" |
| #include "llvm/InitializePasses.h" |
| #include "llvm/Pass.h" |
| #include "llvm/Support/Casting.h" |
| #include "llvm/Support/CommandLine.h" |
| #include "llvm/Support/Compiler.h" |
| #include "llvm/Support/Debug.h" |
| #include "llvm/Support/ErrorHandling.h" |
| #include "llvm/Support/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/LoopSimplify.h" |
| #include "llvm/Transforms/Utils/LoopUtils.h" |
| #include "llvm/Transforms/Utils/LoopVersioning.h" |
| #include "llvm/Transforms/Utils/ScalarEvolutionExpander.h" |
| #include "llvm/Transforms/Utils/SizeOpts.h" |
| #include "llvm/Transforms/Vectorize/LoopVectorizationLegality.h" |
| #include <algorithm> |
| #include <cassert> |
| #include <cstdint> |
| #include <cstdlib> |
| #include <functional> |
| #include <iterator> |
| #include <limits> |
| #include <memory> |
| #include <string> |
| #include <tuple> |
| #include <utility> |
| |
| using namespace llvm; |
| |
| #define LV_NAME "loop-vectorize" |
| #define DEBUG_TYPE LV_NAME |
| |
| #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> PragmaVectorizeMemoryCheckThreshold( |
| "pragma-vectorize-memory-check-threshold", cl::init(128), cl::Hidden, |
| cl::desc("The maximum allowed number of runtime memory checks with a " |
| "vectorize(enable) pragma.")); |
| |
| // Option prefer-predicate-over-epilogue indicates that an epilogue is undesired, |
| // that predication is preferred, and this lists all options. I.e., the |
| // vectorizer will try to fold the tail-loop (epilogue) into the vector body |
| // and predicate the instructions accordingly. If tail-folding fails, there are |
| // different fallback strategies depending on these values: |
| namespace PreferPredicateTy { |
| enum Option { |
| ScalarEpilogue = 0, |
| PredicateElseScalarEpilogue, |
| PredicateOrDontVectorize |
| }; |
| } // namespace PreferPredicateTy |
| |
| static cl::opt<PreferPredicateTy::Option> PreferPredicateOverEpilogue( |
| "prefer-predicate-over-epilogue", |
| cl::init(PreferPredicateTy::ScalarEpilogue), |
| cl::Hidden, |
| cl::desc("Tail-folding and predication preferences over creating a scalar " |
| "epilogue loop."), |
| cl::values(clEnumValN(PreferPredicateTy::ScalarEpilogue, |
| "scalar-epilogue", |
| "Don't tail-predicate loops, create scalar epilogue"), |
| clEnumValN(PreferPredicateTy::PredicateElseScalarEpilogue, |
| "predicate-else-scalar-epilogue", |
| "prefer tail-folding, create scalar epilogue if tail " |
| "folding fails."), |
| clEnumValN(PreferPredicateTy::PredicateOrDontVectorize, |
| "predicate-dont-vectorize", |
| "prefers tail-folding, don't attempt vectorization if " |
| "tail-folding fails."))); |
| |
| static cl::opt<bool> MaximizeBandwidth( |
| "vectorizer-maximize-bandwidth", cl::init(false), cl::Hidden, |
| cl::desc("Maximize bandwidth when selecting vectorization factor which " |
| "will be determined by the smallest type in loop.")); |
| |
| static cl::opt<bool> EnableInterleavedMemAccesses( |
| "enable-interleaved-mem-accesses", cl::init(false), cl::Hidden, |
| cl::desc("Enable vectorization on interleaved memory accesses in a loop")); |
| |
| /// An interleave-group may need masking if it resides in a block that needs |
| /// predication, or in order to mask away gaps. |
| static cl::opt<bool> EnableMaskedInterleavedMemAccesses( |
| "enable-masked-interleaved-mem-accesses", cl::init(false), cl::Hidden, |
| cl::desc("Enable vectorization on masked interleaved memory accesses in a loop")); |
| |
| static cl::opt<unsigned> TinyTripCountInterleaveThreshold( |
| "tiny-trip-count-interleave-threshold", cl::init(128), cl::Hidden, |
| cl::desc("We don't interleave loops with a estimated constant trip count " |
| "below this number")); |
| |
| static cl::opt<unsigned> ForceTargetNumScalarRegs( |
| "force-target-num-scalar-regs", cl::init(0), cl::Hidden, |
| cl::desc("A flag that overrides the target's number of scalar registers.")); |
| |
| static cl::opt<unsigned> ForceTargetNumVectorRegs( |
| "force-target-num-vector-regs", cl::init(0), cl::Hidden, |
| cl::desc("A flag that overrides the target's number of vector registers.")); |
| |
| static cl::opt<unsigned> ForceTargetMaxScalarInterleaveFactor( |
| "force-target-max-scalar-interleave", cl::init(0), cl::Hidden, |
| cl::desc("A flag that overrides the target's max interleave factor for " |
| "scalar loops.")); |
| |
| static cl::opt<unsigned> ForceTargetMaxVectorInterleaveFactor( |
| "force-target-max-vector-interleave", cl::init(0), cl::Hidden, |
| cl::desc("A flag that overrides the target's max interleave factor for " |
| "vectorized loops.")); |
| |
| static cl::opt<unsigned> ForceTargetInstructionCost( |
| "force-target-instruction-cost", cl::init(0), cl::Hidden, |
| cl::desc("A flag that overrides the target's expected cost for " |
| "an instruction to a single constant value. Mostly " |
| "useful for getting consistent testing.")); |
| |
| static cl::opt<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")); |
| |
| /// Interleave small loops with scalar reductions. |
| static cl::opt<bool> InterleaveSmallLoopScalarReduction( |
| "interleave-small-loop-scalar-reduction", cl::init(false), cl::Hidden, |
| cl::desc("Enable interleaving for loops with small iteration counts that " |
| "contain scalar reductions to expose ILP.")); |
| |
| /// The number of stores in a loop that are allowed to need predication. |
| static cl::opt<unsigned> NumberOfStoresToPredicate( |
| "vectorize-num-stores-pred", cl::init(1), cl::Hidden, |
| cl::desc("Max number of stores to be predicated behind an if.")); |
| |
| static cl::opt<bool> EnableIndVarRegisterHeur( |
| "enable-ind-var-reg-heur", cl::init(true), cl::Hidden, |
| cl::desc("Count the induction variable only once when interleaving")); |
| |
| static cl::opt<bool> EnableCondStoresVectorization( |
| "enable-cond-stores-vec", cl::init(true), cl::Hidden, |
| cl::desc("Enable if predication of stores during vectorization.")); |
| |
| static cl::opt<unsigned> MaxNestedScalarReductionIC( |
| "max-nested-scalar-reduction-interleave", cl::init(2), cl::Hidden, |
| cl::desc("The maximum interleave count to use when interleaving a scalar " |
| "reduction in a nested loop.")); |
| |
| static cl::opt<bool> |
| PreferInLoopReductions("prefer-inloop-reductions", cl::init(false), |
| cl::Hidden, |
| cl::desc("Prefer in-loop vector reductions, " |
| "overriding the targets preference.")); |
| |
| static cl::opt<bool> ForceOrderedReductions( |
| "force-ordered-reductions", cl::init(false), cl::Hidden, |
| cl::desc("Enable the vectorisation of loops with in-order (strict) " |
| "FP reductions")); |
| |
| static cl::opt<bool> PreferPredicatedReductionSelect( |
| "prefer-predicated-reduction-select", cl::init(false), cl::Hidden, |
| cl::desc( |
| "Prefer predicating a reduction operation over an after loop select.")); |
| |
| cl::opt<bool> EnableVPlanNativePath( |
| "enable-vplan-native-path", cl::init(false), cl::Hidden, |
| cl::desc("Enable VPlan-native vectorization path with " |
| "support for outer loop vectorization.")); |
| |
| // FIXME: Remove this switch once we have divergence analysis. Currently we |
| // assume divergent non-backedge branches when this switch is true. |
| cl::opt<bool> EnableVPlanPredication( |
| "enable-vplan-predication", cl::init(false), cl::Hidden, |
| cl::desc("Enable VPlan-native vectorization path predicator with " |
| "support for outer loop vectorization.")); |
| |
| // This flag enables the stress testing of the VPlan H-CFG construction in the |
| // VPlan-native vectorization path. It must be used in conjuction with |
| // -enable-vplan-native-path. -vplan-verify-hcfg can also be used to enable the |
| // verification of the H-CFGs built. |
| static cl::opt<bool> VPlanBuildStressTest( |
| "vplan-build-stress-test", cl::init(false), cl::Hidden, |
| cl::desc( |
| "Build VPlan for every supported loop nest in the function and bail " |
| "out right after the build (stress test the VPlan H-CFG construction " |
| "in the VPlan-native vectorization path).")); |
| |
| cl::opt<bool> llvm::EnableLoopInterleaving( |
| "interleave-loops", cl::init(true), cl::Hidden, |
| cl::desc("Enable loop interleaving in Loop vectorization passes")); |
| cl::opt<bool> llvm::EnableLoopVectorization( |
| "vectorize-loops", cl::init(true), cl::Hidden, |
| cl::desc("Run the Loop vectorization passes")); |
| |
| cl::opt<bool> PrintVPlansInDotFormat( |
| "vplan-print-in-dot-format", cl::init(false), cl::Hidden, |
| cl::desc("Use dot format instead of plain text when dumping VPlans")); |
| |
| /// A helper function that returns true if the given type is irregular. The |
| /// type is irregular if its allocated size doesn't equal the store size of an |
| /// element of the corresponding vector type. |
| static bool hasIrregularType(Type *Ty, const DataLayout &DL) { |
| // Determine if an array of N elements of type Ty is "bitcast compatible" |
| // with a <N x Ty> vector. |
| // This is only true if there is no padding between the array elements. |
| return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty); |
| } |
| |
| /// A helper function that returns the reciprocal of the block probability of |
| /// predicated blocks. If we return X, we are assuming the predicated block |
| /// will execute once for every X iterations of the loop header. |
| /// |
| /// TODO: We should use actual block probability here, if available. Currently, |
| /// we always assume predicated blocks have a 50% chance of executing. |
| static unsigned getReciprocalPredBlockProb() { return 2; } |
| |
| /// A helper function that returns an integer or floating-point constant with |
| /// value C. |
| static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) { |
| return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C) |
| : ConstantFP::get(Ty, C); |
| } |
| |
| /// Returns "best known" trip count for the specified loop \p L as defined by |
| /// the following procedure: |
| /// 1) Returns exact trip count if it is known. |
| /// 2) Returns expected trip count according to profile data if any. |
| /// 3) Returns upper bound estimate if it is known. |
| /// 4) Returns None if all of the above failed. |
| static Optional<unsigned> getSmallBestKnownTC(ScalarEvolution &SE, Loop *L) { |
| // Check if exact trip count is known. |
| if (unsigned ExpectedTC = SE.getSmallConstantTripCount(L)) |
| return ExpectedTC; |
| |
| // Check if there is an expected trip count available from profile data. |
| if (LoopVectorizeWithBlockFrequency) |
| if (auto EstimatedTC = getLoopEstimatedTripCount(L)) |
| return EstimatedTC; |
| |
| // Check if upper bound estimate is known. |
| if (unsigned ExpectedTC = SE.getSmallConstantMaxTripCount(L)) |
| return ExpectedTC; |
| |
| return None; |
| } |
| |
| // Forward declare GeneratedRTChecks. |
| class GeneratedRTChecks; |
| |
| namespace llvm { |
| |
| /// InnerLoopVectorizer vectorizes loops which contain only one basic |
| /// block to a specified vectorization factor (VF). |
| /// This class performs the widening of scalars into vectors, or multiple |
| /// scalars. This class also implements the following features: |
| /// * It inserts an epilogue loop for handling loops that don't have iteration |
| /// counts that are known to be a multiple of the vectorization factor. |
| /// * It handles the code generation for reduction variables. |
| /// * Scalarization (implementation using scalars) of un-vectorizable |
| /// instructions. |
| /// InnerLoopVectorizer does not perform any vectorization-legality |
| /// checks, and relies on the caller to check for the different legality |
| /// aspects. The InnerLoopVectorizer relies on the |
| /// LoopVectorizationLegality class to provide information about the induction |
| /// and reduction variables that were found to a given vectorization factor. |
| class InnerLoopVectorizer { |
| public: |
| InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE, |
| LoopInfo *LI, DominatorTree *DT, |
| const TargetLibraryInfo *TLI, |
| const TargetTransformInfo *TTI, AssumptionCache *AC, |
| OptimizationRemarkEmitter *ORE, ElementCount VecWidth, |
| unsigned UnrollFactor, LoopVectorizationLegality *LVL, |
| LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, |
| ProfileSummaryInfo *PSI, 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); |
| } |
| |
| 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. |
| /// In the case of epilogue vectorization, this function is overriden to |
| /// handle the more complex control flow around the loops. |
| virtual BasicBlock *createVectorizedLoopSkeleton(); |
| |
| /// Widen a single call instruction within the innermost loop. |
| void widenCallInstruction(CallInst &I, VPValue *Def, VPUser &ArgOperands, |
| VPTransformState &State); |
| |
| /// Fix the vectorized code, taking care of header phi's, live-outs, and more. |
| void fixVectorizedLoop(VPTransformState &State); |
| |
| // Return true if any runtime check is added. |
| bool areSafetyChecksAdded() { return AddedSafetyChecks; } |
| |
| /// A type for vectorized values in the new loop. Each value from the |
| /// original loop, when vectorized, is represented by UF vector values in the |
| /// new unrolled loop, where UF is the unroll factor. |
| using VectorParts = SmallVector<Value *, 2>; |
| |
| /// Vectorize a single first-order recurrence or pointer induction PHINode in |
| /// a block. This method handles the induction variable canonicalization. It |
| /// supports both VF = 1 for unrolled loops and arbitrary length vectors. |
| void widenPHIInstruction(Instruction *PN, VPWidenPHIRecipe *PhiR, |
| VPTransformState &State); |
| |
| /// 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(Instruction *Instr, VPReplicateRecipe *RepRecipe, |
| const VPIteration &Instance, bool IfPredicateInstr, |
| VPTransformState &State); |
| |
| /// Widen an integer or floating-point induction variable \p IV. If \p Trunc |
| /// is provided, the integer induction variable will first be truncated to |
| /// the corresponding type. |
| void widenIntOrFpInduction(PHINode *IV, Value *Start, TruncInst *Trunc, |
| VPValue *Def, VPValue *CastDef, |
| VPTransformState &State); |
| |
| /// Construct the vector value of a scalarized value \p V one lane at a time. |
| void packScalarIntoVectorValue(VPValue *Def, const VPIteration &Instance, |
| VPTransformState &State); |
| |
| /// Try to vectorize interleaved access group \p Group with the base address |
| /// given in \p Addr, optionally masking the vector operations if \p |
| /// BlockInMask is non-null. Use \p State to translate given VPValues to IR |
| /// values in the vectorized loop. |
| void vectorizeInterleaveGroup(const InterleaveGroup<Instruction> *Group, |
| ArrayRef<VPValue *> VPDefs, |
| VPTransformState &State, VPValue *Addr, |
| ArrayRef<VPValue *> StoredValues, |
| VPValue *BlockInMask = nullptr); |
| |
| /// Vectorize Load and Store instructions with the base address given in \p |
| /// Addr, optionally masking the vector operations if \p BlockInMask is |
| /// non-null. Use \p State to translate given VPValues to IR values in the |
| /// vectorized loop. |
| void vectorizeMemoryInstruction(Instruction *Instr, VPTransformState &State, |
| VPValue *Def, VPValue *Addr, |
| VPValue *StoredValue, VPValue *BlockInMask, |
| bool ConsecutiveStride, bool Reverse); |
| |
| /// Set the debug location in the builder \p Ptr using the debug location in |
| /// \p V. If \p Ptr is None then it uses the class member's Builder. |
| void setDebugLocFromInst(const Value *V, |
| Optional<IRBuilder<> *> CustomBuilder = None); |
| |
| /// Fix the non-induction PHIs in the OrigPHIsToFix vector. |
| void fixNonInductionPHIs(VPTransformState &State); |
| |
| /// Returns true if the reordering of FP operations is not allowed, but we are |
| /// able to vectorize with strict in-order reductions for the given RdxDesc. |
| bool useOrderedReductions(RecurrenceDescriptor &RdxDesc); |
| |
| /// Create a broadcast instruction. This method generates a broadcast |
| /// instruction (shuffle) for loop invariant values and for the induction |
| /// value. If this is the induction variable then we extend it to N, N+1, ... |
| /// this is needed because each iteration in the loop corresponds to a SIMD |
| /// element. |
| virtual Value *getBroadcastInstrs(Value *V); |
| |
| /// Add metadata from one instruction to another. |
| /// |
| /// This includes both the original MDs from \p From and additional ones (\see |
| /// addNewMetadata). Use this for *newly created* instructions in the vector |
| /// loop. |
| void addMetadata(Instruction *To, Instruction *From); |
| |
| /// Similar to the previous function but it adds the metadata to a |
| /// vector of instructions. |
| void addMetadata(ArrayRef<Value *> To, Instruction *From); |
| |
| protected: |
| friend class LoopVectorizationPlanner; |
| |
| /// A small list of PHINodes. |
| using PhiVector = SmallVector<PHINode *, 4>; |
| |
| /// A type for scalarized values in the new loop. Each value from the |
| /// original loop, when scalarized, is represented by UF x VF scalar values |
| /// in the new unrolled loop, where UF is the unroll factor and VF is the |
| /// vectorization factor. |
| using ScalarParts = SmallVector<SmallVector<Value *, 4>, 2>; |
| |
| /// Set up the values of the IVs correctly when exiting the vector loop. |
| void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II, |
| Value *CountRoundDown, Value *EndValue, |
| BasicBlock *MiddleBlock); |
| |
| /// Create a new induction variable inside L. |
| PHINode *createInductionVariable(Loop *L, Value *Start, Value *End, |
| Value *Step, Instruction *DL); |
| |
| /// Handle all cross-iteration phis in the header. |
| void fixCrossIterationPHIs(VPTransformState &State); |
| |
| /// Create the exit value of first order recurrences in the middle block and |
| /// update their users. |
| void fixFirstOrderRecurrence(VPWidenPHIRecipe *PhiR, VPTransformState &State); |
| |
| /// Create code for the loop exit value of the reduction. |
| void fixReduction(VPReductionPHIRecipe *Phi, VPTransformState &State); |
| |
| /// Clear NSW/NUW flags from reduction instructions if necessary. |
| void clearReductionWrapFlags(const RecurrenceDescriptor &RdxDesc, |
| VPTransformState &State); |
| |
| /// Fixup the LCSSA phi nodes in the unique exit block. This simply |
| /// means we need to add the appropriate incoming value from the middle |
| /// block as exiting edges from the scalar epilogue loop (if present) are |
| /// already in place, and we exit the vector loop exclusively to the middle |
| /// block. |
| void fixLCSSAPHIs(VPTransformState &State); |
| |
| /// Iteratively sink the scalarized operands of a predicated instruction into |
| /// the block that was created for it. |
| void sinkScalarOperands(Instruction *PredInst); |
| |
| /// Shrinks vector element sizes to the smallest bitwidth they can be legally |
| /// represented as. |
| void truncateToMinimalBitwidths(VPTransformState &State); |
| |
| /// This function adds |
| /// (StartIdx * Step, (StartIdx + 1) * Step, (StartIdx + 2) * Step, ...) |
| /// to each vector element of Val. The sequence starts at StartIndex. |
| /// \p Opcode is relevant for FP induction variable. |
| virtual Value * |
| getStepVector(Value *Val, Value *StartIdx, Value *Step, |
| Instruction::BinaryOps Opcode = Instruction::BinaryOpsEnd); |
| |
| /// Compute scalar induction steps. \p ScalarIV is the scalar induction |
| /// variable on which to base the steps, \p Step is the size of the step, and |
| /// \p EntryVal is the value from the original loop that maps to the steps. |
| /// Note that \p EntryVal doesn't have to be an induction variable - it |
| /// can also be a truncate instruction. |
| void buildScalarSteps(Value *ScalarIV, Value *Step, Instruction *EntryVal, |
| const InductionDescriptor &ID, VPValue *Def, |
| VPValue *CastDef, VPTransformState &State); |
| |
| /// Create a vector induction phi node based on an existing scalar one. \p |
| /// EntryVal is the value from the original loop that maps to the vector phi |
| /// node, and \p Step is the loop-invariant step. If \p EntryVal is a |
| /// truncate instruction, instead of widening the original IV, we widen a |
| /// version of the IV truncated to \p EntryVal's type. |
| void createVectorIntOrFpInductionPHI(const InductionDescriptor &II, |
| Value *Step, Value *Start, |
| Instruction *EntryVal, VPValue *Def, |
| VPValue *CastDef, |
| VPTransformState &State); |
| |
| /// Returns true if an instruction \p I should be scalarized instead of |
| /// vectorized for the chosen vectorization factor. |
| bool shouldScalarizeInstruction(Instruction *I) const; |
| |
| /// Returns true if we should generate a scalar version of \p IV. |
| bool needsScalarInduction(Instruction *IV) const; |
| |
| /// If there is a cast involved in the induction variable \p ID, which should |
| /// be ignored in the vectorized loop body, this function records the |
| /// VectorLoopValue of the respective Phi also as the VectorLoopValue of the |
| /// cast. We had already proved that the casted Phi is equal to the uncasted |
| /// Phi in the vectorized loop (under a runtime guard), and therefore |
| /// there is no need to vectorize the cast - the same value can be used in the |
| /// vector loop for both the Phi and the cast. |
| /// If \p VectorLoopValue is a scalarized value, \p Lane is also specified, |
| /// Otherwise, \p VectorLoopValue is a widened/vectorized value. |
| /// |
| /// \p EntryVal is the value from the original loop that maps to the vector |
| /// phi node and is used to distinguish what is the IV currently being |
| /// processed - original one (if \p EntryVal is a phi corresponding to the |
| /// original IV) or the "newly-created" one based on the proof mentioned above |
| /// (see also buildScalarSteps() and createVectorIntOrFPInductionPHI()). In the |
| /// latter case \p EntryVal is a TruncInst and we must not record anything for |
| /// that IV, but it's error-prone to expect callers of this routine to care |
| /// about that, hence this explicit parameter. |
| void recordVectorLoopValueForInductionCast( |
| const InductionDescriptor &ID, const Instruction *EntryVal, |
| Value *VectorLoopValue, VPValue *CastDef, VPTransformState &State, |
| unsigned Part, unsigned Lane = UINT_MAX); |
| |
| /// Generate a shuffle sequence that will reverse the vector Vec. |
| virtual Value *reverseVector(Value *Vec); |
| |
| /// Returns (and creates if needed) the original loop trip count. |
| Value *getOrCreateTripCount(Loop *NewLoop); |
| |
| /// Returns (and creates if needed) the trip count of the widened loop. |
| Value *getOrCreateVectorTripCount(Loop *NewLoop); |
| |
| /// Returns a bitcasted value to the requested vector type. |
| /// Also handles bitcasts of vector<float> <-> vector<pointer> types. |
| Value *createBitOrPointerCast(Value *V, VectorType *DstVTy, |
| const DataLayout &DL); |
| |
| /// Emit a bypass check to see if the vector trip count is zero, including if |
| /// it overflows. |
| void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass); |
| |
| /// Emit a bypass check to see if all of the SCEV assumptions we've |
| /// had to make are correct. Returns the block containing the checks or |
| /// nullptr if no checks have been added. |
| BasicBlock *emitSCEVChecks(Loop *L, 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(Loop *L, BasicBlock *Bypass); |
| |
| /// Compute the transformed value of Index at offset StartValue using step |
| /// StepValue. |
| /// For integer induction, returns StartValue + Index * StepValue. |
| /// For pointer induction, returns StartValue[Index * StepValue]. |
| /// FIXME: The newly created binary instructions should contain nsw/nuw |
| /// flags, which can be found from the original scalar operations. |
| Value *emitTransformedIndex(IRBuilder<> &B, Value *Index, ScalarEvolution *SE, |
| const DataLayout &DL, |
| const InductionDescriptor &ID) const; |
| |
| /// Emit basic blocks (prefixed with \p Prefix) for the iteration check, |
| /// vector loop preheader, middle block and scalar preheader. Also |
| /// allocate a loop object for the new vector loop and return it. |
| Loop *createVectorLoopSkeleton(StringRef Prefix); |
| |
| /// Create new phi nodes for the induction variables to resume iteration count |
| /// in the scalar epilogue, from where the vectorized loop left off (given by |
| /// \p VectorTripCount). |
| /// 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( |
| Loop *L, Value *VectorTripCount, |
| 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. Take in the vector loop \p L as argument, and return |
| /// the preheader of the completed vector loop. |
| BasicBlock *completeLoopSkeleton(Loop *L, MDNode *OrigLoopID); |
| |
| /// Add additional metadata to \p To that was not present on \p Orig. |
| /// |
| /// Currently this is used to add the noalias annotations based on the |
| /// inserted memchecks. Use this for instructions that are *cloned* into the |
| /// vector loop. |
| void addNewMetadata(Instruction *To, const Instruction *Orig); |
| |
| /// Collect poison-generating recipes that may generate a poison value that is |
| /// used after vectorization, even when their operands are not poison. Those |
| /// recipes meet the following conditions: |
| /// * Contribute to the address computation of a recipe generating a widen |
| /// memory load/store (VPWidenMemoryInstructionRecipe or |
| /// VPInterleaveRecipe). |
| /// * Such a widen memory load/store has at least one underlying Instruction |
| /// that is in a basic block that needs predication and after vectorization |
| /// the generated instruction won't be predicated. |
| void collectPoisonGeneratingRecipes(VPTransformState &State); |
| |
| /// 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; |
| |
| /// Alias Analysis. |
| AAResults *AA; |
| |
| /// Target Library Info. |
| const TargetLibraryInfo *TLI; |
| |
| /// Target Transform Info. |
| const TargetTransformInfo *TTI; |
| |
| /// Assumption Cache. |
| AssumptionCache *AC; |
| |
| /// Interface to emit optimization remarks. |
| OptimizationRemarkEmitter *ORE; |
| |
| /// LoopVersioning. It's only set up (non-null) if memchecks were |
| /// used. |
| /// |
| /// This is currently only used to add no-alias metadata based on the |
| /// memchecks. The actually versioning is performed manually. |
| std::unique_ptr<LoopVersioning> LVer; |
| |
| /// The vectorization SIMD factor to use. Each vector will have this many |
| /// vector elements. |
| ElementCount VF; |
| |
| /// The vectorization unroll factor to use. Each scalar is vectorized to this |
| /// many different vector instructions. |
| unsigned UF; |
| |
| /// The builder that we use |
| IRBuilder<> Builder; |
| |
| // --- Vectorization state --- |
| |
| /// The vector-loop preheader. |
| BasicBlock *LoopVectorPreHeader; |
| |
| /// The scalar-loop preheader. |
| BasicBlock *LoopScalarPreHeader; |
| |
| /// Middle Block between the vector and the scalar. |
| BasicBlock *LoopMiddleBlock; |
| |
| /// The unique ExitBlock of the scalar loop if one exists. Note that |
| /// there can be multiple exiting edges reaching this block. |
| BasicBlock *LoopExitBlock; |
| |
| /// The vector loop body. |
| BasicBlock *LoopVectorBody; |
| |
| /// The scalar loop body. |
| BasicBlock *LoopScalarBody; |
| |
| /// A list of all bypass blocks. The first block is the entry of the loop. |
| SmallVector<BasicBlock *, 4> LoopBypassBlocks; |
| |
| /// The new Induction variable which was added to the new block. |
| PHINode *Induction = nullptr; |
| |
| /// The induction variable of the old basic block. |
| PHINode *OldInduction = nullptr; |
| |
| /// Store instructions that were predicated. |
| SmallVector<Instruction *, 4> PredicatedInstructions; |
| |
| /// Trip count of the original loop. |
| Value *TripCount = nullptr; |
| |
| /// Trip count of the widened loop (TripCount - TripCount % (VF*UF)) |
| Value *VectorTripCount = nullptr; |
| |
| /// The legality analysis. |
| LoopVectorizationLegality *Legal; |
| |
| /// The profitablity analysis. |
| LoopVectorizationCostModel *Cost; |
| |
| // Record whether runtime checks are added. |
| bool AddedSafetyChecks = false; |
| |
| // Holds the end values for each induction variable. We save the end values |
| // so we can later fix-up the external users of the induction variables. |
| DenseMap<PHINode *, Value *> IVEndValues; |
| |
| // Vector of original scalar PHIs whose corresponding widened PHIs need to be |
| // fixed up at the end of vector code generation. |
| SmallVector<PHINode *, 8> OrigPHIsToFix; |
| |
| /// BFI and PSI are used to check for profile guided size optimizations. |
| BlockFrequencyInfo *BFI; |
| ProfileSummaryInfo *PSI; |
| |
| // Whether this loop should be optimized for size based on profile guided size |
| // optimizatios. |
| bool OptForSizeBasedOnProfile; |
| |
| /// Structure to hold information about generated runtime checks, responsible |
| /// for cleaning the checks, if vectorization turns out unprofitable. |
| GeneratedRTChecks &RTChecks; |
| }; |
| |
| 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), UnrollFactor, LVL, CM, |
| BFI, PSI, Check) {} |
| |
| private: |
| Value *getBroadcastInstrs(Value *V) override; |
| Value *getStepVector( |
| Value *Val, Value *StartIdx, Value *Step, |
| Instruction::BinaryOps Opcode = Instruction::BinaryOpsEnd) override; |
| Value *reverseVector(Value *Vec) override; |
| }; |
| |
| /// 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.MainLoopUF, LVL, CM, BFI, PSI, |
| Checks), |
| EPI(EPI) {} |
| |
| // Override this function to handle the more complex control flow around the |
| // three loops. |
| BasicBlock *createVectorizedLoopSkeleton() final override { |
| return createEpilogueVectorizedLoopSkeleton(); |
| } |
| |
| /// 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 BasicBlock *createEpilogueVectorizedLoopSkeleton() = 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). |
| BasicBlock *createEpilogueVectorizedLoopSkeleton() final override; |
| |
| 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 *emitMinimumIterationCountCheck(Loop *L, 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) {} |
| /// Implements the interface for creating a vectorized skeleton using the |
| /// *epilogue loop* strategy (ie the second pass of vplan execution). |
| BasicBlock *createEpilogueVectorizedLoopSkeleton() final override; |
| |
| 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(Loop *L, |
| 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 Instruction *getDebugLocFromInstOrOperands(Instruction *I) { |
| if (!I) |
| return I; |
| |
| DebugLoc Empty; |
| if (I->getDebugLoc() != Empty) |
| return I; |
| |
| for (Use &Op : I->operands()) { |
| if (Instruction *OpInst = dyn_cast<Instruction>(Op)) |
| if (OpInst->getDebugLoc() != Empty) |
| return OpInst; |
| } |
| |
| return I; |
| } |
| |
| void InnerLoopVectorizer::setDebugLocFromInst( |
| const Value *V, Optional<IRBuilder<> *> CustomBuilder) { |
| IRBuilder<> *B = (CustomBuilder == None) ? &Builder : *CustomBuilder; |
| if (const Instruction *Inst = dyn_cast_or_null<Instruction>(V)) { |
| const DILocation *DIL = Inst->getDebugLoc(); |
| |
| // When a FSDiscriminator is enabled, we don't need to add the multiply |
| // factors to the discriminators. |
| if (DIL && Inst->getFunction()->isDebugInfoForProfiling() && |
| !isa<DbgInfoIntrinsic>(Inst) && !EnableFSDiscriminator) { |
| // FIXME: For scalable vectors, assume vscale=1. |
| auto NewDIL = |
| DIL->cloneByMultiplyingDuplicationFactor(UF * VF.getKnownMinValue()); |
| if (NewDIL) |
| B->SetCurrentDebugLocation(NewDIL.getValue()); |
| else |
| LLVM_DEBUG(dbgs() |
| << "Failed to create new discriminator: " |
| << DIL->getFilename() << " Line: " << DIL->getLine()); |
| } else |
| B->SetCurrentDebugLocation(DIL); |
| } else |
| B->SetCurrentDebugLocation(DebugLoc()); |
| } |
| |
| /// Write a \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. \return the remark object that can be |
| /// streamed to. |
| static OptimizationRemarkAnalysis createLVAnalysis(const char *PassName, |
| StringRef RemarkName, Loop *TheLoop, Instruction *I) { |
| Value *CodeRegion = TheLoop->getHeader(); |
| DebugLoc DL = TheLoop->getStartLoc(); |
| |
| if (I) { |
| CodeRegion = I->getParent(); |
| // If there is no debug location attached to the instruction, revert back to |
| // using the loop's. |
| if (I->getDebugLoc()) |
| DL = I->getDebugLoc(); |
| } |
| |
| return OptimizationRemarkAnalysis(PassName, RemarkName, DL, CodeRegion); |
| } |
| |
| /// Return a value for Step multiplied by VF. |
| static Value *createStepForVF(IRBuilder<> &B, Type *Ty, ElementCount VF, |
| int64_t Step) { |
| assert(Ty->isIntegerTy() && "Expected an integer step"); |
| Constant *StepVal = ConstantInt::get(Ty, Step * VF.getKnownMinValue()); |
| return VF.isScalable() ? B.CreateVScale(StepVal) : StepVal; |
| } |
| |
| namespace llvm { |
| |
| /// Return the runtime value for VF. |
| Value *getRuntimeVF(IRBuilder<> &B, Type *Ty, ElementCount VF) { |
| Constant *EC = ConstantInt::get(Ty, VF.getKnownMinValue()); |
| return VF.isScalable() ? B.CreateVScale(EC) : EC; |
| } |
| |
| static Value *getRuntimeVFAsFloat(IRBuilder<> &B, Type *FTy, ElementCount VF) { |
| assert(FTy->isFloatingPointTy() && "Expected floating point type!"); |
| Type *IntTy = IntegerType::get(FTy->getContext(), FTy->getScalarSizeInBits()); |
| Value *RuntimeVF = getRuntimeVF(B, IntTy, VF); |
| return B.CreateUIToFP(RuntimeVF, FTy); |
| } |
| |
| 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); |
| } |
| |
| void reportVectorizationInfo(const StringRef Msg, const StringRef ORETag, |
| OptimizationRemarkEmitter *ORE, Loop *TheLoop, |
| Instruction *I) { |
| LLVM_DEBUG(debugVectorizationMessage("", Msg, I)); |
| LoopVectorizeHints Hints(TheLoop, true /* doesn't matter */, *ORE); |
| ORE->emit( |
| createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop, I) |
| << Msg); |
| } |
| |
| } // end namespace llvm |
| |
| #ifndef NDEBUG |
| /// \return string containing a file name and a line # for the given loop. |
| static std::string getDebugLocString(const Loop *L) { |
| std::string Result; |
| if (L) { |
| raw_string_ostream OS(Result); |
| if (const DebugLoc LoopDbgLoc = L->getStartLoc()) |
| LoopDbgLoc.print(OS); |
| else |
| // Just print the module name. |
| OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier(); |
| OS.flush(); |
| } |
| return Result; |
| } |
| #endif |
| |
| void InnerLoopVectorizer::addNewMetadata(Instruction *To, |
| const Instruction *Orig) { |
| // If the loop was versioned with memchecks, add the corresponding no-alias |
| // metadata. |
| if (LVer && (isa<LoadInst>(Orig) || isa<StoreInst>(Orig))) |
| LVer->annotateInstWithNoAlias(To, Orig); |
| } |
| |
| void InnerLoopVectorizer::collectPoisonGeneratingRecipes( |
| VPTransformState &State) { |
| |
| // Collect recipes in the backward slice of `Root` that may generate a poison |
| // value that is used after vectorization. |
| SmallPtrSet<VPRecipeBase *, 16> Visited; |
| auto collectPoisonGeneratingInstrsInBackwardSlice([&](VPRecipeBase *Root) { |
| SmallVector<VPRecipeBase *, 16> Worklist; |
| Worklist.push_back(Root); |
| |
| // Traverse the backward slice of Root through its use-def chain. |
| while (!Worklist.empty()) { |
| VPRecipeBase *CurRec = Worklist.back(); |
| Worklist.pop_back(); |
| |
| if (!Visited.insert(CurRec).second) |
| continue; |
| |
| // Prune search if we find another recipe generating a widen memory |
| // instruction. Widen memory instructions involved in address computation |
| // will lead to gather/scatter instructions, which don't need to be |
| // handled. |
| if (isa<VPWidenMemoryInstructionRecipe>(CurRec) || |
| isa<VPInterleaveRecipe>(CurRec)) |
| continue; |
| |
| // This recipe contributes to the address computation of a widen |
| // load/store. Collect recipe if its underlying instruction has |
| // poison-generating flags. |
| Instruction *Instr = CurRec->getUnderlyingInstr(); |
| if (Instr && Instr->hasPoisonGeneratingFlags()) |
| State.MayGeneratePoisonRecipes.insert(CurRec); |
| |
| // Add new definitions to the worklist. |
| for (VPValue *operand : CurRec->operands()) |
| if (VPDef *OpDef = operand->getDef()) |
| Worklist.push_back(cast<VPRecipeBase>(OpDef)); |
| } |
| }); |
| |
| // Traverse all the recipes in the VPlan and collect the poison-generating |
| // recipes in the backward slice starting at the address of a VPWidenRecipe or |
| // VPInterleaveRecipe. |
| auto Iter = depth_first( |
| VPBlockRecursiveTraversalWrapper<VPBlockBase *>(State.Plan->getEntry())); |
| for (VPBasicBlock *VPBB : VPBlockUtils::blocksOnly<VPBasicBlock>(Iter)) { |
| for (VPRecipeBase &Recipe : *VPBB) { |
| if (auto *WidenRec = dyn_cast<VPWidenMemoryInstructionRecipe>(&Recipe)) { |
| Instruction *UnderlyingInstr = WidenRec->getUnderlyingInstr(); |
| VPDef *AddrDef = WidenRec->getAddr()->getDef(); |
| if (AddrDef && WidenRec->isConsecutive() && UnderlyingInstr && |
| Legal->blockNeedsPredication(UnderlyingInstr->getParent())) |
| collectPoisonGeneratingInstrsInBackwardSlice( |
| cast<VPRecipeBase>(AddrDef)); |
| } else if (auto *InterleaveRec = dyn_cast<VPInterleaveRecipe>(&Recipe)) { |
| VPDef *AddrDef = InterleaveRec->getAddr()->getDef(); |
| if (AddrDef) { |
| // Check if any member of the interleave group needs predication. |
| const InterleaveGroup<Instruction> *InterGroup = |
| InterleaveRec->getInterleaveGroup(); |
| bool NeedPredication = false; |
| for (int I = 0, NumMembers = InterGroup->getNumMembers(); |
| I < NumMembers; ++I) { |
| Instruction *Member = InterGroup->getMember(I); |
| if (Member) |
| NeedPredication |= |
| Legal->blockNeedsPredication(Member->getParent()); |
| } |
| |
| if (NeedPredication) |
| collectPoisonGeneratingInstrsInBackwardSlice( |
| cast<VPRecipeBase>(AddrDef)); |
| } |
| } |
| } |
| } |
| } |
| |
| void InnerLoopVectorizer::addMetadata(Instruction *To, |
| Instruction *From) { |
| propagateMetadata(To, From); |
| addNewMetadata(To, From); |
| } |
| |
| void InnerLoopVectorizer::addMetadata(ArrayRef<Value *> To, |
| Instruction *From) { |
| for (Value *V : To) { |
| if (Instruction *I = dyn_cast<Instruction>(V)) |
| addMetadata(I, From); |
| } |
| } |
| |
| namespace llvm { |
| |
| // Loop vectorization cost-model hints how the scalar epilogue loop should be |
| // lowered. |
| enum ScalarEpilogueLowering { |
| |
| // The default: allowing scalar epilogues. |
| CM_ScalarEpilogueAllowed, |
| |
| // Vectorization with OptForSize: don't allow epilogues. |
| CM_ScalarEpilogueNotAllowedOptSize, |
| |
| // A special case of vectorisation with OptForSize: loops with a very small |
| // trip count are considered for vectorization under OptForSize, thereby |
| // making sure the cost of their loop body is dominant, free of runtime |
| // guards and scalar iteration overheads. |
| CM_ScalarEpilogueNotAllowedLowTripLoop, |
| |
| // Loop hint predicate indicating an epilogue is undesired. |
| CM_ScalarEpilogueNotNeededUsePredicate, |
| |
| // Directive indicating we must either tail fold or not vectorize |
| CM_ScalarEpilogueNotAllowedUsePredicate |
| }; |
| |
| /// ElementCountComparator creates a total ordering for ElementCount |
| /// for the purposes of using it in a set structure. |
| struct ElementCountComparator { |
| bool operator()(const ElementCount &LHS, const ElementCount &RHS) const { |
| return std::make_tuple(LHS.isScalable(), LHS.getKnownMinValue()) < |
| std::make_tuple(RHS.isScalable(), RHS.getKnownMinValue()); |
| } |
| }; |
| using ElementCountSet = SmallSet<ElementCount, 16, ElementCountComparator>; |
| |
| /// 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(); |
| |
| /// \return The most profitable vectorization factor and the cost of that VF. |
| /// This method checks every VF in \p CandidateVFs. If UserVF is not ZERO |
| /// then this vectorization factor will be selected if vectorization is |
| /// possible. |
| VectorizationFactor |
| selectVectorizationFactor(const ElementCountSet &CandidateVFs); |
| |
| VectorizationFactor |
| selectEpilogueVectorizationFactor(const ElementCount MaxVF, |
| const LoopVectorizationPlanner &LVP); |
| |
| /// 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).first.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, unsigned LoopCost); |
| |
| /// Memory access instruction may be vectorized in more than one way. |
| /// Form of instruction after vectorization depends on cost. |
| /// This function takes cost-based decisions for Load/Store instructions |
| /// and collects them in a map. This decisions map is used for building |
| /// the lists of loop-uniform and loop-scalar instructions. |
| /// The calculated cost is saved with widening decision in order to |
| /// avoid redundant calculations. |
| void setCostBasedWideningDecision(ElementCount VF); |
| |
| /// A struct that represents some properties of the register usage |
| /// of a loop. |
| struct RegisterUsage { |
| /// Holds the number of loop invariant values that are used in the loop. |
| /// The key is ClassID of target-provided register class. |
| SmallMapVector<unsigned, unsigned, 4> LoopInvariantRegs; |
| /// Holds the maximum number of concurrent live intervals in the loop. |
| /// The key is ClassID of target-provided register class. |
| SmallMapVector<unsigned, unsigned, 4> MaxLocalUsers; |
| }; |
| |
| /// \return Returns information about the register usages of the loop for the |
| /// given vectorization factors. |
| SmallVector<RegisterUsage, 8> |
| calculateRegisterUsage(ArrayRef<ElementCount> VFs); |
| |
| /// Collect values we want to ignore in the cost model. |
| void collectValuesToIgnore(); |
| |
| /// 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 InLoopReductionChains. |
| 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) { |
| 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."); |
| |
| // Cost model is not run in the VPlan-native path - return conservative |
| // result until this changes. |
| if (EnableVPlanNativePath) |
| return false; |
| |
| auto Scalars = InstsToScalarize.find(VF); |
| assert(Scalars != InstsToScalarize.end() && |
| "VF not yet analyzed for scalarization profitability"); |
| return Scalars->second.find(I) != Scalars->second.end(); |
| } |
| |
| /// Returns true if \p I is known to be uniform after vectorization. |
| bool isUniformAfterVectorization(Instruction *I, ElementCount VF) const { |
| if (VF.isScalar()) |
| return true; |
| |
| // Cost model is not run in the VPlan-native path - return conservative |
| // result until this changes. |
| if (EnableVPlanNativePath) |
| return false; |
| |
| auto UniformsPerVF = Uniforms.find(VF); |
| assert(UniformsPerVF != Uniforms.end() && |
| "VF not yet analyzed for uniformity"); |
| return UniformsPerVF->second.count(I); |
| } |
| |
| /// Returns true if \p I is known to be scalar after vectorization. |
| bool isScalarAfterVectorization(Instruction *I, ElementCount VF) const { |
| if (VF.isScalar()) |
| return true; |
| |
| // Cost model is not run in the VPlan-native path - return conservative |
| // result until this changes. |
| if (EnableVPlanNativePath) |
| return false; |
| |
| auto ScalarsPerVF = Scalars.find(VF); |
| assert(ScalarsPerVF != Scalars.end() && |
| "Scalar values are not calculated for VF"); |
| return ScalarsPerVF->second.count(I); |
| } |
| |
| /// \returns True if instruction \p I can be truncated to a smaller bitwidth |
| /// for vectorization factor \p VF. |
| bool canTruncateToMinimalBitwidth(Instruction *I, ElementCount VF) const { |
| return VF.isVector() && MinBWs.find(I) != MinBWs.end() && |
| !isProfitableToScalarize(I, VF) && |
| !isScalarAfterVectorization(I, VF); |
| } |
| |
| /// Decision that was taken during cost calculation for memory instruction. |
| enum InstWidening { |
| CM_Unknown, |
| CM_Widen, // For consecutive accesses with stride +1. |
| CM_Widen_Reverse, // For consecutive accesses with stride -1. |
| CM_Interleave, |
| CM_GatherScatter, |
| CM_Scalarize |
| }; |
| |
| /// Save vectorization decision \p W and \p Cost taken by the cost model for |
| /// instruction \p I and vector width \p VF. |
| void setWideningDecision(Instruction *I, ElementCount VF, InstWidening W, |
| 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"); |
| // Cost model is not run in the VPlan-native path - return conservative |
| // result until this changes. |
| if (EnableVPlanNativePath) |
| return CM_GatherScatter; |
| |
| std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF); |
| auto Itr = WideningDecisions.find(InstOnVF); |
| if (Itr == WideningDecisions.end()) |
| return CM_Unknown; |
| return Itr->second.first; |
| } |
| |
| /// Return the vectorization cost for the given instruction \p I and vector |
| /// width \p VF. |
| InstructionCost getWideningCost(Instruction *I, ElementCount VF) { |
| assert(VF.isVector() && "Expected VF >=2"); |
| std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF); |
| assert(WideningDecisions.find(InstOnVF) != WideningDecisions.end() && |
| "The cost is not calculated"); |
| return WideningDecisions[InstOnVF].second; |
| } |
| |
| /// Return True if instruction \p I is an optimizable truncate whose operand |
| /// is an induction variable. Such a truncate will be removed by adding a new |
| /// induction variable with the destination type. |
| bool isOptimizableIVTruncate(Instruction *I, ElementCount VF) { |
| // If the instruction is not a truncate, return false. |
| auto *Trunc = dyn_cast<TruncInst>(I); |
| if (!Trunc) |
| return false; |
| |
| // Get the source and destination types of the truncate. |
| Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF); |
| Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF); |
| |
| // If the truncate is free for the given types, return false. Replacing a |
| // free truncate with an induction variable would add an induction variable |
| // update instruction to each iteration of the loop. We exclude from this |
| // check the primary induction variable since it will need an update |
| // instruction regardless. |
| Value *Op = Trunc->getOperand(0); |
| if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy)) |
| return false; |
| |
| // If the truncated value is not an induction variable, return false. |
| return Legal->isInductionPhi(Op); |
| } |
| |
| /// Collects the instructions to scalarize for each predicated instruction in |
| /// the loop. |
| void collectInstsToScalarize(ElementCount VF); |
| |
| /// Collect Uniform and Scalar values for the given \p VF. |
| /// The sets depend on CM decision for Load/Store instructions |
| /// that may be vectorized as interleave, gather-scatter or scalarized. |
| void collectUniformsAndScalars(ElementCount VF) { |
| // Do the analysis once. |
| if (VF.isScalar() || Uniforms.find(VF) != Uniforms.end()) |
| return; |
| setCostBasedWideningDecision(VF); |
| collectLoopUniforms(VF); |
| collectLoopScalars(VF); |
| } |
| |
| /// Returns true if the target machine supports masked store operation |
| /// for the given \p DataType and kind of access to \p Ptr. |
| bool isLegalMaskedStore(Type *DataType, Value *Ptr, Align Alignment) 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) { |
| bool LI = isa<LoadInst>(V); |
| bool SI = isa<StoreInst>(V); |
| if (!LI && !SI) |
| return false; |
| auto *Ty = getLoadStoreType(V); |
| Align Align = getLoadStoreAlignment(V); |
| 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); |
| })); |
| } |
| |
| /// Returns true if \p I is an instruction that will be scalarized with |
| /// predication. Such instructions include conditional stores and |
| /// instructions that may divide by zero. |
| /// If a non-zero VF has been calculated, we check if I will be scalarized |
| /// predication for that VF. |
| bool isScalarWithPredication(Instruction *I) const; |
| |
| // Returns true if \p I is an instruction that will be predicated either |
| // through scalar predication or masked load/store or masked gather/scatter. |
| // Superset of instructions that return true for isScalarWithPredication. |
| bool isPredicatedInst(Instruction *I, bool IsKnownUniform = false) { |
| // When we know the load is uniform and the original scalar loop was not |
| // predicated we don't need to mark it as a predicated instruction. Any |
| // vectorised blocks created when tail-folding are something artificial we |
| // have introduced and we know there is always at least one active lane. |
| // That's why we call Legal->blockNeedsPredication here because it doesn't |
| // query tail-folding. |
| if (IsKnownUniform && isa<LoadInst>(I) && |
| !Legal->blockNeedsPredication(I->getParent())) |
| return false; |
| if (!blockNeedsPredicationForAnyReason(I->getParent())) |
| return false; |
| // Loads and stores that need some form of masked operation are predicated |
| // instructions. |
| if (isa<LoadInst>(I) || isa<StoreInst>(I)) |
| return Legal->isMaskRequired(I); |
| return isScalarWithPredication(I); |
| } |
| |
| /// Returns true if \p I is a memory instruction with consecutive memory |
| /// access that can be widened. |
| bool |
| memoryInstructionCanBeWidened(Instruction *I, |
| ElementCount VF = ElementCount::getFixed(1)); |
| |
| /// Returns true if \p I is a memory instruction in an interleaved-group |
| /// of memory accesses that can be vectorized with wide vector loads/stores |
| /// and shuffles. |
| bool |
| interleavedAccessCanBeWidened(Instruction *I, |
| ElementCount VF = ElementCount::getFixed(1)); |
| |
| /// Check if \p Instr belongs to any interleaved access group. |
| bool isAccessInterleaved(Instruction *Instr) { |
| return InterleaveInfo.isInterleaved(Instr); |
| } |
| |
| /// Get the interleaved access group that \p Instr belongs to. |
| const InterleaveGroup<Instruction> * |
| getInterleavedAccessGroup(Instruction *Instr) { |
| return InterleaveInfo.getInterleaveGroup(Instr); |
| } |
| |
| /// Returns true if we're required to use a scalar epilogue for at least |
| /// the final iteration of the original loop. |
| bool requiresScalarEpilogue(ElementCount VF) const { |
| if (!isScalarEpilogueAllowed()) |
| return false; |
| // If we might exit from anywhere but the latch, must run the exiting |
| // iteration in scalar form. |
| if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) |
| return true; |
| return VF.isVector() && InterleaveInfo.requiresScalarEpilogue(); |
| } |
| |
| /// Returns true if a scalar epilogue is not allowed due to optsize or a |
| /// loop hint annotation. |
| bool isScalarEpilogueAllowed() const { |
| return ScalarEpilogueStatus == CM_ScalarEpilogueAllowed; |
| } |
| |
| /// Returns true if all loop blocks should be masked to fold tail loop. |
| bool foldTailByMasking() const { return FoldTailByMasking; } |
| |
| /// 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); |
| } |
| |
| /// A SmallMapVector to store the InLoop reduction op chains, mapping phi |
| /// nodes to the chain of instructions representing the reductions. Uses a |
| /// MapVector to ensure deterministic iteration order. |
| using ReductionChainMap = |
| SmallMapVector<PHINode *, SmallVector<Instruction *, 4>, 4>; |
| |
| /// Return the chain of instructions representing an inloop reduction. |
| const ReductionChainMap &getInLoopReductionChains() const { |
| return InLoopReductionChains; |
| } |
| |
| /// Returns true if the Phi is part of an inloop reduction. |
| bool isInLoopReduction(PHINode *Phi) const { |
| return InLoopReductionChains.count(Phi); |
| } |
| |
| /// Estimate cost of an intrinsic call instruction CI if it were vectorized |
| /// with factor VF. Return the cost of the instruction, including |
| /// scalarization overhead if it's needed. |
| 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. The flag NeedToScalarize shows if the call needs to be |
| /// scalarized - |
| /// i.e. either vector version isn't available, or is too expensive. |
| InstructionCost getVectorCallCost(CallInst *CI, ElementCount VF, |
| bool &NeedToScalarize) const; |
| |
| /// Returns true if the per-lane cost of VectorizationFactor A is lower than |
| /// that of B. |
| bool isMoreProfitable(const VectorizationFactor &A, |
| const VectorizationFactor &B) const; |
| |
| /// Invalidates decisions already taken by the cost model. |
| void invalidateCostModelingDecisions() { |
| WideningDecisions.clear(); |
| Uniforms.clear(); |
| Scalars.clear(); |
| } |
| |
| private: |
| unsigned NumPredStores = 0; |
| |
| /// \return An upper bound for the vectorization 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 ConstTripCount, |
| ElementCount UserVF); |
| |
| /// \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. |
| /// FIXME: MaxSafeVF is currently passed by reference to avoid some obscure |
| /// issue that occurred on one of the buildbots which cannot be reproduced |
| /// without having access to the properietary compiler (see comments on |
| /// D98509). The issue is currently under investigation and this workaround |
| /// will be removed as soon as possible. |
| ElementCount getMaximizedVFForTarget(unsigned ConstTripCount, |
| unsigned SmallestType, |
| unsigned WidestType, |
| const ElementCount &MaxSafeVF); |
| |
| /// \return the maximum legal scalable VF, based on the safe max number |
| /// of elements. |
| ElementCount getMaxLegalScalableVF(unsigned MaxSafeElements); |
| |
| /// The vectorization cost is a combination of the cost itself and a boolean |
| /// indicating whether any of the contributing operations will actually |
| /// operate on vector values after type legalization in the backend. If this |
| /// latter value is false, then all operations will be scalarized (i.e. no |
| /// vectorization has actually taken place). |
| using VectorizationCostTy = std::pair<InstructionCost, bool>; |
| |
| /// Returns the expected execution cost. The unit of the cost does |
| /// not matter because we use the 'cost' units to compare different |
| /// vector widths. The cost that is returned is *not* normalized by |
| /// the factor width. If \p Invalid is not nullptr, this function |
| /// will add a pair(Instruction*, ElementCount) to \p Invalid for |
| /// each instruction that has an Invalid cost for the given VF. |
| using InstructionVFPair = std::pair<Instruction *, ElementCount>; |
| VectorizationCostTy |
| expectedCost(ElementCount VF, |
| SmallVectorImpl<InstructionVFPair> *Invalid = nullptr); |
| |
| /// Returns the execution time cost of an instruction for a given vector |
| /// width. Vector width of one means scalar. |
| VectorizationCostTy getInstructionCost(Instruction *I, ElementCount VF); |
| |
| /// The cost-computation logic from getInstructionCost which provides |
| /// the vector type as an output parameter. |
| InstructionCost getInstructionCost(Instruction *I, ElementCount VF, |
| Type *&VectorTy); |
| |
| /// Return the cost of instructions in an inloop reduction pattern, if I is |
| /// part of that pattern. |
| Optional<InstructionCost> |
| getReductionPatternCost(Instruction *I, ElementCount VF, Type *VectorTy, |
| TTI::TargetCostKind CostKind); |
| |
| /// Calculate vectorization cost of memory instruction \p I. |
| InstructionCost getMemoryInstructionCost(Instruction *I, ElementCount VF); |
| |
| /// The cost computation for scalarized memory instruction. |
| InstructionCost getMemInstScalarizationCost(Instruction *I, ElementCount VF); |
| |
| /// The cost computation for interleaving group of memory instructions. |
| InstructionCost getInterleaveGroupCost(Instruction *I, ElementCount VF); |
| |
| /// The cost computation for Gather/Scatter instruction. |
| InstructionCost getGatherScatterCost(Instruction *I, ElementCount VF); |
| |
| /// The cost computation for widening instruction \p I with consecutive |
| /// memory access. |
| InstructionCost getConsecutiveMemOpCost(Instruction *I, ElementCount VF); |
| |
| /// The cost calculation for Load/Store instruction \p I with uniform pointer - |
| /// Load: scalar load + broadcast. |
| /// Store: scalar store + (loop invariant value stored? 0 : extract of last |
| /// element) |
| InstructionCost getUniformMemOpCost(Instruction *I, ElementCount VF); |
| |
| /// Estimate the overhead of scalarizing an instruction. This is a |
| /// convenience wrapper for the type-based getScalarizationOverhead API. |
| InstructionCost getScalarizationOverhead(Instruction *I, |
| ElementCount VF) const; |
| |
| /// Returns whether the instruction is a load or store and will be a emitted |
| /// as a vector operation. |
| bool isConsecutiveLoadOrStore(Instruction *I); |
| |
| /// Returns true if an artificially high cost for emulated masked memrefs |
| /// should be used. |
| bool useEmulatedMaskMemRefHack(Instruction *I); |
| |
| /// Map of scalar integer values to the smallest bitwidth they can be legally |
| /// represented as. The vector equivalents of these values should be truncated |
| /// to this type. |
| MapVector<Instruction *, uint64_t> MinBWs; |
| |
| /// A type representing the costs for instructions if they were to be |
| /// scalarized rather than vectorized. The entries are Instruction-Cost |
| /// pairs. |
| using ScalarCostsTy = DenseMap<Instruction *, InstructionCost>; |
| |
| /// A set containing all BasicBlocks that are known to present after |
| /// vectorization as a predicated block. |
| SmallPtrSet<BasicBlock *, 4> PredicatedBBsAfterVectorization; |
| |
| /// Records whether it is allowed to have the original scalar loop execute at |
| /// least once. This may be needed as a fallback loop in case runtime |
| /// aliasing/dependence checks fail, or to handle the tail/remainder |
| /// iterations when the trip count is unknown or doesn't divide by the VF, |
| /// or as a peel-loop to handle gaps in interleave-groups. |
| /// Under optsize and when the trip count is very small we don't allow any |
| /// iterations to execute in the scalar loop. |
| ScalarEpilogueLowering ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| |
| /// All blocks of loop are to be masked to fold tail of scalar iterations. |
| bool FoldTailByMasking = false; |
| |
| /// A map holding scalar costs for different vectorization factors. The |
| /// presence of a cost for an instruction in the mapping indicates that the |
| /// instruction will be scalarized when vectorizing with the associated |
| /// vectorization factor. The entries are VF-ScalarCostTy pairs. |
| DenseMap<ElementCount, ScalarCostsTy> InstsToScalarize; |
| |
| /// Holds the instructions known to be uniform after vectorization. |
| /// The data is collected per VF. |
| DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Uniforms; |
| |
| /// Holds the instructions known to be scalar after vectorization. |
| /// The data is collected per VF. |
| DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Scalars; |
| |
| /// Holds the instructions (address computations) that are forced to be |
| /// scalarized. |
| DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> ForcedScalars; |
| |
| /// PHINodes of the reductions that should be expanded in-loop along with |
| /// their associated chains of reduction operations, in program order from top |
| /// (PHI) to bottom |
| ReductionChainMap InLoopReductionChains; |
| |
| /// 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 to the inloop operations, |
| /// without having to loop through InLoopReductionChains. |
| 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. |
| int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts, |
| ElementCount VF); |
| |
| /// Collect the instructions that are uniform after vectorization. An |
| /// instruction is uniform if we represent it with a single scalar value in |
| /// the vectorized loop corresponding to each vector iteration. Examples of |
| /// uniform instructions include pointer operands of consecutive or |
| /// interleaved memory accesses. Note that although uniformity implies an |
| /// instruction will be scalar, the reverse is not true. In general, a |
| /// scalarized instruction will be represented by VF scalar values in the |
| /// vectorized loop, each corresponding to an iteration of the original |
| /// scalar loop. |
| void collectLoopUniforms(ElementCount VF); |
| |
| /// Collect the instructions that are scalar after vectorization. An |
| /// instruction is scalar if it is known to be uniform or will be scalarized |
| /// during vectorization. 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; |
| |
| /// Returns true if \p V is expected to be vectorized and it needs to be |
| /// extracted. |
| bool needsExtract(Value *V, ElementCount VF) const { |
| Instruction *I = dyn_cast<Instruction>(V); |
| if (VF.isScalar() || !I || !TheLoop->contains(I) || |
| TheLoop->isLoopInvariant(I)) |
| return false; |
| |
| // Assume we can vectorize V (and hence we need extraction) if the |
| // scalars are not computed yet. This can happen, because it is called |
| // via getScalarizationOverhead from setCostBasedWideningDecision, before |
| // the scalars are collected. That should be a safe assumption in most |
| // cases, because we check if the operands have vectorizable types |
| // beforehand in LoopVectorizationLegality. |
| return Scalars.find(VF) == Scalars.end() || |
| !isScalarAfterVectorization(I, VF); |
| }; |
| |
| /// Returns a range containing only operands needing to be extracted. |
| SmallVector<Value *, 4> filterExtractingOperands(Instruction::op_range Ops, |
| ElementCount VF) const { |
| return SmallVector<Value *, 4>(make_filter_range( |
| Ops, [this, VF](Value *V) { return this->needsExtract(V, VF); })); |
| } |
| |
| /// Determines if we have the infrastructure to vectorize loop \p L and its |
| /// epilogue, assuming the main loop is vectorized by \p VF. |
| bool isCandidateForEpilogueVectorization(const Loop &L, |
| const ElementCount VF) const; |
| |
| /// 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; |
| |
| 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; |
| |
| /// Profitable vector factors. |
| SmallVector<VectorizationFactor, 8> ProfitableVFs; |
| }; |
| } // end namespace llvm |
| |
| /// 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; |
| |
| SCEVExpander SCEVExp; |
| SCEVExpander MemCheckExp; |
| |
| public: |
| GeneratedRTChecks(ScalarEvolution &SE, DominatorTree *DT, LoopInfo *LI, |
| const DataLayout &DL) |
| : DT(DT), LI(LI), SCEVExp(SE, DL, "scev.check"), |
| MemCheckExp(SE, DL, "scev.check") {} |
| |
| /// 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 SCEVUnionPredicate &UnionPred) { |
| |
| 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"); |
| |
| MemRuntimeCheckCond = |
| addRuntimeChecks(MemCheckBlock->getTerminator(), L, |
| RtPtrChecking.getChecks(), MemCheckExp); |
| 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); |
| } |
| } |
| |
| /// Remove the created SCEV & memory runtime check blocks & instructions, if |
| /// unused. |
| ~GeneratedRTChecks() { |
| SCEVExpanderCleaner SCEVCleaner(SCEVExp, *DT); |
| SCEVExpanderCleaner MemCheckCleaner(MemCheckExp, *DT); |
| 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(Loop *L, BasicBlock *Bypass, |
| BasicBlock *LoopVectorPreHeader, |
| BasicBlock *LoopExitBlock) { |
| if (!SCEVCheckCond) |
| return nullptr; |
| if (auto *C = dyn_cast<ConstantInt>(SCEVCheckCond)) |
| if (C->isZero()) |
| return nullptr; |
| |
| auto *Pred = LoopVectorPreHeader->getSinglePredecessor(); |
| |
| BranchInst::Create(LoopVectorPreHeader, SCEVCheckBlock); |
| // Create new preheader for vector loop. |
| if (auto *PL = LI->getLoopFor(LoopVectorPreHeader)) |
| PL->addBasicBlockToLoop(SCEVCheckBlock, *LI); |
| |
| SCEVCheckBlock->getTerminator()->eraseFromParent(); |
| SCEVCheckBlock->moveBefore(LoopVectorPreHeader); |
| Pred->getTerminator()->replaceSuccessorWith(LoopVectorPreHeader, |
| SCEVCheckBlock); |
| |
| DT->addNewBlock(SCEVCheckBlock, Pred); |
| DT->changeImmediateDominator(LoopVectorPreHeader, SCEVCheckBlock); |
| |
| ReplaceInstWithInst( |
| SCEVCheckBlock->getTerminator(), |
| BranchInst::Create(Bypass, LoopVectorPreHeader, SCEVCheckCond)); |
| // Mark the check as used, to prevent it from being removed during cleanup. |
| SCEVCheckCond = nullptr; |
| 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(Loop *L, 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 (auto *PL = LI->getLoopFor(LoopVectorPreHeader)) |
| PL->addBasicBlockToLoop(MemCheckBlock, *LI); |
| |
| ReplaceInstWithInst( |
| MemCheckBlock->getTerminator(), |
| BranchInst::Create(Bypass, LoopVectorPreHeader, MemRuntimeCheckCond)); |
| MemCheckBlock->getTerminator()->setDebugLoc( |
| Pred->getTerminator()->getDebugLoc()); |
| |
| // Mark the check as used, to prevent it from being removed during cleanup. |
| MemRuntimeCheckCond = nullptr; |
| return MemCheckBlock; |
| } |
| }; |
| |
| // Return true if \p OuterLp is an outer loop annotated with hints for explicit |
| // vectorization. The loop needs to be annotated with #pragma omp simd |
| // simdlen(#) or #pragma clang vectorize(enable) vectorize_width(#). If the |
| // vector length information is not provided, vectorization is not considered |
| // explicit. Interleave hints are not allowed either. These limitations will be |
| // relaxed in the future. |
| // Please, note that we are currently forced to abuse the pragma 'clang |
| // vectorize' semantics. This pragma provides *auto-vectorization hints* |
| // (i.e., LV must check that vectorization is legal) whereas pragma 'omp simd' |
| // provides *explicit vectorization hints* (LV can bypass legal checks and |
| // assume that vectorization is legal). However, both hints are implemented |
| // using the same metadata (llvm.loop.vectorize, processed by |
| // LoopVectorizeHints). This will be fixed in the future when the native IR |
| // representation for pragma 'omp simd' is introduced. |
| static bool isExplicitVecOuterLoop(Loop *OuterLp, |
| OptimizationRemarkEmitter *ORE) { |
| assert(!OuterLp->isInnermost() && "This is not an outer loop"); |
| LoopVectorizeHints Hints(OuterLp, true /*DisableInterleaving*/, *ORE); |
| |
| // Only outer loops with an explicit vectorization hint are supported. |
| // Unannotated outer loops are ignored. |
| if (Hints.getForce() == LoopVectorizeHints::FK_Undefined) |
| return false; |
| |
| Function *Fn = OuterLp->getHeader()->getParent(); |
| if (!Hints.allowVectorization(Fn, OuterLp, |
| true /*VectorizeOnlyWhenForced*/)) { |
| LLVM_DEBUG(dbgs() << "LV: Loop hints prevent outer loop vectorization.\n"); |
| return false; |
| } |
| |
| if (Hints.getInterleave() > 1) { |
| // TODO: Interleave support is future work. |
| LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Interleave is not supported for " |
| "outer loops.\n"); |
| Hints.emitRemarkWithHints(); |
| return false; |
| } |
| |
| return true; |
| } |
| |
| static void collectSupportedLoops(Loop &L, LoopInfo *LI, |
| OptimizationRemarkEmitter *ORE, |
| SmallVectorImpl<Loop *> &V) { |
| // Collect inner loops and outer loops without irreducible control flow. For |
| // now, only collect outer loops that have explicit vectorization hints. If we |
| // are stress testing the VPlan H-CFG construction, we collect the outermost |
| // loop of every loop nest. |
| if (L.isInnermost() || VPlanBuildStressTest || |
| (EnableVPlanNativePath && isExplicitVecOuterLoop(&L, ORE))) { |
| LoopBlocksRPO RPOT(&L); |
| RPOT.perform(LI); |
| if (!containsIrreducibleCFG<const BasicBlock *>(RPOT, *LI)) { |
| V.push_back(&L); |
| // TODO: Collect inner loops inside marked outer loops in case |
| // vectorization fails for the outer loop. Do not invoke |
| // 'containsIrreducibleCFG' again for inner loops when the outer loop is |
| // already known to be reducible. We can use an inherited attribute for |
| // that. |
| return; |
| } |
| } |
| for (Loop *InnerL : L) |
| collectSupportedLoops(*InnerL, LI, ORE, V); |
| } |
| |
| namespace { |
| |
| /// The LoopVectorize Pass. |
| struct LoopVectorize : public FunctionPass { |
| /// Pass identification, replacement for typeid |
| static char ID; |
| |
| LoopVectorizePass Impl; |
| |
| explicit LoopVectorize(bool InterleaveOnlyWhenForced = false, |
| bool VectorizeOnlyWhenForced = false) |
| : FunctionPass(ID), |
| Impl({InterleaveOnlyWhenForced, VectorizeOnlyWhenForced}) { |
| initializeLoopVectorizePass(*PassRegistry::getPassRegistry()); |
| } |
| |
| bool runOnFunction(Function &F) override { |
| if (skipFunction(F)) |
| return false; |
| |
| auto *SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE(); |
| auto *LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo(); |
| auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F); |
| auto *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree(); |
| auto *BFI = &getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI(); |
| auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>(); |
| auto *TLI = TLIP ? &TLIP->getTLI(F) : nullptr; |
| auto *AA = &getAnalysis<AAResultsWrapperPass>().getAAResults(); |
| auto *AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F); |
| auto *LAA = &getAnalysis<LoopAccessLegacyAnalysis>(); |
| auto *DB = &getAnalysis<DemandedBitsWrapperPass>().getDemandedBits(); |
| auto *ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE(); |
| auto *PSI = &getAnalysis<ProfileSummaryInfoWrapperPass>().getPSI(); |
| |
| std::function<const LoopAccessInfo &(Loop &)> GetLAA = |
| [&](Loop &L) -> const LoopAccessInfo & { return LAA->getInfo(&L); }; |
| |
| return Impl.runImpl(F, *SE, *LI, *TTI, *DT, *BFI, TLI, *DB, *AA, *AC, |
| GetLAA, *ORE, PSI).MadeAnyChange; |
| } |
| |
| void getAnalysisUsage(AnalysisUsage &AU) const override { |
| AU.addRequired<AssumptionCacheTracker>(); |
| AU.addRequired<BlockFrequencyInfoWrapperPass>(); |
| AU.addRequired<DominatorTreeWrapperPass>(); |
| AU.addRequired<LoopInfoWrapperPass>(); |
| AU.addRequired<ScalarEvolutionWrapperPass>(); |
| AU.addRequired<TargetTransformInfoWrapperPass>(); |
| AU.addRequired<AAResultsWrapperPass>(); |
| AU.addRequired<LoopAccessLegacyAnalysis>(); |
| AU.addRequired<DemandedBitsWrapperPass>(); |
| AU.addRequired<OptimizationRemarkEmitterWrapperPass>(); |
| AU.addRequired<InjectTLIMappingsLegacy>(); |
| |
| // We currently do not preserve loopinfo/dominator analyses with outer loop |
| // vectorization. Until this is addressed, mark these analyses as preserved |
| // only for non-VPlan-native path. |
| // TODO: Preserve Loop and Dominator analyses for VPlan-native path. |
| if (!EnableVPlanNativePath) { |
| AU.addPreserved<LoopInfoWrapperPass>(); |
| AU.addPreserved<DominatorTreeWrapperPass>(); |
| } |
| |
| AU.addPreserved<BasicAAWrapperPass>(); |
| AU.addPreserved<GlobalsAAWrapperPass>(); |
| AU.addRequired<ProfileSummaryInfoWrapperPass>(); |
| } |
| }; |
| |
| } // end anonymous namespace |
| |
| //===----------------------------------------------------------------------===// |
| // Implementation of LoopVectorizationLegality, InnerLoopVectorizer and |
| // LoopVectorizationCostModel and LoopVectorizationPlanner. |
| //===----------------------------------------------------------------------===// |
| |
| Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) { |
| // We need to place the broadcast of invariant variables outside the loop, |
| // but only if it's proven safe to do so. Else, broadcast will be inside |
| // vector loop body. |
| Instruction *Instr = dyn_cast<Instruction>(V); |
| bool SafeToHoist = OrigLoop->isLoopInvariant(V) && |
| (!Instr || |
| DT->dominates(Instr->getParent(), LoopVectorPreHeader)); |
| // Place the code for broadcasting invariant variables in the new preheader. |
| IRBuilder<>::InsertPointGuard Guard(Builder); |
| if (SafeToHoist) |
| Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| |
| // Broadcast the scalar into all locations in the vector. |
| Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast"); |
| |
| return Shuf; |
| } |
| |
| void InnerLoopVectorizer::createVectorIntOrFpInductionPHI( |
| const InductionDescriptor &II, Value *Step, Value *Start, |
| Instruction *EntryVal, VPValue *Def, VPValue *CastDef, |
| VPTransformState &State) { |
| assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) && |
| "Expected either an induction phi-node or a truncate of it!"); |
| |
| // Construct the initial value of the vector IV in the vector loop preheader |
| auto CurrIP = Builder.saveIP(); |
| Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| if (isa<TruncInst>(EntryVal)) { |
| assert(Start->getType()->isIntegerTy() && |
| "Truncation requires an integer type"); |
| auto *TruncType = cast<IntegerType>(EntryVal->getType()); |
| Step = Builder.CreateTrunc(Step, TruncType); |
| Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType); |
| } |
| |
| Value *Zero = getSignedIntOrFpConstant(Start->getType(), 0); |
| Value *SplatStart = Builder.CreateVectorSplat(VF, Start); |
| Value *SteppedStart = |
| getStepVector(SplatStart, Zero, Step, II.getInductionOpcode()); |
| |
| // We create vector phi nodes for both integer and floating-point induction |
| // variables. Here, we determine the kind of arithmetic we will perform. |
| Instruction::BinaryOps AddOp; |
| Instruction::BinaryOps MulOp; |
| if (Step->getType()->isIntegerTy()) { |
| AddOp = Instruction::Add; |
| MulOp = Instruction::Mul; |
| } else { |
| AddOp = II.getInductionOpcode(); |
| MulOp = Instruction::FMul; |
| } |
| |
| // Multiply the vectorization factor by the step using integer or |
| // floating-point arithmetic as appropriate. |
| Type *StepType = Step->getType(); |
| Value *RuntimeVF; |
| if (Step->getType()->isFloatingPointTy()) |
| RuntimeVF = getRuntimeVFAsFloat(Builder, StepType, VF); |
| else |
| RuntimeVF = getRuntimeVF(Builder, StepType, VF); |
| Value *Mul = Builder.CreateBinOp(MulOp, Step, RuntimeVF); |
| |
| // Create a vector splat to use in the induction update. |
| // |
| // FIXME: If the step is non-constant, we create the vector splat with |
| // IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't |
| // handle a constant vector splat. |
| Value *SplatVF = isa<Constant>(Mul) |
| ? ConstantVector::getSplat(VF, cast<Constant>(Mul)) |
| : Builder.CreateVectorSplat(VF, Mul); |
| Builder.restoreIP(CurrIP); |
| |
| // We may need to add the step a number of times, depending on the unroll |
| // factor. The last of those goes into the PHI. |
| PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind", |
| &*LoopVectorBody->getFirstInsertionPt()); |
| VecInd->setDebugLoc(EntryVal->getDebugLoc()); |
| Instruction *LastInduction = VecInd; |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| State.set(Def, LastInduction, Part); |
| |
| if (isa<TruncInst>(EntryVal)) |
| addMetadata(LastInduction, EntryVal); |
| recordVectorLoopValueForInductionCast(II, EntryVal, LastInduction, CastDef, |
| State, Part); |
| |
| LastInduction = cast<Instruction>( |
| Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add")); |
| LastInduction->setDebugLoc(EntryVal->getDebugLoc()); |
| } |
| |
| // Move the last step to the end of the latch block. This ensures consistent |
| // placement of all induction updates. |
| auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| auto *Br = cast<BranchInst>(LoopVectorLatch->getTerminator()); |
| auto *ICmp = cast<Instruction>(Br->getCondition()); |
| LastInduction->moveBefore(ICmp); |
| LastInduction->setName("vec.ind.next"); |
| |
| VecInd->addIncoming(SteppedStart, LoopVectorPreHeader); |
| VecInd->addIncoming(LastInduction, LoopVectorLatch); |
| } |
| |
| bool InnerLoopVectorizer::shouldScalarizeInstruction(Instruction *I) const { |
| return Cost->isScalarAfterVectorization(I, VF) || |
| Cost->isProfitableToScalarize(I, VF); |
| } |
| |
| bool InnerLoopVectorizer::needsScalarInduction(Instruction *IV) const { |
| if (shouldScalarizeInstruction(IV)) |
| return true; |
| auto isScalarInst = [&](User *U) -> bool { |
| auto *I = cast<Instruction>(U); |
| return (OrigLoop->contains(I) && shouldScalarizeInstruction(I)); |
| }; |
| return llvm::any_of(IV->users(), isScalarInst); |
| } |
| |
| void InnerLoopVectorizer::recordVectorLoopValueForInductionCast( |
| const InductionDescriptor &ID, const Instruction *EntryVal, |
| Value *VectorLoopVal, VPValue *CastDef, VPTransformState &State, |
| unsigned Part, unsigned Lane) { |
| assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) && |
| "Expected either an induction phi-node or a truncate of it!"); |
| |
| // This induction variable is not the phi from the original loop but the |
| // newly-created IV based on the proof that casted Phi is equal to the |
| // uncasted Phi in the vectorized loop (under a runtime guard possibly). It |
| // re-uses the same InductionDescriptor that original IV uses but we don't |
| // have to do any recording in this case - that is done when original IV is |
| // processed. |
| if (isa<TruncInst>(EntryVal)) |
| return; |
| |
| if (!CastDef) { |
| assert(ID.getCastInsts().empty() && |
| "there are casts for ID, but no CastDef"); |
| return; |
| } |
| assert(!ID.getCastInsts().empty() && |
| "there is a CastDef, but no casts for ID"); |
| // Only the first Cast instruction in the Casts vector is of interest. |
| // The rest of the Casts (if exist) have no uses outside the |
| // induction update chain itself. |
| if (Lane < UINT_MAX) |
| State.set(CastDef, VectorLoopVal, VPIteration(Part, Lane)); |
| else |
| State.set(CastDef, VectorLoopVal, Part); |
| } |
| |
| void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, Value *Start, |
| TruncInst *Trunc, VPValue *Def, |
| VPValue *CastDef, |
| VPTransformState &State) { |
| assert((IV->getType()->isIntegerTy() || IV != OldInduction) && |
| "Primary induction variable must have an integer type"); |
| |
| auto II = Legal->getInductionVars().find(IV); |
| assert(II != Legal->getInductionVars().end() && "IV is not an induction"); |
| |
| auto ID = II->second; |
| assert(IV->getType() == ID.getStartValue()->getType() && "Types must match"); |
| |
| // The value from the original loop to which we are mapping the new induction |
| // variable. |
| Instruction *EntryVal = Trunc ? cast<Instruction>(Trunc) : IV; |
| |
| auto &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); |
| |
| // Generate code for the induction step. Note that induction steps are |
| // required to be loop-invariant |
| auto CreateStepValue = [&](const SCEV *Step) -> Value * { |
| assert(PSE.getSE()->isLoopInvariant(Step, OrigLoop) && |
| "Induction step should be loop invariant"); |
| if (PSE.getSE()->isSCEVable(IV->getType())) { |
| SCEVExpander Exp(*PSE.getSE(), DL, "induction"); |
| return Exp.expandCodeFor(Step, Step->getType(), |
| LoopVectorPreHeader->getTerminator()); |
| } |
| return cast<SCEVUnknown>(Step)->getValue(); |
| }; |
| |
| // The scalar value to broadcast. This is derived from the canonical |
| // induction variable. If a truncation type is given, truncate the canonical |
| // induction variable and step. Otherwise, derive these values from the |
| // induction descriptor. |
| auto CreateScalarIV = [&](Value *&Step) -> Value * { |
| Value *ScalarIV = Induction; |
| if (IV != OldInduction) { |
| ScalarIV = IV->getType()->isIntegerTy() |
| ? Builder.CreateSExtOrTrunc(Induction, IV->getType()) |
| : Builder.CreateCast(Instruction::SIToFP, Induction, |
| IV->getType()); |
| ScalarIV = emitTransformedIndex(Builder, ScalarIV, PSE.getSE(), DL, ID); |
| ScalarIV->setName("offset.idx"); |
| } |
| if (Trunc) { |
| auto *TruncType = cast<IntegerType>(Trunc->getType()); |
| assert(Step->getType()->isIntegerTy() && |
| "Truncation requires an integer step"); |
| ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType); |
| Step = Builder.CreateTrunc(Step, TruncType); |
| } |
| return ScalarIV; |
| }; |
| |
| // Create the vector values from the scalar IV, in the absence of creating a |
| // vector IV. |
| auto CreateSplatIV = [&](Value *ScalarIV, Value *Step) { |
| Value *Broadcasted = getBroadcastInstrs(ScalarIV); |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| assert(!VF.isScalable() && "scalable vectors not yet supported."); |
| Value *StartIdx; |
| if (Step->getType()->isFloatingPointTy()) |
| StartIdx = getRuntimeVFAsFloat(Builder, Step->getType(), VF * Part); |
| else |
| StartIdx = getRuntimeVF(Builder, Step->getType(), VF * Part); |
| |
| Value *EntryPart = |
| getStepVector(Broadcasted, StartIdx, Step, ID.getInductionOpcode()); |
| State.set(Def, EntryPart, Part); |
| if (Trunc) |
| addMetadata(EntryPart, Trunc); |
| recordVectorLoopValueForInductionCast(ID, EntryVal, EntryPart, CastDef, |
| State, Part); |
| } |
| }; |
| |
| // Fast-math-flags propagate from the original induction instruction. |
| IRBuilder<>::FastMathFlagGuard FMFG(Builder); |
| if (ID.getInductionBinOp() && isa<FPMathOperator>(ID.getInductionBinOp())) |
| Builder.setFastMathFlags(ID.getInductionBinOp()->getFastMathFlags()); |
| |
| // Now do the actual transformations, and start with creating the step value. |
| Value *Step = CreateStepValue(ID.getStep()); |
| if (VF.isZero() || VF.isScalar()) { |
| Value *ScalarIV = CreateScalarIV(Step); |
| CreateSplatIV(ScalarIV, Step); |
| return; |
| } |
| |
| // Determine if we want a scalar version of the induction variable. This is |
| // true if the induction variable itself is not widened, or if it has at |
| // least one user in the loop that is not widened. |
| auto NeedsScalarIV = needsScalarInduction(EntryVal); |
| if (!NeedsScalarIV) { |
| createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal, Def, CastDef, |
| State); |
| return; |
| } |
| |
| // Try to create a new independent vector induction variable. If we can't |
| // create the phi node, we will splat the scalar induction variable in each |
| // loop iteration. |
| if (!shouldScalarizeInstruction(EntryVal)) { |
| createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal, Def, CastDef, |
| State); |
| Value *ScalarIV = CreateScalarIV(Step); |
| // Create scalar steps that can be used by instructions we will later |
| // scalarize. Note that the addition of the scalar steps will not increase |
| // the number of instructions in the loop in the common case prior to |
| // InstCombine. We will be trading one vector extract for each scalar step. |
| buildScalarSteps(ScalarIV, Step, EntryVal, ID, Def, CastDef, State); |
| return; |
| } |
| |
| // All IV users are scalar instructions, so only emit a scalar IV, not a |
| // vectorised IV. Except when we tail-fold, then the splat IV feeds the |
| // predicate used by the masked loads/stores. |
| Value *ScalarIV = CreateScalarIV(Step); |
| if (!Cost->isScalarEpilogueAllowed()) |
| CreateSplatIV(ScalarIV, Step); |
| buildScalarSteps(ScalarIV, Step, EntryVal, ID, Def, CastDef, State); |
| } |
| |
| Value *InnerLoopVectorizer::getStepVector(Value *Val, Value *StartIdx, |
| Value *Step, |
| Instruction::BinaryOps BinOp) { |
| // Create and check the types. |
| auto *ValVTy = cast<VectorType>(Val->getType()); |
| ElementCount VLen = ValVTy->getElementCount(); |
| |
| Type *STy = Val->getType()->getScalarType(); |
| assert((STy->isIntegerTy() || STy->isFloatingPointTy()) && |
| "Induction Step must be an integer or FP"); |
| assert(Step->getType() == STy && "Step has wrong type"); |
| |
| SmallVector<Constant *, 8> Indices; |
| |
| // Create a vector of consecutive numbers from zero to VF. |
| VectorType *InitVecValVTy = ValVTy; |
| Type *InitVecValSTy = STy; |
| if (STy->isFloatingPointTy()) { |
| InitVecValSTy = |
| IntegerType::get(STy->getContext(), STy->getScalarSizeInBits()); |
| InitVecValVTy = VectorType::get(InitVecValSTy, VLen); |
| } |
| Value *InitVec = Builder.CreateStepVector(InitVecValVTy); |
| |
| // Splat the StartIdx |
| Value *StartIdxSplat = Builder.CreateVectorSplat(VLen, StartIdx); |
| |
| if (STy->isIntegerTy()) { |
| InitVec = Builder.CreateAdd(InitVec, StartIdxSplat); |
| Step = Builder.CreateVectorSplat(VLen, Step); |
| assert(Step->getType() == Val->getType() && "Invalid step vec"); |
| // FIXME: The newly created binary instructions should contain nsw/nuw flags, |
| // which can be found from the original scalar operations. |
| Step = Builder.CreateMul(InitVec, Step); |
| return Builder.CreateAdd(Val, Step, "induction"); |
| } |
| |
| // Floating point induction. |
| assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) && |
| "Binary Opcode should be specified for FP induction"); |
| InitVec = Builder.CreateUIToFP(InitVec, ValVTy); |
| InitVec = Builder.CreateFAdd(InitVec, StartIdxSplat); |
| |
| Step = Builder.CreateVectorSplat(VLen, Step); |
| Value *MulOp = Builder.CreateFMul(InitVec, Step); |
| return Builder.CreateBinOp(BinOp, Val, MulOp, "induction"); |
| } |
| |
| void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step, |
| Instruction *EntryVal, |
| const InductionDescriptor &ID, |
| VPValue *Def, VPValue *CastDef, |
| VPTransformState &State) { |
| // We shouldn't have to build scalar steps if we aren't vectorizing. |
| assert(VF.isVector() && "VF should be greater than one"); |
| // Get the value type and ensure it and the step have the same integer type. |
| Type *ScalarIVTy = ScalarIV->getType()->getScalarType(); |
| assert(ScalarIVTy == Step->getType() && |
| "Val and Step should have the same type"); |
| |
| // We build scalar steps for both integer and floating-point induction |
| // variables. Here, we determine the kind of arithmetic we will perform. |
| Instruction::BinaryOps AddOp; |
| Instruction::BinaryOps MulOp; |
| if (ScalarIVTy->isIntegerTy()) { |
| AddOp = Instruction::Add; |
| MulOp = Instruction::Mul; |
| } else { |
| AddOp = ID.getInductionOpcode(); |
| MulOp = Instruction::FMul; |
| } |
| |
| // Determine the number of scalars we need to generate for each unroll |
| // iteration. If EntryVal is uniform, we only need to generate the first |
| // lane. Otherwise, we generate all VF values. |
| bool IsUniform = |
| Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF); |
| unsigned Lanes = IsUniform ? 1 : VF.getKnownMinValue(); |
| // Compute the scalar steps and save the results in State. |
| Type *IntStepTy = IntegerType::get(ScalarIVTy->getContext(), |
| ScalarIVTy->getScalarSizeInBits()); |
| Type *VecIVTy = nullptr; |
| Value *UnitStepVec = nullptr, *SplatStep = nullptr, *SplatIV = nullptr; |
| if (!IsUniform && VF.isScalable()) { |
| VecIVTy = VectorType::get(ScalarIVTy, VF); |
| UnitStepVec = Builder.CreateStepVector(VectorType::get(IntStepTy, VF)); |
| SplatStep = Builder.CreateVectorSplat(VF, Step); |
| SplatIV = Builder.CreateVectorSplat(VF, ScalarIV); |
| } |
| |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Value *StartIdx0 = createStepForVF(Builder, IntStepTy, VF, Part); |
| |
| if (!IsUniform && VF.isScalable()) { |
| auto *SplatStartIdx = Builder.CreateVectorSplat(VF, StartIdx0); |
| auto *InitVec = Builder.CreateAdd(SplatStartIdx, UnitStepVec); |
| if (ScalarIVTy->isFloatingPointTy()) |
| InitVec = Builder.CreateSIToFP(InitVec, VecIVTy); |
| auto *Mul = Builder.CreateBinOp(MulOp, InitVec, SplatStep); |
| auto *Add = Builder.CreateBinOp(AddOp, SplatIV, Mul); |
| State.set(Def, Add, Part); |
| recordVectorLoopValueForInductionCast(ID, EntryVal, Add, CastDef, State, |
| Part); |
| // It's useful to record the lane values too for the known minimum number |
| // of elements so we do those below. This improves the code quality when |
| // trying to extract the first element, for example. |
| } |
| |
| if (ScalarIVTy->isFloatingPointTy()) |
| StartIdx0 = Builder.CreateSIToFP(StartIdx0, ScalarIVTy); |
| |
| for (unsigned Lane = 0; Lane < Lanes; ++Lane) { |
| Value *StartIdx = Builder.CreateBinOp( |
| AddOp, StartIdx0, getSignedIntOrFpConstant(ScalarIVTy, Lane)); |
| // The step returned by `createStepForVF` is a runtime-evaluated value |
| // when VF is scalable. Otherwise, it should be folded into a Constant. |
| assert((VF.isScalable() || isa<Constant>(StartIdx)) && |
| "Expected StartIdx to be folded to a constant when VF is not " |
| "scalable"); |
| auto *Mul = Builder.CreateBinOp(MulOp, StartIdx, Step); |
| auto *Add = Builder.CreateBinOp(AddOp, ScalarIV, Mul); |
| State.set(Def, Add, VPIteration(Part, Lane)); |
| recordVectorLoopValueForInductionCast(ID, EntryVal, Add, CastDef, State, |
| Part, Lane); |
| } |
| } |
| } |
| |
| void InnerLoopVectorizer::packScalarIntoVectorValue(VPValue *Def, |
| const VPIteration &Instance, |
| VPTransformState &State) { |
| Value *ScalarInst = State.get(Def, Instance); |
| Value *VectorValue = State.get(Def, Instance.Part); |
| VectorValue = Builder.CreateInsertElement( |
| VectorValue, ScalarInst, |
| Instance.Lane.getAsRuntimeExpr(State.Builder, VF)); |
| State.set(Def, VectorValue, Instance.Part); |
| } |
| |
| Value *InnerLoopVectorizer::reverseVector(Value *Vec) { |
| assert(Vec->getType()->isVectorTy() && "Invalid type"); |
| return Builder.CreateVectorReverse(Vec, "reverse"); |
| } |
| |
| // Return whether we allow using masked interleave-groups (for dealing with |
| // strided loads/stores that reside in predicated blocks, or for dealing |
| // with gaps). |
| static bool useMaskedInterleavedAccesses(const TargetTransformInfo &TTI) { |
| // If an override option has been passed in for interleaved accesses, use it. |
| if (EnableMaskedInterleavedMemAccesses.getNumOccurrences() > 0) |
| return EnableMaskedInterleavedMemAccesses; |
| |
| return TTI.enableMaskedInterleavedAccessVectorization(); |
| } |
| |
| // Try to vectorize the interleave group that \p Instr belongs to. |
| // |
| // E.g. Translate following interleaved load group (factor = 3): |
| // for (i = 0; i < N; i+=3) { |
| // R = Pic[i]; // Member of index 0 |
| // G = Pic[i+1]; // Member of index 1 |
| // B = Pic[i+2]; // Member of index 2 |
| // ... // do something to R, G, B |
| // } |
| // To: |
| // %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B |
| // %R.vec = shuffle %wide.vec, poison, <0, 3, 6, 9> ; R elements |
| // %G.vec = shuffle %wide.vec, poison, <1, 4, 7, 10> ; G elements |
| // %B.vec = shuffle %wide.vec, poison, <2, 5, 8, 11> ; B elements |
| // |
| // Or translate following interleaved store group (factor = 3): |
| // for (i = 0; i < N; i+=3) { |
| // ... do something to R, G, B |
| // Pic[i] = R; // Member of index 0 |
| // Pic[i+1] = G; // Member of index 1 |
| // Pic[i+2] = B; // Member of index 2 |
| // } |
| // To: |
| // %R_G.vec = shuffle %R.vec, %G.vec, <0, 1, 2, ..., 7> |
| // %B_U.vec = shuffle %B.vec, poison, <0, 1, 2, 3, u, u, u, u> |
| // %interleaved.vec = shuffle %R_G.vec, %B_U.vec, |
| // <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> ; Interleave R,G,B elements |
| // store <12 x i32> %interleaved.vec ; Write 4 tuples of R,G,B |
| void InnerLoopVectorizer::vectorizeInterleaveGroup( |
| const InterleaveGroup<Instruction> *Group, ArrayRef<VPValue *> VPDefs, |
| VPTransformState &State, VPValue *Addr, ArrayRef<VPValue *> StoredValues, |
| VPValue *BlockInMask) { |
| Instruction *Instr = Group->getInsertPos(); |
| const DataLayout &DL = Instr->getModule()->getDataLayout(); |
| |
| // Prepare for the vector type of the interleaved load/store. |
| Type *ScalarTy = getLoadStoreType(Instr); |
| unsigned InterleaveFactor = Group->getFactor(); |
| assert(!VF.isScalable() && "scalable vectors not yet supported."); |
| auto *VecTy = VectorType::get(ScalarTy, VF * InterleaveFactor); |
| |
| // Prepare for the new pointers. |
| SmallVector<Value *, 2> AddrParts; |
| unsigned Index = Group->getIndex(Instr); |
| |
| // TODO: extend the masked interleaved-group support to reversed access. |
| assert((!BlockInMask || !Group->isReverse()) && |
| "Reversed masked interleave-group not supported."); |
| |
| // If the group is reverse, adjust the index to refer to the last vector lane |
| // instead of the first. We adjust the index from the first vector lane, |
| // rather than directly getting the pointer for lane VF - 1, because the |
| // pointer operand of the interleaved access is supposed to be uniform. For |
| // uniform instructions, we're only required to generate a value for the |
| // first vector lane in each unroll iteration. |
| if (Group->isReverse()) |
| Index += (VF.getKnownMinValue() - 1) * Group->getFactor(); |
| |
| for (unsigned Part = 0; Part < UF; Part++) { |
| Value *AddrPart = State.get(Addr, VPIteration(Part, 0)); |
| setDebugLocFromInst(AddrPart); |
| |
| // Notice current instruction could be any index. Need to adjust the address |
| // to the member of index 0. |
| // |
| // E.g. a = A[i+1]; // Member of index 1 (Current instruction) |
| // b = A[i]; // Member of index 0 |
| // Current pointer is pointed to A[i+1], adjust it to A[i]. |
| // |
| // E.g. A[i+1] = a; // Member of index 1 |
| // A[i] = b; // Member of index 0 |
| // A[i+2] = c; // Member of index 2 (Current instruction) |
| // Current pointer is pointed to A[i+2], adjust it to A[i]. |
| |
| bool InBounds = false; |
| if (auto *gep = dyn_cast<GetElementPtrInst>(AddrPart->stripPointerCasts())) |
| InBounds = gep->isInBounds(); |
| AddrPart = Builder.CreateGEP(ScalarTy, AddrPart, Builder.getInt32(-Index)); |
| cast<GetElementPtrInst>(AddrPart)->setIsInBounds(InBounds); |
| |
| // Cast to the vector pointer type. |
| unsigned AddressSpace = AddrPart->getType()->getPointerAddressSpace(); |
| Type *PtrTy = VecTy->getPointerTo(AddressSpace); |
| AddrParts.push_back(Builder.CreateBitCast(AddrPart, PtrTy)); |
| } |
| |
| setDebugLocFromInst(Instr); |
| Value *PoisonVec = PoisonValue::get(VecTy); |
| |
| Value *MaskForGaps = nullptr; |
| if (Group->requiresScalarEpilogue() && !Cost->isScalarEpilogueAllowed()) { |
| MaskForGaps = createBitMaskForGaps(Builder, VF.getKnownMinValue(), *Group); |
| assert(MaskForGaps && "Mask for Gaps is required but it is null"); |
| } |
| |
| // Vectorize the interleaved load group. |
| if (isa<LoadInst>(Instr)) { |
| // For each unroll part, create a wide load for the group. |
| SmallVector<Value *, 2> NewLoads; |
| for (unsigned Part = 0; Part < UF; Part++) { |
| Instruction *NewLoad; |
| if (BlockInMask || MaskForGaps) { |
| assert(useMaskedInterleavedAccesses(*TTI) && |
| "masked interleaved groups are not allowed."); |
| Value *GroupMask = MaskForGaps; |
| if (BlockInMask) { |
| Value *BlockInMaskPart = State.get(BlockInMask, Part); |
| Value *ShuffledMask = Builder.CreateShuffleVector( |
| BlockInMaskPart, |
| createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), |
| "interleaved.mask"); |
| GroupMask = MaskForGaps |
| ? Builder.CreateBinOp(Instruction::And, ShuffledMask, |
| MaskForGaps) |
| : ShuffledMask; |
| } |
| NewLoad = |
| Builder.CreateMaskedLoad(VecTy, AddrParts[Part], Group->getAlign(), |
| GroupMask, PoisonVec, "wide.masked.vec"); |
| } |
| else |
| NewLoad = Builder.CreateAlignedLoad(VecTy, AddrParts[Part], |
| Group->getAlign(), "wide.vec"); |
| Group->addMetadata(NewLoad); |
| NewLoads.push_back(NewLoad); |
| } |
| |
| // For each member in the group, shuffle out the appropriate data from the |
| // wide loads. |
| unsigned J = 0; |
| for (unsigned I = 0; I < InterleaveFactor; ++I) { |
| Instruction *Member = Group->getMember(I); |
| |
| // Skip the gaps in the group. |
| if (!Member) |
| continue; |
| |
| auto StrideMask = |
| createStrideMask(I, InterleaveFactor, VF.getKnownMinValue()); |
| for (unsigned Part = 0; Part < UF; Part++) { |
| Value *StridedVec = Builder.CreateShuffleVector( |
| NewLoads[Part], StrideMask, "strided.vec"); |
| |
| // If this member has different type, cast the result type. |
| if (Member->getType() != ScalarTy) { |
| assert(!VF.isScalable() && "VF is assumed to be non scalable."); |
| VectorType *OtherVTy = VectorType::get(Member->getType(), VF); |
| StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL); |
| } |
| |
| if (Group->isReverse()) |
| StridedVec = reverseVector(StridedVec); |
| |
| State.set(VPDefs[J], StridedVec, Part); |
| } |
| ++J; |
| } |
| return; |
| } |
| |
| // The sub vector type for current instruction. |
| auto *SubVT = VectorType::get(ScalarTy, VF); |
| |
| // Vectorize the interleaved store group. |
| MaskForGaps = createBitMaskForGaps(Builder, VF.getKnownMinValue(), *Group); |
| assert((!MaskForGaps || useMaskedInterleavedAccesses(*TTI)) && |
| "masked interleaved groups are not allowed."); |
| assert((!MaskForGaps || !VF.isScalable()) && |
| "masking gaps for scalable vectors is not yet supported."); |
| for (unsigned Part = 0; Part < UF; Part++) { |
| // Collect the stored vector from each member. |
| SmallVector<Value *, 4> StoredVecs; |
| for (unsigned i = 0; i < InterleaveFactor; i++) { |
| assert((Group->getMember(i) || MaskForGaps) && |
| "Fail to get a member from an interleaved store group"); |
| Instruction *Member = Group->getMember(i); |
| |
| // Skip the gaps in the group. |
| if (!Member) { |
| Value *Undef = PoisonValue::get(SubVT); |
| StoredVecs.push_back(Undef); |
| continue; |
| } |
| |
| Value *StoredVec = State.get(StoredValues[i], Part); |
| |
| if (Group->isReverse()) |
| StoredVec = reverseVector(StoredVec); |
| |
| // If this member has different type, cast it to a unified type. |
| |
| if (StoredVec->getType() != SubVT) |
| StoredVec = createBitOrPointerCast(StoredVec, SubVT, DL); |
| |
| StoredVecs.push_back(StoredVec); |
| } |
| |
| // Concatenate all vectors into a wide vector. |
| Value *WideVec = concatenateVectors(Builder, StoredVecs); |
| |
| // Interleave the elements in the wide vector. |
| Value *IVec = Builder.CreateShuffleVector( |
| WideVec, createInterleaveMask(VF.getKnownMinValue(), InterleaveFactor), |
| "interleaved.vec"); |
| |
| Instruction *NewStoreInstr; |
| if (BlockInMask || MaskForGaps) { |
| Value *GroupMask = MaskForGaps; |
| if (BlockInMask) { |
| Value *BlockInMaskPart = State.get(BlockInMask, Part); |
| Value *ShuffledMask = Builder.CreateShuffleVector( |
| BlockInMaskPart, |
| createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), |
| "interleaved.mask"); |
| GroupMask = MaskForGaps ? Builder.CreateBinOp(Instruction::And, |
| ShuffledMask, MaskForGaps) |
| : ShuffledMask; |
| } |
| NewStoreInstr = Builder.CreateMaskedStore(IVec, AddrParts[Part], |
| Group->getAlign(), GroupMask); |
| } else |
| NewStoreInstr = |
| Builder.CreateAlignedStore(IVec, AddrParts[Part], Group->getAlign()); |
| |
| Group->addMetadata(NewStoreInstr); |
| } |
| } |
| |
| void InnerLoopVectorizer::vectorizeMemoryInstruction( |
| Instruction *Instr, VPTransformState &State, VPValue *Def, VPValue *Addr, |
| VPValue *StoredValue, VPValue *BlockInMask, bool ConsecutiveStride, |
| bool Reverse) { |
| // Attempt to issue a wide load. |
| LoadInst *LI = dyn_cast<LoadInst>(Instr); |
| StoreInst *SI = dyn_cast<StoreInst>(Instr); |
| |
| assert((LI || SI) && "Invalid Load/Store instruction"); |
| assert((!SI || StoredValue) && "No stored value provided for widened store"); |
| assert((!LI || !StoredValue) && "Stored value provided for widened load"); |
| |
| Type *ScalarDataTy = getLoadStoreType(Instr); |
| |
| auto *DataTy = VectorType::get(ScalarDataTy, VF); |
| const Align Alignment = getLoadStoreAlignment(Instr); |
| bool CreateGatherScatter = !ConsecutiveStride; |
| |
| VectorParts BlockInMaskParts(UF); |
| bool isMaskRequired = BlockInMask; |
| if (isMaskRequired) |
| for (unsigned Part = 0; Part < UF; ++Part) |
| BlockInMaskParts[Part] = State.get(BlockInMask, Part); |
| |
| const auto CreateVecPtr = [&](unsigned Part, Value *Ptr) -> Value * { |
| // Calculate the pointer for the specific unroll-part. |
| GetElementPtrInst *PartPtr = nullptr; |
| |
| bool InBounds = false; |
| if (auto *gep = dyn_cast<GetElementPtrInst>(Ptr->stripPointerCasts())) |
| InBounds = gep->isInBounds(); |
| if (Reverse) { |
| // If the address is consecutive but reversed, then the |
| // wide store needs to start at the last vector element. |
| // RunTimeVF = VScale * VF.getKnownMinValue() |
| // For fixed-width VScale is 1, then RunTimeVF = VF.getKnownMinValue() |
| Value *RunTimeVF = getRuntimeVF(Builder, Builder.getInt32Ty(), VF); |
| // NumElt = -Part * RunTimeVF |
| Value *NumElt = Builder.CreateMul(Builder.getInt32(-Part), RunTimeVF); |
| // LastLane = 1 - RunTimeVF |
| Value *LastLane = Builder.CreateSub(Builder.getInt32(1), RunTimeVF); |
| PartPtr = |
| cast<GetElementPtrInst>(Builder.CreateGEP(ScalarDataTy, Ptr, NumElt)); |
| PartPtr->setIsInBounds(InBounds); |
| PartPtr = cast<GetElementPtrInst>( |
| Builder.CreateGEP(ScalarDataTy, PartPtr, LastLane)); |
| PartPtr->setIsInBounds(InBounds); |
| if (isMaskRequired) // Reverse of a null all-one mask is a null mask. |
| BlockInMaskParts[Part] = reverseVector(BlockInMaskParts[Part]); |
| } else { |
| Value *Increment = |
| createStepForVF(Builder, Builder.getInt32Ty(), VF, Part); |
| PartPtr = cast<GetElementPtrInst>( |
| Builder.CreateGEP(ScalarDataTy, Ptr, Increment)); |
| PartPtr->setIsInBounds(InBounds); |
| } |
| |
| unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace(); |
| return Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace)); |
| }; |
| |
| // Handle Stores: |
| if (SI) { |
| setDebugLocFromInst(SI); |
| |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Instruction *NewSI = nullptr; |
| Value *StoredVal = State.get(StoredValue, Part); |
| if (CreateGatherScatter) { |
| Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; |
| Value *VectorGep = State.get(Addr, Part); |
| NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment, |
| MaskPart); |
| } else { |
| if (Reverse) { |
| // If we store to reverse consecutive memory locations, then we need |
| // to reverse the order of elements in the stored value. |
| StoredVal = reverseVector(StoredVal); |
| // We don't want to update the value in the map as it might be used in |
| // another expression. So don't call resetVectorValue(StoredVal). |
| } |
| auto *VecPtr = CreateVecPtr(Part, State.get(Addr, VPIteration(0, 0))); |
| if (isMaskRequired) |
| NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment, |
| BlockInMaskParts[Part]); |
| else |
| NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment); |
| } |
| addMetadata(NewSI, SI); |
| } |
| return; |
| } |
| |
| // Handle loads. |
| assert(LI && "Must have a load instruction"); |
| setDebugLocFromInst(LI); |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Value *NewLI; |
| if (CreateGatherScatter) { |
| Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; |
| Value *VectorGep = State.get(Addr, Part); |
| NewLI = Builder.CreateMaskedGather(DataTy, VectorGep, Alignment, MaskPart, |
| nullptr, "wide.masked.gather"); |
| addMetadata(NewLI, LI); |
| } else { |
| auto *VecPtr = CreateVecPtr(Part, State.get(Addr, VPIteration(0, 0))); |
| if (isMaskRequired) |
| NewLI = Builder.CreateMaskedLoad( |
| DataTy, VecPtr, Alignment, BlockInMaskParts[Part], |
| PoisonValue::get(DataTy), "wide.masked.load"); |
| else |
| NewLI = |
| Builder.CreateAlignedLoad(DataTy, VecPtr, Alignment, "wide.load"); |
| |
| // Add metadata to the load, but setVectorValue to the reverse shuffle. |
| addMetadata(NewLI, LI); |
| if (Reverse) |
| NewLI = reverseVector(NewLI); |
| } |
| |
| State.set(Def, NewLI, Part); |
| } |
| } |
| |
| void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr, |
| VPReplicateRecipe *RepRecipe, |
| const VPIteration &Instance, |
| bool IfPredicateInstr, |
| 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; |
| |
| setDebugLocFromInst(Instr); |
| |
| // Does this instruction return a value ? |
| bool IsVoidRetTy = Instr->getType()->isVoidTy(); |
| |
| Instruction *Cloned = Instr->clone(); |
| if (!IsVoidRetTy) |
| Cloned->setName(Instr->getName() + ".cloned"); |
| |
| // If the scalarized instruction contributes to the address computation of a |
| // widen masked load/store which was in a basic block that needed predication |
| // and is not predicated after vectorization, we can't propagate |
| // poison-generating flags (nuw/nsw, exact, inbounds, etc.). The scalarized |
| // instruction could feed a poison value to the base address of the widen |
| // load/store. |
| if (State.MayGeneratePoisonRecipes.count(RepRecipe) > 0) |
| Cloned->dropPoisonGeneratingFlags(); |
| |
| State.Builder.SetInsertPoint(Builder.GetInsertBlock(), |
| Builder.GetInsertPoint()); |
| // Replace the operands of the cloned instructions with their scalar |
| // equivalents in the new loop. |
| for (unsigned op = 0, e = RepRecipe->getNumOperands(); op != e; ++op) { |
| auto *Operand = dyn_cast<Instruction>(Instr->getOperand(op)); |
| auto InputInstance = Instance; |
| if (!Operand || !OrigLoop->contains(Operand) || |
| (Cost->isUniformAfterVectorization(Operand, State.VF))) |
| InputInstance.Lane = VPLane::getFirstLane(); |
| auto *NewOp = State.get(RepRecipe->getOperand(op), InputInstance); |
| Cloned->setOperand(op, NewOp); |
| } |
| addNewMetadata(Cloned, Instr); |
| |
| // Place the cloned scalar in the new loop. |
| 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. |
| if (IfPredicateInstr) |
| PredicatedInstructions.push_back(Cloned); |
| } |
| |
| PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start, |
| Value *End, Value *Step, |
| Instruction *DL) { |
| BasicBlock *Header = L->getHeader(); |
| BasicBlock *Latch = L->getLoopLatch(); |
| // As we're just creating this loop, it's possible no latch exists |
| // yet. If so, use the header as this will be a single block loop. |
| if (!Latch) |
| Latch = Header; |
| |
| IRBuilder<> B(&*Header->getFirstInsertionPt()); |
| Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction); |
| setDebugLocFromInst(OldInst, &B); |
| auto *Induction = B.CreatePHI(Start->getType(), 2, "index"); |
| |
| B.SetInsertPoint(Latch->getTerminator()); |
| setDebugLocFromInst(OldInst, &B); |
| |
| // Create i+1 and fill the PHINode. |
| // |
| // If the tail is not folded, we know that End - Start >= Step (either |
| // statically or through the minimum iteration checks). We also know that both |
| // Start % Step == 0 and End % Step == 0. We exit the vector loop if %IV + |
| // %Step == %End. Hence we must exit the loop before %IV + %Step unsigned |
| // overflows and we can mark the induction increment as NUW. |
| Value *Next = B.CreateAdd(Induction, Step, "index.next", |
| /*NUW=*/!Cost->foldTailByMasking(), /*NSW=*/false); |
| Induction->addIncoming(Start, L->getLoopPreheader()); |
| Induction->addIncoming(Next, Latch); |
| // Create the compare. |
| Value *ICmp = B.CreateICmpEQ(Next, End); |
| B.CreateCondBr(ICmp, L->getUniqueExitBlock(), Header); |
| |
| // Now we have two terminators. Remove the old one from the block. |
| Latch->getTerminator()->eraseFromParent(); |
| |
| return Induction; |
| } |
| |
| Value *InnerLoopVectorizer::getOrCreateTripCount(Loop *L) { |
| if (TripCount) |
| return TripCount; |
| |
| assert(L && "Create Trip Count for null loop."); |
| IRBuilder<> Builder(L->getLoopPreheader()->getTerminator()); |
| // Find the loop boundaries. |
| ScalarEvolution *SE = PSE.getSE(); |
| const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount(); |
| assert(!isa<SCEVCouldNotCompute>(BackedgeTakenCount) && |
| "Invalid loop count"); |
| |
| Type *IdxTy = Legal->getWidestInductionType(); |
| assert(IdxTy && "No type for induction"); |
| |
| // The exit count might have the type of i64 while the phi is i32. This can |
| // happen if we have an induction variable that is sign extended before the |
| // compare. The only way that we get a backedge taken count is that the |
| // induction variable was signed and as such will not overflow. In such a case |
| // truncation is legal. |
| if (SE->getTypeSizeInBits(BackedgeTakenCount->getType()) > |
| IdxTy->getPrimitiveSizeInBits()) |
| BackedgeTakenCount = SE->getTruncateOrNoop(BackedgeTakenCount, IdxTy); |
| BackedgeTakenCount = SE->getNoopOrZeroExtend(BackedgeTakenCount, IdxTy); |
| |
| // Get the total trip count from the count by adding 1. |
| const SCEV *ExitCount = SE->getAddExpr( |
| BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType())); |
| |
| const DataLayout &DL = L->getHeader()->getModule()->getDataLayout(); |
| |
| // Expand the trip count and place the new instructions in the preheader. |
| // Notice that the pre-header does not change, only the loop body. |
| SCEVExpander Exp(*SE, DL, "induction"); |
| |
| // Count holds the overall loop count (N). |
| TripCount = Exp.expandCodeFor(ExitCount, ExitCount->getType(), |
| L->getLoopPreheader()->getTerminator()); |
| |
| if (TripCount->getType()->isPointerTy()) |
| TripCount = |
| CastInst::CreatePointerCast(TripCount, IdxTy, "exitcount.ptrcnt.to.int", |
| L->getLoopPreheader()->getTerminator()); |
| |
| return TripCount; |
| } |
| |
| Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) { |
| if (VectorTripCount) |
| return VectorTripCount; |
| |
| Value *TC = getOrCreateTripCount(L); |
| IRBuilder<> Builder(L->getLoopPreheader()->getTerminator()); |
| |
| Type *Ty = TC->getType(); |
| // This is where we can make the step a runtime constant. |
| 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. |
| if (Cost->foldTailByMasking()) { |
| assert(isPowerOf2_32(VF.getKnownMinValue() * UF) && |
| "VF*UF must be a power of 2 when folding tail by masking"); |
| assert(!VF.isScalable() && |
| "Tail folding not yet supported for scalable vectors"); |
| TC = Builder.CreateAdd( |
| TC, ConstantInt::get(Ty, VF.getKnownMinValue() * UF - 1), "n.rnd.up"); |
| } |
| |
| // Now we need to generate the expression for the part of the loop that the |
| // vectorized body will execute. This is equal to N - (N % Step) if scalar |
| // iterations are not required for correctness, or N - Step, otherwise. Step |
| // is equal to the vectorization factor (number of SIMD elements) times the |
| // unroll factor (number of SIMD instructions). |
| Value *R = Builder.CreateURem(TC, Step, "n.mod.vf"); |
| |
| // 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)) { |
| auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0)); |
| R = Builder.CreateSelect(IsZero, Step, R); |
| } |
| |
| VectorTripCount = Builder.CreateSub(TC, R, "n.vec"); |
| |
| return VectorTripCount; |
| } |
| |
| Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy, |
| const DataLayout &DL) { |
| // Verify that V is a vector type with same number of elements as DstVTy. |
| auto *DstFVTy = cast<FixedVectorType>(DstVTy); |
| unsigned VF = DstFVTy->getNumElements(); |
| auto *SrcVecTy = cast<FixedVectorType>(V->getType()); |
| assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match"); |
| Type *SrcElemTy = SrcVecTy->getElementType(); |
| Type *DstElemTy = DstFVTy->getElementType(); |
| assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) && |
| "Vector elements must have same size"); |
| |
| // Do a direct cast if element types are castable. |
| if (CastInst::isBitOrNoopPointerCastable(SrcElemTy, DstElemTy, DL)) { |
| return Builder.CreateBitOrPointerCast(V, DstFVTy); |
| } |
| // V cannot be directly casted to desired vector type. |
| // May happen when V is a floating point vector but DstVTy is a vector of |
| // pointers or vice-versa. Handle this using a two-step bitcast using an |
| // intermediate Integer type for the bitcast i.e. Ptr <-> Int <-> Float. |
| assert((DstElemTy->isPointerTy() != SrcElemTy->isPointerTy()) && |
| "Only one type should be a pointer type"); |
| assert((DstElemTy->isFloatingPointTy() != SrcElemTy->isFloatingPointTy()) && |
| "Only one type should be a floating point type"); |
| Type *IntTy = |
| IntegerType::getIntNTy(V->getContext(), DL.getTypeSizeInBits(SrcElemTy)); |
| auto *VecIntTy = FixedVectorType::get(IntTy, VF); |
| Value *CastVal = Builder.CreateBitOrPointerCast(V, VecIntTy); |
| return Builder.CreateBitOrPointerCast(CastVal, DstFVTy); |
| } |
| |
| void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L, |
| BasicBlock *Bypass) { |
| Value *Count = getOrCreateTripCount(L); |
| // Reuse existing vector loop preheader for TC checks. |
| // Note that new preheader block is generated for vector loop. |
| BasicBlock *const TCCheckBlock = LoopVectorPreHeader; |
| IRBuilder<> Builder(TCCheckBlock->getTerminator()); |
| |
| // Generate code to check if the loop's trip count is less than VF * UF, or |
| // equal to it in case a scalar epilogue is required; this implies that the |
| // vector trip count is zero. This check also covers the case where adding one |
| // to the backedge-taken count overflowed leading to an incorrect trip count |
| // of zero. In this case we will also jump to the scalar loop. |
| auto P = Cost->requiresScalarEpilogue(VF) ? ICmpInst::ICMP_ULE |
| : ICmpInst::ICMP_ULT; |
| |
| // If tail is to be folded, vector loop takes care of all iterations. |
| Value *CheckMinIters = Builder.getFalse(); |
| if (!Cost->foldTailByMasking()) { |
| Value *Step = createStepForVF(Builder, Count->getType(), VF, UF); |
| CheckMinIters = Builder.CreateICmp(P, Count, Step, "min.iters.check"); |
| } |
| // Create new preheader for vector loop. |
| LoopVectorPreHeader = |
| SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), DT, LI, nullptr, |
| "vector.ph"); |
| |
| assert(DT->properlyDominates(DT->getNode(TCCheckBlock), |
| DT->getNode(Bypass)->getIDom()) && |
| "TC check is expected to dominate Bypass"); |
| |
| // Update dominator for Bypass & LoopExit (if needed). |
| DT->changeImmediateDominator(Bypass, TCCheckBlock); |
| if (!Cost->requiresScalarEpilogue(VF)) |
| // 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, TCCheckBlock); |
| |
| ReplaceInstWithInst( |
| TCCheckBlock->getTerminator(), |
| BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| LoopBypassBlocks.push_back(TCCheckBlock); |
| } |
| |
| BasicBlock *InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) { |
| |
| BasicBlock *const SCEVCheckBlock = |
| RTChecks.emitSCEVChecks(L, 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)) |
| // 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(Loop *L, |
| BasicBlock *Bypass) { |
| // VPlan-native path does not do any analysis for runtime checks currently. |
| if (EnableVPlanNativePath) |
| return nullptr; |
| |
| BasicBlock *const MemCheckBlock = |
| RTChecks.emitMemRuntimeChecks(L, 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", |
| L->getStartLoc(), L->getHeader()) |
| << "Code-size may be reduced by not forcing " |
| "vectorization, or by source-code modifications " |
| "eliminating the need for runtime checks " |
| "(e.g., adding 'restrict')."; |
| }); |
| } |
| |
| LoopBypassBlocks.push_back(MemCheckBlock); |
| |
| AddedSafetyChecks = true; |
| |
| // We currently don't use LoopVersioning for the actual loop cloning but we |
| // still use it to add the noalias metadata. |
| LVer = std::make_unique<LoopVersioning>( |
| *Legal->getLAI(), |
| Legal->getLAI()->getRuntimePointerChecking()->getChecks(), OrigLoop, LI, |
| DT, PSE.getSE()); |
| LVer->prepareNoAliasMetadata(); |
| return MemCheckBlock; |
| } |
| |
| Value *InnerLoopVectorizer::emitTransformedIndex( |
| IRBuilder<> &B, Value *Index, ScalarEvolution *SE, const DataLayout &DL, |
| const InductionDescriptor &ID) const { |
| |
| SCEVExpander Exp(*SE, DL, "induction"); |
| auto Step = ID.getStep(); |
| auto StartValue = ID.getStartValue(); |
| assert(Index->getType()->getScalarType() == Step->getType() && |
| "Index scalar type does not match StepValue type"); |
| |
| // Note: the IR at this point is broken. We cannot use SE to create any new |
| // SCEV and then expand it, hoping that SCEV's simplification will give us |
| // a more optimal code. Unfortunately, attempt of doing so on invalid IR may |
| // lead to various SCEV crashes. So all we can do is to use builder and rely |
| // on InstCombine for future simplifications. Here we handle some trivial |
| // cases only. |
| auto CreateAdd = [&B](Value *X, Value *Y) { |
| assert(X->getType() == Y->getType() && "Types don't match!"); |
| if (auto *CX = dyn_cast<ConstantInt>(X)) |
| if (CX->isZero()) |
| return Y; |
| if (auto *CY = dyn_cast<ConstantInt>(Y)) |
| if (CY->isZero()) |
| return X; |
| return B.CreateAdd(X, Y); |
| }; |
| |
| // 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); |
| }; |
| |
| // Get a suitable insert point for SCEV expansion. For blocks in the vector |
| // loop, choose the end of the vector loop header (=LoopVectorBody), because |
| // the DomTree is not kept up-to-date for additional blocks generated in the |
| // vector loop. By using the header as insertion point, we guarantee that the |
| // expanded instructions dominate all their uses. |
| auto GetInsertPoint = [this, &B]() { |
| BasicBlock *InsertBB = B.GetInsertPoint()->getParent(); |
| if (InsertBB != LoopVectorBody && |
| LI->getLoopFor(LoopVectorBody) == LI->getLoopFor(InsertBB)) |
| return LoopVectorBody->getTerminator(); |
| return &*B.GetInsertPoint(); |
| }; |
| |
| switch (ID.getKind()) { |
| case InductionDescriptor::IK_IntInduction: { |
| assert(!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 (ID.getConstIntStepValue() && ID.getConstIntStepValue()->isMinusOne()) |
| return B.CreateSub(StartValue, Index); |
| auto *Offset = CreateMul( |
| Index, Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint())); |
| return CreateAdd(StartValue, Offset); |
| } |
| case InductionDescriptor::IK_PtrInduction: { |
| assert(isa<SCEVConstant>(Step) && |
| "Expected constant step for pointer induction"); |
| return B.CreateGEP( |
| ID.getElementType(), StartValue, |
| CreateMul(Index, |
| Exp.expandCodeFor(Step, Index->getType()->getScalarType(), |
| GetInsertPoint()))); |
| } |
| 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"); |
| auto InductionBinOp = ID.getInductionBinOp(); |
| assert(InductionBinOp && |
| (InductionBinOp->getOpcode() == Instruction::FAdd || |
| InductionBinOp->getOpcode() == Instruction::FSub) && |
| "Original bin op should be defined for FP induction"); |
| |
| Value *StepValue = cast<SCEVUnknown>(Step)->getValue(); |
| Value *MulExp = B.CreateFMul(StepValue, Index); |
| return B.CreateBinOp(InductionBinOp->getOpcode(), StartValue, MulExp, |
| "induction"); |
| } |
| case InductionDescriptor::IK_NoInduction: |
| return nullptr; |
| } |
| llvm_unreachable("invalid enum"); |
| } |
| |
| Loop *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)) && |
| "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"); |
| |
| auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); |
| |
| // Set up the middle block terminator. Two cases: |
| // 1) If we know that we must execute the scalar epilogue, emit an |
| // unconditional branch. |
| // 2) Otherwise, we must have a single unique exit block (due to how we |
| // implement the multiple exit case). In this case, set up a conditonal |
| // branch from the middle block to the loop scalar preheader, and the |
| // exit block. completeLoopSkeleton will update the condition to use an |
| // iteration check, if required to decide whether to execute the remainder. |
| BranchInst *BrInst = Cost->requiresScalarEpilogue(VF) ? |
| BranchInst::Create(LoopScalarPreHeader) : |
| BranchInst::Create(LoopExitBlock, LoopScalarPreHeader, |
| Builder.getTrue()); |
| BrInst->setDebugLoc(ScalarLatchTerm->getDebugLoc()); |
| ReplaceInstWithInst(LoopMiddleBlock->getTerminator(), BrInst); |
| |
| // We intentionally don't let SplitBlock to update LoopInfo since |
| // LoopVectorBody should belong to another loop than LoopVectorPreHeader. |
| // LoopVectorBody is explicitly added to the correct place few lines later. |
| LoopVectorBody = |
| SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| nullptr, nullptr, Twine(Prefix) + "vector.body"); |
| |
| // Update dominator for loop exit. |
| if (!Cost->requiresScalarEpilogue(VF)) |
| // 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, LoopMiddleBlock); |
| |
| // Create and register the new vector loop. |
| Loop *Lp = LI->AllocateLoop(); |
| Loop *ParentLoop = OrigLoop->getParentLoop(); |
| |
| // Insert the new loop into the loop nest and register the new basic blocks |
| // before calling any utilities such as SCEV that require valid LoopInfo. |
| if (ParentLoop) { |
| ParentLoop->addChildLoop(Lp); |
| } else { |
| LI->addTopLevelLoop(Lp); |
| } |
| Lp->addBasicBlockToLoop(LoopVectorBody, *LI); |
| return Lp; |
| } |
| |
| void InnerLoopVectorizer::createInductionResumeValues( |
| Loop *L, Value *VectorTripCount, |
| std::pair<BasicBlock *, Value *> AdditionalBypass) { |
| assert(VectorTripCount && L && "Expected valid arguments"); |
| 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 (auto &InductionEntry : Legal->getInductionVars()) { |
| PHINode *OrigPhi = InductionEntry.first; |
| InductionDescriptor II = InductionEntry.second; |
| |
| // Create phi nodes to merge from the backedge-taken check block. |
| PHINode *BCResumeVal = |
| PHINode::Create(OrigPhi->getType(), 3, "bc.resume.val", |
| LoopScalarPreHeader->getTerminator()); |
| // Copy original phi DL over to the new one. |
| BCResumeVal->setDebugLoc(OrigPhi->getDebugLoc()); |
| Value *&EndValue = IVEndValues[OrigPhi]; |
| Value *EndValueFromAdditionalBypass = AdditionalBypass.second; |
| if (OrigPhi == OldInduction) { |
| // We know what the end value is. |
| EndValue = VectorTripCount; |
| } else { |
| IRBuilder<> B(L->getLoopPreheader()->getTerminator()); |
| |
| // Fast-math-flags propagate from the original induction instruction. |
| if (II.getInductionBinOp() && isa<FPMathOperator>(II.getInductionBinOp())) |
| B.setFastMathFlags(II.getInductionBinOp()->getFastMathFlags()); |
| |
| Type *StepType = II.getStep()->getType(); |
| Instruction::CastOps CastOp = |
| CastInst::getCastOpcode(VectorTripCount, true, StepType, true); |
| Value *CRD = B.CreateCast(CastOp, VectorTripCount, StepType, "cast.crd"); |
| const DataLayout &DL = LoopScalarBody->getModule()->getDataLayout(); |
| EndValue = emitTransformedIndex(B, CRD, PSE.getSE(), DL, II); |
| EndValue->setName("ind.end"); |
| |
| // Compute the end value for the additional bypass (if applicable). |
| if (AdditionalBypass.first) { |
| B.SetInsertPoint(&(*AdditionalBypass.first->getFirstInsertionPt())); |
| CastOp = CastInst::getCastOpcode(AdditionalBypass.second, true, |
| StepType, true); |
| CRD = |
| B.CreateCast(CastOp, AdditionalBypass.second, StepType, "cast.crd"); |
| EndValueFromAdditionalBypass = |
| emitTransformedIndex(B, CRD, PSE.getSE(), DL, II); |
| EndValueFromAdditionalBypass->setName("ind.end"); |
| } |
| } |
| // The new PHI merges the original incoming value, in case of a bypass, |
| // or the value at the end of the vectorized loop. |
| BCResumeVal->addIncoming(EndValue, LoopMiddleBlock); |
| |
| // Fix the scalar body counter (PHI node). |
| // The old induction's phi node in the scalar body needs the truncated |
| // value. |
| for (BasicBlock *BB : LoopBypassBlocks) |
| BCResumeVal->addIncoming(II.getStartValue(), BB); |
| |
| if (AdditionalBypass.first) |
| BCResumeVal->setIncomingValueForBlock(AdditionalBypass.first, |
| EndValueFromAdditionalBypass); |
| |
| OrigPhi->setIncomingValueForBlock(LoopScalarPreHeader, BCResumeVal); |
| } |
| } |
| |
| BasicBlock *InnerLoopVectorizer::completeLoopSkeleton(Loop *L, |
| MDNode *OrigLoopID) { |
| assert(L && "Expected valid loop."); |
| |
| // The trip counts should be cached by now. |
| Value *Count = getOrCreateTripCount(L); |
| Value *VectorTripCount = getOrCreateVectorTripCount(L); |
| |
| auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); |
| |
| // Add a check in the middle block to see if we have completed |
| // all of the iterations in the first vector loop. Three cases: |
| // 1) If we require a scalar epilogue, there is no conditional branch as |
| // we unconditionally branch to the scalar preheader. Do nothing. |
| // 2) If (N - N%VF) == N, then we *don't* need to run the remainder. |
| // Thus if tail is to be folded, we know we don't need to run the |
| // remainder and we can use the previous value for the condition (true). |
| // 3) Otherwise, construct a runtime check. |
| if (!Cost->requiresScalarEpilogue(VF) && !Cost->foldTailByMasking()) { |
| Instruction *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, |
| Count, VectorTripCount, "cmp.n", |
| LoopMiddleBlock->getTerminator()); |
| |
| // Here we use the same DebugLoc as the scalar loop latch terminator instead |
| // of the corresponding compare because they may have ended up with |
| // different line numbers and we want to avoid awkward line stepping while |
| // debugging. Eg. if the compare has got a line number inside the loop. |
| CmpN->setDebugLoc(ScalarLatchTerm->getDebugLoc()); |
| cast<BranchInst>(LoopMiddleBlock->getTerminator())->setCondition(CmpN); |
| } |
| |
| // Get ready to start creating new instructions into the vectorized body. |
| assert(LoopVectorPreHeader == L->getLoopPreheader() && |
| "Inconsistent vector loop preheader"); |
| Builder.SetInsertPoint(&*LoopVectorBody->getFirstInsertionPt()); |
| |
| Optional<MDNode *> VectorizedLoopID = |
| makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll, |
| LLVMLoopVectorizeFollowupVectorized}); |
| if (VectorizedLoopID.hasValue()) { |
| L->setLoopID(VectorizedLoopID.getValue()); |
| |
| // Do not setAlreadyVectorized if loop attributes have been defined |
| // explicitly. |
| return LoopVectorPreHeader; |
| } |
| |
| // Keep all loop hints from the original loop on the vector loop (we'll |
| // replace the vectorizer-specific hints below). |
| if (MDNode *LID = OrigLoop->getLoopID()) |
| L->setLoopID(LID); |
| |
| LoopVectorizeHints Hints(L, true, *ORE); |
| Hints.setAlreadyVectorized(); |
| |
| #ifdef EXPENSIVE_CHECKS |
| assert(DT->verify(DominatorTree::VerificationLevel::Fast)); |
| LI->verify(*DT); |
| #endif |
| |
| return LoopVectorPreHeader; |
| } |
| |
| BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() { |
| /* |
| In this function we generate a new loop. The new loop will contain |
| the vectorized instructions while the old loop will continue to run the |
| scalar remainder. |
| |
| [ ] <-- loop iteration number check. |
| / | |
| / v |
| | [ ] <-- vector loop bypass (may consist of multiple blocks). |
| | / | |
| | / v |
| || [ ] <-- vector pre header. |
| |/ | |
| | v |
| | [ ] \ |
| | [ ]_| <-- vector loop. |
| | | |
| | v |
| \ -[ ] <--- middle-block. |
| \/ | |
| /\ v |
| | ->[ ] <--- new preheader. |
| | | |
| (opt) v <-- edge from middle to exit iff epilogue is not required. |
| | [ ] \ |
| | [ ]_| <-- old scalar loop to handle remainder (scalar epilogue). |
| \ | |
| \ v |
| >[ ] <-- exit block(s). |
| ... |
| */ |
| |
| // Get the metadata of the original loop before it gets modified. |
| MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| |
| // Workaround! Compute the trip count of the original loop and cache it |
| // before we start modifying the CFG. This code has a systemic problem |
| // wherein it tries to run analysis over partially constructed IR; this is |
| // wrong, and not simply for SCEV. The trip count of the original loop |
| // simply happens to be prone to hitting this in practice. In theory, we |
| // can hit the same issue for any SCEV, or ValueTracking query done during |
| // mutation. See PR49900. |
| getOrCreateTripCount(OrigLoop); |
| |
| // Create an empty vector loop, and prepare basic blocks for the runtime |
| // checks. |
| Loop *Lp = createVectorLoopSkeleton(""); |
| |
| // Now, compare the new count to zero. If it is zero skip the vector loop and |
| // jump to the scalar loop. This check also covers the case where the |
| // backedge-taken count is uint##_max: adding one to it will overflow leading |
| // to an incorrect trip count of zero. In this (rare) case we will also jump |
| // to the scalar loop. |
| emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader); |
| |
| // Generate the code to check any assumptions that we've made for SCEV |
| // expressions. |
| emitSCEVChecks(Lp, LoopScalarPreHeader); |
| |
| // Generate the code that checks in runtime if arrays overlap. We put the |
| // checks into a separate block to make the more common case of few elements |
| // faster. |
| emitMemRuntimeChecks(Lp, LoopScalarPreHeader); |
| |
| // Some loops have a single integer induction variable, while other loops |
| // don't. One example is c++ iterators that often have multiple pointer |
| // induction variables. In the code below we also support a case where we |
| // don't have a single induction variable. |
| // |
| // We try to obtain an induction variable from the original loop as hard |
| // as possible. However if we don't find one that: |
| // - is an integer |
| // - counts from zero, stepping by one |
| // - is the size of the widest induction variable type |
| // then we create a new one. |
| OldInduction = Legal->getPrimaryInduction(); |
| Type *IdxTy = Legal->getWidestInductionType(); |
| Value *StartIdx = ConstantInt::get(IdxTy, 0); |
| // The loop step is equal to the vectorization factor (num of SIMD elements) |
| // times the unroll factor (num of SIMD instructions). |
| Builder.SetInsertPoint(&*Lp->getHeader()->getFirstInsertionPt()); |
| Value *Step = createStepForVF(Builder, IdxTy, VF, UF); |
| Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| Induction = |
| createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| getDebugLocFromInstOrOperands(OldInduction)); |
| |
| // Emit phis for the new starting index of the scalar loop. |
| createInductionResumeValues(Lp, CountRoundDown); |
| |
| return completeLoopSkeleton(Lp, OrigLoopID); |
| } |
| |
| // Fix up external users of the induction variable. At this point, we are |
| // in LCSSA form, with all external PHIs that use the IV having one input value, |
| // coming from the remainder loop. We need those PHIs to also have a correct |
| // value for the IV when arriving directly from the middle block. |
| void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi, |
| const InductionDescriptor &II, |
| Value *CountRoundDown, Value *EndValue, |
| BasicBlock *MiddleBlock) { |
| // There are two kinds of external IV usages - those that use the value |
| // computed in the last iteration (the PHI) and those that use the penultimate |
| // value (the value that feeds into the phi from the loop latch). |
| // We allow both, but they, obviously, have different values. |
| |
| assert(OrigLoop->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)) { |
| const DataLayout &DL = |
| OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 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( |
| CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1)); |
| Value *CMO = |
| !II.getStep()->getType()->isIntegerTy() |
| ? B.CreateCast(Instruction::SIToFP, CountMinusOne, |
| II.getStep()->getType()) |
| : B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType()); |
| CMO->setName("cast.cmo"); |
| Value *Escape = emitTransformedIndex(B, CMO, PSE.getSE(), DL, II); |
| Escape->setName("ind.escape"); |
| MissingVals[UI] = Escape; |
| } |
| } |
| |
| for (auto &I : MissingVals) { |
| PHINode *PHI = cast<PHINode>(I.first); |
| // One corner case we have to handle is two IVs "chasing" each-other, |
| // that is %IV2 = phi [...], [ %IV1, %latch ] |
| // In this case, if IV1 has an external use, we need to avoid adding both |
| // "last value of IV1" and "penultimate value of IV2". So, verify that we |
| // don't already have an incoming value for the middle block. |
| if (PHI->getBasicBlockIndex(MiddleBlock) == -1) |
| PHI->addIncoming(I.second, MiddleBlock); |
| } |
| } |
| |
| namespace { |
| |
| struct CSEDenseMapInfo { |
| static bool canHandle(const Instruction *I) { |
| return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) || |
| isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I); |
| } |
| |
| static inline Instruction *getEmptyKey() { |
| return DenseMapInfo<Instruction *>::getEmptyKey(); |
| } |
| |
| static inline Instruction *getTombstoneKey() { |
| return DenseMapInfo<Instruction *>::getTombstoneKey(); |
| } |
| |
| static unsigned getHashValue(const Instruction *I) { |
| assert(canHandle(I) && "Unknown instruction!"); |
| return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(), |
| I->value_op_end())); |
| } |
| |
| static bool isEqual(const Instruction *LHS, const Instruction *RHS) { |
| if (LHS == getEmptyKey() || RHS == getEmptyKey() || |
| LHS == getTombstoneKey() || RHS == getTombstoneKey()) |
| return LHS == RHS; |
| return LHS->isIdenticalTo(RHS); |
| } |
| }; |
| |
| } // end anonymous namespace |
| |
| ///Perform cse of induction variable instructions. |
| static void cse(BasicBlock *BB) { |
| // Perform simple cse. |
| SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap; |
| for (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, |
| bool &NeedToScalarize) const { |
| Function *F = CI->getCalledFunction(); |
| Type *ScalarRetTy = CI->getType(); |
| SmallVector<Type *, 4> Tys, ScalarTys; |
| for (auto &ArgOp : CI->args()) |
| ScalarTys.push_back(ArgOp->getType()); |
| |
| // Estimate cost of scalarized vector call. The source operands are assumed |
| // to be vectors, so we need to extract individual elements from there, |
| // execute VF scalar calls, and then gather the result into the vector return |
| // value. |
| InstructionCost ScalarCallCost = |
| TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys, TTI::TCK_RecipThroughput); |
| if (VF.isScalar()) |
| return ScalarCallCost; |
| |
| // Compute corresponding vector type for return value and arguments. |
| Type *RetTy = ToVectorTy(ScalarRetTy, VF); |
| for (Type *ScalarTy : ScalarTys) |
| Tys.push_back(ToVectorTy(ScalarTy, VF)); |
| |
| // Compute costs of unpacking argument values for the scalar calls and |
| // packing the return values to a vector. |
| InstructionCost ScalarizationCost = getScalarizationOverhead(CI, VF); |
| |
| InstructionCost Cost = |
| ScalarCallCost * VF.getKnownMinValue() + ScalarizationCost; |
| |
| // If we can't emit a vector call for this function, then the currently found |
| // cost is the cost we need to return. |
| NeedToScalarize = true; |
| VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); |
| Function *VecFunc = VFDatabase(*CI).getVectorizedFunction(Shape); |
| |
| if (!TLI || CI->isNoBuiltin() || !VecFunc) |
| return Cost; |
| |
| // If the corresponding vector cost is cheaper, return its cost. |
| InstructionCost VectorCallCost = |
| TTI.getCallInstrCost(nullptr, RetTy, Tys, TTI::TCK_RecipThroughput); |
| if (VectorCallCost < Cost) { |
| NeedToScalarize = false; |
| Cost = VectorCallCost; |
| } |
| return Cost; |
| } |
| |
| 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); |
| } |
| |
| static Type *smallestIntegerVectorType(Type *T1, Type *T2) { |
| auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType()); |
| auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType()); |
| return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2; |
| } |
| |
| static Type *largestIntegerVectorType(Type *T1, Type *T2) { |
| auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType()); |
| auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType()); |
| return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2; |
| } |
| |
| void InnerLoopVectorizer::truncateToMinimalBitwidths(VPTransformState &State) { |
| // For every instruction `I` in MinBWs, truncate the operands, create a |
| // truncated version of `I` and reextend its result. InstCombine runs |
| // later and will remove any ext/trunc pairs. |
| SmallPtrSet<Value *, 4> Erased; |
| for (const auto &KV : Cost->getMinimalBitwidths()) { |
| // If the value wasn't vectorized, we must maintain the original scalar |
| // type. The absence of the value from State indicates that it |
| // wasn't vectorized. |
| // FIXME: Should not rely on getVPValue at this point. |
| VPValue *Def = State.Plan->getVPValue(KV.first, true); |
| if (!State.hasAnyVectorValue(Def)) |
| continue; |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Value *I = State.get(Def, Part); |
| if (Erased.count(I) || I->use_empty() || !isa<Instruction>(I)) |
| continue; |
| Type *OriginalTy = I->getType(); |
| Type *ScalarTruncatedTy = |
| IntegerType::get(OriginalTy->getContext(), KV.second); |
| auto *TruncatedTy = VectorType::get( |
| ScalarTruncatedTy, cast<VectorType>(OriginalTy)->getElementCount()); |
| if (TruncatedTy == OriginalTy) |
| continue; |
| |
| IRBuilder<> B(cast<Instruction>(I)); |
| auto ShrinkOperand = [&](Value *V) -> Value * { |
| if (auto *ZI = dyn_cast<ZExtInst>(V)) |
| if (ZI->getSrcTy() == TruncatedTy) |
| return ZI->getOperand(0); |
| return B.CreateZExtOrTrunc(V, TruncatedTy); |
| }; |
| |
| // The actual instruction modification depends on the instruction type, |
| // unfortunately. |
| Value *NewI = nullptr; |
| if (auto *BO = dyn_cast<BinaryOperator>(I)) { |
| NewI = B.CreateBinOp(BO->getOpcode(), ShrinkOperand(BO->getOperand(0)), |
| ShrinkOperand(BO->getOperand(1))); |
| |
| // Any wrapping introduced by shrinking this operation shouldn't be |
| // considered undefined behavior. So, we can't unconditionally copy |
| // arithmetic wrapping flags to NewI. |
| cast<BinaryOperator>(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false); |
| } else if (auto *CI = dyn_cast<ICmpInst>(I)) { |
| NewI = |
| B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)), |
| ShrinkOperand(CI->getOperand(1))); |
| } else if (auto *SI = dyn_cast<SelectInst>(I)) { |
| NewI = B.CreateSelect(SI->getCondition(), |
| ShrinkOperand(SI->getTrueValue()), |
| ShrinkOperand(SI->getFalseValue())); |
| } else if (auto *CI = dyn_cast<CastInst>(I)) { |
| switch (CI->getOpcode()) { |
| default: |
| llvm_unreachable("Unhandled cast!"); |
| case Instruction::Trunc: |
| NewI = ShrinkOperand(CI->getOperand(0)); |
| break; |
| case Instruction::SExt: |
| NewI = B.CreateSExtOrTrunc( |
| CI->getOperand(0), |
| smallestIntegerVectorType(OriginalTy, TruncatedTy)); |
| break; |
| case Instruction::ZExt: |
| NewI = B.CreateZExtOrTrunc( |
| CI->getOperand(0), |
| smallestIntegerVectorType(OriginalTy, TruncatedTy)); |
| break; |
| } |
| } else if (auto *SI = dyn_cast<ShuffleVectorInst>(I)) { |
| auto Elements0 = |
| cast<VectorType>(SI->getOperand(0)->getType())->getElementCount(); |
| auto *O0 = B.CreateZExtOrTrunc( |
| SI->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements0)); |
| auto Elements1 = |
| cast<VectorType>(SI->getOperand(1)->getType())->getElementCount(); |
| auto *O1 = B.CreateZExtOrTrunc( |
| SI->getOperand(1), VectorType::get(ScalarTruncatedTy, Elements1)); |
| |
| NewI = B.CreateShuffleVector(O0, O1, SI->getShuffleMask()); |
| } else if (isa<LoadInst>(I) || isa<PHINode>(I)) { |
| // Don't do anything with the operands, just extend the result. |
| continue; |
| } else if (auto *IE = dyn_cast<InsertElementInst>(I)) { |
| auto Elements = |
| cast<VectorType>(IE->getOperand(0)->getType())->getElementCount(); |
| auto *O0 = B.CreateZExtOrTrunc( |
| IE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements)); |
| auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy); |
| NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2)); |
| } else if (auto *EE = dyn_cast<ExtractElementInst>(I)) { |
| auto Elements = |
| cast<VectorType>(EE->getOperand(0)->getType())->getElementCount(); |
| auto *O0 = B.CreateZExtOrTrunc( |
| EE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements)); |
| NewI = B.CreateExtractElement(O0, EE->getOperand(2)); |
| } else { |
| // If we don't know what to do, be conservative and don't do anything. |
| continue; |
| } |
| |
| // Lastly, extend the result. |
| NewI->takeName(cast<Instruction>(I)); |
| Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy); |
| I->replaceAllUsesWith(Res); |
| cast<Instruction>(I)->eraseFromParent(); |
| Erased.insert(I); |
| State.reset(Def, Res, Part); |
| } |
| } |
| |
| // We'll have created a bunch of ZExts that are now parentless. Clean up. |
| for (const auto &KV : Cost->getMinimalBitwidths()) { |
| // If the value wasn't vectorized, we must maintain the original scalar |
| // type. The absence of the value from State indicates that it |
| // wasn't vectorized. |
| // FIXME: Should not rely on getVPValue at this point. |
| VPValue *Def = State.Plan->getVPValue(KV.first, true); |
| if (!State.hasAnyVectorValue(Def)) |
| continue; |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Value *I = State.get(Def, Part); |
| ZExtInst *Inst = dyn_cast<ZExtInst>(I); |
| if (Inst && Inst->use_empty()) { |
| Value *NewI = Inst->getOperand(0); |
| Inst->eraseFromParent(); |
| State.reset(Def, NewI, Part); |
| } |
| } |
| } |
| } |
| |
| void InnerLoopVectorizer::fixVectorizedLoop(VPTransformState &State) { |
| // Insert truncates and extends for any truncated instructions as hints to |
| // InstCombine. |
| if (VF.isVector()) |
| truncateToMinimalBitwidths(State); |
| |
| // Fix widened non-induction PHIs by setting up the PHI operands. |
| if (OrigPHIsToFix.size()) { |
| assert(EnableVPlanNativePath && |
| "Unexpected non-induction PHIs for fixup in non VPlan-native path"); |
| fixNonInductionPHIs(State); |
| } |
| |
| // At this point every instruction in the original loop is widened to a |
| // vector form. Now we need to fix the recurrences in the loop. These PHI |
| // nodes are currently empty because we did not want to introduce cycles. |
| // This is the second stage of vectorizing recurrences. |
| fixCrossIterationPHIs(State); |
| |
| // Forget the original basic block. |
| PSE.getSE()->forgetLoop(OrigLoop); |
| |
| // 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. |
| if (!Cost->requiresScalarEpilogue(VF)) { |
| // Fix-up external users of the induction variables. |
| for (auto &Entry : Legal->getInductionVars()) |
| fixupIVUsers(Entry.first, Entry.second, |
| getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)), |
| IVEndValues[Entry.first], LoopMiddleBlock); |
| |
| fixLCSSAPHIs(State); |
| } |
| |
| for (Instruction *PI : PredicatedInstructions) |
| sinkScalarOperands(&*PI); |
| |
| // Remove redundant induction instructions. |
| cse(LoopVectorBody); |
| |
| // Set/update profile weights for the vector and remainder loops as original |
| // loop iterations are now distributed among them. Note that original loop |
| // represented by LoopScalarBody becomes remainder loop after vectorization. |
| // |
| // For cases like foldTailByMasking() and requiresScalarEpiloque() we may |
| // end up getting slightly roughened result but that should be OK since |
| // profile is not inherently precise anyway. Note also possible bypass of |
| // vector code caused by legality checks is ignored, assigning all the weight |
| // to the vector loop, optimistically. |
| // |
| // 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), LI->getLoopFor(LoopVectorBody), |
| LI->getLoopFor(LoopScalarBody), VF.getKnownMinValue() * UF); |
| } |
| |
| void InnerLoopVectorizer::fixCrossIterationPHIs(VPTransformState &State) { |
| // In order to support recurrences we need to be able to vectorize Phi nodes. |
| // Phi nodes have cycles, so we need to vectorize them in two stages. This is |
| // stage #2: We now need to fix the recurrences by adding incoming edges to |
| // the currently empty PHI nodes. At this point every instruction in the |
| // original loop is widened to a vector form so we can use them to construct |
| // the incoming edges. |
| VPBasicBlock *Header = State.Plan->getEntry()->getEntryBasicBlock(); |
| for (VPRecipeBase &R : Header->phis()) { |
| if (auto *ReductionPhi = dyn_cast<VPReductionPHIRecipe>(&R)) |
| fixReduction(ReductionPhi, State); |
| else if (auto *FOR = dyn_cast<VPFirstOrderRecurrencePHIRecipe>(&R)) |
| fixFirstOrderRecurrence(FOR, State); |
| } |
| } |
| |
| void InnerLoopVectorizer::fixFirstOrderRecurrence(VPWidenPHIRecipe *PhiR, |
| VPTransformState &State) { |
| // This is the second phase of vectorizing first-order recurrences. An |
| // overview of the transformation is described below. Suppose we have the |
| // following loop. |
| // |
| // for (int i = 0; i < n; ++i) |
| // b[i] = a[i] - a[i - 1]; |
| // |
| // There is a first-order recurrence on "a". For this loop, the shorthand |
| // scalar IR looks like: |
| // |
| // scalar.ph: |
| // s_init = a[-1] |
| // br scalar.body |
| // |
| // scalar.body: |
| // i = phi [0, scalar.ph], [i+1, scalar.body] |
| // s1 = phi [s_init, scalar.ph], [s2, scalar.body] |
| // s2 = a[i] |
| // b[i] = s2 - s1 |
| // br cond, scalar.body, ... |
| // |
| // In this example, s1 is a recurrence because it's value depends on the |
| // previous iteration. In the first phase of vectorization, we created a |
| // vector phi v1 for s1. We now complete the vectorization and produce the |
| // shorthand vector IR shown below (for VF = 4, UF = 1). |
| // |
| // vector.ph: |
| // v_init = vector(..., ..., ..., a[-1]) |
| // br vector.body |
| // |
| // vector.body |
| // i = phi [0, vector.ph], [i+4, vector.body] |
| // v1 = phi [v_init, vector.ph], [v2, vector.body] |
| // v2 = a[i, i+1, i+2, i+3]; |
| // v3 = vector(v1(3), v2(0, 1, 2)) |
| // b[i, i+1, i+2, i+3] = v2 - v3 |
| // br cond, vector.body, middle.block |
| // |
| // middle.block: |
| // x = v2(3) |
| // br scalar.ph |
| // |
| // scalar.ph: |
| // s_init = phi [x, middle.block], [a[-1], otherwise] |
| // br scalar.body |
| // |
| // After execution completes the vector loop, we extract the next value of |
| // the recurrence (x) to use as the initial value in the scalar loop. |
| |
| // Extract the last vector element in the middle block. This will be the |
| // initial value for the recurrence when jumping to the scalar loop. |
| VPValue *PreviousDef = PhiR->getBackedgeValue(); |
| Value *Incoming = State.get(PreviousDef, UF - 1); |
| auto *ExtractForScalar = Incoming; |
| auto *IdxTy = Builder.getInt32Ty(); |
| if (VF.isVector()) { |
| auto *One = ConstantInt::get(IdxTy, 1); |
| Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); |
| auto *RuntimeVF = getRuntimeVF(Builder, IdxTy, VF); |
| auto *LastIdx = Builder.CreateSub(RuntimeVF, One); |
| ExtractForScalar = Builder.CreateExtractElement(ExtractForScalar, LastIdx, |
| "vector.recur.extract"); |
| } |
| // Extract the second last element in the middle block if the |
| // Phi is used outside the loop. We need to extract the phi itself |
| // and not the last element (the phi update in the current iteration). This |
| // will be the value when jumping to the exit block from the LoopMiddleBlock, |
| // when the scalar loop is not run at all. |
| Value *ExtractForPhiUsedOutsideLoop = nullptr; |
| if (VF.isVector()) { |
| auto *RuntimeVF = getRuntimeVF(Builder, IdxTy, VF); |
| auto *Idx = Builder.CreateSub(RuntimeVF, ConstantInt::get(IdxTy, 2)); |
| ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement( |
| Incoming, Idx, "vector.recur.extract.for.phi"); |
| } else if (UF > 1) |
| // When loop is unrolled without vectorizing, initialize |
| // ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value |
| // of `Incoming`. This is analogous to the vectorized case above: extracting |
| // the second last element when VF > 1. |
| ExtractForPhiUsedOutsideLoop = State.get(PreviousDef, UF - 2); |
| |
| // Fix the initial value of the original recurrence in the scalar loop. |
| Builder.SetInsertPoint(&*LoopScalarPreHeader->begin()); |
| PHINode *Phi = cast<PHINode>(PhiR->getUnderlyingValue()); |
| auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init"); |
| auto *ScalarInit = PhiR->getStartValue()->getLiveInIRValue(); |
| for (auto *BB : predecessors(LoopScalarPreHeader)) { |
| auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit; |
| Start->addIncoming(Incoming, BB); |
| } |
| |
| Phi->setIncomingValueForBlock(LoopScalarPreHeader, Start); |
| Phi->setName("scalar.recur"); |
| |
| // Finally, fix users of the recurrence outside the loop. The users will need |
| // either the last value of the scalar recurrence or the last value of the |
| // vector recurrence we extracted in the middle block. Since the loop is in |
| // LCSSA form, we just need to find all the phi nodes for the original scalar |
| // recurrence in the exit block, and then add an edge for the middle block. |
| // Note that LCSSA does not imply single entry when the original scalar loop |
| // had multiple exiting edges (as we always run the last iteration in the |
| // scalar epilogue); in that case, there is no edge from middle to exit and |
| // and thus no phis which needed updated. |
| if (!Cost->requiresScalarEpilogue(VF)) |
| for (PHINode &LCSSAPhi : LoopExitBlock->phis()) |
| if (llvm::is_contained(LCSSAPhi.incoming_values(), Phi)) |
| LCSSAPhi.addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock); |
| } |
| |
| void InnerLoopVectorizer::fixReduction(VPReductionPHIRecipe *PhiR, |
| VPTransformState &State) { |
| PHINode *OrigPhi = cast<PHINode>(PhiR->getUnderlyingValue()); |
| // Get it's reduction variable descriptor. |
| assert(Legal->isReductionVariable(OrigPhi) && |
| "Unable to find the reduction variable"); |
| const RecurrenceDescriptor &RdxDesc = PhiR->getRecurrenceDescriptor(); |
| |
| RecurKind RK = RdxDesc.getRecurrenceKind(); |
| TrackingVH<Value> ReductionStartValue = RdxDesc.getRecurrenceStartValue(); |
| Instruction *LoopExitInst = RdxDesc.getLoopExitInstr(); |
| setDebugLocFromInst(ReductionStartValue); |
| |
| VPValue *LoopExitInstDef = PhiR->getBackedgeValue(); |
| // This is the vector-clone of the value that leaves the loop. |
| Type *VecTy = State.get(LoopExitInstDef, 0)->getType(); |
| |
| // Wrap flags are in general invalid after vectorization, clear them. |
| clearReductionWrapFlags(RdxDesc, State); |
| |
| // Before each round, move the insertion point right between |
| // the PHIs and the values we are going to write. |
| // This allows us to write both PHINodes and the extractelement |
| // instructions. |
| Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); |
| |
| setDebugLocFromInst(LoopExitInst); |
| |
| Type *PhiTy = OrigPhi->getType(); |
| // If tail is folded by masking, the vector value to leave the loop should be |
| // a Select choosing between the vectorized LoopExitInst and vectorized Phi, |
| // instead of the former. For an inloop reduction the reduction will already |
| // be predicated, and does not need to be handled here. |
| if (Cost->foldTailByMasking() && !PhiR->isInLoop()) { |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Value *VecLoopExitInst = State.get(LoopExitInstDef, Part); |
| Value *Sel = nullptr; |
| for (User *U : VecLoopExitInst->users()) { |
| if (isa<SelectInst>(U)) { |
| assert(!Sel && "Reduction exit feeding two selects"); |
| Sel = U; |
| } else |
| assert(isa<PHINode>(U) && "Reduction exit must feed Phi's or select"); |
| } |
| assert(Sel && "Reduction exit feeds no select"); |
| State.reset(LoopExitInstDef, Sel, Part); |
| |
| // If the target can create a predicated operator for the reduction at no |
| // extra cost in the loop (for example a predicated vadd), it can be |
| // cheaper for the select to remain in the loop than be sunk out of it, |
| // and so use the select value for the phi instead of the old |
| // LoopExitValue. |
| if (PreferPredicatedReductionSelect || |
| TTI->preferPredicatedReductionSelect( |
| RdxDesc.getOpcode(), PhiTy, |
| TargetTransformInfo::ReductionFlags())) { |
| auto *VecRdxPhi = |
| cast<PHINode>(State.get(PhiR, Part)); |
| VecRdxPhi->setIncomingValueForBlock( |
| LI->getLoopFor(LoopVectorBody)->getLoopLatch(), Sel); |
| } |
| } |
| } |
| |
| // If the vector reduction can be performed in a smaller type, we truncate |
| // then extend the loop exit value to enable InstCombine to evaluate the |
| // entire expression in the smaller type. |
| if (VF.isVector() && PhiTy != RdxDesc.getRecurrenceType()) { |
| assert(!PhiR->isInLoop() && "Unexpected truncated inloop reduction!"); |
| Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF); |
| Builder.SetInsertPoint( |
| LI->getLoopFor(LoopVectorBody)->getLoopLatch()->getTerminator()); |
| VectorParts RdxParts(UF); |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| RdxParts[Part] = State.get(LoopExitInstDef, Part); |
| Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); |
| Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy) |
| : Builder.CreateZExt(Trunc, VecTy); |
| for (User *U : llvm::make_early_inc_range(RdxParts[Part]->users())) |
| if (U != Trunc) { |
| U->replaceUsesOfWith(RdxParts[Part], Extnd); |
| RdxParts[Part] = Extnd; |
| } |
| } |
| Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); |
| State.reset(LoopExitInstDef, RdxParts[Part], Part); |
| } |
| } |
| |
| // Reduce all of the unrolled parts into a single vector. |
| Value *ReducedPartRdx = State.get(LoopExitInstDef, 0); |
| unsigned Op = RecurrenceDescriptor::getOpcode(RK); |
| |
| // The middle block terminator has already been assigned a DebugLoc here (the |
| // OrigLoop's single latch terminator). We want the whole middle block to |
| // appear to execute on this line because: (a) it is all compiler generated, |
| // (b) these instructions are always executed after evaluating the latch |
| // conditional branch, and (c) other passes may add new predecessors which |
| // terminate on this line. This is the easiest way to ensure we don't |
| // accidentally cause an extra step back into the loop while debugging. |
| setDebugLocFromInst(LoopMiddleBlock->getTerminator()); |
| if (PhiR->isOrdered()) |
| ReducedPartRdx = State.get(LoopExitInstDef, UF - 1); |
| else { |
| // Floating-point operations should have some FMF to enable the reduction. |
| IRBuilderBase::FastMathFlagGuard FMFG(Builder); |
| Builder.setFastMathFlags(RdxDesc.getFastMathFlags()); |
| for (unsigned Part = 1; Part < UF; ++Part) { |
| Value *RdxPart = State.get(LoopExitInstDef, Part); |
| if (Op != Instruction::ICmp && Op != Instruction::FCmp) { |
| ReducedPartRdx = Builder.CreateBinOp( |
| (Instruction::BinaryOps)Op, RdxPart, ReducedPartRdx, "bin.rdx"); |
| } else if (RecurrenceDescriptor::isSelectCmpRecurrenceKind(RK)) |
| ReducedPartRdx = createSelectCmpOp(Builder, ReductionStartValue, RK, |
| ReducedPartRdx, RdxPart); |
| else |
| ReducedPartRdx = createMinMaxOp(Builder, RK, ReducedPartRdx, RdxPart); |
| } |
| } |
| |
| // Create the reduction after the loop. Note that inloop reductions create the |
| // target reduction in the loop using a Reduction recipe. |
| if (VF.isVector() && !PhiR->isInLoop()) { |
| ReducedPartRdx = |
| createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx, OrigPhi); |
| // If the reduction can be performed in a smaller type, we need to extend |
| // the reduction to the wider type before we branch to the original loop. |
| if (PhiTy != RdxDesc.getRecurrenceType()) |
| ReducedPartRdx = RdxDesc.isSigned() |
| ? Builder.CreateSExt(ReducedPartRdx, PhiTy) |
| : Builder.CreateZExt(ReducedPartRdx, PhiTy); |
| } |
| |
| // Create a phi node that merges control-flow from the backedge-taken check |
| // block and the middle block. |
| PHINode *BCBlockPhi = PHINode::Create(PhiTy, 2, "bc.merge.rdx", |
| LoopScalarPreHeader->getTerminator()); |
| for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) |
| BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]); |
| BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock); |
| |
| // Now, we need to fix the users of the reduction variable |
| // inside and outside of the scalar remainder loop. |
| |
| // We know that the loop is in LCSSA form. We need to update the PHI nodes |
| // in the exit blocks. See comment on analogous loop in |
| // fixFirstOrderRecurrence for a more complete explaination of the logic. |
| if (!Cost->requiresScalarEpilogue(VF)) |
| for (PHINode &LCSSAPhi : LoopExitBlock->phis()) |
| if (llvm::is_contained(LCSSAPhi.incoming_values(), LoopExitInst)) |
| LCSSAPhi.addIncoming(ReducedPartRdx, LoopMiddleBlock); |
| |
| // 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); |
| OrigPhi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst); |
| } |
| |
| void InnerLoopVectorizer::clearReductionWrapFlags(const RecurrenceDescriptor &RdxDesc, |
| VPTransformState &State) { |
| RecurKind RK = RdxDesc.getRecurrenceKind(); |
| if (RK != RecurKind::Add && RK != RecurKind::Mul) |
| return; |
| |
| Instruction *LoopExitInstr = RdxDesc.getLoopExitInstr(); |
| assert(LoopExitInstr && "null loop exit instruction"); |
| SmallVector<Instruction *, 8> Worklist; |
| SmallPtrSet<Instruction *, 8> Visited; |
| Worklist.push_back(LoopExitInstr); |
| Visited.insert(LoopExitInstr); |
| |
| while (!Worklist.empty()) { |
| Instruction *Cur = Worklist.pop_back_val(); |
| if (isa<OverflowingBinaryOperator>(Cur)) |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| // FIXME: Should not rely on getVPValue at this point. |
| Value *V = State.get(State.Plan->getVPValue(Cur, true), Part); |
| cast<Instruction>(V)->dropPoisonGeneratingFlags(); |
| } |
| |
| for (User *U : Cur->users()) { |
| Instruction *UI = cast<Instruction>(U); |
| if ((Cur != LoopExitInstr || OrigLoop->contains(UI->getParent())) && |
| Visited.insert(UI).second) |
| Worklist.push_back(UI); |
| } |
| } |
| } |
| |
| void InnerLoopVectorizer::fixLCSSAPHIs(VPTransformState &State) { |
| for (PHINode &LCSSAPhi : LoopExitBlock->phis()) { |
| if (LCSSAPhi.getBasicBlockIndex(LoopMiddleBlock) != -1) |
| // Some phis were already hand updated by the reduction and recurrence |
| // code above, leave them alone. |
| continue; |
| |
| auto *IncomingValue = LCSSAPhi.getIncomingValue(0); |
| // Non-instruction incoming values will have only one value. |
| |
| VPLane Lane = VPLane::getFirstLane(); |
| if (isa<Instruction>(IncomingValue) && |
| !Cost->isUniformAfterVectorization(cast<Instruction>(IncomingValue), |
| VF)) |
| Lane = VPLane::getLastLaneForVF(VF); |
| |
| // Can be a loop invariant incoming value or the last scalar value to be |
| // extracted from the vectorized loop. |
| // FIXME: Should not rely on getVPValue at this point. |
| Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); |
| Value *lastIncomingValue = |
| OrigLoop->isLoopInvariant(IncomingValue) |
| ? IncomingValue |
| : State.get(State.Plan->getVPValue(IncomingValue, true), |
| VPIteration(UF - 1, Lane)); |
| LCSSAPhi.addIncoming(lastIncomingValue, LoopMiddleBlock); |
| } |
| } |
| |
| void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) { |
| // The basic block and loop containing the predicated instruction. |
| auto *PredBB = PredInst->getParent(); |
| auto *VectorLoop = LI->getLoopFor(PredBB); |
| |
| // Initialize a worklist with the operands of the predicated instruction. |
| SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end()); |
| |
| // Holds instructions that we need to analyze again. An instruction may be |
| // reanalyzed if we don't yet know if we can sink it or not. |
| SmallVector<Instruction *, 8> InstsToReanalyze; |
| |
| // Returns true if a given use occurs in the predicated block. Phi nodes use |
| // their operands in their corresponding predecessor blocks. |
| auto isBlockOfUsePredicated = [&](Use &U) -> bool { |
| auto *I = cast<Instruction>(U.getUser()); |
| BasicBlock *BB = I->getParent(); |
| if (auto *Phi = dyn_cast<PHINode>(I)) |
| BB = Phi->getIncomingBlock( |
| PHINode::getIncomingValueNumForOperand(U.getOperandNo())); |
| return BB == PredBB; |
| }; |
| |
| // Iteratively sink the scalarized operands of the predicated instruction |
| // into the block we created for it. When an instruction is sunk, it's |
| // operands are then added to the worklist. The algorithm ends after one pass |
| // through the worklist doesn't sink a single instruction. |
| bool Changed; |
| do { |
| // Add the instructions that need to be reanalyzed to the worklist, and |
| // reset the changed indicator. |
| Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end()); |
| InstsToReanalyze.clear(); |
| Changed = false; |
| |
| while (!Worklist.empty()) { |
| auto *I = dyn_cast<Instruction>(Worklist.pop_back_val()); |
| |
| // We can't sink an instruction if it is a phi node, is not in the loop, |
| // or may have side effects. |
| if (!I || isa<PHINode>(I) || !VectorLoop->contains(I) || |
| I->mayHaveSideEffects()) |
| 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(VPTransformState &State) { |
| for (PHINode *OrigPhi : OrigPHIsToFix) { |
| VPWidenPHIRecipe *VPPhi = |
| cast<VPWidenPHIRecipe>(State.Plan->getVPValue(OrigPhi)); |
| 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]); |
| } |
| } |
| } |
| |
| bool InnerLoopVectorizer::useOrderedReductions(RecurrenceDescriptor &RdxDesc) { |
| return Cost->useOrderedReductions(RdxDesc); |
| } |
| |
| void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, |
| VPWidenPHIRecipe *PhiR, |
| VPTransformState &State) { |
| PHINode *P = cast<PHINode>(PN); |
| if (EnableVPlanNativePath) { |
| // Currently we enter here in the VPlan-native path for non-induction |
| // PHIs where all control flow is uniform. We simply widen these PHIs. |
| // Create a vector phi with no operands - the vector phi operands will be |
| // set at the end of vector code generation. |
| Type *VecTy = (State.VF.isScalar()) |
| ? PN->getType() |
| : VectorType::get(PN->getType(), State.VF); |
| Value *VecPhi = Builder.CreatePHI(VecTy, PN->getNumOperands(), "vec.phi"); |
| State.set(PhiR, VecPhi, 0); |
| OrigPHIsToFix.push_back(P); |
| |
| return; |
| } |
| |
| assert(PN->getParent() == OrigLoop->getHeader() && |
| "Non-header phis should have been handled elsewhere"); |
| |
| // In order to support recurrences we need to be able to vectorize Phi nodes. |
| // Phi nodes have cycles, so we need to vectorize them in two stages. This is |
| // stage #1: We create a new vector PHI node with no incoming edges. We'll use |
| // this value when we vectorize all of the instructions that use the PHI. |
| |
| assert(!Legal->isReductionVariable(P) && |
| "reductions should be handled elsewhere"); |
| |
| setDebugLocFromInst(P); |
| |
| // This PHINode must be an induction variable. |
| // Make sure that we know about it. |
| assert(Legal->getInductionVars().count(P) && "Not an induction variable"); |
| |
| InductionDescriptor II = Legal->getInductionVars().lookup(P); |
| const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); |
| |
| // FIXME: The newly created binary instructions should contain nsw/nuw flags, |
| // which can be found from the original scalar operations. |
| switch (II.getKind()) { |
| case InductionDescriptor::IK_NoInduction: |
| llvm_unreachable("Unknown induction"); |
| case InductionDescriptor::IK_IntInduction: |
| case InductionDescriptor::IK_FpInduction: |
| llvm_unreachable("Integer/fp induction is handled elsewhere."); |
| case InductionDescriptor::IK_PtrInduction: { |
| // Handle the pointer induction variable case. |
| assert(P->getType()->isPointerTy() && "Unexpected type."); |
| |
| if (Cost->isScalarAfterVectorization(P, State.VF)) { |
| // This is the normalized GEP that starts counting at zero. |
| Value *PtrInd = |
| Builder.CreateSExtOrTrunc(Induction, II.getStep()->getType()); |
| // Determine the number of scalars we need to generate for each unroll |
| // iteration. If the instruction is uniform, we only need to generate the |
| // first lane. Otherwise, we generate all VF values. |
| bool IsUniform = Cost->isUniformAfterVectorization(P, State.VF); |
| assert((IsUniform || !State.VF.isScalable()) && |
| "Cannot scalarize a scalable VF"); |
| unsigned Lanes = IsUniform ? 1 : State.VF.getFixedValue(); |
| |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| Value *PartStart = |
| createStepForVF(Builder, PtrInd->getType(), VF, Part); |
| |
| for (unsigned Lane = 0; Lane < Lanes; ++Lane) { |
| Value *Idx = Builder.CreateAdd( |
| PartStart, ConstantInt::get(PtrInd->getType(), Lane)); |
| Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx); |
| Value *SclrGep = |
| emitTransformedIndex(Builder, GlobalIdx, PSE.getSE(), DL, II); |
| SclrGep->setName("next.gep"); |
| State.set(PhiR, SclrGep, VPIteration(Part, Lane)); |
| } |
| } |
| return; |
| } |
| assert(isa<SCEVConstant>(II.getStep()) && |
| "Induction step not a SCEV constant!"); |
| Type *PhiType = II.getStep()->getType(); |
| |
| // Build a pointer phi |
| Value *ScalarStartValue = II.getStartValue(); |
| Type *ScStValueType = ScalarStartValue->getType(); |
| PHINode *NewPointerPhi = |
| PHINode::Create(ScStValueType, 2, "pointer.phi", Induction); |
| NewPointerPhi->addIncoming(ScalarStartValue, LoopVectorPreHeader); |
| |
| // A pointer induction, performed by using a gep |
| BasicBlock *LoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| Instruction *InductionLoc = LoopLatch->getTerminator(); |
| const SCEV *ScalarStep = II.getStep(); |
| SCEVExpander Exp(*PSE.getSE(), DL, "induction"); |
| Value *ScalarStepValue = |
| Exp.expandCodeFor(ScalarStep, PhiType, InductionLoc); |
| Value *RuntimeVF = getRuntimeVF(Builder, PhiType, VF); |
| Value *NumUnrolledElems = |
| Builder.CreateMul(RuntimeVF, ConstantInt::get(PhiType, State.UF)); |
| Value *InductionGEP = GetElementPtrInst::Create( |
| II.getElementType(), NewPointerPhi, |
| Builder.CreateMul(ScalarStepValue, NumUnrolledElems), "ptr.ind", |
| InductionLoc); |
| NewPointerPhi->addIncoming(InductionGEP, LoopLatch); |
| |
| // Create UF many actual address geps that use the pointer |
| // phi as base and a vectorized version of the step value |
| // (<step*0, ..., step*N>) as offset. |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| Type *VecPhiType = VectorType::get(PhiType, State.VF); |
| Value *StartOffsetScalar = |
| Builder.CreateMul(RuntimeVF, ConstantInt::get(PhiType, Part)); |
| Value *StartOffset = |
| Builder.CreateVectorSplat(State.VF, StartOffsetScalar); |
| // Create a vector of consecutive numbers from zero to VF. |
| StartOffset = |
| Builder.CreateAdd(StartOffset, Builder.CreateStepVector(VecPhiType)); |
| |
| Value *GEP = Builder.CreateGEP( |
| II.getElementType(), NewPointerPhi, |
| Builder.CreateMul( |
| StartOffset, Builder.CreateVectorSplat(State.VF, ScalarStepValue), |
| "vector.gep")); |
| State.set(PhiR, GEP, Part); |
| } |
| } |
| } |
| } |
| |
| /// A helper function for checking whether an integer division-related |
| /// instruction may divide by zero (in which case it must be predicated if |
| /// executed conditionally in the scalar code). |
| /// TODO: It may be worthwhile to generalize and check isKnownNonZero(). |
| /// Non-zero divisors that are non compile-time constants will not be |
| /// converted into multiplication, so we will still end up scalarizing |
| /// the division, but can do so w/o predication. |
| static bool mayDivideByZero(Instruction &I) { |
| assert((I.getOpcode() == Instruction::UDiv || |
| I.getOpcode() == Instruction::SDiv || |
| I.getOpcode() == Instruction::URem || |
| I.getOpcode() == Instruction::SRem) && |
| "Unexpected instruction"); |
| Value *Divisor = I.getOperand(1); |
| auto *CInt = dyn_cast<ConstantInt>(Divisor); |
| return !CInt || CInt->isZero(); |
| } |
| |
| void InnerLoopVectorizer::widenCallInstruction(CallInst &I, VPValue *Def, |
| VPUser &ArgOperands, |
| VPTransformState &State) { |
| assert(!isa<DbgInfoIntrinsic>(I) && |
| "DbgInfoIntrinsic should have been dropped during VPlan construction"); |
| setDebugLocFromInst(&I); |
| |
| Module *M = I.getParent()->getParent()->getParent(); |
| auto *CI = cast<CallInst>(&I); |
| |
| SmallVector<Type *, 4> Tys; |
| for (Value *ArgOperand : CI->args()) |
| Tys.push_back(ToVectorTy(ArgOperand->getType(), VF.getKnownMinValue())); |
| |
| Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| |
| // The flag shows whether we use Intrinsic or a usual Call for vectorized |
| // version of the instruction. |
| // Is it beneficial to perform intrinsic call compared to lib call? |
| bool NeedToScalarize = false; |
| InstructionCost CallCost = Cost->getVectorCallCost(CI, VF, NeedToScalarize); |
| InstructionCost IntrinsicCost = ID ? Cost->getVectorIntrinsicCost(CI, VF) : 0; |
| bool UseVectorIntrinsic = ID && IntrinsicCost <= CallCost; |
| assert((UseVectorIntrinsic || !NeedToScalarize) && |
| "Instruction should be scalarized elsewhere."); |
| assert((IntrinsicCost.isValid() || CallCost.isValid()) && |
| "Either the intrinsic cost or vector call cost must be valid"); |
| |
| for (unsigned Part = 0; Part < UF; ++Part) { |
| SmallVector<Type *, 2> TysForDecl = {CI->getType()}; |
| SmallVector<Value *, 4> Args; |
| for (auto &I : enumerate(ArgOperands.operands())) { |
| // Some intrinsics have a scalar argument - don't replace it with a |
| // vector. |
| Value *Arg; |
| if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, I.index())) |
| Arg = State.get(I.value(), Part); |
| else { |
| Arg = State.get(I.value(), VPIteration(0, 0)); |
| if (hasVectorInstrinsicOverloadedScalarOpd(ID, I.index())) |
| TysForDecl.push_back(Arg->getType()); |
| } |
| Args.push_back(Arg); |
| } |
| |
| Function *VectorF; |
| if (UseVectorIntrinsic) { |
| // Use vector version of the intrinsic. |
| if (VF.isVector()) |
| TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF); |
| VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl); |
| assert(VectorF && "Can't retrieve vector intrinsic."); |
| } else { |
| // Use vector version of the function call. |
| const VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); |
| #ifndef NDEBUG |
| assert(VFDatabase(*CI).getVectorizedFunction(Shape) != nullptr && |
| "Can't create vector function."); |
| #endif |
| VectorF = VFDatabase(*CI).getVectorizedFunction(Shape); |
| } |
| SmallVector<OperandBundleDef, 1> OpBundles; |
| CI->getOperandBundlesAsDefs(OpBundles); |
| CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles); |
| |
| if (isa<FPMathOperator>(V)) |
| V->copyFastMathFlags(CI); |
| |
| State.set(Def, V, Part); |
| addMetadata(V, &I); |
| } |
| } |
| |
| void LoopVectorizationCostModel::collectLoopScalars(ElementCount VF) { |
| // We should not collect Scalars more than once per VF. Right now, this |
| // function is called from collectUniformsAndScalars(), which already does |
| // this check. Collecting Scalars for VF=1 does not make any sense. |
| assert(VF.isVector() && Scalars.find(VF) == Scalars.end() && |
| "This function should not be visited twice for the same VF"); |
| |
| SmallSetVector<Instruction *, 8> Worklist; |
| |
| // These sets are used to seed the analysis with pointers used by memory |
| // accesses that will remain scalar. |
| SmallSetVector<Instruction *, 8> ScalarPtrs; |
| SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs; |
| auto *Latch = TheLoop->getLoopLatch(); |
| |
| // A helper that returns true if the use of Ptr by MemAccess will be scalar. |
| // The pointer operands of loads and stores will be scalar as long as the |
| // memory access is not a gather or scatter operation. The value operand of a |
| // store will remain scalar if the store is scalarized. |
| auto isScalarUse = [&](Instruction *MemAccess, Value *Ptr) { |
| InstWidening WideningDecision = getWideningDecision(MemAccess, VF); |
| assert(WideningDecision != CM_Unknown && |
| "Widening decision should be ready at this moment"); |
| if (auto *Store = dyn_cast<StoreInst>(MemAccess)) |
| if (Ptr == Store->getValueOperand()) |
| return WideningDecision == CM_Scalarize; |
| assert(Ptr == getLoadStorePointerOperand(MemAccess) && |
| "Ptr is neither a value or pointer operand"); |
| return WideningDecision != CM_GatherScatter; |
| }; |
| |
| // A helper that returns true if the given value is a bitcast or |
| // getelementptr instruction contained in the loop. |
| auto isLoopVaryingBitCastOrGEP = [&](Value *V) { |
| return ((isa<BitCastInst>(V) && V->getType()->isPointerTy()) || |
| isa<GetElementPtrInst>(V)) && |
| !TheLoop->isLoopInvariant(V); |
| }; |
| |
| // 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 (!isLoopVaryingBitCastOrGEP(Ptr)) |
| return; |
| |
| // If the pointer has already been identified as scalar (e.g., if it was |
| // also identified as uniform), there's nothing to do. |
| auto *I = cast<Instruction>(Ptr); |
| if (Worklist.count(I)) |
| return; |
| |
| // If the use of the pointer will be a scalar use, and all users of the |
| // pointer are memory accesses, place the pointer in ScalarPtrs. Otherwise, |
| // place the pointer in PossibleNonScalarPtrs. |
| if (isScalarUse(MemAccess, Ptr) && llvm::all_of(I->users(), [&](User *U) { |
| return isa<LoadInst>(U) || isa<StoreInst>(U); |
| })) |
| ScalarPtrs.insert(I); |
| else |
| PossibleNonScalarPtrs.insert(I); |
| }; |
| |
| // We seed the scalars analysis with three classes of instructions: (1) |
| // instructions marked uniform-after-vectorization and (2) bitcast, |
| // getelementptr and (pointer) phi instructions used by memory accesses |
| // requiring a scalar use. |
| // |
| // (1) Add to the worklist all instructions that have been identified as |
| // uniform-after-vectorization. |
| Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end()); |
| |
| // (2) Add to the worklist all bitcast and getelementptr instructions used by |
| // memory accesses requiring a scalar use. The pointer operands of loads and |
| // stores will be scalar as long as the memory accesses is not a gather or |
| // scatter operation. The value operand of a store will remain scalar if the |
| // store is scalarized. |
| for (auto *BB : TheLoop->blocks()) |
| for (auto &I : *BB) { |
| if (auto *Load = dyn_cast<LoadInst>(&I)) { |
| evaluatePtrUse(Load, Load->getPointerOperand()); |
| } else if (auto *Store = dyn_cast<StoreInst>(&I)) { |
| evaluatePtrUse(Store, Store->getPointerOperand()); |
| evaluatePtrUse(Store, Store->getValueOperand()); |
| } |
| } |
| for (auto *I : ScalarPtrs) |
| if (!PossibleNonScalarPtrs.count(I)) { |
| LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n"); |
| Worklist.insert(I); |
| } |
| |
| // Insert the forced scalars. |
| // FIXME: Currently widenPHIInstruction() often creates a dead vector |
| // induction variable when the PHI user is scalarized. |
| auto ForcedScalar = ForcedScalars.find(VF); |
| if (ForcedScalar != ForcedScalars.end()) |
| for (auto *I : ForcedScalar->second) |
| Worklist.insert(I); |
| |
| // Expand the worklist by looking through any bitcasts and getelementptr |
| // instructions we've already identified as scalar. This is similar to the |
| // expansion step in collectLoopUniforms(); however, here we're only |
| // expanding to include additional bitcasts and getelementptr instructions. |
| unsigned Idx = 0; |
| while (Idx != Worklist.size()) { |
| Instruction *Dst = Worklist[Idx++]; |
| if (!isLoopVaryingBitCastOrGEP(Dst->getOperand(0))) |
| continue; |
| auto *Src = cast<Instruction>(Dst->getOperand(0)); |
| if (llvm::all_of(Src->users(), [&](User *U) -> bool { |
| auto *J = cast<Instruction>(U); |
| return !TheLoop->contains(J) || Worklist.count(J) || |
| ((isa<LoadInst>(J) || isa<StoreInst>(J)) && |
| isScalarUse(J, Src)); |
| })) { |
| Worklist.insert(Src); |
| LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Src << "\n"); |
| } |
| } |
| |
| // An induction variable will remain scalar if all users of the induction |
| // variable and induction variable update remain scalar. |
| for (auto &Induction : Legal->getInductionVars()) { |
| auto *Ind = Induction.first; |
| auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| |
| // If tail-folding is applied, the primary induction variable will be used |
| // to feed a vector compare. |
| if (Ind == Legal->getPrimaryInduction() && foldTailByMasking()) |
| continue; |
| |
| // 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; |
| |
| // 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) const { |
| if (!blockNeedsPredicationForAnyReason(I->getParent())) |
| return false; |
| switch(I->getOpcode()) { |
| default: |
| break; |
| case Instruction::Load: |
| case Instruction::Store: { |
| if (!Legal->isMaskRequired(I)) |
| return false; |
| auto *Ptr = getLoadStorePointerOperand(I); |
| auto *Ty = getLoadStoreType(I); |
| const Align Alignment = getLoadStoreAlignment(I); |
| return isa<LoadInst>(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) || |
| TTI.isLegalMaskedGather(Ty, Alignment)) |
| : !(isLegalMaskedStore(Ty, Ptr, Alignment) || |
| TTI.isLegalMaskedScatter(Ty, Alignment)); |
| } |
| case Instruction::UDiv: |
| case Instruction::SDiv: |
| case Instruction::SRem: |
| case Instruction::URem: |
| return mayDivideByZero(*I); |
| } |
| return false; |
| } |
| |
| bool LoopVectorizationCostModel::interleavedAccessCanBeWidened( |
| Instruction *I, ElementCount VF) { |
| assert(isAccessInterleaved(I) && "Expecting interleaved access."); |
| assert(getWideningDecision(I, VF) == CM_Unknown && |
| "Decision should not be set yet."); |
| auto *Group = getInterleavedAccessGroup(I); |
| assert(Group && "Must have a group."); |
| |
| // If the instruction's allocated size doesn't equal it's type size, it |
| // requires padding and will be scalarized. |
| auto &DL = I->getModule()->getDataLayout(); |
| auto *ScalarTy = getLoadStoreType(I); |
| if (hasIrregularType(ScalarTy, DL)) |
| 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)) |
| return false; |
| |
| // If the instruction's allocated size doesn't equal it's type size, it |
| // requires padding and will be scalarized. |
| auto &DL = I->getModule()->getDataLayout(); |
| 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.find(VF) == Uniforms.end() && |
| "This function should not be visited twice for the same VF"); |
| |
| // Visit the list of Uniforms. If we'll not find any uniform value, we'll |
| // not analyze again. Uniforms.count(VF) will return 1. |
| Uniforms[VF].clear(); |
| |
| // We now know that the loop is vectorizable! |
| // Collect instructions inside the loop that will remain uniform after |
| // vectorization. |
| |
| // Global values, params and instructions outside of current loop are out of |
| // scope. |
| auto isOutOfScope = [&](Value *V) -> bool { |
| Instruction *I = dyn_cast<Instruction>(V); |
| return (!I || !TheLoop->contains(I)); |
| }; |
| |
| // Worklist containing uniform instructions demanding lane 0. |
| SetVector<Instruction *> Worklist; |
| BasicBlock *Latch = TheLoop->getLoopLatch(); |
| |
| // Add uniform instructions demanding lane 0 to the worklist. Instructions |
| // that are scalar with predication must not be considered uniform after |
| // vectorization, because that would create an erroneous replicating region |
| // where only a single instance out of VF should be formed. |
| // TODO: optimize such seldom cases if found important, see PR40816. |
| auto addToWorklistIfAllowed = [&](Instruction *I) -> void { |
| if (isOutOfScope(I)) { |
| LLVM_DEBUG(dbgs() << "LV: Found not uniform due to scope: " |
| << *I << "\n"); |
| return; |
| } |
| if (isScalarWithPredication(I)) { |
| LLVM_DEBUG(dbgs() << "LV: Found not uniform being ScalarWithPredication: " |
| << *I << "\n"); |
| return; |
| } |
| LLVM_DEBUG(dbgs() << "LV: Found uniform instruction: " << *I << "\n"); |
| Worklist.insert(I); |
| }; |
| |
| // Start with the conditional branch. If the branch condition is an |
| // instruction contained in the loop that is only used by the branch, it is |
| // uniform. |
| auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0)); |
| if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse()) |
| addToWorklistIfAllowed(Cmp); |
| |
| auto isUniformDecision = [&](Instruction *I, ElementCount VF) { |
| InstWidening WideningDecision = getWideningDecision(I, VF); |
| assert(WideningDecision != CM_Unknown && |
| "Widening decision should be ready at this moment"); |
| |
| // A uniform memory op is itself uniform. We exclude uniform stores |
| // here as they demand the last lane, not the first one. |
| if (isa<LoadInst>(I) && Legal->isUniformMemOp(*I)) { |
| assert(WideningDecision == CM_Scalarize); |
| 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, and I is known to not require scalarization. |
| auto isVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool { |
| return getLoadStorePointerOperand(I) == Ptr && isUniformDecision(I, VF); |
| }; |
| |
| // 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; |
| |
| // A uniform memory op is itself uniform. We exclude uniform stores |
| // here as they demand the last lane, not the first one. |
| if (isa<LoadInst>(I) && Legal->isUniformMemOp(I)) |
| addToWorklistIfAllowed(&I); |
| |
| if (isUniformDecision(&I, VF)) { |
| assert(isVectorizedMemAccessUse(&I, Ptr) && "consistency check"); |
| 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 { |
| return isVectorizedMemAccessUse(cast<Instruction>(U), 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->isFirstOrderRecurrence(OP)) |
| continue; |
| // If all the users of the operand are uniform, then add the |
| // operand into the uniform worklist. |
| auto *OI = cast<Instruction>(OV); |
| if (llvm::all_of(OI->users(), [&](User *U) -> bool { |
| auto *J = cast<Instruction>(U); |
| return Worklist.count(J) || 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. |
| for (auto &Induction : Legal->getInductionVars()) { |
| auto *Ind = Induction.first; |
| auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| |
| // Determine if all users of the induction variable are uniform after |
| // vectorization. |
| auto UniformInd = llvm::all_of(Ind->users(), [&](User *U) -> bool { |
| auto *I = cast<Instruction>(U); |
| return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) || |
| isVectorizedMemAccessUse(I, Ind); |
| }); |
| if (!UniformInd) |
| continue; |
| |
| // Determine if all users of the induction variable update instruction are |
| // uniform after vectorization. |
| auto UniformIndUpdate = |
| llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| auto *I = cast<Instruction>(U); |
| return I == Ind || !TheLoop->contains(I) || Worklist.count(I) || |
| isVectorizedMemAccessUse(I, IndUpdate); |
| }); |
| if (!UniformIndUpdate) |
| continue; |
| |
| // The induction variable and its update instruction will remain uniform. |
| addToWorklistIfAllowed(Ind); |
| addToWorklistIfAllowed(IndUpdate); |
| } |
| |
| Uniforms[VF].insert(Worklist.begin(), Worklist.end()); |
| } |
| |
| bool LoopVectorizationCostModel::runtimeChecksRequired() { |
| LLVM_DEBUG(dbgs() << "LV: Performing code size checks.\n"); |
| |
| if (Legal->getRuntimePointerChecking()->Need) { |
| reportVectorizationFailure("Runtime ptr check is required with -Os/-Oz", |
| "runtime pointer checks needed. Enable vectorization of this " |
| "loop with '#pragma clang loop vectorize(enable)' when " |
| "compiling with -Os/-Oz", |
| "CantVersionLoopWithOptForSize", ORE, TheLoop); |
| return true; |
| } |
| |
| if (!PSE.getUnionPredicate().getPredicates().empty()) { |
| reportVectorizationFailure("Runtime SCEV check is required with -Os/-Oz", |
| "runtime SCEV checks needed. Enable vectorization of this " |
| "loop with '#pragma clang loop vectorize(enable)' when " |
| "compiling with -Os/-Oz", |
| "CantVersionLoopWithOptForSize", ORE, TheLoop); |
| return true; |
| } |
| |
| // FIXME: Avoid specializing for stride==1 instead of bailing out. |
| if (!Legal->getLAI()->getSymbolicStrides().empty()) { |
| reportVectorizationFailure("Runtime stride check for small trip count", |
| "runtime stride == 1 checks needed. Enable vectorization of " |
| "this loop without such check by compiling with -Os/-Oz", |
| "CantVersionLoopWithOptForSize", ORE, TheLoop); |
| return true; |
| } |
| |
| return false; |
| } |
| |
| ElementCount |
| LoopVectorizationCostModel::getMaxLegalScalableVF(unsigned MaxSafeElements) { |
| if (!TTI.supportsScalableVectors() && !ForceTargetSupportsScalableVectors) |
| return ElementCount::getScalable(0); |
| |
| if (Hints->isScalableVectorizationDisabled()) { |
| reportVectorizationInfo("Scalable vectorization is explicitly disabled", |
| "ScalableVectorizationDisabled", ORE, TheLoop); |
| return ElementCount::getScalable(0); |
| } |
| |
| 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 ElementCount::getScalable(0); |
| } |
| |
| // 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 ElementCount::getScalable(0); |
| } |
| |
| if (Legal->isSafeForAnyVectorWidth()) |
| return MaxScalableVF; |
| |
| // Limit MaxScalableVF by the maximum safe dependence distance. |
| Optional<unsigned> MaxVScale = TTI.getMaxVScale(); |
| if (!MaxVScale && TheFunction->hasFnAttribute(Attribute::VScaleRange)) { |
| unsigned VScaleMax = TheFunction->getFnAttribute(Attribute::VScaleRange) |
| .getVScaleRangeArgs() |
| .second; |
| if (VScaleMax > 0) |
| MaxVScale = VScaleMax; |
| } |
| MaxScalableVF = ElementCount::getScalable( |
| MaxVScale ? (MaxSafeElements / MaxVScale.getValue()) : 0); |
| if (!MaxScalableVF) |
| reportVectorizationInfo( |
| "Max legal vector width too small, scalable vectorization " |
| "unfeasible.", |
| "ScalableVFUnfeasible", ORE, TheLoop); |
| |
| return MaxScalableVF; |
| } |
| |
| FixedScalableVFPair |
| LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount, |
| ElementCount UserVF) { |
| 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 = |
| PowerOf2Floor(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(ConstTripCount, SmallestType, |
| WidestType, MaxSafeFixedVF)) |
| Result.FixedVF = MaxVF; |
| |
| if (auto MaxVF = getMaximizedVFForTarget(ConstTripCount, SmallestType, |
| WidestType, MaxSafeScalableVF)) |
| 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); |
| 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(TC, UserVF); |
| case CM_ScalarEpilogueNotAllowedUsePredicate: |
| LLVM_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(TC, UserVF); |
| } |
| 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(TC, UserVF); |
| // Avoid tail folding if the trip count is known to be a multiple of any VF |
| // we chose. |
| // FIXME: The condition below pessimises the case for fixed-width vectors, |
| // when scalable VFs are also candidates for vectorization. |
| if (MaxFactors.FixedVF.isVector() && !MaxFactors.ScalableVF) { |
| ElementCount MaxFixedVF = MaxFactors.FixedVF; |
| assert((UserVF.isNonZero() || isPowerOf2_32(MaxFixedVF.getFixedValue())) && |
| "MaxFixedVF must be a power of 2"); |
| unsigned MaxVFtimesIC = UserIC ? MaxFixedVF.getFixedValue() * UserIC |
| : MaxFixedVF.getFixedValue(); |
| 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; |
| } |
| } |
| |
| // For scalable vectors, don't use tail folding as this is currently not yet |
| // supported. The code is likely to have ended up here if the tripcount is |
| // low, in which case it makes sense not to use scalable vectors. |
| if (MaxFactors.ScalableVF.isVector()) |
| MaxFactors.ScalableVF = ElementCount::getScalable(0); |
| |
| // If we don't know the precise trip count, or if the trip count that we |
| // found modulo the vectorization factor is not zero, try to fold the tail |
| // by masking. |
| // FIXME: look for a smaller MaxVF that does divide TC rather than masking. |
| if (Legal->prepareToFoldTailByMasking()) { |
| FoldTailByMasking = true; |
| return 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 ConstTripCount, unsigned SmallestType, unsigned WidestType, |
| const ElementCount &MaxSafeVF) { |
| bool ComputeScalableMaxVF = MaxSafeVF.isScalable(); |
| 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( |
| PowerOf2Floor(WidestRegister.getKnownMinSize() / 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); |
| } |
| |
| const auto TripCountEC = ElementCount::getFixed(ConstTripCount); |
| if (ConstTripCount && |
| ElementCount::isKnownLE(TripCountEC, MaxVectorElementCount) && |
| isPowerOf2_32(ConstTripCount)) { |
| // We need to clamp the VF to be the ConstTripCount. There is no point in |
| // choosing a higher viable VF as done in the loop below. 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. |
| LLVM_DEBUG(dbgs() << "LV: Clamping the MaxVF to the constant trip count: " |
| << ConstTripCount << "\n"); |
| return TripCountEC; |
| } |
| |
| ElementCount MaxVF = MaxVectorElementCount; |
| if (TTI.shouldMaximizeVectorBandwidth() || |
| (MaximizeBandwidth && isScalarEpilogueAllowed())) { |
| auto MaxVectorElementCountMaxBW = ElementCount::get( |
| PowerOf2Floor(WidestRegister.getKnownMinSize() / 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) { |
| bool Selected = true; |
| for (auto &pair : RUs[i].MaxLocalUsers) { |
| unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first); |
| if (pair.second > TargetNumRegisters) |
| Selected = false; |
| } |
| if (Selected) { |
| MaxVF = VFs[i]; |
| 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; |
| } |
| } |
| } |
| return MaxVF; |
| } |
| |
| bool LoopVectorizationCostModel::isMoreProfitable( |
| const VectorizationFactor &A, const VectorizationFactor &B) const { |
| InstructionCost CostA = A.Cost; |
| InstructionCost CostB = B.Cost; |
| |
| unsigned MaxTripCount = PSE.getSE()->getSmallConstantMaxTripCount(TheLoop); |
| |
| if (!A.Width.isScalable() && !B.Width.isScalable() && FoldTailByMasking && |
| MaxTripCount) { |
| // If we are folding the tail and the trip count is a known (possibly small) |
| // constant, the trip count will be rounded up to an integer number of |
| // iterations. The total cost will be PerIterationCost*ceil(TripCount/VF), |
| // which we compare directly. When not folding the tail, the total cost will |
| // be PerIterationCost*floor(TC/VF) + Scalar remainder cost, and so is |
| // approximated with the per-lane cost below instead of using the tripcount |
| // as here. |
| auto RTCostA = CostA * divideCeil(MaxTripCount, A.Width.getFixedValue()); |
| auto RTCostB = CostB * divideCeil(MaxTripCount, B.Width.getFixedValue()); |
| return RTCostA < RTCostB; |
| } |
| |
| // Improve estimate for the vector width if it is scalable. |
| unsigned EstimatedWidthA = A.Width.getKnownMinValue(); |
| unsigned EstimatedWidthB = B.Width.getKnownMinValue(); |
| if (Optional<unsigned> VScale = TTI.getVScaleForTuning()) { |
| if (A.Width.isScalable()) |
| EstimatedWidthA *= VScale.getValue(); |
| if (B.Width.isScalable()) |
| EstimatedWidthB *= VScale.getValue(); |
| } |
| |
| // When set to preferred, for now assume vscale may be larger than 1 (or the |
| // one being tuned for), so that scalable vectorization is slightly favorable |
| // over fixed-width vectorization. |
| if (Hints->isScalableVectorizationPreferred()) |
| if (A.Width.isScalable() && !B.Width.isScalable()) |
| return (CostA * B.Width.getFixedValue()) <= (CostB * EstimatedWidthA); |
| |
| // To avoid the need for FP division: |
| // (CostA / A.Width) < (CostB / B.Width) |
| // <=> (CostA * B.Width) < (CostB * A.Width) |
| return (CostA * EstimatedWidthB) < (CostB * EstimatedWidthA); |
| } |
| |
| VectorizationFactor LoopVectorizationCostModel::selectVectorizationFactor( |
| const ElementCountSet &VFCandidates) { |
| InstructionCost ExpectedCost = expectedCost(ElementCount::getFixed(1)).first; |
| LLVM_DEBUG(dbgs() << "LV: Scalar loop costs: " << ExpectedCost << ".\n"); |
| assert(ExpectedCost.isValid() && "Unexpected invalid cost for scalar loop"); |
| assert(VFCandidates.count(ElementCount::getFixed(1)) && |
| "Expected Scalar VF to be a candidate"); |
| |
| const VectorizationFactor ScalarCost(ElementCount::getFixed(1), ExpectedCost); |
| VectorizationFactor ChosenFactor = ScalarCost; |
| |
| bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled; |
| if (ForceVectorization && VFCandidates.size() > 1) { |
| // Ignore scalar width, because the user explicitly wants vectorization. |
| // Initialize cost to max so that VF = 2 is, at least, chosen during cost |
| // evaluation. |
| ChosenFactor.Cost = InstructionCost::getMax(); |
| } |
| |
| SmallVector<InstructionVFPair> InvalidCosts; |
| for (const auto &i : VFCandidates) { |
| // The cost for scalar VF=1 is already calculated, so ignore it. |
| if (i.isScalar()) |
| continue; |
| |
| VectorizationCostTy C = expectedCost(i, &InvalidCosts); |
| VectorizationFactor Candidate(i, C.first); |
| |
| #ifndef NDEBUG |
| unsigned AssumedMinimumVscale = 1; |
| if (Optional<unsigned> VScale = TTI.getVScaleForTuning()) |
| AssumedMinimumVscale = VScale.getValue(); |
| unsigned Width = |
| Candidate.Width.isScalable() |
| ? Candidate.Width.getKnownMinValue() * AssumedMinimumVscale |
| : Candidate.Width.getFixedValue(); |
| LLVM_DEBUG(dbgs() << "LV: Vector loop of width " << i |
| << " costs: " << (Candidate.Cost / Width)); |
| if (i.isScalable()) |
| LLVM_DEBUG(dbgs() << " (assuming a minimum vscale of " |
| << AssumedMinimumVscale << ")"); |
| LLVM_DEBUG(dbgs() << ".\n"); |
| #endif |
| |
| if (!C.second && !ForceVectorization) { |
| LLVM_DEBUG( |
| dbgs() << "LV: Not considering vector loop of width " << i |
| << " because it will not generate any vector instructions.\n"); |
| continue; |
| } |
| |
| // If profitable add it to ProfitableVF list. |
| if (isMoreProfitable(Candidate, ScalarCost)) |
| ProfitableVFs.push_back(Candidate); |
| |
| if (isMoreProfitable(Candidate, ChosenFactor)) |
| ChosenFactor = Candidate; |
| } |
| |
| // Emit a report of VFs with invalid costs in the loop. |
| if (!InvalidCosts.empty()) { |
| // Group the remarks per instruction, keeping the instruction order from |
| // InvalidCosts. |
| std::map<Instruction *, unsigned> Numbering; |
| unsigned I = 0; |
| for (auto &Pair : InvalidCosts) |
| if (!Numbering.count(Pair.first)) |
| Numbering[Pair.first] = I++; |
| |
| // Sort the list, first on instruction(number) then on VF. |
| llvm::sort(InvalidCosts, |
| [&Numbering](InstructionVFPair &A, InstructionVFPair &B) { |
| if (Numbering[A.first] != Numbering[B.first]) |
| return Numbering[A.first] < Numbering[B.first]; |
| ElementCountComparator ECC; |
| return ECC(A.second, B.second); |
| }); |
| |
| // For a list of ordered instruction-vf pairs: |
| // [(load, vf1), (load, vf2), (store, vf1)] |
| // Group the instructions together to emit separate remarks for: |
| // load (vf1, vf2) |
| // store (vf1) |
| auto Tail = ArrayRef<InstructionVFPair>(InvalidCosts); |
| auto Subset = ArrayRef<InstructionVFPair>(); |
| do { |
| if (Subset.empty()) |
| Subset = Tail.take_front(1); |
| |
| Instruction *I = Subset.front().first; |
| |
| // If the next instruction 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=(vf, vf2) |
| if (Subset == Tail || Tail[Subset.size()].first != I) { |
| std::string OutString; |
| raw_string_ostream OS(OutString); |
| assert(!Subset.empty() && "Unexpected empty range"); |
| OS << "Instruction with invalid costs prevented vectorization at VF=("; |
| for (auto &Pair : Subset) |
| OS << (Pair.second == Subset.front().second ? "" : ", ") |
| << Pair.second; |
| OS << "):"; |
| if (auto *CI = dyn_cast<CallInst>(I)) |
| OS << " call to " << CI->getCalledFunction()->getName(); |
| else |
| OS << " " << I->getOpcodeName(); |
| OS.flush(); |
| reportVectorizationInfo(OutString, "InvalidCost", ORE, TheLoop, I); |
| Tail = Tail.drop_front(Subset.size()); |
| Subset = {}; |
| } else |
| // Grow the subset by one element |
| Subset = Tail.take_front(Subset.size() + 1); |
| } while (!Tail.empty()); |
| } |
| |
| if (!EnableCondStoresVectorization && NumPredStores) { |
| reportVectorizationFailure("There are conditional stores.", |
| "store that is conditionally executed prevents vectorization", |
| "ConditionalStore", ORE, TheLoop); |
| ChosenFactor = ScalarCost; |
| } |
| |
| LLVM_DEBUG(if (ForceVectorization && !ChosenFactor.Width.isScalar() && |
| ChosenFactor.Cost >= ScalarCost.Cost) 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; |
| } |
| |
| bool LoopVectorizationCostModel::isCandidateForEpilogueVectorization( |
| const Loop &L, ElementCount VF) const { |
| // Cross iteration phis such as reductions need special handling and are |
| // currently unsupported. |
| if (any_of(L.getHeader()->phis(), [&](PHINode &Phi) { |
| return Legal->isFirstOrderRecurrence(&Phi) || |
| Legal->isReductionVariable(&Phi); |
| })) |
| return false; |
| |
| // Phis with uses outside of the loop require special handling and are |
| // currently unsupported. |
| for (auto &Entry : Legal->getInductionVars()) { |
| // Look for uses of the value of the induction at the last iteration. |
| Value *PostInc = Entry.first->getIncomingValueForBlock(L.getLoopLatch()); |
| for (User *U : PostInc->users()) |
| if (!L.contains(cast<Instruction>(U))) |
| return false; |
| // Look for uses of penultimate value of the induction. |
| for (User *U : Entry.first->users()) |
| if (!L.contains(cast<Instruction>(U))) |
| return false; |
| } |
| |
| // Induction variables that are widened require special handling that is |
| // currently not supported. |
| if (any_of(Legal->getInductionVars(), [&](auto &Entry) { |
| return !(this->isScalarAfterVectorization(Entry.first, VF) || |
| this->isProfitableToScalarize(Entry.first, VF)); |
| })) |
| 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 (L.getExitingBlock() != L.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. |
| // We also consider epilogue vectorization unprofitable for targets that don't |
| // consider interleaving beneficial (eg. MVE). |
| if (TTI.getMaxInterleaveFactor(VF.getKnownMinValue()) <= 1) |
| return false; |
| if (VF.getFixedValue() >= EpilogueVectorizationMinVF) |
| return true; |
| return false; |
| } |
| |
| VectorizationFactor |
| LoopVectorizationCostModel::selectEpilogueVectorizationFactor( |
| const ElementCount MainLoopVF, const LoopVectorizationPlanner &LVP) { |
| VectorizationFactor Result = VectorizationFactor::Disabled(); |
| if (!EnableEpilogueVectorization) { |
| LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is disabled.\n";); |
| return Result; |
| } |
| |
| if (!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(*TheLoop, 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 (LVP.hasPlanWithVF(ForcedEC)) |
| return {ForcedEC, 0}; |
| else { |
| LLVM_DEBUG( |
| dbgs() |
| << "LEV: Epilogue vectorization forced factor is not viable.\n";); |
| return Result; |
| } |
| } |
| |
| if (TheLoop->getHeader()->getParent()->hasOptSize() || |
| TheLoop->getHeader()->getParent()->hasMinSize()) { |
| LLVM_DEBUG( |
| dbgs() |
| << "LEV: Epilogue vectorization skipped due to opt for size.\n";); |
| return Result; |
| } |
| |
| auto FixedMainLoopVF = ElementCount::getFixed(MainLoopVF.getKnownMinValue()); |
| if (MainLoopVF.isScalable()) |
| LLVM_DEBUG( |
| dbgs() << "LEV: Epilogue vectorization using scalable vectors not " |
| "yet supported. Converting to fixed-width (VF=" |
| << FixedMainLoopVF << ") instead\n"); |
| |
| if (!isEpilogueVectorizationProfitable(FixedMainLoopVF)) { |
| LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization is not profitable for " |
| "this loop\n"); |
| return Result; |
| } |
| |
| for (auto &NextVF : ProfitableVFs) |
| if (ElementCount::isKnownLT(NextVF.Width, FixedMainLoopVF) && |
| (Result.Width.getFixedValue() == 1 || |
| isMoreProfitable(NextVF, Result)) && |
| LVP.hasPlanWithVF(NextVF.Width)) |
| Result = NextVF; |
| |
| if (Result != VectorizationFactor::Disabled()) |
| LLVM_DEBUG(dbgs() << "LEV: Vectorizing epilogue loop with VF = " |
| << Result.Width.getFixedValue() << "\n";); |
| return Result; |
| } |
| |
| std::pair<unsigned, unsigned> |
| LoopVectorizationCostModel::getSmallestAndWidestTypes() { |
| unsigned MinWidth = -1U; |
| unsigned MaxWidth = 8; |
| const DataLayout &DL = TheFunction->getParent()->getDataLayout(); |
| for (Type *T : ElementTypesInLoop) { |
| MinWidth = std::min<unsigned>( |
| MinWidth, DL.getTypeSizeInBits(T->getScalarType()).getFixedSize()); |
| MaxWidth = std::max<unsigned>( |
| MaxWidth, DL.getTypeSizeInBits(T->getScalarType()).getFixedSize()); |
| } |
| 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()[PN]; |
| 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(); |
| |
| // Ignore loaded pointer types and stored pointer types that are not |
| // vectorizable. |
| // |
| // FIXME: The check here attempts to predict whether a load or store will |
| // be vectorized. We only know this for certain after a VF has |
| // been selected. Here, we assume that if an access can be |
| // vectorized, it will be. We should also look at extending this |
| // optimization to non-pointer types. |
| // |
| if (T->isPointerTy() && !isConsecutiveLoadOrStore(&I) && |
| !isAccessInterleaved(&I) && !isLegalGatherOrScatter(&I)) |
| continue; |
| |
| ElementTypesInLoop.insert(T); |
| } |
| } |
| } |
| |
| unsigned LoopVectorizationCostModel::selectInterleaveCount(ElementCount VF, |
| unsigned LoopCost) { |
| // -- The interleave heuristics -- |
| // We interleave the loop in order to expose ILP and reduce the loop overhead. |
| // There are many micro-architectural considerations that we can't predict |
| // at this level. For example, frontend pressure (on decode or fetch) due to |
| // code size, or the number and capabilities of the execution ports. |
| // |
| // We use the following heuristics to select the interleave count: |
| // 1. If the code has reductions, then we interleave to break the cross |
| // iteration dependency. |
| // 2. If the loop is really small, then we interleave to reduce the loop |
| // overhead. |
| // 3. We don't interleave if we think that we will spill registers to memory |
| // due to the increased register pressure. |
| |
| if (!isScalarEpilogueAllowed()) |
| return 1; |
| |
| // We used the distance for the interleave count. |
| if (Legal->getMaxSafeDepDistBytes() != -1U) |
| return 1; |
| |
| auto BestKnownTC = getSmallBestKnownTC(*PSE.getSE(), TheLoop); |
| const bool HasReductions = !Legal->getReductionVars().empty(); |
| // Do not interleave loops with a relatively small known or estimated trip |
| // count. But we will interleave when InterleaveSmallLoopScalarReduction is |
| // enabled, and the code has scalar reductions(HasReductions && VF = 1), |
| // because with the above conditions interleaving can expose ILP and break |
| // cross iteration dependences for reductions. |
| if (BestKnownTC && (*BestKnownTC < TinyTripCountInterleaveThreshold) && |
| !(InterleaveSmallLoopScalarReduction && HasReductions && VF.isScalar())) |
| return 1; |
| |
| RegisterUsage R = calculateRegisterUsage({VF})[0]; |
| // We divide by these constants so assume that we have at least one |
| // instruction that uses at least one register. |
| for (auto& pair : R.MaxLocalUsers) { |
| pair.second = std::max(pair.second, 1U); |
| } |
| |
| // We calculate the interleave count using the following formula. |
| // Subtract the number of loop invariants from the number of available |
| // registers. These registers are used by all of the interleaved instances. |
| // Next, divide the remaining registers by the number of registers that is |
| // required by the loop, in order to estimate how many parallel instances |
| // fit without causing spills. All of this is rounded down if necessary to be |
| // a power of two. We want power of two interleave count to simplify any |
| // addressing operations or alignment considerations. |
| // We also want power of two interleave counts to ensure that the induction |
| // variable of the vector loop wraps to zero, when tail is folded by masking; |
| // this currently happens when OptForSize, in which case IC is set to 1 above. |
| unsigned IC = UINT_MAX; |
| |
| for (auto& pair : R.MaxLocalUsers) { |
| unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first); |
| LLVM_DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters |
| << " registers of " |
| << TTI.getRegisterClassName(pair.first) << " register class\n"); |
| if (VF.isScalar()) { |
| if (ForceTargetNumScalarRegs.getNumOccurrences() > 0) |
| TargetNumRegisters = ForceTargetNumScalarRegs; |
| } else { |
| if (ForceTargetNumVectorRegs.getNumOccurrences() > 0) |
| TargetNumRegisters = ForceTargetNumVectorRegs; |
| } |
| unsigned MaxLocalUsers = pair.second; |
| unsigned LoopInvariantRegs = 0; |
| if (R.LoopInvariantRegs.find(pair.first) != R.LoopInvariantRegs.end()) |
| LoopInvariantRegs = R.LoopInvariantRegs[pair.first]; |
| |
| unsigned TmpIC = PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs) / MaxLocalUsers); |
| // Don't count the induction variable as interleaved. |
| if (EnableIndVarRegisterHeur) { |
| TmpIC = |
| PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs - 1) / |
| std::max(1U, (MaxLocalUsers - 1))); |
| } |
| |
| IC = std::min(IC, TmpIC); |
| } |
| |
| // Clamp the interleave ranges to reasonable counts. |
| unsigned MaxInterleaveCount = |
| TTI.getMaxInterleaveFactor(VF.getKnownMinValue()); |
| |
| // Check if the user has overridden the max. |
| if (VF.isScalar()) { |
| if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0) |
| MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor; |
| } else { |
| if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0) |
| MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor; |
| } |
| |
| // If trip count is known or estimated compile time constant, limit the |
| // interleave count to be less than the trip count divided by VF, provided it |
| // is at least 1. |
| // |
| // For scalable vectors we can't know if interleaving is beneficial. It may |
| // not be beneficial for small loops if none of the lanes in the second vector |
| // iterations is enabled. However, for larger loops, there is likely to be a |
| // similar benefit as for fixed-width vectors. For now, we choose to leave |
| // the InterleaveCount as if vscale is '1', although if some information about |
| // the vector is known (e.g. min vector size), we can make a better decision. |
| if (BestKnownTC) { |
| MaxInterleaveCount = |
| std::min(*BestKnownTC / VF.getKnownMinValue(), MaxInterleaveCount); |
| // Make sure MaxInterleaveCount is greater than 0. |
| MaxInterleaveCount = std::max(1u, 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."); |
| |
| // 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) { |
| InstructionCost C = expectedCost(VF).first; |
| assert(C.isValid() && "Expected to have chosen a VF with valid cost"); |
| LoopCost = *C.getValue(); |
| } |
| |
| assert(LoopCost && "Non-zero loop cost expected"); |
| |
| // Interleave if we vectorized this loop and there is a reduction that could |
| // benefit from interleaving. |
| if (VF.isVector() && HasReductions) { |
| LLVM_DEBUG(dbgs() << "LV: Interleaving because of reductions.\n"); |
| return IC; |
| } |
| |
| // Note that if we've already vectorized the loop we will have done the |
| // runtime check and so interleaving won't require further checks. |
| bool InterleavingRequiresRuntimePointerCheck = |
| (VF.isScalar() && Legal->getRuntimePointerChecking()->Need); |
| |
| // We want to interleave small loops in order to reduce the loop overhead and |
| // potentially expose ILP opportunities. |
| LLVM_DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n' |
| << "LV: IC is " << IC << '\n' |
| << "LV: VF is " << VF << '\n'); |
| const bool AggressivelyInterleaveReductions = |
| TTI.enableAggressiveInterleaving(HasReductions); |
| if (!InterleavingRequiresRuntimePointerCheck && LoopCost < SmallLoopCost) { |
| // We assume that the cost overhead is 1 and we use the cost model |
| // to estimate the cost of the loop and interleave until the cost of the |
| // loop overhead is about 5% of the cost of the loop. |
| unsigned SmallIC = |
| std::min(IC, (unsigned)PowerOf2Floor(SmallLoopCost / LoopCost)); |
| |
| // Interleave until store/load ports (estimated by max interleave count) are |
| // saturated. |
| unsigned NumStores = Legal->getNumStores(); |
| unsigned NumLoads = Legal->getNumLoads(); |
| unsigned StoresIC = IC / (NumStores ? NumStores : 1); |
| unsigned LoadsIC = IC / (NumLoads ? NumLoads : 1); |
| |
| // 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::isSelectCmpRecurrenceKind( |
| 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 (InterleaveSmallLoopScalarReduction && VF.isScalar() && |
| AggressivelyInterleaveReductions) { |
| LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n"); |
| // Interleave no less than SmallIC but not as aggressive as the normal IC |
| // to satisfy the rare situation when resources are too limited. |
| return std::max(IC / 2, SmallIC); |
| } else { |
| LLVM_DEBUG(dbgs() << "LV: Interleaving to reduce branch cost.\n"); |
| return SmallIC; |
| } |
| } |
| |
| // Interleave if this is a large loop (small loops are already dealt with by |
| // this point) that could benefit from interleaving. |
| if (AggressivelyInterleaveReductions) { |
| LLVM_DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n"); |
| return IC; |
| } |
| |
| LLVM_DEBUG(dbgs() << "LV: Not Interleaving.\n"); |
| return 1; |
| } |
| |
| SmallVector<LoopVectorizationCostModel::RegisterUsage, 8> |
| LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<ElementCount> VFs) { |
| // This function calculates the register usage by measuring the highest number |
| // of values that are alive at a single location. Obviously, this is a very |
| // rough estimation. We scan the loop in a topological order in order and |
| // assign a number to each instruction. We use RPO to ensure that defs are |
| // met before their users. We assume that each instruction that has in-loop |
| // users starts an interval. We record every time that an in-loop value is |
| // used, so we have a list of the first and last occurrences of each |
| // instruction. Next, we transpose this data structure into a multi map that |
| // holds the list of intervals that *end* at a specific location. This multi |
| // map allows us to perform a linear search. We scan the instructions linearly |
| // and record each time that a new interval starts, by placing it in a set. |
| // If we find this value in the multi-map then we remove it from the set. |
| // The max register usage is the maximum size of the set. |
| // We also search for instructions that are defined outside the loop, but are |
| // used inside the loop. We need this number separately from the max-interval |
| // usage number because when we unroll, loop-invariant values do not take |
| // more register. |
| LoopBlocksDFS DFS(TheLoop); |
| DFS.perform(LI); |
| |
| RegisterUsage RU; |
| |
| // Each 'key' in the map opens a new interval. The values |
| // of the map are the index of the 'last seen' usage of the |
| // instruction that is the key. |
| using IntervalMap = DenseMap<Instruction *, unsigned>; |
| |
| // Maps instruction to its index. |
| SmallVector<Instruction *, 64> IdxToInstr; |
| // Marks the end of each interval. |
| IntervalMap EndPoint; |
| // Saves the list of instruction indices that are used in the loop. |
| SmallPtrSet<Instruction *, 8> Ends; |
| // Saves the list of values that are used in the loop but are |
| // defined outside the loop, such as arguments and constants. |
| SmallPtrSet<Value *, 8> LoopInvariants; |
| |
| for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) { |
| for (Instruction &I : BB->instructionsWithoutDebug()) { |
| IdxToInstr.push_back(&I); |
| |
| // Save the end location of each USE. |
| for (Value *U : I.operands()) { |
| auto *Instr = dyn_cast<Instruction>(U); |
| |
| // Ignore non-instruction values such as arguments, constants, etc. |
| if (!Instr) |
| continue; |
| |
| // If this instruction is outside the loop then record it and continue. |
| if (!TheLoop->contains(Instr)) { |
| LoopInvariants.insert(Instr); |
| continue; |
| } |
| |
| // Overwrite previous end points. |
| EndPoint[Instr] = IdxToInstr.size(); |
| Ends.insert(Instr); |
| } |
| } |
| } |
| |
| // Saves the list of intervals that end with the index in 'key'. |
| using InstrList = SmallVector<Instruction *, 2>; |
| DenseMap<unsigned, InstrList> TransposeEnds; |
| |
| // Transpose the EndPoints to a list of values that end at each index. |
| for (auto &Interval : EndPoint) |
| TransposeEnds[Interval.second].push_back(Interval.first); |
| |
| SmallPtrSet<Instruction *, 8> OpenIntervals; |
| SmallVector<RegisterUsage, 8> RUs(VFs.size()); |
| SmallVector<SmallMapVector<unsigned, unsigned, 4>, 8> MaxUsages(VFs.size()); |
| |
| LLVM_DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n"); |
| |
| // A lambda that gets the register usage for the given type and VF. |
| const auto &TTICapture = TTI; |
| auto GetRegUsage = [&TTICapture](Type *Ty, ElementCount VF) -> unsigned { |
| if (Ty->isTokenTy() || !VectorType::isValidElementType(Ty)) |
| return 0; |
| InstructionCost::CostType RegUsage = |
| *TTICapture.getRegUsageForType(VectorType::get(Ty, VF)).getValue(); |
| assert(RegUsage >= 0 && RegUsage <= std::numeric_limits<unsigned>::max() && |
| "Nonsensical values for register usage."); |
| return RegUsage; |
| }; |
| |
| for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) { |
| Instruction *I = IdxToInstr[i]; |
| |
| // Remove all of the instructions that end at this location. |
| InstrList &List = TransposeEnds[i]; |
| for (Instruction *ToRemove : List) |
| OpenIntervals.erase(ToRemove); |
| |
| // Ignore instructions that are never used within the loop. |
| if (!Ends.count(I)) |
| continue; |
| |
| // Skip ignored values. |
| if (ValuesToIgnore.count(I)) |
| continue; |
| |
| // For each VF find the maximum usage of registers. |
| for (unsigned j = 0, e = VFs.size(); j < e; ++j) { |
| // Count the number of live intervals. |
| SmallMapVector<unsigned, unsigned, 4> RegUsage; |
| |
| if (VFs[j].isScalar()) { |
| for (auto Inst : OpenIntervals) { |
| unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType()); |
| if (RegUsage.find(ClassID) == RegUsage.end()) |
| RegUsage[ClassID] = 1; |
| else |
| RegUsage[ClassID] += 1; |
| } |
| } else { |
| collectUniformsAndScalars(VFs[j]); |
| for (auto Inst : OpenIntervals) { |
| // Skip ignored values for VF > 1. |
| if (VecValuesToIgnore.count(Inst)) |
| continue; |
| if (isScalarAfterVectorization(Inst, VFs[j])) { |
| unsigned ClassID = TTI.getRegisterClassForType(false, Inst->getType()); |
| if (RegUsage.find(ClassID) == RegUsage.end()) |
| RegUsage[ClassID] = 1; |
| else |
| RegUsage[ClassID] += 1; |
| } else { |
| unsigned ClassID = TTI.getRegisterClassForType(true, Inst->getType()); |
| if (RegUsage.find(ClassID) == RegUsage.end()) |
| RegUsage[ClassID] = GetRegUsage(Inst->getType(), VFs[j]); |
| else |
| RegUsage[ClassID] += GetRegUsage(Inst->getType(), VFs[j]); |
| } |
| } |
| } |
| |
| for (auto& pair : RegUsage) { |
| if (MaxUsages[j].find(pair.first) != MaxUsages[j].end()) |
| MaxUsages[j][pair.first] = std::max(MaxUsages[j][pair.first], pair.second); |
| else |
| MaxUsages[j][pair.first] = pair.second; |
| } |
| } |
| |
| LLVM_DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " |
| << OpenIntervals.size() << '\n'); |
| |
| // Add the current instruction to the list of open intervals. |
| OpenIntervals.insert(I); |
| } |
| |
| for (unsigned i = 0, e = VFs.size(); i < e; ++i) { |
| SmallMapVector<unsigned, unsigned, 4> Invariant; |
| |
| for (auto Inst : LoopInvariants) { |
| unsigned Usage = |
| VFs[i].isScalar() ? 1 : GetRegUsage(Inst->getType(), VFs[i]); |
| unsigned ClassID = |
| TTI.getRegisterClassForType(VFs[i].isVector(), Inst->getType()); |
| if (Invariant.find(ClassID) == Invariant.end()) |
| Invariant[ClassID] = Usage; |
| else |
| Invariant[ClassID] += Usage; |
| } |
| |
| LLVM_DEBUG({ |
| dbgs() << "LV(REG): VF = " << VFs[i] << '\n'; |
| dbgs() << "LV(REG): Found max usage: " << MaxUsages[i].size() |
| << " item\n"; |
| for (const auto &pair : MaxUsages[i]) { |
| dbgs() << "LV(REG): RegisterClass: " |
| << TTI.getRegisterClassName(pair.first) << ", " << pair.second |
| << " registers\n"; |
| } |
| dbgs() << "LV(REG): Found invariant usage: " << Invariant.size() |
| << " item\n"; |
| for (const auto &pair : Invariant) { |
| dbgs() << "LV(REG): RegisterClass: " |
| << TTI.getRegisterClassName(pair.first) << ", " << pair.second |
| << " registers\n"; |
| } |
| }); |
| |
| RU.LoopInvariantRegs = Invariant; |
| RU.MaxLocalUsers = MaxUsages[i]; |
| RUs[i] = RU; |
| } |
| |
| return RUs; |
| } |
| |
| bool LoopVectorizationCostModel::useEmulatedMaskMemRefHack(Instruction *I){ |
| // TODO: Cost model for emulated masked load/store is completely |
| // broken. This hack guides the cost model to use an artificially |
| // high enough value to practically disable vectorization with such |
| // operations, except where previously deployed legality hack allowed |
| // using very low cost values. This is to avoid regressions coming simply |
| // from moving "masked load/store" check from legality to cost model. |
| // Masked Load/Gather emulation was previously never allowed. |
| // Limited number of Masked Store/Scatter emulation was allowed. |
| assert(isPredicatedInst(I) && |
| "Expecting a scalar emulated instruction"); |
| return isa<LoadInst>(I) || |
| (isa<StoreInst>(I) && |
| NumPredStores > NumberOfStoresToPredicate); |
| } |
| |
| void LoopVectorizationCostModel::collectInstsToScalarize(ElementCount VF) { |
| // If we aren't vectorizing the loop, or if we've already collected the |
| // instructions to scalarize, there's nothing to do. Collection may already |
| // have occurred if we have a user-selected VF and are now computing the |
| // expected cost for interleaving. |
| if (VF.isScalar() || VF.isZero() || |
| InstsToScalarize.find(VF) != InstsToScalarize.end()) |
| return; |
| |
| // Initialize a mapping for VF in InstsToScalalarize. If we find that it's |
| // not profitable to scalarize any instructions, the presence of VF in the |
| // map will indicate that we've analyzed it already. |
| ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF]; |
| |
| // Find all the instructions that are scalar with predication in the loop and |
| // determine if it would be better to not if-convert the blocks they are in. |
| // If so, we also record the instructions to scalarize. |
| for (BasicBlock *BB : TheLoop->blocks()) { |
| if (!blockNeedsPredicationForAnyReason(BB)) |
| continue; |
| for (Instruction &I : *BB) |
| if (isScalarWithPredication(&I)) { |
| ScalarCostsTy ScalarCosts; |
| // Do not apply discount if scalable, because that would lead to |
| // invalid scalarization costs. |
| // Do not apply discount logic if hacked cost is needed |
| // for emulated masked memrefs. |
| if (!VF.isScalable() && !useEmulatedMaskMemRefHack(&I) && |
| computePredInstDiscount(&I, ScalarCosts, VF) >= 0) |
| ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end()); |
| // Remember that BB will remain after vectorization. |
| PredicatedBBsAfterVectorization.insert(BB); |
| } |
| } |
| } |
| |
| int LoopVectorizationCostModel::computePredInstDiscount( |
| Instruction *PredInst, 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)) |
| return false; |
| |
| // If any of the instruction's operands are uniform after vectorization, |
| // the instruction cannot be scalarized. This prevents, for example, a |
| // masked load from being scalarized. |
| // |
| // We assume we will only emit a value for lane zero of an instruction |
| // marked uniform after vectorization, rather than VF identical values. |
| // Thus, if we scalarize an instruction that uses a uniform, we would |
| // create uses of values corresponding to the lanes we aren't emitting code |
| // for. This behavior can be changed by allowing getScalarValue to clone |
| // the lane zero values for uniforms rather than asserting. |
| for (Use &U : I->operands()) |
| if (auto *J = dyn_cast<Instruction>(U.get())) |
| if (isUniformAfterVectorization(J, VF)) |
| return false; |
| |
| // Otherwise, we can scalarize the instruction. |
| return true; |
| }; |
| |
| // Compute the expected cost discount from scalarizing the entire expression |
| // feeding the predicated instruction. We currently only consider expressions |
| // that are single-use instruction chains. |
| Worklist.push_back(PredInst); |
| while (!Worklist.empty()) { |
| Instruction *I = Worklist.pop_back_val(); |
| |
| // If we've already analyzed the instruction, there's nothing to do. |
| if (ScalarCosts.find(I) != ScalarCosts.end()) |
| continue; |
| |
| // Compute the cost of the vector instruction. Note that this cost already |
| // includes the scalarization overhead of the predicated instruction. |
| InstructionCost VectorCost = getInstructionCost(I, VF).first; |
| |
| // Compute the cost of the scalarized instruction. This cost is the cost of |
| // the instruction as if it wasn't if-converted and instead remained in the |
| // predicated block. We will scale this cost by block probability after |
| // computing the scalarization overhead. |
| InstructionCost ScalarCost = |
| VF.getFixedValue() * |
| getInstructionCost(I, ElementCount::getFixed(1)).first; |
| |
| // Compute the scalarization overhead of needed insertelement instructions |
| // and phi nodes. |
| if (isScalarWithPredication(I) && !I->getType()->isVoidTy()) { |
| ScalarCost += TTI.getScalarizationOverhead( |
| cast<VectorType>(ToVectorTy(I->getType(), VF)), |
| APInt::getAllOnes(VF.getFixedValue()), true, false); |
| ScalarCost += |
| VF.getFixedValue() * |
| TTI.getCFInstrCost(Instruction::PHI, TTI::TCK_RecipThroughput); |
| } |
| |
| // Compute the scalarization overhead of needed extractelement |
| // instructions. For each of the instruction's operands, if the operand can |
| // be scalarized, add it to the worklist; otherwise, account for the |
| // overhead. |
| for (Use &U : I->operands()) |
| if (auto *J = dyn_cast<Instruction>(U.get())) { |
| assert(VectorType::isValidElementType(J->getType()) && |
| "Instruction has non-scalar type"); |
| if (canBeScalarized(J)) |
| Worklist.push_back(J); |
| else if (needsExtract(J, VF)) { |
| ScalarCost += TTI.getScalarizationOverhead( |
| cast<VectorType>(ToVectorTy(J->getType(), VF)), |
| APInt::getAllOnes(VF.getFixedValue()), false, true); |
| } |
| } |
| |
| // Scale the total scalar cost by block probability. |
| ScalarCost /= getReciprocalPredBlockProb(); |
| |
| // Compute the discount. A non-negative discount means the vector version |
| // of the instruction costs more, and scalarizing would be beneficial. |
| Discount += VectorCost - ScalarCost; |
| ScalarCosts[I] = ScalarCost; |
| } |
| |
| return *Discount.getValue(); |
| } |
| |
| LoopVectorizationCostModel::VectorizationCostTy |
| LoopVectorizationCostModel::expectedCost( |
| ElementCount VF, SmallVectorImpl<InstructionVFPair> *Invalid) { |
| VectorizationCostTy Cost; |
| |
| // For each block. |
| for (BasicBlock *BB : TheLoop->blocks()) { |
| VectorizationCostTy BlockCost; |
| |
| // For each instruction in the old loop. |
| for (Instruction &I : BB->instructionsWithoutDebug()) { |
| // Skip ignored values. |
| if (ValuesToIgnore.count(&I) || |
| (VF.isVector() && VecValuesToIgnore.count(&I))) |
| continue; |
| |
| VectorizationCostTy C = getInstructionCost(&I, VF); |
| |
| // Check if we should override the cost. |
| if (C.first.isValid() && |
| ForceTargetInstructionCost.getNumOccurrences() > 0) |
| C.first = InstructionCost(ForceTargetInstructionCost); |
| |
| // Keep a list of instructions with invalid costs. |
| if (Invalid && !C.first.isValid()) |
| Invalid->emplace_back(&I, VF); |
| |
| BlockCost.first += C.first; |
| BlockCost.second |= C.second; |
| LLVM_DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first |
| << " for VF " << VF << " For instruction: " << I |
| << '\n'); |
| } |
| |
| // If we are vectorizing a predicated block, it will have been |
| // if-converted. This means that the block's instructions (aside from |
| // stores and instructions that may divide by zero) will now be |
| // unconditionally executed. For the scalar case, we may not always execute |
| // the predicated block, 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.first /= getReciprocalPredBlockProb(); |
| |
| Cost.first += BlockCost.first; |
| Cost.second |= BlockCost.second; |
| } |
| |
| return Cost; |
| } |
| |
| /// Gets Address Access SCEV after verifying that the access pattern |
| /// is loop invariant except the induction variable dependence. |
| /// |
| /// This SCEV can be sent to the Target in order to estimate the address |
| /// calculation cost. |
| static const SCEV *getAddressAccessSCEV( |
| Value *Ptr, |
| LoopVectorizationLegality *Legal, |
| PredicatedScalarEvolution &PSE, |
| const Loop *TheLoop) { |
| |
| auto *Gep = dyn_cast<GetElementPtrInst>(Ptr); |
| if (!Gep) |
| return nullptr; |
| |
| // We are looking for a gep with all loop invariant indices except for one |
| // which should be an induction variable. |
| auto SE = PSE.getSE(); |
| unsigned NumOperands = Gep->getNumOperands(); |
| for (unsigned i = 1; i < NumOperands; ++i) { |
| Value *Opd = Gep->getOperand(i); |
| if (!SE->isLoopInvariant(SE->getSCEV(Opd), TheLoop) && |
| !Legal->isInductionVariable(Opd)) |
| return nullptr; |
| } |
| |
| // Now we know we have a GEP ptr, %inv, %ind, %inv. return the Ptr SCEV. |
| return PSE.getSCEV(Ptr); |
| } |
| |
| static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) { |
| return Legal->hasStride(I->getOperand(0)) || |
| Legal->hasStride(I->getOperand(1)); |
| } |
| |
| 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. |
| const Align Alignment = getLoadStoreAlignment(I); |
| Cost += VF.getKnownMinValue() * |
| TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment, |
| AS, TTI::TCK_RecipThroughput); |
| |
| // Get the overhead of the extractelement and insertelement instructions |
| // we might create due to scalarization. |
| Cost += getScalarizationOverhead(I, VF); |
| |
| // If we have a predicated 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); |
| Cost += TTI.getCFInstrCost(Instruction::Br, TTI::TCK_RecipThroughput); |
| |
| if (useEmulatedMaskMemRefHack(I)) |
| // 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 |
| Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS, |
| CostKind, I); |
| |
| bool Reverse = ConsecutiveStride < 0; |
| if (Reverse) |
| Cost += |
| TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, None, 0); |
| return Cost; |
| } |
| |
| InstructionCost |
| LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I, |
| ElementCount VF) { |
| assert(Legal->isUniformMemOp(*I)); |
| |
| 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->isUniform(SI->getValueOperand()); |
| return TTI.getAddressComputationCost(ValTy) + |
| TTI.getMemoryOpCost(Instruction::Store, ValTy, Alignment, AS, |
| CostKind) + |
| (isLoopInvariantStoreValue |
| ? 0 |
| : TTI.getVectorInstrCost(Instruction::ExtractElement, VectorTy, |
| VF.getKnownMinValue() - 1)); |
| } |
| |
| 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) { |
| // TODO: Once we have support for interleaving with scalable vectors |
| // we can calculate the cost properly here. |
| if (VF.isScalable()) |
| return InstructionCost::getInvalid(); |
| |
| Type *ValTy = getLoadStoreType(I); |
| auto *VectorTy = cast<VectorType>(ToVectorTy(ValTy, VF)); |
| unsigned AS = getLoadStoreAddressSpace(I); |
| |
| auto Group = getInterleavedAccessGroup(I); |
| assert(Group && "Fail to get an interleaved access group."); |
| |
| unsigned InterleaveFactor = Group->getFactor(); |
| 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, TTI::TCK_RecipThroughput, Legal->isMaskRequired(I), UseMaskForGaps); |
| |
| if (Group->isReverse()) { |
| // TODO: Add support for reversed masked interleaved access. |
| assert(!Legal->isMaskRequired(I) && |
| "Reverse masked interleaved access not supported."); |
| Cost += |
| Group->getNumMembers() * |
| TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, None, 0); |
| } |
| return Cost; |
| } |
| |
| Optional<InstructionCost> LoopVectorizationCostModel::getReductionPatternCost( |
| Instruction *I, ElementCount VF, Type *Ty, TTI::TargetCostKind CostKind) { |
| using namespace llvm::PatternMatch; |
| // Early exit for no inloop reductions |
| if (InLoopReductionChains.empty() || VF.isScalar() || !isa<VectorType>(Ty)) |
| return None; |
| 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 None; |
| RetI = RetI->user_back(); |
| } |
| if (match(RetI, m_Mul(m_Value(), m_Value())) && |
| RetI->user_back()->getOpcode() == Instruction::Add) { |
| if (!RetI->hasOneUser()) |
| return None; |
| 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 None; |
| |
| // Find the reduction this chain is a part of and calculate the basic cost of |
| // the reduction on its own. |
| Instruction *LastChain = InLoopReductionImmediateChains[RetI]; |
| Instruction *ReductionPhi = LastChain; |
| while (!isa<PHINode>(ReductionPhi)) |
| ReductionPhi = InLoopReductionImmediateChains[ReductionPhi]; |
| |
| const RecurrenceDescriptor &RdxDesc = |
| Legal->getReductionVars()[cast<PHINode>(ReductionPhi)]; |
| |
| InstructionCost 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 (RdxDesc.getRecurrenceKind() == 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 && |
| 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(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.getExtendedAddReductionCost( |
| /*IsMLA=*/true, 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.getExtendedAddReductionCost( |
| /*IsMLA=*/false, IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, |
| 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 && |
| match(RedOp, m_Mul(m_Instruction(Op0), m_Instruction(Op1)))) { |
| if (match(Op0, m_ZExtOrSExt(m_Value())) && |
| Op0->getOpcode() == Op1->getOpcode() && |
| Op0->getOperand(0)->getType() == Op1->getOperand(0)->getType() && |
| !TheLoop->isLoopInvariant(Op0) && !TheLoop->isLoopInvariant(Op1)) { |
| bool IsUnsigned = isa<ZExtInst>(Op0); |
| auto *ExtType = VectorType::get(Op0->getOperand(0)->getType(), VectorTy); |
| // Matched reduce(mul(ext, ext)) |
| InstructionCost ExtCost = |
| TTI.getCastInstrCost(Op0->getOpcode(), VectorTy, ExtType, |
| TTI::CastContextHint::None, CostKind, Op0); |
| InstructionCost MulCost = |
| TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy, CostKind); |
| |
| InstructionCost RedCost = TTI.getExtendedAddReductionCost( |
| /*IsMLA=*/true, IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, |
| CostKind); |
| |
| if (RedCost.isValid() && RedCost < ExtCost * 2 + MulCost + BaseCost) |
| return I == RetI ? RedCost : 0; |
| } else if (!match(I, m_ZExtOrSExt(m_Value()))) { |
| // Matched reduce(mul()) |
| InstructionCost MulCost = |
| TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy, CostKind); |
| |
| InstructionCost RedCost = TTI.getExtendedAddReductionCost( |
| /*IsMLA=*/true, true, RdxDesc.getRecurrenceType(), VectorTy, |
| CostKind); |
| |
| if (RedCost.isValid() && RedCost < MulCost + BaseCost) |
| return I == RetI ? RedCost : 0; |
| } |
| } |
| |
| return I == RetI ? Optional<InstructionCost>(BaseCost) : None; |
| } |
| |
| 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); |
| |
| return TTI.getAddressComputationCost(ValTy) + |
| TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS, |
| TTI::TCK_RecipThroughput, I); |
| } |
| return getWideningCost(I, VF); |
| } |
| |
| LoopVectorizationCostModel::VectorizationCostTy |
| LoopVectorizationCostModel::getInstructionCost(Instruction *I, |
| ElementCount VF) { |
| // If we know that this instruction will remain uniform, check the cost of |
| // the scalar version. |
| if (isUniformAfterVectorization(I, VF)) |
| VF = ElementCount::getFixed(1); |
| |
| if (VF.isVector() && isProfitableToScalarize(I, VF)) |
| return VectorizationCostTy(InstsToScalarize[VF][I], false); |
| |
| // Forced scalars do not have any scalarization overhead. |
| auto ForcedScalar = ForcedScalars.find(VF); |
| if (VF.isVector() && ForcedScalar != ForcedScalars.end()) { |
| auto InstSet = ForcedScalar->second; |
| if (InstSet.count(I)) |
| return VectorizationCostTy( |
| (getInstructionCost(I, ElementCount::getFixed(1)).first * |
| VF.getKnownMinValue()), |
| false); |
| } |
| |
| Type *VectorTy; |
| InstructionCost C = getInstructionCost(I, VF, VectorTy); |
| |
| bool TypeNotScalarized = false; |
| if (VF.isVector() && VectorTy->isVectorTy()) { |
| unsigned NumParts = TTI.getNumberOfParts(VectorTy); |
| if (NumParts) |
| TypeNotScalarized = NumParts < VF.getKnownMinValue(); |
| else |
| C = InstructionCost::getInvalid(); |
| } |
| return VectorizationCostTy(C, TypeNotScalarized); |
| } |
| |
| InstructionCost |
| LoopVectorizationCostModel::getScalarizationOverhead(Instruction *I, |
| ElementCount VF) const { |
| |
| // There is no mechanism yet to create a scalable scalarization loop, |
| // so this is currently Invalid. |
| if (VF.isScalable()) |
| return InstructionCost::getInvalid(); |
| |
| if (VF.isScalar()) |
| return 0; |
| |
| InstructionCost Cost = 0; |
| Type *RetTy = ToVectorTy(I->getType(), VF); |
| if (!RetTy->isVoidTy() && |
| (!isa<LoadInst>(I) || !TTI.supportsEfficientVectorElementLoadStore())) |
| Cost += TTI.getScalarizationOverhead( |
| cast<VectorType>(RetTy), APInt::getAllOnes(VF.getKnownMinValue()), true, |
| false); |
| |
| // Some targets keep addresses scalar. |
| if (isa<LoadInst>(I) && !TTI.prefersVectorizedAddressing()) |
| return Cost; |
| |
| // Some targets support efficient element stores. |
| if (isa<StoreInst>(I) && TTI.supportsEfficientVectorElementLoadStore()) |
| return Cost; |
| |
| // Collect operands to consider. |
| CallInst *CI = dyn_cast<CallInst>(I); |
| Instruction::op_range Ops = CI ? CI->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); |
| } |
| |
| 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)) |
| NumPredStores++; |
| |
| if (Legal->isUniformMemOp(I)) { |
| // TODO: Avoid replicating loads and stores instead of |
| // relying on instcombine to remove them. |
| // Load: Scalar load + broadcast |
| // Store: Scalar store + isLoopInvariantStoreValue ? 0 : extract |
| InstructionCost Cost; |
| if (isa<StoreInst>(&I) && VF.isScalable() && |
| isLegalGatherOrScatter(&I)) { |
| Cost = getGatherScatterCost(&I, VF); |
| setWideningDecision(&I, VF, CM_GatherScatter, Cost); |
| } else { |
| assert((isa<LoadInst>(&I) || !VF.isScalable()) && |
| "Cannot yet scalarize uniform stores"); |
| Cost = getUniformMemOpCost(&I, VF); |
| setWideningDecision(&I, VF, CM_Scalarize, Cost); |
| } |
| continue; |
| } |
| |
| // We assume that widening is the best solution when possible. |
| if (memoryInstructionCanBeWidened(&I, VF)) { |
| 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) |
| ? 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); |
| } |
| } |
| |
| InstructionCost |
| LoopVectorizationCostModel::getInstructionCost(Instruction *I, ElementCount VF, |
| Type *&VectorTy) { |
| 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; |
| |
| 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 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); |
| |
| // TODO: We need to estimate the cost of intrinsic calls. |
| switch (I->getOpcode()) { |
| case Instruction::GetElementPtr: |
| // We mark this instruction as zero-cost because the cost of GEPs in |
| // vectorized code depends on whether the corresponding memory instruction |
| // is scalarized or not. Therefore, we handle GEPs with the memory |
| // instruction cost. |
| return 0; |
| case Instruction::Br: { |
| // In cases of scalarized and predicated instructions, there will be VF |
| // predicated blocks in the vectorized loop. Each branch around these |
| // blocks requires also an extract of its vector compare i1 element. |
| bool ScalarPredicatedBB = false; |
| BranchInst *BI = cast<BranchInst>(I); |
| if (VF.isVector() && BI->isConditional() && |
| (PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) || |
| PredicatedBBsAfterVectorization.count(BI->getSuccessor(1)))) |
| ScalarPredicatedBB = true; |
| |
| if (ScalarPredicatedBB) { |
| // 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()), false, true) + |
| (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::PHI: { |
| auto *Phi = cast<PHINode>(I); |
| |
| // First-order recurrences are replaced by vector shuffles inside the loop. |
| // NOTE: Don't use ToVectorTy as SK_ExtractSubvector expects a vector type. |
| if (VF.isVector() && Legal->isFirstOrderRecurrence(Phi)) |
| return TTI.getShuffleCost( |
| TargetTransformInfo::SK_ExtractSubvector, cast<VectorType>(VectorTy), |
| None, VF.getKnownMinValue() - 1, FixedVectorType::get(RetTy, 1)); |
| |
| // Phi nodes in non-header blocks (not inductions, reductions, etc.) are |
| // converted into select instructions. We require N - 1 selects per phi |
| // node, where N is the number of incoming values. |
| if (VF.isVector() && Phi->getParent() != TheLoop->getHeader()) |
| return (Phi->getNumIncomingValues() - 1) * |
| TTI.getCmpSelInstrCost( |
| Instruction::Select, ToVectorTy(Phi->getType(), VF), |
| ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF), |
| CmpInst::BAD_ICMP_PREDICATE, CostKind); |
| |
| return TTI.getCFInstrCost(Instruction::PHI, CostKind); |
| } |
| case Instruction::UDiv: |
| case Instruction::SDiv: |
| case Instruction::URem: |
| case Instruction::SRem: |
| // If we have a predicated instruction, it may not be executed for each |
| // vector lane. Get the scalarization cost and scale this amount by the |
| // probability of executing the predicated block. If the instruction is not |
| // predicated, we fall through to the next case. |
| if (VF.isVector() && isScalarWithPredication(I)) { |
| InstructionCost Cost = 0; |
| |
| // These instructions have a non-void type, so account for the phi nodes |
| // that we will create. This cost is likely to be zero. The phi node |
| // cost, if any, should be scaled by the block probability because it |
| // models a copy at the end of each predicated block. |
| Cost += VF.getKnownMinValue() * |
| TTI.getCFInstrCost(Instruction::PHI, CostKind); |
| |
| // The cost of the non-predicated instruction. |
| Cost += VF.getKnownMinValue() * |
| TTI.getArithmeticInstrCost(I->getOpcode(), RetTy, CostKind); |
| |
| // The cost of insertelement and extractelement instructions needed for |
| // scalarization. |
| Cost += getScalarizationOverhead(I, VF); |
| |
| // Scale the cost by the probability of executing the predicated blocks. |
| // This assumes the predicated block for each vector lane is equally |
| // likely. |
| return Cost / getReciprocalPredBlockProb(); |
| } |
| LLVM_FALLTHROUGH; |
| case Instruction::Add: |
| case Instruction::FAdd: |
| case Instruction::Sub: |
| case Instruction::FSub: |
| case Instruction::Mul: |
| case Instruction::FMul: |
| case Instruction::FDiv: |
| case Instruction::FRem: |
| case Instruction::Shl: |
| case Instruction::LShr: |
| case Instruction::AShr: |
| case Instruction::And: |
| case Instruction::Or: |
| case Instruction::Xor: { |
| // Since we will replace the stride by 1 the multiplication should go away. |
| if (I->getOpcode() == Instruction::Mul && isStrideMul(I, Legal)) |
| return 0; |
| |
| // 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); |
| TargetTransformInfo::OperandValueProperties Op2VP; |
| TargetTransformInfo::OperandValueKind Op2VK = |
| TTI.getOperandInfo(Op2, Op2VP); |
| if (Op2VK == TargetTransformInfo::OK_AnyValue && Legal->isUniform(Op2)) |
| Op2VK = TargetTransformInfo::OK_UniformValue; |
| |
| SmallVector<const Value *, 4> Operands(I->operand_values()); |
| return TTI.getArithmeticInstrCost( |
| I->getOpcode(), VectorTy, CostKind, TargetTransformInfo::OK_AnyValue, |
| Op2VK, TargetTransformInfo::OP_None, Op2VP, Operands, I); |
| } |
| case Instruction::FNeg: { |
| return TTI.getArithmeticInstrCost( |
| I->getOpcode(), VectorTy, CostKind, TargetTransformInfo::OK_AnyValue, |
| TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None, |
| TargetTransformInfo::OP_None, I->getOperand(0), I); |
| } |
| case Instruction::Select: { |
| SelectInst *SI = cast<SelectInst>(I); |
| const SCEV *CondSCEV = SE->getSCEV(SI->getCondition()); |
| bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop)); |
| |
| 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 |
| TTI::OperandValueProperties Op1VP = TTI::OP_None; |
| TTI::OperandValueProperties Op2VP = TTI::OP_None; |
| TTI::OperandValueKind Op1VK = TTI::getOperandInfo(Op0, Op1VP); |
| TTI::OperandValueKind Op2VK = TTI::getOperandInfo(Op1, Op2VP); |
| 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, Op2VK, Op1VP, Op2VP, Operands, I); |
| } |
| |
| Type *CondTy = SI->getCondition()->getType(); |
| if (!ScalarCond) |
| CondTy = VectorType::get(CondTy, VF); |
| return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy, |
| CmpInst::BAD_ICMP_PREDICATE, 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, |
| CmpInst::BAD_ICMP_PREDICATE, CostKind, I); |
| } |
| case Instruction::Store: |
| case Instruction::Load: { |
| ElementCount Width = VF; |
| if (Width.isVector()) { |
| InstWidening Decision = getWideningDecision(I, Width); |
| assert(Decision != CM_Unknown && |
| "CM decision should be taken at this point"); |
| if (Decision == CM_Scalarize) |
| Width = ElementCount::getFixed(1); |
| } |
| VectorTy = ToVectorTy(getLoadStoreType(I), Width); |
| return getMemoryInstructionCost(I, VF); |
| } |
| case Instruction::BitCast: |
| if (I->getType()->isPointerTy()) |
| return 0; |
| LLVM_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?"); |
| } |
| |
| 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(); |
| Type *SrcVecTy = |
| VectorTy->isVectorTy() ? ToVectorTy(SrcScalarTy, VF) : SrcScalarTy; |
| if (canTruncateToMinimalBitwidth(I, VF)) { |
| // This cast is going to be shrunk. This may remove the cast or it might |
| // turn it into slightly different cast. For example, if MinBW == 16, |
| // "zext i8 %1 to i32" becomes "zext i8 %1 to i16". |
| // |
| // Calculate the modified src and dest types. |
| Type *MinVecTy = VectorTy; |
| if (Opcode == Instruction::Trunc) { |
| SrcVecTy = smallestIntegerVectorType(SrcVecTy, MinVecTy); |
| VectorTy = |
| largestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy); |
| } else if (Opcode == Instruction::ZExt || Opcode == Instruction::SExt) { |
| SrcVecTy = largestIntegerVectorType(SrcVecTy, MinVecTy); |
| VectorTy = |
| smallestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy); |
| } |
| } |
| |
| return TTI.getCastInstrCost(Opcode, VectorTy, SrcVecTy, CCH, CostKind, I); |
| } |
| case Instruction::Call: { |
| if (RecurrenceDescriptor::isFMulAddIntrinsic(I)) |
| if (auto RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind)) |
| return *RedCost; |
| bool NeedToScalarize; |
| CallInst *CI = cast<CallInst>(I); |
| InstructionCost CallCost = getVectorCallCost(CI, VF, NeedToScalarize); |
| if (getVectorIntrinsicIDForCall(CI, TLI)) { |
| InstructionCost IntrinsicCost = getVectorIntrinsicCost(CI, VF); |
| return std::min(CallCost, IntrinsicCost); |
| } |
| return CallCost; |
| } |
| 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(); |
| LLVM_FALLTHROUGH; |
| default: |
| // This opcode is unknown. Assume that it is the same as 'mul'. |
| return TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy, CostKind); |
| } // end of switch. |
| } |
| |
| char LoopVectorize::ID = 0; |
| |
| static const char lv_name[] = "Loop Vectorization"; |
| |
| INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false) |
| INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(BasicAAWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(GlobalsAAWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker) |
| INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(LoopAccessLegacyAnalysis) |
| INITIALIZE_PASS_DEPENDENCY(DemandedBitsWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(ProfileSummaryInfoWrapperPass) |
| INITIALIZE_PASS_DEPENDENCY(InjectTLIMappingsLegacy) |
| INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false) |
| |
| namespace llvm { |
| |
| Pass *createLoopVectorizePass() { return new LoopVectorize(); } |
| |
| Pass *createLoopVectorizePass(bool InterleaveOnlyWhenForced, |
| bool VectorizeOnlyWhenForced) { |
| return new LoopVectorize(InterleaveOnlyWhenForced, VectorizeOnlyWhenForced); |
| } |
| |
| } // end namespace llvm |
| |
| bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) { |
| // Check if the pointer operand of a load or store instruction is |
| // consecutive. |
| if (auto *Ptr = getLoadStorePointerOperand(Inst)) |
| return Legal->isConsecutivePtr(getLoadStoreType(Inst), Ptr); |
| return false; |
| } |
| |
| void LoopVectorizationCostModel::collectValuesToIgnore() { |
| // Ignore ephemeral values. |
| CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore); |
| |
| // Ignore type-promoting instructions we identified during reduction |
| // detection. |
| for (auto &Reduction : Legal->getReductionVars()) { |
| RecurrenceDescriptor &RedDes = Reduction.second; |
| const SmallPtrSetImpl<Instruction *> &Casts = RedDes.getCastInsts(); |
| VecValuesToIgnore.insert(Casts.begin(), Casts.end()); |
| } |
| // Ignore type-casting instructions we identified during induction |
| // detection. |
| for (auto &Induction : Legal->getInductionVars()) { |
| InductionDescriptor &IndDes = Induction.second; |
| const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts(); |
| VecValuesToIgnore.insert(Casts.begin(), Casts.end()); |
| } |
| } |
| |
| void LoopVectorizationCostModel::collectInLoopReductions() { |
| for (auto &Reduction : Legal->getReductionVars()) { |
| PHINode *Phi = Reduction.first; |
| RecurrenceDescriptor &RdxDesc = Reduction.second; |
| |
| // We don't collect reductions that are type promoted (yet). |
| if (RdxDesc.getRecurrenceType() != Phi->getType()) |
| continue; |
| |
| // If the target would prefer this reduction to happen "in-loop", then we |
| // want to record it as such. |
| unsigned Opcode = RdxDesc.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) { |
| InLoopReductionChains[Phi] = ReductionOperations; |
| // 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"); |
| } |
| } |
| |
| // TODO: we could return a pair of values that specify the max VF and |
| // min VF, to be used in `buildVPlans(MinVF, MaxVF)` instead of |
| // `buildVPlans(VF, VF)`. We cannot do it because VPLAN at the moment |
| // doesn't have a cost model that can choose which plan to execute if |
| // more than one is generated. |
| static unsigned determineVPlanVF(const unsigned WidestVectorRegBits, |
| LoopVectorizationCostModel &CM) { |
| unsigned WidestType; |
| std::tie(std::ignore, WidestType) = CM.getSmallestAndWidestTypes(); |
| return WidestVectorRegBits / WidestType; |
| } |
| |
| VectorizationFactor |
| LoopVectorizationPlanner::planInVPlanNativePath(ElementCount UserVF) { |
| assert(!UserVF.isScalable() && "scalable vectors not yet supported"); |
| ElementCount VF = UserVF; |
| // Outer loop handling: They may require CFG and instruction level |
| // transformations before even evaluating whether vectorization is profitable. |
| // Since we cannot modify the incoming IR, we need to build VPlan upfront in |
| // the vectorization pipeline. |
| if (!OrigLoop->isInnermost()) { |
| // If the user doesn't provide a vectorization factor, determine a |
| // reasonable one. |
| if (UserVF.isZero()) { |
| VF = ElementCount::getFixed(determineVPlanVF( |
| TTI->getRegisterBitWidth(TargetTransformInfo::RGK_FixedWidthVector) |
| .getFixedSize(), |
| CM)); |
| LLVM_DEBUG(dbgs() << "LV: VPlan computed VF " << VF << ".\n"); |
| |
| // Make sure we have a VF > 1 for stress testing. |
| if (VPlanBuildStressTest && (VF.isScalar() || VF.isZero())) { |
| LLVM_DEBUG(dbgs() << "LV: VPlan stress testing: " |
| << "overriding computed VF.\n"); |
| VF = ElementCount::getFixed(4); |
| } |
| } |
| assert(EnableVPlanNativePath && "VPlan-native path is not enabled."); |
| assert(isPowerOf2_32(VF.getKnownMinValue()) && |
| "VF needs to be a power of two"); |
| LLVM_DEBUG(dbgs() << "LV: Using " << (!UserVF.isZero() ? "user " : "") |
| << "VF " << VF << " to build VPlans.\n"); |
| buildVPlans(VF, VF); |
| |
| // For VPlan build stress testing, we bail out after VPlan construction. |
| if (VPlanBuildStressTest) |
| return VectorizationFactor::Disabled(); |
| |
| return {VF, 0 /*Cost*/}; |
| } |
| |
| LLVM_DEBUG( |
| dbgs() << "LV: Not vectorizing. Inner loops aren't supported in the " |
| "VPlan-native path.\n"); |
| return VectorizationFactor::Disabled(); |
| } |
| |
| Optional<VectorizationFactor> |
| LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) { |
| assert(OrigLoop->isInnermost() && "Inner loop expected."); |
| FixedScalableVFPair MaxFactors = CM.computeMaxVF(UserVF, UserIC); |
| if (!MaxFactors) // Cases that should not to be vectorized nor interleaved. |
| return None; |
| |
| // 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(); |
| } |
| |
| ElementCount MaxUserVF = |
| UserVF.isScalable() ? MaxFactors.ScalableVF : MaxFactors.FixedVF; |
| bool UserVFIsLegal = ElementCount::isKnownLE(UserVF, MaxUserVF); |
| if (!UserVF.isZero() && UserVFIsLegal) { |
| 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. |
| if (CM.selectUserVectorizationFactor(UserVF)) { |
| LLVM_DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n"); |
| CM.collectInLoopReductions(); |
| buildVPlansWithVPRecipes(UserVF, UserVF); |
| LLVM_DEBUG(printPlans(dbgs())); |
| return {{UserVF, 0}}; |
| } else |
| reportVectorizationInfo("UserVF ignored because of invalid costs.", |
| "InvalidCost", ORE, OrigLoop); |
| } |
| |
| // Populate the set of Vectorization Factor Candidates. |
| ElementCountSet VFCandidates; |
| for (auto VF = ElementCount::getFixed(1); |
| ElementCount::isKnownLE(VF, MaxFactors.FixedVF); VF *= 2) |
| VFCandidates.insert(VF); |
| for (auto VF = ElementCount::getScalable(1); |
| ElementCount::isKnownLE(VF, MaxFactors.ScalableVF); VF *= 2) |
| VFCandidates.insert(VF); |
| |
| 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); |
| } |
| |
| CM.collectInLoopReductions(); |
| buildVPlansWithVPRecipes(ElementCount::getFixed(1), MaxFactors.FixedVF); |
| buildVPlansWithVPRecipes(ElementCount::getScalable(1), MaxFactors.ScalableVF); |
| |
| LLVM_DEBUG(printPlans(dbgs())); |
| if (!MaxFactors.hasVector()) |
| return VectorizationFactor::Disabled(); |
| |
| // Select the optimal vectorization factor. |
| auto SelectedVF = CM.selectVectorizationFactor(VFCandidates); |
| |
| // Check if it is profitable to vectorize with runtime checks. |
| unsigned NumRuntimePointerChecks = Requirements.getNumRuntimePointerChecks(); |
| if (SelectedVF.Width.getKnownMinValue() > 1 && NumRuntimePointerChecks) { |
| bool PragmaThresholdReached = |
| NumRuntimePointerChecks > PragmaVectorizeMemoryCheckThreshold; |
| bool ThresholdReached = |
| NumRuntimePointerChecks > VectorizerParams::RuntimeMemoryCheckThreshold; |
| if ((ThresholdReached && !Hints.allowReordering()) || |
| PragmaThresholdReached) { |
| ORE->emit([&]() { |
| return OptimizationRemarkAnalysisAliasing( |
| DEBUG_TYPE, "CantReorderMemOps", OrigLoop->getStartLoc(), |
| OrigLoop->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 VectorizationFactor::Disabled(); |
| } |
| } |
| return SelectedVF; |
| } |
| |
| VPlan &LoopVectorizationPlanner::getBestPlanFor(ElementCount VF) const { |
| assert(count_if(VPlans, |
| [VF](const VPlanPtr &Plan) { return Plan->hasVF(VF); }) == |
| 1 && |
| "Best VF has not a single VPlan."); |
| |
| for (const VPlanPtr &Plan : VPlans) { |
| if (Plan->hasVF(VF)) |
| return *Plan.get(); |
| } |
| llvm_unreachable("No plan found!"); |
| } |
| |
| void LoopVectorizationPlanner::executePlan(ElementCount BestVF, unsigned BestUF, |
| VPlan &BestVPlan, |
| InnerLoopVectorizer &ILV, |
| DominatorTree *DT) { |
| LLVM_DEBUG(dbgs() << "Executing best plan with VF=" << BestVF << ", UF=" << BestUF |
| << '\n'); |
| |
| // Perform the actual loop transformation. |
| |
| // 1. Create a new empty loop. Unlink the old loop and connect the new one. |
| VPTransformState State{BestVF, BestUF, LI, DT, ILV.Builder, &ILV, &BestVPlan}; |
| State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton(); |
| State.TripCount = ILV.getOrCreateTripCount(nullptr); |
| State.CanonicalIV = ILV.Induction; |
| ILV.collectPoisonGeneratingRecipes(State); |
| |
| 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.execute(&State); |
| |
| // 3. Fix the vectorized code: take care of header phi's, live-outs, |
| // predication, updating analyses. |
| ILV.fixVectorizedLoop(State); |
| |
| ILV.printDebugTracesAtEnd(); |
| } |
| |
| #if !defined(NDEBUG) || defined(LLVM_ENABLE_DUMP) |
| void LoopVectorizationPlanner::printPlans(raw_ostream &O) { |
| for (const auto &Plan : VPlans) |
| if (PrintVPlansInDotFormat) |
| Plan->printDOT(O); |
| else |
| Plan->print(O); |
| } |
| #endif |
| |
| void LoopVectorizationPlanner::collectTriviallyDeadInstructions( |
| SmallPtrSetImpl<Instruction *> &DeadInstructions) { |
| |
| // We create new control-flow for the vectorized loop, so the original exit |
| // conditions will be dead after vectorization if it's only used by the |
| // terminator |
| SmallVector<BasicBlock*> ExitingBlocks; |
| OrigLoop->getExitingBlocks(ExitingBlocks); |
| for (auto *BB : ExitingBlocks) { |
| auto *Cmp = dyn_cast<Instruction>(BB->getTerminator()->getOperand(0)); |
| if (!Cmp || !Cmp->hasOneUse()) |
| continue; |
| |
| // TODO: we should introduce a getUniqueExitingBlocks on Loop |
| if (!DeadInstructions.insert(Cmp).second) |
| continue; |
| |
| // The operands of the icmp is often a dead trunc, used by IndUpdate. |
| // TODO: can recurse through operands in general |
| for (Value *Op : Cmp->operands()) { |
| if (isa<TruncInst>(Op) && Op->hasOneUse()) |
| DeadInstructions.insert(cast<Instruction>(Op)); |
| } |
| } |
| |
| // We create new "steps" for induction variable updates to which the original |
| // induction variables map. An original update instruction will be dead if |
| // all its users except the induction variable are dead. |
| auto *Latch = OrigLoop->getLoopLatch(); |
| for (auto &Induction : Legal->getInductionVars()) { |
| PHINode *Ind = Induction.first; |
| auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| |
| // If the tail is to be folded by masking, the primary induction variable, |
| // if exists, isn't dead: it will be used for masking. Don't kill it. |
| if (CM.foldTailByMasking() && IndUpdate == Legal->getPrimaryInduction()) |
| continue; |
| |
| if (llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| return U == Ind || DeadInstructions.count(cast<Instruction>(U)); |
| })) |
| DeadInstructions.insert(IndUpdate); |
| |
| // We record as "Dead" also the type-casting instructions we had identified |
| // during induction analysis. We don't need any handling for them in the |
| // vectorized loop because we have proven that, under a proper runtime |
| // test guarding the vectorized loop, the value of the phi, and the casted |
| // value of the phi, are the same. The last instruction in this casting chain |
| // will get its scalar/vector/widened def from the scalar/vector/widened def |
| // of the respective phi node. Any other casts in the induction def-use chain |
| // have no other uses outside the phi update chain, and will be ignored. |
| InductionDescriptor &IndDes = Induction.second; |
| const SmallVectorImpl<Instruction *> &Casts = IndDes.getCastInsts(); |
| DeadInstructions.insert(Casts.begin(), Casts.end()); |
| } |
| } |
| |
| Value *InnerLoopUnroller::reverseVector(Value *Vec) { return Vec; } |
| |
| Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) { return V; } |
| |
| Value *InnerLoopUnroller::getStepVector(Value *Val, Value *StartIdx, |
| Value *Step, |
| Instruction::BinaryOps BinOp) { |
| // When unrolling and the VF is 1, we only need to add a simple scalar. |
| Type *Ty = Val->getType(); |
| assert(!Ty->isVectorTy() && "Val must be a scalar"); |
| |
| if (Ty->isFloatingPointTy()) { |
| // Floating-point operations inherit FMF via the builder's flags. |
| Value *MulOp = Builder.CreateFMul(StartIdx, Step); |
| return Builder.CreateBinOp(BinOp, Val, MulOp); |
| } |
| return Builder.CreateAdd(Val, Builder.CreateMul(StartIdx, Step), "induction"); |
| } |
| |
| static void AddRuntimeUnrollDisableMetaData(Loop *L) { |
| SmallVector<Metadata *, 4> MDs; |
| // Reserve first location for self reference to the LoopID metadata node. |
| MDs.push_back(nullptr); |
| bool IsUnrollMetadata = false; |
| MDNode *LoopID = L->getLoopID(); |
| if (LoopID) { |
| // First find existing loop unrolling disable metadata. |
| for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) { |
| auto *MD = dyn_cast<MDNode>(LoopID->getOperand(i)); |
| if (MD) { |
| const auto *S = dyn_cast<MDString>(MD->getOperand(0)); |
| IsUnrollMetadata = |
| S && S->getString().startswith("llvm.loop.unroll.disable"); |
| } |
| MDs.push_back(LoopID->getOperand(i)); |
| } |
| } |
| |
| if (!IsUnrollMetadata) { |
| // Add runtime unroll disable metadata. |
| LLVMContext &Context = L->getHeader()->getContext(); |
| SmallVector<Metadata *, 1> DisableOperands; |
| DisableOperands.push_back( |
| MDString::get(Context, "llvm.loop.unroll.runtime.disable")); |
| MDNode *DisableNode = MDNode::get(Context, DisableOperands); |
| MDs.push_back(DisableNode); |
| MDNode *NewLoopID = MDNode::get(Context, MDs); |
| // Set operand 0 to refer to the loop id itself. |
| NewLoopID->replaceOperandWith(0, NewLoopID); |
| L->setLoopID(NewLoopID); |
| } |
| } |
| |
| //===--------------------------------------------------------------------===// |
| // EpilogueVectorizerMainLoop |
| //===--------------------------------------------------------------------===// |
| |
| /// This function is partially responsible for generating the control flow |
| /// depicted in https://llvm.org/docs/Vectorizers.html#epilogue-vectorization. |
| BasicBlock *EpilogueVectorizerMainLoop::createEpilogueVectorizedLoopSkeleton() { |
| MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| Loop *Lp = createVectorLoopSkeleton(""); |
| |
| // Generate the code to check the minimum iteration count of the vector |
| // epilogue (see below). |
| EPI.EpilogueIterationCountCheck = |
| emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader, true); |
| EPI.EpilogueIterationCountCheck->setName("iter.check"); |
| |
| // Generate the code to check any assumptions that we've made for SCEV |
| // expressions. |
| EPI.SCEVSafetyCheck = emitSCEVChecks(Lp, 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(Lp, 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 = |
| emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader, false); |
| |
| // Generate the induction variable. |
| OldInduction = Legal->getPrimaryInduction(); |
| Type *IdxTy = Legal->getWidestInductionType(); |
| Value *StartIdx = ConstantInt::get(IdxTy, 0); |
| |
| IRBuilder<> B(&*Lp->getLoopPreheader()->getFirstInsertionPt()); |
| Value *Step = getRuntimeVF(B, IdxTy, VF * UF); |
| Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| EPI.VectorTripCount = CountRoundDown; |
| Induction = |
| createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| getDebugLocFromInstOrOperands(OldInduction)); |
| |
| // Skip induction resume value creation here because they will be created in |
| // the second pass. If we created them here, they wouldn't be used anyway, |
| // because the vplan in the second pass still contains the inductions from the |
| // original loop. |
| |
| return completeLoopSkeleton(Lp, OrigLoopID); |
| } |
| |
| 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::emitMinimumIterationCountCheck( |
| Loop *L, BasicBlock *Bypass, bool ForEpilogue) { |
| assert(L && "Expected valid Loop."); |
| assert(Bypass && "Expected valid bypass basic block."); |
| ElementCount VFactor = ForEpilogue ? EPI.EpilogueVF : VF; |
| unsigned UFactor = ForEpilogue ? EPI.EpilogueUF : UF; |
| Value *Count = getOrCreateTripCount(L); |
| // Reuse existing vector loop preheader for TC checks. |
| // Note that new preheader block is generated for vector loop. |
| BasicBlock *const TCCheckBlock = LoopVectorPreHeader; |
| IRBuilder<> Builder(TCCheckBlock->getTerminator()); |
| |
| // Generate code to check if the loop's trip count is less than VF * UF of the |
| // main vector loop. |
| auto P = Cost->requiresScalarEpilogue(ForEpilogue ? EPI.EpilogueVF : VF) ? |
| 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 & LoopExit. |
| DT->changeImmediateDominator(Bypass, TCCheckBlock); |
| if (!Cost->requiresScalarEpilogue(EPI.EpilogueVF)) |
| // For loops with multiple exits, there's no edge from the middle block |
| // to exit blocks (as the epilogue must run) and thus no need to update |
| // the immediate dominator of the exit blocks. |
| DT->changeImmediateDominator(LoopExitBlock, 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; |
| } |
| |
| ReplaceInstWithInst( |
| TCCheckBlock->getTerminator(), |
| BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| |
| return TCCheckBlock; |
| } |
| |
| //===--------------------------------------------------------------------===// |
| // EpilogueVectorizerEpilogueLoop |
| //===--------------------------------------------------------------------===// |
| |
| /// This function is partially responsible for generating the control flow |
| /// depicted in https://llvm.org/docs/Vectorizers.html#epilogue-vectorization. |
| BasicBlock * |
| EpilogueVectorizerEpilogueLoop::createEpilogueVectorizedLoopSkeleton() { |
| MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| Loop *Lp = 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. |
| BasicBlock *VecEpilogueIterationCountCheck = LoopVectorPreHeader; |
| VecEpilogueIterationCountCheck->setName("vec.epilog.iter.check"); |
| LoopVectorPreHeader = |
| SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| LI, nullptr, "vec.epilog.ph"); |
| emitMinimumVectorEpilogueIterCountCheck(Lp, 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)) |
| // 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 |
| // 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); |
| |
| // 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", |
| LoopVectorPreHeader->getFirstNonPHI()); |
| EPResumeVal->addIncoming(EPI.VectorTripCount, VecEpilogueIterationCountCheck); |
| EPResumeVal->addIncoming(ConstantInt::get(IdxTy, 0), |
| EPI.MainLoopIterationCountCheck); |
| |
| // Generate the induction variable. |
| OldInduction = Legal->getPrimaryInduction(); |
| Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| Constant *Step = ConstantInt::get(IdxTy, VF.getKnownMinValue() * UF); |
| Value *StartIdx = EPResumeVal; |
| Induction = |
| createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| getDebugLocFromInstOrOperands(OldInduction)); |
| |
| // 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(Lp, CountRoundDown, |
| {VecEpilogueIterationCountCheck, |
| EPI.VectorTripCount} /* AdditionalBypass */); |
| |
| AddRuntimeUnrollDisableMetaData(Lp); |
| return completeLoopSkeleton(Lp, OrigLoopID); |
| } |
| |
| BasicBlock * |
| EpilogueVectorizerEpilogueLoop::emitMinimumVectorEpilogueIterCountCheck( |
| Loop *L, 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) ? |
| ICmpInst::ICMP_ULE : ICmpInst::ICMP_ULT; |
| |
| Value *CheckMinIters = |
| Builder.CreateICmp(P, Count, |
| createStepForVF(Builder, Count->getType(), |
| EPI.EpilogueVF, EPI.EpilogueUF), |
| "min.epilog.iters.check"); |
| |
| ReplaceInstWithInst( |
| Insert->getTerminator(), |
| BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| |
| 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"; |
| }); |
| } |
| |
| bool LoopVectorizationPlanner::getDecisionAndClampRange( |
| const std::function<bool(ElementCount)> &Predicate, VFRange &Range) { |
| assert(!Range.isEmpty() && "Trying to test an empty VF range."); |
| bool PredicateAtRangeStart = Predicate(Range.Start); |
| |
| for (ElementCount TmpVF = Range.Start * 2; |
| ElementCount::isKnownLT(TmpVF, Range.End); TmpVF *= 2) |
| if (Predicate(TmpVF) != PredicateAtRangeStart) { |
| Range.End = TmpVF; |
| break; |
| } |
| |
| return PredicateAtRangeStart; |
| } |
| |
| /// Build VPlans for the full range of feasible VF's = {\p MinVF, 2 * \p MinVF, |
| /// 4 * \p MinVF, ..., \p MaxVF} by repeatedly building a VPlan for a sub-range |
| /// of VF's starting at a given VF and extending it as much as possible. Each |
| /// vectorization decision can potentially shorten this sub-range during |
| /// buildVPlan(). |
| void LoopVectorizationPlanner::buildVPlans(ElementCount MinVF, |
| ElementCount MaxVF) { |
| auto MaxVFPlusOne = MaxVF.getWithIncrement(1); |
| for (ElementCount VF = MinVF; ElementCount::isKnownLT(VF, MaxVFPlusOne);) { |
| VFRange SubRange = {VF, MaxVFPlusOne}; |
| VPlans.push_back(buildVPlan(SubRange)); |
| VF = SubRange.End; |
| } |
| } |
| |
| VPValue *VPRecipeBuilder::createEdgeMask(BasicBlock *Src, BasicBlock *Dst, |
| VPlanPtr &Plan) { |
| assert(is_contained(predecessors(Dst), Src) && "Invalid edge"); |
| |
| // Look for cached value. |
| std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst); |
| EdgeMaskCacheTy::iterator ECEntryIt = EdgeMaskCache.find(Edge); |
| if (ECEntryIt != EdgeMaskCache.end()) |
| return ECEntryIt->second; |
| |
| VPValue *SrcMask = createBlockInMask(Src, Plan); |
| |
| // The terminator has to be a branch inst! |
| BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator()); |
| assert(BI && "Unexpected terminator found"); |
| |
| if (!BI->isConditional() || BI->getSuccessor(0) == BI->getSuccessor(1)) |
| return EdgeMaskCache[Edge] = SrcMask; |
| |
| // 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 = Plan->getOrAddVPValue(BI->getCondition()); |
| assert(EdgeMask && "No Edge Mask found for condition"); |
| |
| if (BI->getSuccessor(0) != Dst) |
| EdgeMask = Builder.createNot(EdgeMask); |
| |
| if (SrcMask) { // Otherwise block in-mask is all-one, no need to AND. |
| // The condition is 'SrcMask && EdgeMask', which is equivalent to |
| // 'select i1 SrcMask, i1 EdgeMask, i1 false'. |
| // The select version does not introduce new UB if SrcMask is false and |
| // EdgeMask is poison. Using 'and' here introduces undefined behavior. |
| VPValue *False = Plan->getOrAddVPValue( |
| ConstantInt::getFalse(BI->getCondition()->getType())); |
| EdgeMask = Builder.createSelect(SrcMask, EdgeMask, False); |
| } |
| |
| return EdgeMaskCache[Edge] = EdgeMask; |
| } |
| |
| VPValue *VPRecipeBuilder::createBlockInMask(BasicBlock *BB, VPlanPtr &Plan) { |
| assert(OrigLoop->contains(BB) && "Block is not a part of a loop"); |
| |
| // Look for cached value. |
| BlockMaskCacheTy::iterator BCEntryIt = BlockMaskCache.find(BB); |
| if (BCEntryIt != BlockMaskCache.end()) |
| return BCEntryIt->second; |
| |
| // All-one mask is modelled as no-mask following the convention for masked |
| // load/store/gather/scatter. Initialize BlockMask to no-mask. |
| VPValue *BlockMask = nullptr; |
| |
| if (OrigLoop->getHeader() == BB) { |
| if (!CM.blockNeedsPredicationForAnyReason(BB)) |
| return BlockMaskCache[BB] = BlockMask; // Loop incoming mask is all-one. |
| |
| // Create the block in mask as the first non-phi instruction in the block. |
| VPBuilder::InsertPointGuard Guard(Builder); |
| auto NewInsertionPoint = Builder.getInsertBlock()->getFirstNonPhi(); |
| Builder.setInsertPoint(Builder.getInsertBlock(), NewInsertionPoint); |
| |
| // Introduce the early-exit compare IV <= BTC to form header block mask. |
| // This is used instead of IV < TC because TC may wrap, unlike BTC. |
| // Start by constructing the desired canonical IV. |
| VPValue *IV = nullptr; |
| if (Legal->getPrimaryInduction()) |
| IV = Plan->getOrAddVPValue(Legal->getPrimaryInduction()); |
| else { |
| auto *IVRecipe = new VPWidenCanonicalIVRecipe(); |
| Builder.getInsertBlock()->insert(IVRecipe, NewInsertionPoint); |
| IV = IVRecipe; |
| } |
| VPValue *BTC = Plan->getOrCreateBackedgeTakenCount(); |
| bool TailFolded = !CM.isScalarEpilogueAllowed(); |
| |
| if (TailFolded && CM.TTI.emitGetActiveLaneMask()) { |
| // While ActiveLaneMask is a binary op that consumes the loop tripcount |
| // as a second argument, we only pass the IV here and extract the |
| // tripcount from the transform state where codegen of the VP instructions |
| // happen. |
| BlockMask = Builder.createNaryOp(VPInstruction::ActiveLaneMask, {IV}); |
| } else { |
| BlockMask = Builder.createNaryOp(VPInstruction::ICmpULE, {IV, BTC}); |
| } |
| return BlockMaskCache[BB] = BlockMask; |
| } |
| |
| // This is the block mask. We OR all incoming edges. |
| for (auto *Predecessor : predecessors(BB)) { |
| VPValue *EdgeMask = createEdgeMask(Predecessor, BB, Plan); |
| if (!EdgeMask) // Mask of predecessor is all-one so mask of block is too. |
| return BlockMaskCache[BB] = EdgeMask; |
| |
| if (!BlockMask) { // BlockMask has its initialized nullptr value. |
| BlockMask = EdgeMask; |
| continue; |
| } |
| |
| BlockMask = Builder.createOr(BlockMask, EdgeMask); |
| } |
| |
| return BlockMaskCache[BB] = BlockMask; |
| } |
| |
| VPRecipeBase *VPRecipeBuilder::tryToWidenMemory(Instruction *I, |
| ArrayRef<VPValue *> Operands, |
| VFRange &Range, |
| VPlanPtr &Plan) { |
| assert((isa<LoadInst>(I) || isa<StoreInst>(I)) && |
| "Must be called with either a load or store"); |
| |
| auto willWiden = [&](ElementCount VF) -> bool { |
| if (VF.isScalar()) |
| return false; |
| LoopVectorizationCostModel::InstWidening Decision = |
| CM.getWideningDecision(I, VF); |
| assert(Decision != LoopVectorizationCostModel::CM_Unknown && |
| "CM decision should be taken at this point."); |
| if (Decision == LoopVectorizationCostModel::CM_Interleave) |
| return true; |
| if (CM.isScalarAfterVectorization(I, VF) || |
| CM.isProfitableToScalarize(I, VF)) |
| return false; |
| return Decision != LoopVectorizationCostModel::CM_Scalarize; |
| }; |
| |
| if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range)) |
| return nullptr; |
| |
| VPValue *Mask = nullptr; |
| if (Legal->isMaskRequired(I)) |
| Mask = createBlockInMask(I->getParent(), Plan); |
| |
| // 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; |
| |
| if (LoadInst *Load = dyn_cast<LoadInst>(I)) |
| return new VPWidenMemoryInstructionRecipe(*Load, Operands[0], Mask, |
| Consecutive, Reverse); |
| |
| StoreInst *Store = cast<StoreInst>(I); |
| return new VPWidenMemoryInstructionRecipe(*Store, Operands[1], Operands[0], |
| Mask, Consecutive, Reverse); |
| } |
| |
| VPWidenIntOrFpInductionRecipe * |
| VPRecipeBuilder::tryToOptimizeInductionPHI(PHINode *Phi, |
| ArrayRef<VPValue *> Operands) const { |
| // Check if this is an integer or fp induction. If so, build the recipe that |
| // produces its scalar and vector values. |
| InductionDescriptor II = Legal->getInductionVars().lookup(Phi); |
| if (II.getKind() == InductionDescriptor::IK_IntInduction || |
| II.getKind() == InductionDescriptor::IK_FpInduction) { |
| assert(II.getStartValue() == |
| Phi->getIncomingValueForBlock(OrigLoop->getLoopPreheader())); |
| const SmallVectorImpl<Instruction *> &Casts = II.getCastInsts(); |
| return new VPWidenIntOrFpInductionRecipe( |
| Phi, Operands[0], Casts.empty() ? nullptr : Casts.front()); |
| } |
| |
| return nullptr; |
| } |
| |
| VPWidenIntOrFpInductionRecipe *VPRecipeBuilder::tryToOptimizeInductionTruncate( |
| TruncInst *I, ArrayRef<VPValue *> Operands, VFRange &Range, |
| VPlan &Plan) const { |
| // Optimize the special case where the source is a constant integer |
| // induction variable. Notice that we can only optimize the 'trunc' case |
| // because (a) FP conversions lose precision, (b) sext/zext may wrap, and |
| // (c) other casts depend on pointer size. |
| |
| // Determine whether \p K is a truncation based on an induction variable that |
| // can be optimized. |
| auto isOptimizableIVTruncate = |
| [&](Instruction *K) -> std::function<bool(ElementCount)> { |
| return [=](ElementCount VF) -> bool { |
| return CM.isOptimizableIVTruncate(K, VF); |
| }; |
| }; |
| |
| if (LoopVectorizationPlanner::getDecisionAndClampRange( |
| isOptimizableIVTruncate(I), Range)) { |
| |
| InductionDescriptor II = |
| Legal->getInductionVars().lookup(cast<PHINode>(I->getOperand(0))); |
| VPValue *Start = Plan.getOrAddVPValue(II.getStartValue()); |
| return new VPWidenIntOrFpInductionRecipe(cast<PHINode>(I->getOperand(0)), |
| Start, nullptr, I); |
| } |
| return nullptr; |
| } |
| |
| VPRecipeOrVPValueTy VPRecipeBuilder::tryToBlend(PHINode *Phi, |
| ArrayRef<VPValue *> Operands, |
| VPlanPtr &Plan) { |
| // If all incoming values are equal, the incoming VPValue can be used directly |
| // instead of creating a new VPBlendRecipe. |
| VPValue *FirstIncoming = Operands[0]; |
| if (all_of(Operands, [FirstIncoming](const VPValue *Inc) { |
| return FirstIncoming == Inc; |
| })) { |
| return Operands[0]; |
| } |
| |
| // 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; |
| unsigned NumIncoming = Phi->getNumIncomingValues(); |
| |
| for (unsigned In = 0; In < NumIncoming; In++) { |
| VPValue *EdgeMask = |
| createEdgeMask(Phi->getIncomingBlock(In), Phi->getParent(), Plan); |
| assert((EdgeMask || NumIncoming == 1) && |
| "Multiple predecessors with one having a full mask"); |
| OperandsWithMask.push_back(Operands[In]); |
| if (EdgeMask) |
| OperandsWithMask.push_back(EdgeMask); |
| } |
| return toVPRecipeResult(new VPBlendRecipe(Phi, OperandsWithMask)); |
| } |
| |
| VPWidenCallRecipe *VPRecipeBuilder::tryToWidenCall(CallInst *CI, |
| ArrayRef<VPValue *> Operands, |
| VFRange &Range) const { |
| |
| bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange( |
| [this, CI](ElementCount VF) { return CM.isScalarWithPredication(CI); }, |
| 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; |
| |
| auto willWiden = [&](ElementCount VF) -> bool { |
| Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| // The following case may be scalarized depending on the VF. |
| // The flag shows whether we use Intrinsic or a usual Call for vectorized |
| // version of the instruction. |
| // Is it beneficial to perform intrinsic call compared to lib call? |
| bool NeedToScalarize = false; |
| InstructionCost CallCost = CM.getVectorCallCost(CI, VF, NeedToScalarize); |
| InstructionCost IntrinsicCost = ID ? CM.getVectorIntrinsicCost(CI, VF) : 0; |
| bool UseVectorIntrinsic = ID && IntrinsicCost <= CallCost; |
| return UseVectorIntrinsic || !NeedToScalarize; |
| }; |
| |
| if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range)) |
| return nullptr; |
| |
| ArrayRef<VPValue *> Ops = Operands.take_front(CI->arg_size()); |
| return new VPWidenCallRecipe(*CI, make_range(Ops.begin(), Ops.end())); |
| } |
| |
| 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); |
| }; |
| return !LoopVectorizationPlanner::getDecisionAndClampRange(WillScalarize, |
| Range); |
| } |
| |
| VPWidenRecipe *VPRecipeBuilder::tryToWiden(Instruction *I, |
| ArrayRef<VPValue *> Operands) const { |
| auto IsVectorizableOpcode = [](unsigned Opcode) { |
| switch (Opcode) { |
| case Instruction::Add: |
| case Instruction::And: |
| case Instruction::AShr: |
| case Instruction::BitCast: |
| case Instruction::FAdd: |
| case Instruction::FCmp: |
| case Instruction::FDiv: |
| case Instruction::FMul: |
| case Instruction::FNeg: |
| case Instruction::FPExt: |
| case Instruction::FPToSI: |
| case Instruction::FPToUI: |
| case Instruction::FPTrunc: |
| case Instruction::FRem: |
| case Instruction::FSub: |
| case Instruction::ICmp: |
| case Instruction::IntToPtr: |
| case Instruction::LShr: |
| case Instruction::Mul: |
| case Instruction::Or: |
| case Instruction::PtrToInt: |
| case Instruction::SDiv: |
| case Instruction::Select: |
| case Instruction::SExt: |
| case Instruction::Shl: |
| case Instruction::SIToFP: |
| case Instruction::SRem: |
| case Instruction::Sub: |
| case Instruction::Trunc: |
| case Instruction::UDiv: |
| case Instruction::UIToFP: |
| case Instruction::URem: |
| case Instruction::Xor: |
| case Instruction::ZExt: |
| return true; |
| } |
| return false; |
| }; |
| |
| if (!IsVectorizableOpcode(I->getOpcode())) |
| return nullptr; |
| |
| // Success: widen this instruction. |
| return new VPWidenRecipe(*I, make_range(Operands.begin(), Operands.end())); |
| } |
| |
| void VPRecipeBuilder::fixHeaderPhis() { |
| BasicBlock *OrigLatch = OrigLoop->getLoopLatch(); |
| for (VPWidenPHIRecipe *R : PhisToFix) { |
| auto *PN = cast<PHINode>(R->getUnderlyingValue()); |
| VPRecipeBase *IncR = |
| getRecipe(cast<Instruction>(PN->getIncomingValueForBlock(OrigLatch))); |
| R->addOperand(IncR->getVPSingleValue()); |
| } |
| } |
| |
| VPBasicBlock *VPRecipeBuilder::handleReplication( |
| Instruction *I, VFRange &Range, VPBasicBlock *VPBB, |
| VPlanPtr &Plan) { |
| bool IsUniform = LoopVectorizationPlanner::getDecisionAndClampRange( |
| [&](ElementCount VF) { return CM.isUniformAfterVectorization(I, VF); }, |
| Range); |
| |
| bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange( |
| [&](ElementCount VF) { return CM.isPredicatedInst(I, IsUniform); }, |
| Range); |
| |
| // 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; |
| } |
| } |
| |
| auto *Recipe = new VPReplicateRecipe(I, Plan->mapToVPValues(I->operands()), |
| IsUniform, IsPredicated); |
| setRecipe(I, Recipe); |
| Plan->addVPValue(I, Recipe); |
| |
| // Find if I uses a predicated instruction. If so, it will use its scalar |
| // value. Avoid hoisting the insert-element which packs the scalar value into |
| // a vector value, as that happens iff all users use the vector value. |
| for (VPValue *Op : Recipe->operands()) { |
| auto *PredR = dyn_cast_or_null<VPPredInstPHIRecipe>(Op->getDef()); |
| if (!PredR) |
| continue; |
| auto *RepR = |
| cast_or_null<VPReplicateRecipe>(PredR->getOperand(0)->getDef()); |
| assert(RepR->isPredicated() && |
| "expected Replicate recipe to be predicated"); |
| RepR->setAlsoPack(false); |
| } |
| |
| // Finalize the recipe for Instr, first if it is not predicated. |
| if (!IsPredicated) { |
| LLVM_DEBUG(dbgs() << "LV: Scalarizing:" << *I << "\n"); |
| VPBB->appendRecipe(Recipe); |
| return VPBB; |
| } |
| LLVM_DEBUG(dbgs() << "LV: Scalarizing and predicating:" << *I << "\n"); |
| assert(VPBB->getSuccessors().empty() && |
| "VPBB has successors when handling predicated replication."); |
| // Record predicated instructions for above packing optimizations. |
| VPBlockBase *Region = createReplicateRegion(I, Recipe, Plan); |
| VPBlockUtils::insertBlockAfter(Region, VPBB); |
| auto *RegSucc = new VPBasicBlock(); |
| VPBlockUtils::insertBlockAfter(RegSucc, Region); |
| return RegSucc; |
| } |
| |
| VPRegionBlock *VPRecipeBuilder::createReplicateRegion(Instruction *Instr, |
| VPRecipeBase *PredRecipe, |
| VPlanPtr &Plan) { |
| // Instructions marked for predication are replicated and placed under an |
| // if-then construct to prevent side-effects. |
| |
| // Generate recipes to compute the block mask for this region. |
| VPValue *BlockInMask = createBlockInMask(Instr->getParent(), Plan); |
| |
| // Build the triangular if-then region. |
| std::string RegionName = (Twine("pred.") + Instr->getOpcodeName()).str(); |
| assert(Instr->getParent() && "Predicated instruction not in any basic block"); |
| auto *BOMRecipe = new VPBranchOnMaskRecipe(BlockInMask); |
| auto *Entry = new VPBasicBlock(Twine(RegionName) + ".entry", BOMRecipe); |
| auto *PHIRecipe = Instr->getType()->isVoidTy() |
| ? nullptr |
| : new VPPredInstPHIRecipe(Plan->getOrAddVPValue(Instr)); |
| if (PHIRecipe) { |
| Plan->removeVPValueFor(Instr); |
| Plan->addVPValue(Instr, PHIRecipe); |
| } |
| auto *Exit = new VPBasicBlock(Twine(RegionName) + ".continue", PHIRecipe); |
| auto *Pred = new VPBasicBlock(Twine(RegionName) + ".if", PredRecipe); |
| VPRegionBlock *Region = new VPRegionBlock(Entry, Exit, RegionName, true); |
| |
| // Note: first set Entry as region entry and then connect successors starting |
| // from it in order, to propagate the "parent" of each VPBasicBlock. |
| VPBlockUtils::insertTwoBlocksAfter(Pred, Exit, BlockInMask, Entry); |
| VPBlockUtils::connectBlocks(Pred, Exit); |
| |
| return Region; |
| } |
| |
| VPRecipeOrVPValueTy |
| VPRecipeBuilder::tryToCreateWidenRecipe(Instruction *Instr, |
| ArrayRef<VPValue *> Operands, |
| VFRange &Range, VPlanPtr &Plan) { |
| // First, check for specific widening recipes that deal with calls, memory |
| // operations, inductions and Phi nodes. |
| if (auto *CI = dyn_cast<CallInst>(Instr)) |
| return toVPRecipeResult(tryToWidenCall(CI, Operands, Range)); |
| |
| if (isa<LoadInst>(Instr) || isa<StoreInst>(Instr)) |
| return toVPRecipeResult(tryToWidenMemory(Instr, Operands, Range, Plan)); |
| |
| VPRecipeBase *Recipe; |
| if (auto Phi = dyn_cast<PHINode>(Instr)) { |
| if (Phi->getParent() != OrigLoop->getHeader()) |
| return tryToBlend(Phi, Operands, Plan); |
| if ((Recipe = tryToOptimizeInductionPHI(Phi, Operands))) |
| return toVPRecipeResult(Recipe); |
| |
| VPWidenPHIRecipe *PhiRecipe = nullptr; |
| if (Legal->isReductionVariable(Phi) || Legal->isFirstOrderRecurrence(Phi)) { |
| VPValue *StartV = Operands[0]; |
| if (Legal->isReductionVariable(Phi)) { |
| RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi]; |
| assert(RdxDesc.getRecurrenceStartValue() == |
| Phi->getIncomingValueForBlock(OrigLoop->getLoopPreheader())); |
| PhiRecipe = new VPReductionPHIRecipe(Phi, RdxDesc, *StartV, |
| CM.isInLoopReduction(Phi), |
| CM.useOrderedReductions(RdxDesc)); |
| } else { |
| PhiRecipe = new VPFirstOrderRecurrencePHIRecipe(Phi, *StartV); |
| } |
| |
| // Record the incoming value from the backedge, so we can add the incoming |
| // value from the backedge after all recipes have been created. |
| recordRecipeOf(cast<Instruction>( |
| Phi->getIncomingValueForBlock(OrigLoop->getLoopLatch()))); |
| PhisToFix.push_back(PhiRecipe); |
| } else { |
| // TODO: record start and backedge value for remaining pointer induction |
| // phis. |
| assert(Phi->getType()->isPointerTy() && |
| "only pointer phis should be handled here"); |
| PhiRecipe = new VPWidenPHIRecipe(Phi); |
| } |
| |
| return toVPRecipeResult(PhiRecipe); |
| } |
| |
| if (isa<TruncInst>(Instr) && |
| (Recipe = tryToOptimizeInductionTruncate(cast<TruncInst>(Instr), Operands, |
| Range, *Plan))) |
| return toVPRecipeResult(Recipe); |
| |
| if (!shouldWiden(Instr, Range)) |
| return nullptr; |
| |
| if (auto GEP = dyn_cast<GetElementPtrInst>(Instr)) |
| return toVPRecipeResult(new VPWidenGEPRecipe( |
| GEP, make_range(Operands.begin(), Operands.end()), OrigLoop)); |
| |
| if (auto *SI = dyn_cast<SelectInst>(Instr)) { |
| bool InvariantCond = |
| PSE.getSE()->isLoopInvariant(PSE.getSCEV(SI->getOperand(0)), OrigLoop); |
| return toVPRecipeResult(new VPWidenSelectRecipe( |
| *SI, make_range(Operands.begin(), Operands.end()), InvariantCond)); |
| } |
| |
| return toVPRecipeResult(tryToWiden(Instr, Operands)); |
| } |
| |
| void LoopVectorizationPlanner::buildVPlansWithVPRecipes(ElementCount MinVF, |
| ElementCount MaxVF) { |
| assert(OrigLoop->isInnermost() && "Inner loop expected."); |
| |
| // Collect instructions from the original loop that will become trivially dead |
| // in the vectorized loop. We don't need to vectorize these instructions. For |
| // example, original induction update instructions can become dead because we |
| // separately emit induction "steps" when generating code for the new loop. |
| // Similarly, we create a new latch condition when setting up the structure |
| // of the new loop, so the old one can become dead. |
| SmallPtrSet<Instruction *, 4> DeadInstructions; |
| collectTriviallyDeadInstructions(DeadInstructions); |
| |
| // Add assume instructions we need to drop to DeadInstructions, to prevent |
| // them from being added to the VPlan. |
| // TODO: We only need to drop assumes in blocks that get flattend. If the |
| // control flow is preserved, we should keep them. |
| auto &ConditionalAssumes = Legal->getConditionalAssumes(); |
| DeadInstructions.insert(ConditionalAssumes.begin(), ConditionalAssumes.end()); |
| |
| MapVector<Instruction *, Instruction *> &SinkAfter = Legal->getSinkAfter(); |
| // Dead instructions do not need sinking. Remove them from SinkAfter. |
| for (Instruction *I : DeadInstructions) |
| SinkAfter.erase(I); |
| |
| // Cannot sink instructions after dead instructions (there won't be any |
| // recipes for them). Instead, find the first non-dead previous instruction. |
| for (auto &P : Legal->getSinkAfter()) { |
| Instruction *SinkTarget = P.second; |
| Instruction *FirstInst = &*SinkTarget->getParent()->begin(); |
| (void)FirstInst; |
| while (DeadInstructions.contains(SinkTarget)) { |
| assert( |
| SinkTarget != FirstInst && |
| "Must find a live instruction (at least the one feeding the " |
| "first-order recurrence PHI) before reaching beginning of the block"); |
| SinkTarget = SinkTarget->getPrevNode(); |
| assert(SinkTarget != P.first && |
| "sink source equals target, no sinking required"); |
| } |
| P.second = SinkTarget; |
| } |
| |
| auto MaxVFPlusOne = MaxVF.getWithIncrement(1); |
| for (ElementCount VF = MinVF; ElementCount::isKnownLT(VF, MaxVFPlusOne);) { |
| VFRange SubRange = {VF, MaxVFPlusOne}; |
| VPlans.push_back( |
| buildVPlanWithVPRecipes(SubRange, DeadInstructions, SinkAfter)); |
| VF = SubRange.End; |
| } |
| } |
| |
| VPlanPtr LoopVectorizationPlanner::buildVPlanWithVPRecipes( |
| VFRange &Range, SmallPtrSetImpl<Instruction *> &DeadInstructions, |
| const MapVector<Instruction *, Instruction *> &SinkAfter) { |
| |
| SmallPtrSet<const InterleaveGroup<Instruction> *, 1> InterleaveGroups; |
| |
| VPRecipeBuilder RecipeBuilder(OrigLoop, TLI, Legal, CM, PSE, Builder); |
| |
| // --------------------------------------------------------------------------- |
| // Pre-construction: record ingredients whose recipes we'll need to further |
| // process after constructing the initial VPlan. |
| // --------------------------------------------------------------------------- |
| |
| // Mark instructions we'll need to sink later and their targets as |
| // ingredients whose recipe we'll need to record. |
| for (auto &Entry : SinkAfter) { |
| RecipeBuilder.recordRecipeOf(Entry.first); |
| RecipeBuilder.recordRecipeOf(Entry.second); |
| } |
| for (auto &Reduction : CM.getInLoopReductionChains()) { |
| PHINode *Phi = Reduction.first; |
| RecurKind Kind = Legal->getReductionVars()[Phi].getRecurrenceKind(); |
| const SmallVector<Instruction *, 4> &ReductionOperations = Reduction.second; |
| |
| RecipeBuilder.recordRecipeOf(Phi); |
| for (auto &R : ReductionOperations) { |
| RecipeBuilder.recordRecipeOf(R); |
| // For min/max reducitons, where we have a pair of icmp/select, we also |
| // need to record the ICmp recipe, so it can be removed later. |
| assert(!RecurrenceDescriptor::isSelectCmpRecurrenceKind(Kind) && |
| "Only min/max recurrences allowed for inloop reductions"); |
| if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) |
| RecipeBuilder.recordRecipeOf(cast<Instruction>(R->getOperand(0))); |
| } |
| } |
| |
| // For each interleave group which is relevant for this (possibly trimmed) |
| // Range, add it to the set of groups to be later applied to the VPlan and add |
| // placeholders for its members' Recipes which we'll be replacing with a |
| // single VPInterleaveRecipe. |
| for (InterleaveGroup<Instruction> *IG : IAI.getInterleaveGroups()) { |
| auto applyIG = [IG, this](ElementCount VF) -> bool { |
| return (VF.isVector() && // Query is illegal for VF == 1 |
| CM.getWideningDecision(IG->getInsertPos(), VF) == |
| LoopVectorizationCostModel::CM_Interleave); |
| }; |
| if (!getDecisionAndClampRange(applyIG, Range)) |
| continue; |
| InterleaveGroups.insert(IG); |
| for (unsigned i = 0; i < IG->getFactor(); i++) |
| if (Instruction *Member = IG->getMember(i)) |
| RecipeBuilder.recordRecipeOf(Member); |
| }; |
| |
| // --------------------------------------------------------------------------- |
| // Build initial VPlan: Scan the body of the loop in a topological order to |
| // visit each basic block after having visited its predecessor basic blocks. |
| // --------------------------------------------------------------------------- |
| |
| auto Plan = std::make_unique<VPlan>(); |
| |
| // 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 *VPBB = nullptr; |
| VPBasicBlock *HeaderVPBB = nullptr; |
| SmallVector<VPWidenIntOrFpInductionRecipe *> InductionsToMove; |
| for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) { |
| // Relevant instructions from basic block BB will be grouped into VPRecipe |
| // ingredients and fill a new VPBasicBlock. |
| unsigned VPBBsForBB = 0; |
| auto *FirstVPBBForBB = new VPBasicBlock(BB->getName()); |
| if (VPBB) |
| VPBlockUtils::insertBlockAfter(FirstVPBBForBB, VPBB); |
| else { |
| auto *TopRegion = new VPRegionBlock("vector loop"); |
| TopRegion->setEntry(FirstVPBBForBB); |
| Plan->setEntry(TopRegion); |
| HeaderVPBB = FirstVPBBForBB; |
| } |
| VPBB = FirstVPBBForBB; |
| Builder.setInsertPoint(VPBB); |
| |
| // Introduce each ingredient into VPlan. |
| // TODO: Model and preserve debug instrinsics in VPlan. |
| for (Instruction &I : BB->instructionsWithoutDebug()) { |
| Instruction *Instr = &I; |
| |
| // First filter out irrelevant instructions, to ensure no recipes are |
| // built for them. |
| if (isa<BranchInst>(Instr) || DeadInstructions.count(Instr)) |
| continue; |
| |
| SmallVector<VPValue *, 4> Operands; |
| auto *Phi = dyn_cast<PHINode>(Instr); |
| if (Phi && Phi->getParent() == OrigLoop->getHeader()) { |
| Operands.push_back(Plan->getOrAddVPValue( |
| Phi->getIncomingValueForBlock(OrigLoop->getLoopPreheader()))); |
| } else { |
| auto OpRange = Plan->mapToVPValues(Instr->operands()); |
| Operands = {OpRange.begin(), OpRange.end()}; |
| } |
| if (auto RecipeOrValue = RecipeBuilder.tryToCreateWidenRecipe( |
| Instr, Operands, Range, Plan)) { |
| // If Instr can be simplified to an existing VPValue, use it. |
| if (RecipeOrValue.is<VPValue *>()) { |
| auto *VPV = RecipeOrValue.get<VPValue *>(); |
| Plan->addVPValue(Instr, VPV); |
| // If the re-used value is a recipe, register the recipe for the |
| // instruction, in case the recipe for Instr needs to be recorded. |
| if (auto *R = dyn_cast_or_null<VPRecipeBase>(VPV->getDef())) |
| RecipeBuilder.setRecipe(Instr, R); |
| continue; |
| } |
| // Otherwise, add the new recipe. |
| VPRecipeBase *Recipe = RecipeOrValue.get<VPRecipeBase *>(); |
| for (auto *Def : Recipe->definedValues()) { |
| auto *UV = Def->getUnderlyingValue(); |
| Plan->addVPValue(UV, Def); |
| } |
| |
| if (isa<VPWidenIntOrFpInductionRecipe>(Recipe) && |
| HeaderVPBB->getFirstNonPhi() != VPBB->end()) { |
| // Keep track of VPWidenIntOrFpInductionRecipes not in the phi section |
| // of the header block. That can happen for truncates of induction |
| // variables. Those recipes are moved to the phi section of the header |
| // block after applying SinkAfter, which relies on the original |
| // position of the trunc. |
| assert(isa<TruncInst>(Instr)); |
| InductionsToMove.push_back( |
| cast<VPWidenIntOrFpInductionRecipe>(Recipe)); |
| } |
| RecipeBuilder.setRecipe(Instr, Recipe); |
| VPBB->appendRecipe(Recipe); |
| continue; |
| } |
| |
| // Otherwise, if all widening options failed, Instruction is to be |
| // replicated. This may create a successor for VPBB. |
| VPBasicBlock *NextVPBB = |
| RecipeBuilder.handleReplication(Instr, Range, VPBB, Plan); |
| if (NextVPBB != VPBB) { |
| VPBB = NextVPBB; |
| VPBB->setName(BB->hasName() ? BB->getName() + "." + Twine(VPBBsForBB++) |
| : ""); |
| } |
| } |
| } |
| |
| assert(isa<VPRegionBlock>(Plan->getEntry()) && |
| !Plan->getEntry()->getEntryBasicBlock()->empty() && |
| "entry block must be set to a VPRegionBlock having a non-empty entry " |
| "VPBasicBlock"); |
| cast<VPRegionBlock>(Plan->getEntry())->setExit(VPBB); |
| RecipeBuilder.fixHeaderPhis(); |
| |
| // --------------------------------------------------------------------------- |
| // Transform initial VPlan: Apply previously taken decisions, in order, to |
| // bring the VPlan to its final state. |
| // --------------------------------------------------------------------------- |
| |
| // Apply Sink-After legal constraints. |
| auto GetReplicateRegion = [](VPRecipeBase *R) -> VPRegionBlock * { |
| auto *Region = dyn_cast_or_null<VPRegionBlock>(R->getParent()->getParent()); |
| if (Region && Region->isReplicator()) { |
| assert(Region->getNumSuccessors() == 1 && |
| Region->getNumPredecessors() == 1 && "Expected SESE region!"); |
| assert(R->getParent()->size() == 1 && |
| "A recipe in an original replicator region must be the only " |
| "recipe in its block"); |
| return Region; |
| } |
| return nullptr; |
| }; |
| for (auto &Entry : SinkAfter) { |
| VPRecipeBase *Sink = RecipeBuilder.getRecipe(Entry.first); |
| VPRecipeBase *Target = RecipeBuilder.getRecipe(Entry.second); |
| |
| auto *TargetRegion = GetReplicateRegion(Target); |
| auto *SinkRegion = GetReplicateRegion(Sink); |
| if (!SinkRegion) { |
| // If the sink source is not a replicate region, sink the recipe directly. |
| if (TargetRegion) { |
| // The target is in a replication region, make sure to move Sink to |
| // the block after it, not into the replication region itself. |
| VPBasicBlock *NextBlock = |
| cast<VPBasicBlock>(TargetRegion->getSuccessors().front()); |
| Sink->moveBefore(*NextBlock, NextBlock->getFirstNonPhi()); |
| } else |
| Sink->moveAfter(Target); |
| continue; |
| } |
| |
| // The sink source is in a replicate region. Unhook the region from the CFG. |
| auto *SinkPred = SinkRegion->getSinglePredecessor(); |
| auto *SinkSucc = SinkRegion->getSingleSuccessor(); |
| VPBlockUtils::disconnectBlocks(SinkPred, SinkRegion); |
| VPBlockUtils::disconnectBlocks(SinkRegion, SinkSucc); |
| VPBlockUtils::connectBlocks(SinkPred, SinkSucc); |
| |
| if (TargetRegion) { |
| // The target recipe is also in a replicate region, move the sink region |
| // after the target region. |
| auto *TargetSucc = TargetRegion->getSingleSuccessor(); |
| VPBlockUtils::disconnectBlocks(TargetRegion, TargetSucc); |
| VPBlockUtils::connectBlocks(TargetRegion, SinkRegion); |
| VPBlockUtils::connectBlocks(SinkRegion, TargetSucc); |
| } else { |
| // The sink source is in a replicate region, we need to move the whole |
| // replicate region, which should only contain a single recipe in the |
| // main block. |
| auto *SplitBlock = |
| Target->getParent()->splitAt(std::next(Target->getIterator())); |
| |
| auto *SplitPred = SplitBlock->getSinglePredecessor(); |
| |
| VPBlockUtils::disconnectBlocks(SplitPred, SplitBlock); |
| VPBlockUtils::connectBlocks(SplitPred, SinkRegion); |
| VPBlockUtils::connectBlocks(SinkRegion, SplitBlock); |
| if (VPBB == SplitPred) |
| VPBB = SplitBlock; |
| } |
| } |
| |
| // Now that sink-after is done, move induction recipes for optimized truncates |
| // to the phi section of the header block. |
| for (VPWidenIntOrFpInductionRecipe *Ind : InductionsToMove) |
| Ind->moveBefore(*HeaderVPBB, HeaderVPBB->getFirstNonPhi()); |
| |
| // Adjust the recipes for any inloop reductions. |
| adjustRecipesForReductions(VPBB, Plan, RecipeBuilder, Range.Start); |
| |
| // Introduce a recipe to combine the incoming and previous values of a |
| // first-order recurrence. |
| for (VPRecipeBase &R : Plan->getEntry()->getEntryBasicBlock()->phis()) { |
| auto *RecurPhi = dyn_cast<VPFirstOrderRecurrencePHIRecipe>(&R); |
| if (!RecurPhi) |
| continue; |
| |
| VPRecipeBase *PrevRecipe = RecurPhi->getBackedgeRecipe(); |
| VPBasicBlock *InsertBlock = PrevRecipe->getParent(); |
| auto *Region = GetReplicateRegion(PrevRecipe); |
| if (Region) |
| InsertBlock = cast<VPBasicBlock>(Region->getSingleSuccessor()); |
| if (Region || PrevRecipe->isPhi()) |
| Builder.setInsertPoint(InsertBlock, InsertBlock->getFirstNonPhi()); |
| else |
| Builder.setInsertPoint(InsertBlock, std::next(PrevRecipe->getIterator())); |
| |
| auto *RecurSplice = cast<VPInstruction>( |
| Builder.createNaryOp(VPInstruction::FirstOrderRecurrenceSplice, |
| {RecurPhi, RecurPhi->getBackedgeValue()})); |
| |
| RecurPhi->replaceAllUsesWith(RecurSplice); |
| // Set the first operand of RecurSplice to RecurPhi again, after replacing |
| // all users. |
| RecurSplice->setOperand(0, RecurPhi); |
| } |
| |
| // Interleave memory: for each Interleave Group we marked earlier as relevant |
| // for this VPlan, replace the Recipes widening its memory instructions with a |
| // single VPInterleaveRecipe at its insertion point. |
| for (auto IG : InterleaveGroups) { |
| auto *Recipe = cast<VPWidenMemoryInstructionRecipe>( |
| RecipeBuilder.getRecipe(IG->getInsertPos())); |
| 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<VPWidenMemoryInstructionRecipe>(RecipeBuilder.getRecipe(SI)); |
| StoredValues.push_back(StoreR->getStoredValue()); |
| } |
| |
| auto *VPIG = new VPInterleaveRecipe(IG, Recipe->getAddr(), StoredValues, |
| Recipe->getMask()); |
| VPIG->insertBefore(Recipe); |
| unsigned J = 0; |
| for (unsigned i = 0; i < IG->getFactor(); ++i) |
| if (Instruction *Member = IG->getMember(i)) { |
| if (!Member->getType()->isVoidTy()) { |
| VPValue *OriginalV = Plan->getVPValue(Member); |
| Plan->removeVPValueFor(Member); |
| Plan->addVPValue(Member, VPIG->getVPValue(J)); |
| OriginalV->replaceAllUsesWith(VPIG->getVPValue(J)); |
| J++; |
| } |
| RecipeBuilder.getRecipe(Member)->eraseFromParent(); |
| } |
| } |
| |
| // From this point onwards, VPlan-to-VPlan transformations may change the plan |
| // in ways that accessing values using original IR values is incorrect. |
| Plan->disableValue2VPValue(); |
| |
| VPlanTransforms::sinkScalarOperands(*Plan); |
| VPlanTransforms::mergeReplicateRegions(*Plan); |
| |
| std::string PlanName; |
| raw_string_ostream RSO(PlanName); |
| ElementCount VF = Range.Start; |
| Plan->addVF(VF); |
| RSO << "Initial VPlan for VF={" << VF; |
| for (VF *= 2; ElementCount::isKnownLT(VF, Range.End); VF *= 2) { |
| Plan->addVF(VF); |
| RSO << "," << VF; |
| } |
| RSO << "},UF>=1"; |
| RSO.flush(); |
| Plan->setName(PlanName); |
| |
| assert(VPlanVerifier::verifyPlanIsValid(*Plan) && "VPlan is invalid"); |
| return Plan; |
| } |
| |
| VPlanPtr LoopVectorizationPlanner::buildVPlan(VFRange &Range) { |
| // Outer loop handling: They may require CFG and instruction level |
| // transformations before even evaluating whether vectorization is profitable. |
| // Since we cannot modify the incoming IR, we need to build VPlan upfront in |
| // the vectorization pipeline. |
| assert(!OrigLoop->isInnermost()); |
| assert(EnableVPlanNativePath && "VPlan-native path is not enabled."); |
| |
| // Create new empty VPlan |
| auto Plan = std::make_unique<VPlan>(); |
| |
| // Build hierarchical CFG |
| VPlanHCFGBuilder HCFGBuilder(OrigLoop, LI, *Plan); |
| HCFGBuilder.buildHierarchicalCFG(); |
| |
| for (ElementCount VF = Range.Start; ElementCount::isKnownLT(VF, Range.End); |
| VF *= 2) |
| Plan->addVF(VF); |
| |
| if (EnableVPlanPredication) { |
| VPlanPredicator VPP(*Plan); |
| VPP.predicate(); |
| |
| // Avoid running transformation to recipes until masked code generation in |
| // VPlan-native path is in place. |
| return Plan; |
| } |
| |
| SmallPtrSet<Instruction *, 1> DeadInstructions; |
| VPlanTransforms::VPInstructionsToVPRecipes(OrigLoop, Plan, |
| Legal->getInductionVars(), |
| DeadInstructions, *PSE.getSE()); |
| 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. |
| void LoopVectorizationPlanner::adjustRecipesForReductions( |
| VPBasicBlock *LatchVPBB, VPlanPtr &Plan, VPRecipeBuilder &RecipeBuilder, |
| ElementCount MinVF) { |
| for (auto &Reduction : CM.getInLoopReductionChains()) { |
| PHINode *Phi = Reduction.first; |
| RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi]; |
| const SmallVector<Instruction *, 4> &ReductionOperations = Reduction.second; |
| |
| if (MinVF.isScalar() && !CM.useOrderedReductions(RdxDesc)) |
| continue; |
| |
| // ReductionOperations are orders top-down from the phi's use to the |
| // LoopExitValue. We keep a track of the previous item (the Chain) to tell |
| // which of the two operands will remain scalar and which will be reduced. |
| // For minmax the chain will be the select instructions. |
| Instruction *Chain = Phi; |
| for (Instruction *R : ReductionOperations) { |
| VPRecipeBase *WidenRecipe = RecipeBuilder.getRecipe(R); |
| RecurKind Kind = RdxDesc.getRecurrenceKind(); |
| |
| VPValue *ChainOp = Plan->getVPValue(Chain); |
| unsigned FirstOpId; |
| assert(!RecurrenceDescriptor::isSelectCmpRecurrenceKind(Kind) && |
| "Only min/max recurrences allowed for inloop reductions"); |
| // Recognize a call to the llvm.fmuladd intrinsic. |
| bool IsFMulAdd = (Kind == RecurKind::FMulAdd); |
| assert((!IsFMulAdd || RecurrenceDescriptor::isFMulAddIntrinsic(R)) && |
| "Expected instruction to be a call to the llvm.fmuladd intrinsic"); |
| if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { |
| assert(isa<VPWidenSelectRecipe>(WidenRecipe) && |
| "Expected to replace a VPWidenSelectSC"); |
| FirstOpId = 1; |
| } else { |
| assert((MinVF.isScalar() || isa<VPWidenRecipe>(WidenRecipe) || |
| (IsFMulAdd && isa<VPWidenCallRecipe>(WidenRecipe))) && |
| "Expected to replace a VPWidenSC"); |
| FirstOpId = 0; |
| } |
| unsigned VecOpId = |
| R->getOperand(FirstOpId) == Chain ? FirstOpId + 1 : FirstOpId; |
| VPValue *VecOp = Plan->getVPValue(R->getOperand(VecOpId)); |
| |
| auto *CondOp = CM.foldTailByMasking() |
| ? RecipeBuilder.createBlockInMask(R->getParent(), Plan) |
| : nullptr; |
| |
| if (IsFMulAdd) { |
| // If the instruction is a call to the llvm.fmuladd intrinsic then we |
| // need to create an fmul recipe to use as the vector operand for the |
| // fadd reduction. |
| VPInstruction *FMulRecipe = new VPInstruction( |
| Instruction::FMul, {VecOp, Plan->getVPValue(R->getOperand(1))}); |
| FMulRecipe->setFastMathFlags(R->getFastMathFlags()); |
| WidenRecipe->getParent()->insert(FMulRecipe, |
| WidenRecipe->getIterator()); |
| VecOp = FMulRecipe; |
| } |
| VPReductionRecipe *RedRecipe = |
| new VPReductionRecipe(&RdxDesc, R, ChainOp, VecOp, CondOp, TTI); |
| WidenRecipe->getVPSingleValue()->replaceAllUsesWith(RedRecipe); |
| Plan->removeVPValueFor(R); |
| Plan->addVPValue(R, RedRecipe); |
| WidenRecipe->getParent()->insert(RedRecipe, WidenRecipe->getIterator()); |
| WidenRecipe->getVPSingleValue()->replaceAllUsesWith(RedRecipe); |
| WidenRecipe->eraseFromParent(); |
| |
| if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { |
| VPRecipeBase *CompareRecipe = |
| RecipeBuilder.getRecipe(cast<Instruction>(R->getOperand(0))); |
| assert(isa<VPWidenRecipe>(CompareRecipe) && |
| "Expected to replace a VPWidenSC"); |
| assert(cast<VPWidenRecipe>(CompareRecipe)->getNumUsers() == 0 && |
| "Expected no remaining users"); |
| CompareRecipe->eraseFromParent(); |
| } |
| Chain = R; |
| } |
| } |
| |
| // If tail is folded by masking, introduce selects between the phi |
| // and the live-out instruction of each reduction, at the end of the latch. |
| if (CM.foldTailByMasking()) { |
| for (VPRecipeBase &R : Plan->getEntry()->getEntryBasicBlock()->phis()) { |
| VPReductionPHIRecipe *PhiR = dyn_cast<VPReductionPHIRecipe>(&R); |
| if (!PhiR || PhiR->isInLoop()) |
| continue; |
| Builder.setInsertPoint(LatchVPBB); |
| VPValue *Cond = |
| RecipeBuilder.createBlockInMask(OrigLoop->getHeader(), Plan); |
| VPValue *Red = PhiR->getBackedgeValue(); |
| Builder.createNaryOp(Instruction::Select, {Cond, Red, PhiR}); |
| } |
| } |
| } |
| |
| #if !defined(NDEBUG) || defined(LLVM_ENABLE_DUMP) |
| void VPInterleaveRecipe::print(raw_ostream &O, const Twine &Indent, |
| VPSlotTracker &SlotTracker) const { |
| O << Indent << "INTERLEAVE-GROUP with factor " << IG->getFactor() << " at "; |
| IG->getInsertPos()->printAsOperand(O, false); |
| O << ", "; |
| getAddr()->printAsOperand(O, SlotTracker); |
| VPValue *Mask = getMask(); |
| if (Mask) { |
| O << ", "; |
| Mask->printAsOperand(O, SlotTracker); |
| } |
| |
| unsigned OpIdx = 0; |
| for (unsigned i = 0; i < IG->getFactor(); ++i) { |
| if (!IG->getMember(i)) |
| continue; |
| if (getNumStoreOperands() > 0) { |
| O << "\n" << Indent << " store "; |
| getOperand(1 + OpIdx)->printAsOperand(O, SlotTracker); |
| O << " to index " << i; |
| } else { |
| O << "\n" << Indent << " "; |
| getVPValue(OpIdx)->printAsOperand(O, SlotTracker); |
| O << " = load from index " << i; |
| } |
| ++OpIdx; |
| } |
| } |
| #endif |
| |
| void VPWidenCallRecipe::execute(VPTransformState &State) { |
| State.ILV->widenCallInstruction(*cast<CallInst>(getUnderlyingInstr()), this, |
| *this, State); |
| } |
| |
| void VPWidenSelectRecipe::execute(VPTransformState &State) { |
| auto &I = *cast<SelectInst>(getUnderlyingInstr()); |
| State.ILV->setDebugLocFromInst(&I); |
| |
| // The condition can be loop invariant but still defined inside the |
| // loop. This means that we can't just use the original 'cond' value. |
| // We have to take the 'vectorized' value and pick the first lane. |
| // Instcombine will make this a no-op. |
| auto *InvarCond = |
| InvariantCond ? State.get(getOperand(0), VPIteration(0, 0)) : nullptr; |
| |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| Value *Cond = InvarCond ? InvarCond : State.get(getOperand(0), Part); |
| Value *Op0 = State.get(getOperand(1), Part); |
| Value *Op1 = State.get(getOperand(2), Part); |
| Value *Sel = State.Builder.CreateSelect(Cond, Op0, Op1); |
| State.set(this, Sel, Part); |
| State.ILV->addMetadata(Sel, &I); |
| } |
| } |
| |
| void VPWidenRecipe::execute(VPTransformState &State) { |
| auto &I = *cast<Instruction>(getUnderlyingValue()); |
| auto &Builder = State.Builder; |
| switch (I.getOpcode()) { |
| case Instruction::Call: |
| case Instruction::Br: |
| case Instruction::PHI: |
| case Instruction::GetElementPtr: |
| case Instruction::Select: |
| llvm_unreachable("This instruction is handled by a different recipe."); |
| case Instruction::UDiv: |
| case Instruction::SDiv: |
| case Instruction::SRem: |
| case Instruction::URem: |
| case Instruction::Add: |
| case Instruction::FAdd: |
| case Instruction::Sub: |
| case Instruction::FSub: |
| case Instruction::FNeg: |
| case Instruction::Mul: |
| case Instruction::FMul: |
| case Instruction::FDiv: |
| case Instruction::FRem: |
| case Instruction::Shl: |
| case Instruction::LShr: |
| case Instruction::AShr: |
| case Instruction::And: |
| case Instruction::Or: |
| case Instruction::Xor: { |
| // Just widen unops and binops. |
| State.ILV->setDebugLocFromInst(&I); |
| |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| SmallVector<Value *, 2> Ops; |
| for (VPValue *VPOp : operands()) |
| Ops.push_back(State.get(VPOp, Part)); |
| |
| Value *V = Builder.CreateNAryOp(I.getOpcode(), Ops); |
| |
| if (auto *VecOp = dyn_cast<Instruction>(V)) { |
| VecOp->copyIRFlags(&I); |
| |
| // If the instruction is vectorized and was in a basic block that needed |
| // predication, we can't propagate poison-generating flags (nuw/nsw, |
| // exact, etc.). The control flow has been linearized and the |
| // instruction is no longer guarded by the predicate, which could make |
| // the flag properties to no longer hold. |
| if (State.MayGeneratePoisonRecipes.count(this) > 0) |
| VecOp->dropPoisonGeneratingFlags(); |
| } |
| |
| // Use this vector value for all users of the original instruction. |
| State.set(this, V, Part); |
| State.ILV->addMetadata(V, &I); |
| } |
| |
| break; |
| } |
| case Instruction::ICmp: |
| case Instruction::FCmp: { |
| // Widen compares. Generate vector compares. |
| bool FCmp = (I.getOpcode() == Instruction::FCmp); |
| auto *Cmp = cast<CmpInst>(&I); |
| State.ILV->setDebugLocFromInst(Cmp); |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| Value *A = State.get(getOperand(0), Part); |
| Value *B = State.get(getOperand(1), Part); |
| Value *C = nullptr; |
| if (FCmp) { |
| // Propagate fast math flags. |
| IRBuilder<>::FastMathFlagGuard FMFG(Builder); |
| Builder.setFastMathFlags(Cmp->getFastMathFlags()); |
| C = Builder.CreateFCmp(Cmp->getPredicate(), A, B); |
| } else { |
| C = Builder.CreateICmp(Cmp->getPredicate(), A, B); |
| } |
| State.set(this, C, Part); |
| State.ILV->addMetadata(C, &I); |
| } |
| |
| break; |
| } |
| |
| case Instruction::ZExt: |
| case Instruction::SExt: |
| case Instruction::FPToUI: |
| case Instruction::FPToSI: |
| case Instruction::FPExt: |
| case Instruction::PtrToInt: |
| case Instruction::IntToPtr: |
| case Instruction::SIToFP: |
| case Instruction::UIToFP: |
| case Instruction::Trunc: |
| case Instruction::FPTrunc: |
| case Instruction::BitCast: { |
| auto *CI = cast<CastInst>(&I); |
| State.ILV->setDebugLocFromInst(CI); |
| |
| /// Vectorize casts. |
| Type *DestTy = (State.VF.isScalar()) |
| ? CI->getType() |
| : VectorType::get(CI->getType(), State.VF); |
| |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| Value *A = State.get(getOperand(0), Part); |
| Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy); |
| State.set(this, Cast, Part); |
| State.ILV->addMetadata(Cast, &I); |
| } |
| break; |
| } |
| default: |
| // This instruction is not vectorized by simple widening. |
| LLVM_DEBUG(dbgs() << "LV: Found an unhandled instruction: " << I); |
| llvm_unreachable("Unhandled instruction!"); |
| } // end of switch. |
| } |
| |
| void VPWidenGEPRecipe::execute(VPTransformState &State) { |
| auto *GEP = cast<GetElementPtrInst>(getUnderlyingInstr()); |
| // Construct a vector GEP by widening the operands of the scalar GEP as |
| // necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP |
| // results in a vector of pointers when at least one operand of the GEP |
| // is vector-typed. Thus, to keep the representation compact, we only use |
| // vector-typed operands for loop-varying values. |
| |
| if (State.VF.isVector() && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) { |
| // If we are vectorizing, but the GEP has only loop-invariant operands, |
| // the GEP we build (by only using vector-typed operands for |
| // loop-varying values) would be a scalar pointer. Thus, to ensure we |
| // produce a vector of pointers, we need to either arbitrarily pick an |
| // operand to broadcast, or broadcast a clone of the original GEP. |
| // Here, we broadcast a clone of the original. |
| // |
| // TODO: If at some point we decide to scalarize instructions having |
| // loop-invariant operands, this special case will no longer be |
| // required. We would add the scalarization decision to |
| // collectLoopScalars() and teach getVectorValue() to broadcast |
| // the lane-zero scalar value. |
| auto *Clone = State.Builder.Insert(GEP->clone()); |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| Value *EntryPart = State.Builder.CreateVectorSplat(State.VF, Clone); |
| State.set(this, EntryPart, Part); |
| State.ILV->addMetadata(EntryPart, GEP); |
| } |
| } else { |
| // If the GEP has at least one loop-varying operand, we are sure to |
| // produce a vector of pointers. But if we are only unrolling, we want |
| // to produce a scalar GEP for each unroll part. Thus, the GEP we |
| // produce with the code below will be scalar (if VF == 1) or vector |
| // (otherwise). Note that for the unroll-only case, we still maintain |
| // values in the vector mapping with initVector, as we do for other |
| // instructions. |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| // The pointer operand of the new GEP. If it's loop-invariant, we |
| // won't broadcast it. |
| auto *Ptr = IsPtrLoopInvariant |
| ? State.get(getOperand(0), VPIteration(0, 0)) |
| : State.get(getOperand(0), Part); |
| |
| // Collect all the indices for the new GEP. If any index is |
| // loop-invariant, we won't broadcast it. |
| SmallVector<Value *, 4> Indices; |
| for (unsigned I = 1, E = getNumOperands(); I < E; I++) { |
| VPValue *Operand = getOperand(I); |
| if (IsIndexLoopInvariant[I - 1]) |
| Indices.push_back(State.get(Operand, VPIteration(0, 0))); |
| else |
| Indices.push_back(State.get(Operand, Part)); |
| } |
| |
| // If the GEP instruction is vectorized and was in a basic block that |
| // needed predication, we can't propagate the poison-generating 'inbounds' |
| // flag. The control flow has been linearized and the GEP is no longer |
| // guarded by the predicate, which could make the 'inbounds' properties to |
| // no longer hold. |
| bool IsInBounds = |
| GEP->isInBounds() && State.MayGeneratePoisonRecipes.count(this) == 0; |
| |
| // Create the new GEP. Note that this GEP may be a scalar if VF == 1, |
| // but it should be a vector, otherwise. |
| auto *NewGEP = IsInBounds |
| ? State.Builder.CreateInBoundsGEP( |
| GEP->getSourceElementType(), Ptr, Indices) |
| : State.Builder.CreateGEP(GEP->getSourceElementType(), |
| Ptr, Indices); |
| assert((State.VF.isScalar() || NewGEP->getType()->isVectorTy()) && |
| "NewGEP is not a pointer vector"); |
| State.set(this, NewGEP, Part); |
| State.ILV->addMetadata(NewGEP, GEP); |
| } |
| } |
| } |
| |
| void VPWidenIntOrFpInductionRecipe::execute(VPTransformState &State) { |
| assert(!State.Instance && "Int or FP induction being replicated."); |
| State.ILV->widenIntOrFpInduction(IV, getStartValue()->getLiveInIRValue(), |
| getTruncInst(), getVPValue(0), |
| getCastValue(), State); |
| } |
| |
| void VPWidenPHIRecipe::execute(VPTransformState &State) { |
| State.ILV->widenPHIInstruction(cast<PHINode>(getUnderlyingValue()), this, |
| State); |
| } |
| |
| void VPBlendRecipe::execute(VPTransformState &State) { |
| State.ILV->setDebugLocFromInst(Phi, &State.Builder); |
| // We know that all PHIs in non-header blocks are converted into |
| // selects, so we don't have to worry about the insertion order and we |
| // can just use the builder. |
| // At this point we generate the predication tree. There may be |
| // duplications since this is a simple recursive scan, but future |
| // optimizations will clean it up. |
| |
| unsigned NumIncoming = getNumIncomingValues(); |
| |
| // Generate a sequence of selects of the form: |
| // SELECT(Mask3, In3, |
| // SELECT(Mask2, In2, |
| // SELECT(Mask1, In1, |
| // In0))) |
| // Note that Mask0 is never used: lanes for which no path reaches this phi and |
| // are essentially undef are taken from In0. |
| InnerLoopVectorizer::VectorParts Entry(State.UF); |
| for (unsigned In = 0; In < NumIncoming; ++In) { |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| // We might have single edge PHIs (blocks) - use an identity |
| // 'select' for the first PHI operand. |
| Value *In0 = State.get(getIncomingValue(In), Part); |
| if (In == 0) |
| Entry[Part] = In0; // Initialize with the first incoming value. |
| else { |
| // Select between the current value and the previous incoming edge |
| // based on the incoming mask. |
| Value *Cond = State.get(getMask(In), Part); |
| Entry[Part] = |
| State.Builder.CreateSelect(Cond, In0, Entry[Part], "predphi"); |
| } |
| } |
| } |
| for (unsigned Part = 0; Part < State.UF; ++Part) |
| State.set(this, Entry[Part], Part); |
| } |
| |
| void VPInterleaveRecipe::execute(VPTransformState &State) { |
| assert(!State.Instance && "Interleave group being replicated."); |
| State.ILV->vectorizeInterleaveGroup(IG, definedValues(), State, getAddr(), |
| getStoredValues(), getMask()); |
| } |
| |
| void VPReductionRecipe::execute(VPTransformState &State) { |
| assert(!State.Instance && "Reduction being replicated."); |
| Value *PrevInChain = State.get(getChainOp(), 0); |
| RecurKind Kind = RdxDesc->getRecurrenceKind(); |
| bool IsOrdered = State.ILV->useOrderedReductions(*RdxDesc); |
| // Propagate the fast-math flags carried by the underlying instruction. |
| IRBuilderBase::FastMathFlagGuard FMFGuard(State.Builder); |
| State.Builder.setFastMathFlags(RdxDesc->getFastMathFlags()); |
| for (unsigned Part = 0; Part < State.UF; ++Part) { |
| Value *NewVecOp = State.get(getVecOp(), Part); |
| if (VPValue *Cond = getCondOp()) { |
| Value *NewCond = State.get(Cond, Part); |
| VectorType *VecTy = cast<VectorType>(NewVecOp->getType()); |
| Value *Iden = RdxDesc->getRecurrenceIdentity( |
| Kind, VecTy->getElementType(), RdxDesc->getFastMathFlags()); |
| Value *IdenVec = |
| State.Builder.CreateVectorSplat(VecTy->getElementCount(), Iden); |
| Value *Select = State.Builder.CreateSelect(NewCond, NewVecOp, IdenVec); |
| NewVecOp = Select; |
| } |
| Value *NewRed; |
| Value *NextInChain; |
| if (IsOrdered) { |
| if (State.VF.isVector()) |
| NewRed = createOrderedReduction(State.Builder, *RdxDesc, NewVecOp, |
| PrevInChain); |
| else |
| NewRed = State.Builder.CreateBinOp( |
| (Instruction::BinaryOps)RdxDesc->getOpcode(Kind), PrevInChain, |
| NewVecOp); |
| PrevInChain = NewRed; |
| } else { |
| PrevInChain = State.get(getChainOp(), Part); |
| NewRed = createTargetReduction(State.Builder, TTI, *RdxDesc, NewVecOp); |
| } |
| if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { |
| NextInChain = |
| createMinMaxOp(State.Builder, RdxDesc->getRecurrenceKind(), |
| NewRed, PrevInChain); |
| } else if (IsOrdered) |
| NextInChain = NewRed; |
| else |
| NextInChain = State.Builder.CreateBinOp( |
| (Instruction::BinaryOps)RdxDesc->getOpcode(Kind), NewRed, |
| PrevInChain); |
| State.set(this, NextInChain, Part); |
| } |
| } |
| |
| void VPReplicateRecipe::execute(VPTransformState &State) { |
| if (State.Instance) { // Generate a single instance. |
| assert(!State.VF.isScalable() && "Can't scalarize a scalable vector"); |
| State.ILV->scalarizeInstruction(getUnderlyingInstr(), this, *State.Instance, |
| IsPredicated, State); |
| // Insert scalar instance packing it into a vector. |
| if (AlsoPack && State.VF.isVector()) { |
| // If we're constructing lane 0, initialize to start from poison. |
| if (State.Instance->Lane.isFirstLane()) { |
| assert(!State.VF.isScalable() && "VF is assumed to be non scalable."); |
| Value *Poison = PoisonValue::get( |
| VectorType::get(getUnderlyingValue()->getType(), State.VF)); |
| State.set(this, Poison, State.Instance->Part); |
| } |
| State.ILV->packScalarIntoVectorValue(this, *State.Instance, State); |
| } |
| return; |
| } |
| |
| // Generate scalar instances for all VF lanes of all UF parts, unless the |
| // instruction is uniform inwhich case generate only the first lane for each |
| // of the UF parts. |
| unsigned EndLane = IsUniform ? 1 : State.VF.getKnownMinValue(); |
| assert((!State.VF.isScalable() || IsUniform) && |
| "Can't scalarize a scalable vector"); |
| for (unsigned Part = 0; Part < State.UF; ++Part) |
| for (unsigned Lane = 0; Lane < EndLane; ++Lane) |
| State.ILV->scalarizeInstruction(getUnderlyingInstr(), this, |
| VPIteration(Part, Lane), IsPredicated, |
| State); |
| } |
| |
| void VPBranchOnMaskRecipe::execute(VPTransformState &State) { |
| assert(State.Instance && "Branch on Mask works only on single instance."); |
| |
| unsigned Part = State.Instance->Part; |
| unsigned Lane = State.Instance->Lane.getKnownLane(); |
| |
| Value *ConditionBit = nullptr; |
| VPValue *BlockInMask = getMask(); |
| if (BlockInMask) { |
| ConditionBit = State.get(BlockInMask, Part); |
| if (ConditionBit->getType()->isVectorTy()) |
| ConditionBit = State.Builder.CreateExtractElement( |
| ConditionBit, State.Builder.getInt32(Lane)); |
| } else // Block in mask is all-one. |
| ConditionBit = State.Builder.getTrue(); |
| |
| // Replace the temporary unreachable terminator with a new conditional branch, |
| // whose two destinations will be set later when they are created. |
| auto *CurrentTerminator = State.CFG.PrevBB->getTerminator(); |
| assert(isa<UnreachableInst>(CurrentTerminator) && |
| "Expected to replace unreachable terminator with conditional branch."); |
| auto *CondBr = BranchInst::Create(State.CFG.PrevBB, nullptr, ConditionBit); |
| CondBr->setSuccessor(0, nullptr); |
| ReplaceInstWithInst(CurrentTerminator, CondBr); |
| } |
| |
| void VPPredInstPHIRecipe::execute(VPTransformState &State) { |
| assert(State.Instance && "Predicated instruction PHI works per instance."); |
| Instruction *ScalarPredInst = |
| cast<Instruction>(State.get(getOperand(0), *State.Instance)); |
| BasicBlock *PredicatedBB = ScalarPredInst->getParent(); |
| BasicBlock *PredicatingBB = PredicatedBB->getSinglePredecessor(); |
| assert(PredicatingBB && "Predicated block has no single predecessor."); |
| assert(isa<VPReplicateRecipe>(getOperand(0)) && |
| "operand must be VPReplicateRecipe"); |
| |
| // By current pack/unpack logic we need to generate only a single phi node: if |
| // a vector value for the predicated instruction exists at this point it means |
| // the instruction has vector users only, and a phi for the vector value is |
| // needed. In this case the recipe of the predicated instruction is marked to |
| // also do that packing, thereby "hoisting" the insert-element sequence. |
| // Otherwise, a phi node for the scalar value is needed. |
| unsigned Part = State.Instance->Part; |
| if (State.hasVectorValue(getOperand(0), Part)) { |
| Value *VectorValue = State.get(getOperand(0), Part); |
| InsertElementInst *IEI = cast<InsertElementInst>(VectorValue); |
| PHINode *VPhi = State.Builder.CreatePHI(IEI->getType(), 2); |
| VPhi->addIncoming(IEI->getOperand(0), PredicatingBB); // Unmodified vector. |
| VPhi->addIncoming(IEI, PredicatedBB); // New vector with inserted element. |
| if (State.hasVectorValue(this, Part)) |
| State.reset(this, VPhi, Part); |
| else |
| State.set(this, VPhi, Part); |
| // NOTE: Currently we need to update the value of the operand, so the next |
| // predicated iteration inserts its generated value in the correct vector. |
| State.reset(getOperand(0), VPhi, Part); |
| } else { |
| Type *PredInstType = getOperand(0)->getUnderlyingValue()->getType(); |
| PHINode *Phi = State.Builder.CreatePHI(PredInstType, 2); |
| Phi->addIncoming(PoisonValue::get(ScalarPredInst->getType()), |
| PredicatingBB); |
| Phi->addIncoming(ScalarPredInst, PredicatedBB); |
| if (State.hasScalarValue(this, *State.Instance)) |
| State.reset(this, Phi, *State.Instance); |
| else |
| State.set(this, Phi, *State.Instance); |
| // NOTE: Currently we need to update the value of the operand, so the next |
| // predicated iteration inserts its generated value in the correct vector. |
| State.reset(getOperand(0), Phi, *State.Instance); |
| } |
| } |
| |
| void VPWidenMemoryInstructionRecipe::execute(VPTransformState &State) { |
| VPValue *StoredValue = isStore() ? getStoredValue() : nullptr; |
| State.ILV->vectorizeMemoryInstruction( |
| &Ingredient, State, StoredValue ? nullptr : getVPSingleValue(), getAddr(), |
| StoredValue, getMask(), Consecutive, Reverse); |
| } |
| |
| // Determine how to lower the scalar epilogue, which depends on 1) optimising |
| // for minimum code-size, 2) predicate compiler options, 3) loop hints forcing |
| // predication, and 4) a TTI hook that analyses whether the loop is suitable |
| // for predication. |
| static ScalarEpilogueLowering getScalarEpilogueLowering( |
| Function *F, Loop *L, LoopVectorizeHints &Hints, ProfileSummaryInfo *PSI, |
| BlockFrequencyInfo *BFI, TargetTransformInfo *TTI, TargetLibraryInfo *TLI, |
| AssumptionCache *AC, LoopInfo *LI, ScalarEvolution *SE, DominatorTree *DT, |
| LoopVectorizationLegality &LVL) { |
| // 1) OptSize takes precedence over all other options, i.e. if this is set, |
| // don't look at hints or options, and don't request a scalar epilogue. |
| // (For PGSO, as shouldOptimizeForSize isn't currently accessible from |
| // LoopAccessInfo (due to code dependency and not being able to reliably get |
| // PSI/BFI from a loop analysis under NPM), we cannot suppress the collection |
| // of strides in LoopAccessInfo::analyzeLoop() and vectorize without |
| // versioning when the vectorization is forced, unlike hasOptSize. So revert |
| // back to the old way and vectorize with versioning when forced. See D81345.) |
| if (F->hasOptSize() || (llvm::shouldOptimizeForSize(L->getHeader(), PSI, BFI, |
| PGSOQueryType::IRPass) && |
| Hints.getForce() != LoopVectorizeHints::FK_Enabled)) |
| return CM_ScalarEpilogueNotAllowedOptSize; |
| |
| // 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. |
| if (TTI->preferPredicateOverEpilogue(L, LI, *SE, *AC, TLI, DT, |
| LVL.getLAI())) |
| return CM_ScalarEpilogueNotNeededUsePredicate; |
| |
| return CM_ScalarEpilogueAllowed; |
| } |
| |
| Value *VPTransformState::get(VPValue *Def, unsigned Part) { |
| // If Values have been set for this Def return the one relevant for \p Part. |
| if (hasVectorValue(Def, Part)) |
| return Data.PerPartOutput[Def][Part]; |
| |
| if (!hasScalarValue(Def, {Part, 0})) { |
| Value *IRV = Def->getLiveInIRValue(); |
| Value *B = ILV->getBroadcastInstrs(IRV); |
| set(Def, B, Part); |
| return B; |
| } |
| |
| Value *ScalarValue = get(Def, {Part, 0}); |
| // If we aren't vectorizing, we can just copy the scalar map values over |
| // to the vector map. |
| if (VF.isScalar()) { |
| set(Def, ScalarValue, Part); |
| return ScalarValue; |
| } |
| |
| auto *RepR = dyn_cast<VPReplicateRecipe>(Def); |
| bool IsUniform = RepR && RepR->isUniform(); |
| |
| unsigned LastLane = IsUniform ? 0 : VF.getKnownMinValue() - 1; |
| // Check if there is a scalar value for the selected lane. |
| if (!hasScalarValue(Def, {Part, LastLane})) { |
| // At the moment, VPWidenIntOrFpInductionRecipes can also be uniform. |
| assert(isa<VPWidenIntOrFpInductionRecipe>(Def->getDef()) && |
| "unexpected recipe found to be invariant"); |
| IsUniform = true; |
| LastLane = 0; |
| } |
| |
| auto *LastInst = cast<Instruction>(get(Def, {Part, LastLane})); |
| // Set the insert point after the last scalarized instruction or after the |
| // last PHI, if LastInst is a PHI. This ensures the insertelement sequence |
| // will directly follow the scalar definitions. |
| auto OldIP = Builder.saveIP(); |
| auto NewIP = |
| isa<PHINode>(LastInst) |
| ? BasicBlock::iterator(LastInst->getParent()->getFirstNonPHI()) |
| : std::next(BasicBlock::iterator(LastInst)); |
| Builder.SetInsertPoint(&*NewIP); |
| |
| // However, if we are vectorizing, we need to construct the vector values. |
| // If the value is known to be uniform after vectorization, we can just |
| // broadcast the scalar value corresponding to lane zero for each unroll |
| // iteration. Otherwise, we construct the vector values using |
| // insertelement instructions. Since the resulting vectors are stored in |
| // State, we will only generate the insertelements once. |
| Value *VectorValue = nullptr; |
| if (IsUniform) { |
| VectorValue = ILV->getBroadcastInstrs(ScalarValue); |
| set(Def, VectorValue, Part); |
| } else { |
| // Initialize packing with insertelements to start from undef. |
| assert(!VF.isScalable() && "VF is assumed to be non scalable."); |
| Value *Undef = PoisonValue::get(VectorType::get(LastInst->getType(), VF)); |
| set(Def, Undef, Part); |
| for (unsigned Lane = 0; Lane < VF.getKnownMinValue(); ++Lane) |
| ILV->packScalarIntoVectorValue(Def, {Part, Lane}, *this); |
| VectorValue = get(Def, Part); |
| } |
| Builder.restoreIP(OldIP); |
| return VectorValue; |
| } |
| |
| // 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, AC, LI, PSE.getSE(), DT, *LVL); |
| |
| LoopVectorizationCostModel CM(SEL, L, PSE, LI, LVL, *TTI, TLI, DB, AC, ORE, F, |
| &Hints, IAI); |
| // Use the planner for outer loop vectorization. |
| // TODO: CM is not used at this point inside the planner. Turn CM into an |
| // optional argument if we don't need it in the future. |
| LoopVectorizationPlanner LVP(L, LI, TLI, TTI, LVL, CM, IAI, PSE, Hints, |
| Requirements, 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 || EnableVPlanPredication || |
| VectorizationFactor::Disabled() == VF) |
| return false; |
| |
| VPlan &BestPlan = LVP.getBestPlanFor(VF.Width); |
| |
| { |
| GeneratedRTChecks Checks(*PSE.getSE(), DT, LI, |
| F->getParent()->getDataLayout()); |
| InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, 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); |
| } |
| |
| // 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); |
| } |
| } |
| |
| LoopVectorizePass::LoopVectorizePass(LoopVectorizeOptions Opts) |
| : InterleaveOnlyWhenForced(Opts.InterleaveOnlyWhenForced || |
| !EnableLoopInterleaving), |
| VectorizeOnlyWhenForced(Opts.VectorizeOnlyWhenForced || |
| !EnableLoopVectorization) {} |
| |
| bool LoopVectorizePass::processLoop(Loop *L) { |
| assert((EnableVPlanNativePath || L->isInnermost()) && |
| "VPlan-native path is not enabled. Only process inner loops."); |
| |
| #ifndef NDEBUG |
| const std::string DebugLocStr = getDebugLocString(L); |
| #endif /* NDEBUG */ |
| |
| LLVM_DEBUG(dbgs() << "\nLV: Checking a loop in \"" |
| << L->getHeader()->getParent()->getName() << "\" from " |
| << DebugLocStr << "\n"); |
| |
| LoopVectorizeHints Hints(L, InterleaveOnlyWhenForced, *ORE); |
| |
| LLVM_DEBUG( |
| dbgs() << "LV: Loop hints:" |
| << " force=" |
| << (Hints.getForce() == LoopVectorizeHints::FK_Disabled |
| ? "disabled" |
| : (Hints.getForce() == LoopVectorizeHints::FK_Enabled |
| ? "enabled" |
| : "?")) |
| << " width=" << Hints.getWidth() |
| << " 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, AA, F, GetLAA, LI, ORE, |
| &Requirements, &Hints, DB, AC, BFI, PSI); |
| if (!LVL.canVectorize(EnableVPlanNativePath)) { |
| LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n"); |
| Hints.emitRemarkWithHints(); |
| return false; |
| } |
| |
| // Check the function attributes and profiles to find out if this function |
| // should be optimized for size. |
| ScalarEpilogueLowering SEL = getScalarEpilogueLowering( |
| F, L, Hints, PSI, BFI, TTI, TLI, AC, LI, PSE.getSE(), DT, LVL); |
| |
| // Entrance to the VPlan-native vectorization path. Outer loops are processed |
| // here. They may require CFG and instruction level transformations before |
| // even evaluating whether vectorization is profitable. Since we cannot modify |
| // the incoming IR, we need to build VPlan upfront in the vectorization |
| // pipeline. |
| if (!L->isInnermost()) |
| return processLoopInVPlanNativePath(L, PSE, LI, DT, &LVL, TTI, TLI, DB, AC, |
| ORE, BFI, PSI, Hints, Requirements); |
| |
| assert(L->isInnermost() && "Inner loop expected."); |
| |
| // Check the loop for a trip count threshold: vectorize loops with a tiny trip |
| // count by optimizing for size, to minimize overheads. |
| auto ExpectedTC = getSmallBestKnownTC(*SE, L); |
| if (ExpectedTC && *ExpectedTC < TinyTripCountVectorThreshold) { |
| LLVM_DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " |
| << "This loop is worth vectorizing only if no scalar " |
| << "iteration overheads are incurred."); |
| if (Hints.getForce() == LoopVectorizeHints::FK_Enabled) |
| LLVM_DEBUG(dbgs() << " But vectorizing was explicitly forced.\n"); |
| else { |
| LLVM_DEBUG(dbgs() << "\n"); |
| SEL = CM_ScalarEpilogueNotAllowedLowTripLoop; |
| } |
| } |
| |
| // Check the function attributes to see if implicit floats are allowed. |
| // FIXME: This check doesn't seem possibly correct -- what if the loop is |
| // an integer loop and the vector instructions selected are purely integer |
| // vector instructions? |
| if (F->hasFnAttribute(Attribute::NoImplicitFloat)) { |
| reportVectorizationFailure( |
| "Can't vectorize when the NoImplicitFloat attribute is used", |
| "loop not vectorized due to NoImplicitFloat attribute", |
| "NoImplicitFloat", ORE, L); |
| Hints.emitRemarkWithHints(); |
| return false; |
| } |
| |
| // Check if the target supports potentially unsafe FP vectorization. |
| // FIXME: Add a check for the type of safety issue (denormal, signaling) |
| // for the target we're vectorizing for, to make sure none of the |
| // additional fp-math flags can help. |
| if (Hints.isPotentiallyUnsafe() && |
| TTI->isFPVectorizationPotentiallyUnsafe()) { |
| reportVectorizationFailure( |
| "Potentially unsafe FP op prevents vectorization", |
| "loop not vectorized due to unsafe FP support.", |
| "UnsafeFP", ORE, L); |
| Hints.emitRemarkWithHints(); |
| return false; |
| } |
| |
| bool 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; |
| } |
| |
| bool UseInterleaved = TTI->enableInterleavedAccessVectorization(); |
| InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL.getLAI()); |
| |
| // If an override option has been passed in for interleaved accesses, use it. |
| if (EnableInterleavedMemAccesses.getNumOccurrences() > 0) |
| UseInterleaved = EnableInterleavedMemAccesses; |
| |
| // Analyze interleaved memory accesses. |
| if (UseInterleaved) { |
| IAI.analyzeInterleaving(useMaskedInterleavedAccesses(*TTI)); |
| } |
| |
| // Use the cost model. |
| LoopVectorizationCostModel CM(SEL, L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE, |
| F, &Hints, IAI); |
| CM.collectValuesToIgnore(); |
| CM.collectElementTypesForWidening(); |
| |
| // Use the planner for vectorization. |
| LoopVectorizationPlanner LVP(L, LI, TLI, TTI, &LVL, CM, IAI, PSE, Hints, |
| Requirements, ORE); |
| |
| // Get user vectorization factor and interleave count. |
| ElementCount UserVF = Hints.getWidth(); |
| unsigned UserIC = Hints.getInterleave(); |
| |
| // Plan how to best vectorize, return the best VF and its cost. |
| Optional<VectorizationFactor> MaybeVF = LVP.plan(UserVF, UserIC); |
| |
| VectorizationFactor VF = VectorizationFactor::Disabled(); |
| unsigned IC = 1; |
| |
| if (MaybeVF) { |
| VF = *MaybeVF; |
| // Select the interleave count. |
| IC = CM.selectInterleaveCount(VF.Width, *VF.Cost.getValue()); |
| } |
| |
| // 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 (!MaybeVF && UserIC > 1) { |
| // Tell the user interleaving was avoided up-front, despite being explicitly |
| // requested. |
| LLVM_DEBUG(dbgs() << "LV: Ignoring UserIC, because vectorization and " |
| "interleaving should be avoided up front\n"); |
| IntDiagMsg = std::make_pair( |
| "InterleavingAvoided", |
| "Ignoring UserIC, because interleaving was avoided up front"); |
| InterleaveLoop = false; |
| } else if (IC == 1 && UserIC <= 1) { |
| // Tell the user interleaving is not beneficial. |
| LLVM_DEBUG(dbgs() << "LV: Interleaving is not beneficial.\n"); |
| IntDiagMsg = std::make_pair( |
| "InterleavingNotBeneficial", |
| "the cost-model indicates that interleaving is not beneficial"); |
| InterleaveLoop = false; |
| if (UserIC == 1) { |
| IntDiagMsg.first = "InterleavingNotBeneficialAndDisabled"; |
| IntDiagMsg.second += |
| " and is explicitly disabled or interleave count is set to 1"; |
| } |
| } else if (IC > 1 && UserIC == 1) { |
| // Tell the user interleaving is beneficial, but it explicitly disabled. |
| LLVM_DEBUG( |
| dbgs() << "LV: Interleaving is beneficial but is explicitly disabled."); |
| IntDiagMsg = std::make_pair( |
| "InterleavingBeneficialButDisabled", |
| "the cost-model indicates that interleaving is beneficial " |
| "but is explicitly disabled or interleave count is set to 1"); |
| InterleaveLoop = false; |
| } |
| |
| // Override IC if user provided an interleave count. |
| IC = UserIC > 0 ? UserIC : IC; |
| |
| // Emit diagnostic messages, if any. |
| const char *VAPassName = Hints.vectorizeAnalysisPassName(); |
| if (!VectorizeLoop && !InterleaveLoop) { |
| // Do not vectorize or interleaving the loop. |
| ORE->emit([&]() { |
| return OptimizationRemarkMissed(VAPassName, VecDiagMsg.first, |
| L->getStartLoc(), L->getHeader()) |
| << VecDiagMsg.second; |
| }); |
| ORE->emit([&]() { |
| return OptimizationRemarkMissed(LV_NAME, IntDiagMsg.first, |
| L->getStartLoc(), L->getHeader()) |
| << IntDiagMsg.second; |
| }); |
| return false; |
| } else if (!VectorizeLoop && InterleaveLoop) { |
| LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n'); |
| ORE->emit([&]() { |
| return OptimizationRemarkAnalysis(VAPassName, VecDiagMsg.first, |
| L->getStartLoc(), L->getHeader()) |
| << VecDiagMsg.second; |
| }); |
| } else if (VectorizeLoop && !InterleaveLoop) { |
| LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width |
| << ") in " << DebugLocStr << '\n'); |
| ORE->emit([&]() { |
| return OptimizationRemarkAnalysis(LV_NAME, IntDiagMsg.first, |
| L->getStartLoc(), L->getHeader()) |
| << IntDiagMsg.second; |
| }); |
| } else if (VectorizeLoop && InterleaveLoop) { |
| LLVM_DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width |
| << ") in " << DebugLocStr << '\n'); |
| LLVM_DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n'); |
| } |
| |
| bool DisableRuntimeUnroll = false; |
| MDNode *OrigLoopID = L->getLoopID(); |
| { |
| // Optimistically generate runtime checks. Drop them if they turn out to not |
| // be profitable. Limit the scope of Checks, so the cleanup happens |
| // immediately after vector codegeneration is done. |
| GeneratedRTChecks Checks(*PSE.getSE(), DT, LI, |
| F->getParent()->getDataLayout()); |
| if (!VF.Width.isScalar() || IC > 1) |
| Checks.Create(L, *LVL.getLAI(), PSE.getUnionPredicate()); |
| |
| 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.getBestPlanFor(VF.Width); |
| LVP.executePlan(VF.Width, IC, BestPlan, Unroller, DT); |
| |
| ORE->emit([&]() { |
| return OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(), |
| L->getHeader()) |
| << "interleaved loop (interleaved count: " |
| << NV("InterleaveCount", IC) << ")"; |
| }); |
| } else { |
| // If we decided that it is *legal* to vectorize the loop, then do it. |
| |
| // Consider vectorizing the epilogue too if it's profitable. |
| VectorizationFactor EpilogueVF = |
| CM.selectEpilogueVectorizationFactor(VF.Width, LVP); |
| 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); |
| |
| VPlan &BestMainPlan = LVP.getBestPlanFor(EPI.MainLoopVF); |
| LVP.executePlan(EPI.MainLoopVF, EPI.MainLoopUF, BestMainPlan, MainILV, |
| DT); |
| ++LoopsVectorized; |
| |
| simplifyLoop(L, DT, LI, SE, AC, nullptr, false /* PreserveLCSSA */); |
| formLCSSARecursively(*L, *DT, LI, SE); |
| |
| // 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.getBestPlanFor(EPI.EpilogueVF); |
| LVP.executePlan(EPI.EpilogueVF, EPI.EpilogueUF, BestEpiPlan, EpilogILV, |
| DT); |
| ++LoopsEpilogueVectorized; |
| |
| if (!MainILV.areSafetyChecksAdded()) |
| DisableRuntimeUnroll = true; |
| } else { |
| InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, IC, |
| &LVL, &CM, BFI, PSI, Checks); |
| |
| VPlan &BestPlan = LVP.getBestPlanFor(VF.Width); |
| LVP.executePlan(VF.Width, IC, BestPlan, LB, DT); |
| ++LoopsVectorized; |
| |
| // Add metadata to disable runtime unrolling a scalar loop when there |
| // are no runtime checks about strides and memory. A scalar loop that is |
| // rarely used is not worth unrolling. |
| if (!LB.areSafetyChecksAdded()) |
| DisableRuntimeUnroll = true; |
| } |
| // Report the vectorization decision. |
| ORE->emit([&]() { |
| return OptimizationRemark(LV_NAME, "Vectorized", L->getStartLoc(), |
| L->getHeader()) |
| << "vectorized loop (vectorization width: " |
| << NV("VectorizationFactor", VF.Width) |
| << ", interleaved count: " << NV("InterleaveCount", IC) << ")"; |
| }); |
| } |
| |
| if (ORE->allowExtraAnalysis(LV_NAME)) |
| checkMixedPrecision(L, ORE); |
| } |
| |
| Optional<MDNode *> RemainderLoopID = |
| makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll, |
| LLVMLoopVectorizeFollowupEpilogue}); |
| if (RemainderLoopID.hasValue()) { |
| L->setLoopID(RemainderLoopID.getValue()); |
| } else { |
| if (DisableRuntimeUnroll) |
| AddRuntimeUnrollDisableMetaData(L); |
| |
| // Mark the loop as already vectorized to avoid vectorizing again. |
| Hints.setAlreadyVectorized(); |
| } |
| |
| assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs())); |
| return true; |
| } |
| |
| LoopVectorizeResult LoopVectorizePass::runImpl( |
| Function &F, ScalarEvolution &SE_, LoopInfo &LI_, TargetTransformInfo &TTI_, |
| DominatorTree &DT_, BlockFrequencyInfo &BFI_, TargetLibraryInfo *TLI_, |
| DemandedBits &DB_, AAResults &AA_, AssumptionCache &AC_, |
| std::function<const LoopAccessInfo &(Loop &)> &GetLAA_, |
| OptimizationRemarkEmitter &ORE_, ProfileSummaryInfo *PSI_) { |
| SE = &SE_; |
| LI = &LI_; |
| TTI = &TTI_; |
| DT = &DT_; |
| BFI = &BFI_; |
| TLI = TLI_; |
| AA = &AA_; |
| AC = &AC_; |
| GetLAA = &GetLAA_; |
| DB = &DB_; |
| ORE = &ORE_; |
| PSI = PSI_; |
| |
| // Don't attempt if |
| // 1. the target claims to have no vector registers, and |
| // 2. interleaving won't help ILP. |
| // |
| // The second condition is necessary because, even if the target has no |
| // vector registers, loop vectorization may still enable scalar |
| // interleaving. |
| if (!TTI->getNumberOfRegisters(TTI->getRegisterClassForType(true)) && |
| TTI->getMaxInterleaveFactor(1) < 2) |
| return LoopVectorizeResult(false, false); |
| |
| bool Changed = false, CFGChanged = false; |
| |
| // The vectorizer requires loops to be in simplified form. |
| // Since simplification may add new inner loops, it has to run before the |
| // legality and profitability checks. This means running the loop vectorizer |
| // will simplify all loops, regardless of whether anything end up being |
| // vectorized. |
| for (auto &L : *LI) |
| Changed |= CFGChanged |= |
| simplifyLoop(L, DT, LI, SE, AC, nullptr, false /* PreserveLCSSA */); |
| |
| // Build up a worklist of inner-loops to vectorize. This is necessary as |
| // the act of vectorizing or partially unrolling a loop creates new loops |
| // and can invalidate iterators across the loops. |
| SmallVector<Loop *, 8> Worklist; |
| |
| for (Loop *L : *LI) |
| collectSupportedLoops(*L, LI, ORE, Worklist); |
| |
| LoopsAnalyzed += Worklist.size(); |
| |
| // Now walk the identified inner loops. |
| while (!Worklist.empty()) { |
| Loop *L = Worklist.pop_back_val(); |
| |
| // For the inner loops we actually process, form LCSSA to simplify the |
| // transform. |
| Changed |= formLCSSARecursively(*L, *DT, LI, SE); |
| |
| Changed |= CFGChanged |= processLoop(L); |
| } |
| |
| // Process each loop nest in the function. |
| return LoopVectorizeResult(Changed, CFGChanged); |
| } |
| |
| PreservedAnalyses LoopVectorizePass::run(Function &F, |
| FunctionAnalysisManager &AM) { |
| auto &SE = AM.getResult<ScalarEvolutionAnalysis>(F); |
| auto &LI = AM.getResult<LoopAnalysis>(F); |
| auto &TTI = AM.getResult<TargetIRAnalysis>(F); |
| auto &DT = AM.getResult<DominatorTreeAnalysis>(F); |
| auto &BFI = AM.getResult<BlockFrequencyAnalysis>(F); |
| auto &TLI = AM.getResult<TargetLibraryAnalysis>(F); |
| auto &AA = AM.getResult<AAManager>(F); |
| auto &AC = AM.getResult<AssumptionAnalysis>(F); |
| auto &DB = AM.getResult<DemandedBitsAnalysis>(F); |
| auto &ORE = AM.getResult<OptimizationRemarkEmitterAnalysis>(F); |
| |
| auto &LAM = AM.getResult<LoopAnalysisManagerFunctionProxy>(F).getManager(); |
| std::function<const LoopAccessInfo &(Loop &)> GetLAA = |
| [&](Loop &L) -> const LoopAccessInfo & { |
| LoopStandardAnalysisResults AR = {AA, AC, DT, LI, SE, |
| TLI, TTI, nullptr, nullptr, nullptr}; |
| return LAM.getResult<LoopAccessAnalysis>(L, AR); |
| }; |
| auto &MAMProxy = AM.getResult<ModuleAnalysisManagerFunctionProxy>(F); |
| ProfileSummaryInfo *PSI = |
| MAMProxy.getCachedResult<ProfileSummaryAnalysis>(*F.getParent()); |
| LoopVectorizeResult Result = |
| runImpl(F, SE, LI, TTI, DT, BFI, &TLI, DB, AA, AC, GetLAA, ORE, PSI); |
| if (!Result.MadeAnyChange) |
| return PreservedAnalyses::all(); |
| PreservedAnalyses PA; |
| |
| // We currently do not preserve loopinfo/dominator analyses with outer loop |
| // vectorization. Until this is addressed, mark these analyses as preserved |
| // only for non-VPlan-native path. |
| // TODO: Preserve Loop and Dominator analyses for VPlan-native path. |
| if (!EnableVPlanNativePath) { |
| PA.preserve<LoopAnalysis>(); |
| PA.preserve<DominatorTreeAnalysis>(); |
| } |
| if (!Result.MadeCFGChange) |
| 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 << ">"; |
| } |