//===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===// // // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. // See https://llvm.org/LICENSE.txt for license information. // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception // //===----------------------------------------------------------------------===// // // This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops // and generates target-independent LLVM-IR. // The vectorizer uses the TargetTransformInfo analysis to estimate the costs // of instructions in order to estimate the profitability of vectorization. // // The loop vectorizer combines consecutive loop iterations into a single // 'wide' iteration. After this transformation the index is incremented // by the SIMD vector width, and not by one. // // This pass has three parts: // 1. The main loop pass that drives the different parts. // 2. LoopVectorizationLegality - A unit that checks for the legality // of the vectorization. // 3. InnerLoopVectorizer - A unit that performs the actual // widening of instructions. // 4. LoopVectorizationCostModel - A unit that checks for the profitability // of vectorization. It decides on the optimal vector width, which // can be one, if vectorization is not profitable. // // There is a development effort going on to migrate loop vectorizer to the // VPlan infrastructure and to introduce outer loop vectorization support (see // docs/Proposal/VectorizationPlan.rst and // http://lists.llvm.org/pipermail/llvm-dev/2017-December/119523.html). For this // purpose, we temporarily introduced the VPlan-native vectorization path: an // alternative vectorization path that is natively implemented on top of the // VPlan infrastructure. See EnableVPlanNativePath for enabling. // //===----------------------------------------------------------------------===// // // The reduction-variable vectorization is based on the paper: // D. Nuzman and R. Henderson. Multi-platform Auto-vectorization. // // Variable uniformity checks are inspired by: // Karrenberg, R. and Hack, S. Whole Function Vectorization. // // The interleaved access vectorization is based on the paper: // Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved // Data for SIMD // // Other ideas/concepts are from: // A. Zaks and D. Nuzman. Autovectorization in GCC-two years later. // // S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of // Vectorizing Compilers. // //===----------------------------------------------------------------------===// #include "llvm/Transforms/Vectorize/LoopVectorize.h" #include "LoopVectorizationPlanner.h" #include "VPRecipeBuilder.h" #include "VPlan.h" #include "VPlanHCFGBuilder.h" #include "VPlanPredicator.h" #include "VPlanTransforms.h" #include "llvm/ADT/APInt.h" #include "llvm/ADT/ArrayRef.h" #include "llvm/ADT/DenseMap.h" #include "llvm/ADT/DenseMapInfo.h" #include "llvm/ADT/Hashing.h" #include "llvm/ADT/MapVector.h" #include "llvm/ADT/None.h" #include "llvm/ADT/Optional.h" #include "llvm/ADT/STLExtras.h" #include "llvm/ADT/SetVector.h" #include "llvm/ADT/SmallPtrSet.h" #include "llvm/ADT/SmallVector.h" #include "llvm/ADT/Statistic.h" #include "llvm/ADT/StringRef.h" #include "llvm/ADT/Twine.h" #include "llvm/ADT/iterator_range.h" #include "llvm/Analysis/AssumptionCache.h" #include "llvm/Analysis/BasicAliasAnalysis.h" #include "llvm/Analysis/BlockFrequencyInfo.h" #include "llvm/Analysis/CFG.h" #include "llvm/Analysis/CodeMetrics.h" #include "llvm/Analysis/DemandedBits.h" #include "llvm/Analysis/GlobalsModRef.h" #include "llvm/Analysis/LoopAccessAnalysis.h" #include "llvm/Analysis/LoopAnalysisManager.h" #include "llvm/Analysis/LoopInfo.h" #include "llvm/Analysis/LoopIterator.h" #include "llvm/Analysis/MemorySSA.h" #include "llvm/Analysis/OptimizationRemarkEmitter.h" #include "llvm/Analysis/ProfileSummaryInfo.h" #include "llvm/Analysis/ScalarEvolution.h" #include "llvm/Analysis/ScalarEvolutionExpressions.h" #include "llvm/Analysis/TargetLibraryInfo.h" #include "llvm/Analysis/TargetTransformInfo.h" #include "llvm/Analysis/VectorUtils.h" #include "llvm/IR/Attributes.h" #include "llvm/IR/BasicBlock.h" #include "llvm/IR/CFG.h" #include "llvm/IR/Constant.h" #include "llvm/IR/Constants.h" #include "llvm/IR/DataLayout.h" #include "llvm/IR/DebugInfoMetadata.h" #include "llvm/IR/DebugLoc.h" #include "llvm/IR/DerivedTypes.h" #include "llvm/IR/DiagnosticInfo.h" #include "llvm/IR/Dominators.h" #include "llvm/IR/Function.h" #include "llvm/IR/IRBuilder.h" #include "llvm/IR/InstrTypes.h" #include "llvm/IR/Instruction.h" #include "llvm/IR/Instructions.h" #include "llvm/IR/IntrinsicInst.h" #include "llvm/IR/Intrinsics.h" #include "llvm/IR/LLVMContext.h" #include "llvm/IR/Metadata.h" #include "llvm/IR/Module.h" #include "llvm/IR/Operator.h" #include "llvm/IR/Type.h" #include "llvm/IR/Use.h" #include "llvm/IR/User.h" #include "llvm/IR/Value.h" #include "llvm/IR/ValueHandle.h" #include "llvm/IR/Verifier.h" #include "llvm/InitializePasses.h" #include "llvm/Pass.h" #include "llvm/Support/Casting.h" #include "llvm/Support/CommandLine.h" #include "llvm/Support/Compiler.h" #include "llvm/Support/Debug.h" #include "llvm/Support/ErrorHandling.h" #include "llvm/Support/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 #include #include #include #include #include #include #include #include #include #include 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 EnableEpilogueVectorization( "enable-epilogue-vectorization", cl::init(true), cl::Hidden, cl::desc("Enable vectorization of epilogue loops.")); static cl::opt 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 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 TinyTripCountVectorThreshold( "vectorizer-min-trip-count", cl::init(16), cl::Hidden, cl::desc("Loops with a constant trip count that is smaller than this " "value are vectorized only if no scalar iteration overheads " "are incurred.")); // Option prefer-predicate-over-epilogue indicates that an epilogue is undesired, // that predication is preferred, and this lists all options. I.e., the // vectorizer will try to fold the tail-loop (epilogue) into the vector body // and predicate the instructions accordingly. If tail-folding fails, there are // different fallback strategies depending on these values: namespace PreferPredicateTy { enum Option { ScalarEpilogue = 0, PredicateElseScalarEpilogue, PredicateOrDontVectorize }; } // namespace PreferPredicateTy static cl::opt 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 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 EnableIndVarRegisterHeur( "enable-ind-var-reg-heur", cl::init(true), cl::Hidden, cl::desc("Count the induction variable only once when interleaving")); static cl::opt EnableCondStoresVectorization( "enable-cond-stores-vec", cl::init(true), cl::Hidden, cl::desc("Enable if predication of stores during vectorization.")); static cl::opt 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 PreferInLoopReductions("prefer-inloop-reductions", cl::init(false), cl::Hidden, cl::desc("Prefer in-loop vector reductions, " "overriding the targets preference.")); static cl::opt PreferPredicatedReductionSelect( "prefer-predicated-reduction-select", cl::init(false), cl::Hidden, cl::desc( "Prefer predicating a reduction operation over an after loop select.")); cl::opt 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 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 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 llvm::EnableLoopInterleaving( "interleave-loops", cl::init(true), cl::Hidden, cl::desc("Enable loop interleaving in Loop vectorization passes")); cl::opt llvm::EnableLoopVectorization( "vectorize-loops", cl::init(true), cl::Hidden, cl::desc("Run the Loop vectorization passes")); /// A helper function that returns the type of loaded or stored value. static Type *getMemInstValueType(Value *I) { assert((isa(I) || isa(I)) && "Expected Load or Store instruction"); if (auto *LI = dyn_cast(I)) return LI->getType(); return cast(I)->getValueOperand()->getType(); } /// A helper function that returns true if the given type is irregular. The /// type is irregular if its allocated size doesn't equal the store size of an /// element of the corresponding vector type. static bool hasIrregularType(Type *Ty, const DataLayout &DL) { // Determine if an array of N elements of type Ty is "bitcast compatible" // with a 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 adds a 'fast' flag to floating-point operations. static Value *addFastMathFlag(Value *V) { if (isa(V)) cast(V)->setFastMathFlags(FastMathFlags::getFast()); return V; } static Value *addFastMathFlag(Value *V, FastMathFlags FMF) { if (isa(V)) cast(V)->setFastMathFlags(FMF); return V; } /// A helper function that returns an integer or floating-point constant with /// value C. static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) { return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C) : ConstantFP::get(Ty, C); } /// Returns "best known" trip count for the specified loop \p L as defined by /// the following procedure: /// 1) Returns exact trip count if it is known. /// 2) Returns expected trip count according to profile data if any. /// 3) Returns upper bound estimate if it is known. /// 4) Returns None if all of the above failed. static Optional getSmallBestKnownTC(ScalarEvolution &SE, Loop *L) { // Check if exact trip count is known. if (unsigned ExpectedTC = SE.getSmallConstantTripCount(L)) return ExpectedTC; // Check if there is an expected trip count available from profile data. if (LoopVectorizeWithBlockFrequency) if (auto EstimatedTC = getLoopEstimatedTripCount(L)) return EstimatedTC; // Check if upper bound estimate is known. if (unsigned ExpectedTC = SE.getSmallConstantMaxTripCount(L)) return ExpectedTC; return None; } namespace llvm { /// InnerLoopVectorizer vectorizes loops which contain only one basic /// block to a specified vectorization factor (VF). /// This class performs the widening of scalars into vectors, or multiple /// scalars. This class also implements the following features: /// * It inserts an epilogue loop for handling loops that don't have iteration /// counts that are known to be a multiple of the vectorization factor. /// * It handles the code generation for reduction variables. /// * Scalarization (implementation using scalars) of un-vectorizable /// instructions. /// InnerLoopVectorizer does not perform any vectorization-legality /// checks, and relies on the caller to check for the different legality /// aspects. The InnerLoopVectorizer relies on the /// LoopVectorizationLegality class to provide information about the induction /// and reduction variables that were found to a given vectorization factor. class InnerLoopVectorizer { public: InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, DominatorTree *DT, const TargetLibraryInfo *TLI, const TargetTransformInfo *TTI, AssumptionCache *AC, OptimizationRemarkEmitter *ORE, ElementCount VecWidth, unsigned UnrollFactor, LoopVectorizationLegality *LVL, LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI) : OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI), AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor), Builder(PSE.getSE()->getContext()), VectorLoopValueMap(UnrollFactor, VecWidth), Legal(LVL), Cost(CM), BFI(BFI), PSI(PSI) { // Query this against the original loop and save it here because the profile // of the original loop header may change as the transformation happens. OptForSizeBasedOnProfile = llvm::shouldOptimizeForSize( OrigLoop->getHeader(), PSI, BFI, PGSOQueryType::IRPass); } virtual ~InnerLoopVectorizer() = default; /// Create a new empty loop that will contain vectorized instructions later /// on, while the old loop will be used as the scalar remainder. Control flow /// is generated around the vectorized (and scalar epilogue) loops consisting /// of various checks and bypasses. Return the pre-header block of the new /// loop. /// 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 instruction within the innermost loop. void widenInstruction(Instruction &I, VPValue *Def, VPUser &Operands, VPTransformState &State); /// Widen a single call instruction within the innermost loop. void widenCallInstruction(CallInst &I, VPValue *Def, VPUser &ArgOperands, VPTransformState &State); /// Widen a single select instruction within the innermost loop. void widenSelectInstruction(SelectInst &I, VPValue *VPDef, VPUser &Operands, bool InvariantCond, VPTransformState &State); /// Fix the vectorized code, taking care of header phi's, live-outs, and more. void fixVectorizedLoop(); // Return true if any runtime check is added. bool areSafetyChecksAdded() { return AddedSafetyChecks; } /// A type for vectorized values in the new loop. Each value from the /// original loop, when vectorized, is represented by UF vector values in the /// new unrolled loop, where UF is the unroll factor. using VectorParts = SmallVector; /// Vectorize a single GetElementPtrInst based on information gathered and /// decisions taken during planning. void widenGEP(GetElementPtrInst *GEP, VPValue *VPDef, VPUser &Indices, unsigned UF, ElementCount VF, bool IsPtrLoopInvariant, SmallBitVector &IsIndexLoopInvariant, VPTransformState &State); /// Vectorize a single PHINode in a block. This method handles the induction /// variable canonicalization. It supports both VF = 1 for unrolled loops and /// arbitrary length vectors. void widenPHIInstruction(Instruction *PN, RecurrenceDescriptor *RdxDesc, Value *StartV, unsigned UF, ElementCount VF); /// A helper function to scalarize a single Instruction in the innermost loop. /// Generates a sequence of scalar instances for each lane between \p MinLane /// and \p MaxLane, times each part between \p MinPart and \p MaxPart, /// inclusive. Uses the VPValue operands from \p Operands instead of \p /// Instr's operands. void scalarizeInstruction(Instruction *Instr, VPUser &Operands, const VPIteration &Instance, bool IfPredicateInstr, VPTransformState &State); /// Widen an integer or floating-point induction variable \p IV. If \p Trunc /// is provided, the integer induction variable will first be truncated to /// the corresponding type. void widenIntOrFpInduction(PHINode *IV, Value *Start, TruncInst *Trunc = nullptr); /// getOrCreateVectorValue and getOrCreateScalarValue coordinate to generate a /// vector or scalar value on-demand if one is not yet available. When /// vectorizing a loop, we visit the definition of an instruction before its /// uses. When visiting the definition, we either vectorize or scalarize the /// instruction, creating an entry for it in the corresponding map. (In some /// cases, such as induction variables, we will create both vector and scalar /// entries.) Then, as we encounter uses of the definition, we derive values /// for each scalar or vector use unless such a value is already available. /// For example, if we scalarize a definition and one of its uses is vector, /// we build the required vector on-demand with an insertelement sequence /// when visiting the use. Otherwise, if the use is scalar, we can use the /// existing scalar definition. /// /// Return a value in the new loop corresponding to \p V from the original /// loop at unroll index \p Part. If the value has already been vectorized, /// the corresponding vector entry in VectorLoopValueMap is returned. If, /// however, the value has a scalar entry in VectorLoopValueMap, we construct /// a new vector value on-demand by inserting the scalar values into a vector /// with an insertelement sequence. If the value has been neither vectorized /// nor scalarized, it must be loop invariant, so we simply broadcast the /// value into a vector. Value *getOrCreateVectorValue(Value *V, unsigned Part); void setVectorValue(Value *Scalar, unsigned Part, Value *Vector) { VectorLoopValueMap.setVectorValue(Scalar, Part, Vector); } /// Return a value in the new loop corresponding to \p V from the original /// loop at unroll and vector indices \p Instance. If the value has been /// vectorized but not scalarized, the necessary extractelement instruction /// will be generated. Value *getOrCreateScalarValue(Value *V, const VPIteration &Instance); /// Construct the vector value of a scalarized value \p V one lane at a time. void packScalarIntoVectorValue(Value *V, const VPIteration &Instance); /// Try to vectorize interleaved access group \p Group with the base address /// given in \p Addr, optionally masking the vector operations if \p /// BlockInMask is non-null. Use \p State to translate given VPValues to IR /// values in the vectorized loop. void vectorizeInterleaveGroup(const InterleaveGroup *Group, ArrayRef VPDefs, VPTransformState &State, VPValue *Addr, ArrayRef 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); /// Set the debug location in the builder using the debug location in /// the instruction. void setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr); /// Fix the non-induction PHIs in the OrigPHIsToFix vector. void fixNonInductionPHIs(void); protected: friend class LoopVectorizationPlanner; /// A small list of PHINodes. using PhiVector = SmallVector; /// 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, 2>; /// Set up the values of the IVs correctly when exiting the vector loop. void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II, Value *CountRoundDown, Value *EndValue, BasicBlock *MiddleBlock); /// Create a new induction variable inside L. PHINode *createInductionVariable(Loop *L, Value *Start, Value *End, Value *Step, Instruction *DL); /// Handle all cross-iteration phis in the header. void fixCrossIterationPHIs(); /// Fix a first-order recurrence. This is the second phase of vectorizing /// this phi node. void fixFirstOrderRecurrence(PHINode *Phi); /// Fix a reduction cross-iteration phi. This is the second phase of /// vectorizing this phi node. void fixReduction(PHINode *Phi); /// Clear NSW/NUW flags from reduction instructions if necessary. void clearReductionWrapFlags(RecurrenceDescriptor &RdxDesc); /// 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(); /// Iteratively sink the scalarized operands of a predicated instruction into /// the block that was created for it. void sinkScalarOperands(Instruction *PredInst); /// Shrinks vector element sizes to the smallest bitwidth they can be legally /// represented as. void truncateToMinimalBitwidths(); /// Create a broadcast instruction. This method generates a broadcast /// instruction (shuffle) for loop invariant values and for the induction /// value. If this is the induction variable then we extend it to N, N+1, ... /// this is needed because each iteration in the loop corresponds to a SIMD /// element. virtual Value *getBroadcastInstrs(Value *V); /// This function adds (StartIdx, StartIdx + Step, StartIdx + 2*Step, ...) /// to each vector element of Val. The sequence starts at StartIndex. /// \p Opcode is relevant for FP induction variable. virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step, Instruction::BinaryOps Opcode = Instruction::BinaryOpsEnd); /// Compute scalar induction steps. \p ScalarIV is the scalar induction /// variable on which to base the steps, \p Step is the size of the step, and /// \p EntryVal is the value from the original loop that maps to the steps. /// Note that \p EntryVal doesn't have to be an induction variable - it /// can also be a truncate instruction. void buildScalarSteps(Value *ScalarIV, Value *Step, Instruction *EntryVal, const InductionDescriptor &ID); /// Create a vector induction phi node based on an existing scalar one. \p /// EntryVal is the value from the original loop that maps to the vector phi /// node, and \p Step is the loop-invariant step. If \p EntryVal is a /// truncate instruction, instead of widening the original IV, we widen a /// version of the IV truncated to \p EntryVal's type. void createVectorIntOrFpInductionPHI(const InductionDescriptor &II, Value *Step, Value *Start, Instruction *EntryVal); /// Returns true if an instruction \p I should be scalarized instead of /// vectorized for the chosen vectorization factor. bool shouldScalarizeInstruction(Instruction *I) const; /// Returns true if we should generate a scalar version of \p IV. bool needsScalarInduction(Instruction *IV) const; /// If there is a cast involved in the induction variable \p ID, which should /// be ignored in the vectorized loop body, this function records the /// VectorLoopValue of the respective Phi also as the VectorLoopValue of the /// cast. We had already proved that the casted Phi is equal to the uncasted /// Phi in the vectorized loop (under a runtime guard), and therefore /// there is no need to vectorize the cast - the same value can be used in the /// vector loop for both the Phi and the cast. /// If \p VectorLoopValue is a scalarized value, \p Lane is also specified, /// Otherwise, \p VectorLoopValue is a widened/vectorized value. /// /// \p EntryVal is the value from the original loop that maps to the vector /// phi node and is used to distinguish what is the IV currently being /// processed - original one (if \p EntryVal is a phi corresponding to the /// original IV) or the "newly-created" one based on the proof mentioned above /// (see also buildScalarSteps() and createVectorIntOrFPInductionPHI()). In the /// latter case \p EntryVal is a TruncInst and we must not record anything for /// that IV, but it's error-prone to expect callers of this routine to care /// about that, hence this explicit parameter. void recordVectorLoopValueForInductionCast(const InductionDescriptor &ID, const Instruction *EntryVal, Value *VectorLoopValue, unsigned Part, unsigned Lane = UINT_MAX); /// Generate a shuffle sequence that will reverse the vector Vec. virtual Value *reverseVector(Value *Vec); /// Returns (and creates if needed) the original loop trip count. Value *getOrCreateTripCount(Loop *NewLoop); /// Returns (and creates if needed) the trip count of the widened loop. Value *getOrCreateVectorTripCount(Loop *NewLoop); /// Returns a bitcasted value to the requested vector type. /// Also handles bitcasts of vector <-> vector types. Value *createBitOrPointerCast(Value *V, VectorType *DstVTy, const DataLayout &DL); /// Emit a bypass check to see if the vector trip count is zero, including if /// it overflows. void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass); /// Emit a bypass check to see if all of the SCEV assumptions we've /// had to make are correct. void emitSCEVChecks(Loop *L, BasicBlock *Bypass); /// Emit bypass checks to check any memory assumptions we may have made. void emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass); /// Compute the transformed value of Index at offset StartValue using step /// StepValue. /// For integer induction, returns StartValue + Index * StepValue. /// For pointer induction, returns StartValue[Index * StepValue]. /// FIXME: The newly created binary instructions should contain nsw/nuw /// flags, which can be found from the original scalar operations. Value *emitTransformedIndex(IRBuilder<> &B, Value *Index, ScalarEvolution *SE, const DataLayout &DL, const InductionDescriptor &ID) const; /// Emit basic blocks (prefixed with \p Prefix) for the iteration check, /// vector loop preheader, middle block and scalar preheader. Also /// allocate a loop object for the new vector loop and return it. Loop *createVectorLoopSkeleton(StringRef Prefix); /// Create new phi nodes for the induction variables to resume iteration count /// in the scalar epilogue, from where the vectorized loop left off (given by /// \p VectorTripCount). /// 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 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); /// 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 To, Instruction *From); /// 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 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. 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 LoopBypassBlocks; /// The new Induction variable which was added to the new block. PHINode *Induction = nullptr; /// The induction variable of the old basic block. PHINode *OldInduction = nullptr; /// Maps values from the original loop to their corresponding values in the /// vectorized loop. A key value can map to either vector values, scalar /// values or both kinds of values, depending on whether the key was /// vectorized and scalarized. VectorizerValueMap VectorLoopValueMap; /// Store instructions that were predicated. SmallVector 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 IVEndValues; // Vector of original scalar PHIs whose corresponding widened PHIs need to be // fixed up at the end of vector code generation. SmallVector OrigPHIsToFix; /// BFI and PSI are used to check for profile guided size optimizations. BlockFrequencyInfo *BFI; ProfileSummaryInfo *PSI; // Whether this loop should be optimized for size based on profile guided size // optimizatios. bool OptForSizeBasedOnProfile; }; class InnerLoopUnroller : public InnerLoopVectorizer { public: InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, DominatorTree *DT, const TargetLibraryInfo *TLI, const TargetTransformInfo *TTI, AssumptionCache *AC, OptimizationRemarkEmitter *ORE, unsigned UnrollFactor, LoopVectorizationLegality *LVL, LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI) : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, ElementCount::getFixed(1), UnrollFactor, LVL, CM, BFI, PSI) {} private: Value *getBroadcastInstrs(Value *V) override; Value *getStepVector(Value *Val, int StartIdx, Value *Step, Instruction::BinaryOps Opcode = Instruction::BinaryOpsEnd) override; Value *reverseVector(Value *Vec) override; }; /// 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(unsigned MVF, unsigned MUF, unsigned EVF, unsigned EUF) : MainLoopVF(ElementCount::getFixed(MVF)), MainLoopUF(MUF), EpilogueVF(ElementCount::getFixed(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) : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, EPI.MainLoopVF, EPI.MainLoopUF, LVL, CM, BFI, PSI), 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) : InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, EPI, LVL, CM, BFI, PSI) {} /// 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) : InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, EPI, LVL, CM, BFI, PSI) {} /// 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 (User::op_iterator OI = I->op_begin(), OE = I->op_end(); OI != OE; ++OI) { if (Instruction *OpInst = dyn_cast(*OI)) if (OpInst->getDebugLoc() != Empty) return OpInst; } return I; } void InnerLoopVectorizer::setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) { if (const Instruction *Inst = dyn_cast_or_null(Ptr)) { const DILocation *DIL = Inst->getDebugLoc(); if (DIL && Inst->getFunction()->isDebugInfoForProfiling() && !isa(Inst)) { assert(!VF.isScalable() && "scalable vectors not yet supported."); auto NewDIL = DIL->cloneByMultiplyingDuplicationFactor(UF * VF.getKnownMinValue()); if (NewDIL) B.SetCurrentDebugLocation(NewDIL.getValue()); else LLVM_DEBUG(dbgs() << "Failed to create new discriminator: " << DIL->getFilename() << " Line: " << DIL->getLine()); } else B.SetCurrentDebugLocation(DIL); } else B.SetCurrentDebugLocation(DebugLoc()); } /// Write a record \p DebugMsg about vectorization failure to the debug /// output stream. If \p I is passed, it is an instruction that prevents /// vectorization. #ifndef NDEBUG static void debugVectorizationFailure(const StringRef DebugMsg, Instruction *I) { dbgs() << "LV: Not vectorizing: " << DebugMsg; if (I != nullptr) dbgs() << " " << *I; else dbgs() << '.'; dbgs() << '\n'; } #endif /// Create an analysis remark that explains why vectorization failed /// /// \p PassName is the name of the pass (e.g. can be AlwaysPrint). \p /// RemarkName is the identifier for the remark. If \p I is passed it is an /// instruction that prevents vectorization. Otherwise \p TheLoop is used for /// the location of the remark. \return the remark object that can be /// streamed to. static OptimizationRemarkAnalysis createLVAnalysis(const char *PassName, StringRef RemarkName, Loop *TheLoop, Instruction *I) { Value *CodeRegion = TheLoop->getHeader(); DebugLoc DL = TheLoop->getStartLoc(); if (I) { CodeRegion = I->getParent(); // If there is no debug location attached to the instruction, revert back to // using the loop's. if (I->getDebugLoc()) DL = I->getDebugLoc(); } OptimizationRemarkAnalysis R(PassName, RemarkName, DL, CodeRegion); R << "loop not vectorized: "; return R; } /// Return a value for Step multiplied by VF. static Value *createStepForVF(IRBuilder<> &B, Constant *Step, ElementCount VF) { assert(isa(Step) && "Expected an integer step"); Constant *StepVal = ConstantInt::get( Step->getType(), cast(Step)->getSExtValue() * VF.getKnownMinValue()); return VF.isScalable() ? B.CreateVScale(StepVal) : StepVal; } namespace llvm { void reportVectorizationFailure(const StringRef DebugMsg, const StringRef OREMsg, const StringRef ORETag, OptimizationRemarkEmitter *ORE, Loop *TheLoop, Instruction *I) { LLVM_DEBUG(debugVectorizationFailure(DebugMsg, I)); LoopVectorizeHints Hints(TheLoop, true /* doesn't matter */, *ORE); ORE->emit(createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop, I) << OREMsg); } } // end namespace llvm #ifndef NDEBUG /// \return string containing a file name and a line # for the given loop. static std::string getDebugLocString(const Loop *L) { std::string Result; if (L) { raw_string_ostream OS(Result); if (const DebugLoc LoopDbgLoc = L->getStartLoc()) LoopDbgLoc.print(OS); else // Just print the module name. OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier(); OS.flush(); } return Result; } #endif void InnerLoopVectorizer::addNewMetadata(Instruction *To, const Instruction *Orig) { // If the loop was versioned with memchecks, add the corresponding no-alias // metadata. if (LVer && (isa(Orig) || isa(Orig))) LVer->annotateInstWithNoAlias(To, Orig); } void InnerLoopVectorizer::addMetadata(Instruction *To, Instruction *From) { propagateMetadata(To, From); addNewMetadata(To, From); } void InnerLoopVectorizer::addMetadata(ArrayRef To, Instruction *From) { for (Value *V : To) { if (Instruction *I = dyn_cast(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 }; /// LoopVectorizationCostModel - estimates the expected speedups due to /// vectorization. /// In many cases vectorization is not profitable. This can happen because of /// a number of reasons. In this class we mainly attempt to predict the /// expected speedup/slowdowns due to the supported instruction set. We use the /// TargetTransformInfo to query the different backends for the cost of /// different operations. class LoopVectorizationCostModel { public: LoopVectorizationCostModel(ScalarEpilogueLowering SEL, Loop *L, PredicatedScalarEvolution &PSE, LoopInfo *LI, LoopVectorizationLegality *Legal, const TargetTransformInfo &TTI, const TargetLibraryInfo *TLI, DemandedBits *DB, AssumptionCache *AC, OptimizationRemarkEmitter *ORE, const Function *F, const LoopVectorizeHints *Hints, InterleavedAccessInfo &IAI) : ScalarEpilogueStatus(SEL), TheLoop(L), PSE(PSE), LI(LI), Legal(Legal), TTI(TTI), TLI(TLI), DB(DB), AC(AC), ORE(ORE), TheFunction(F), Hints(Hints), InterleaveInfo(IAI) {} /// \return An upper bound for the vectorization factor, or None if /// vectorization and interleaving should be avoided up front. Optional 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 power of two up to MaxVF. If UserVF is not ZERO /// then this vectorization factor will be selected if vectorization is /// possible. VectorizationFactor selectVectorizationFactor(ElementCount MaxVF); VectorizationFactor selectEpilogueVectorizationFactor(const ElementCount MaxVF, const LoopVectorizationPlanner &LVP); /// Setup cost-based decisions for user vectorization factor. void selectUserVectorizationFactor(ElementCount UserVF) { collectUniformsAndScalars(UserVF); collectInstsToScalarize(UserVF); } /// \return The size (in bits) of the smallest and widest types in the code /// that needs to be vectorized. We ignore values that remain scalar such as /// 64 bit loop indices. std::pair 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 LoopInvariantRegs; /// Holds the maximum number of concurrent live intervals in the loop. /// The key is ClassID of target-provided register class. SmallMapVector MaxLocalUsers; }; /// \return Returns information about the register usages of the loop for the /// given vectorization factors. SmallVector calculateRegisterUsage(ArrayRef VFs); /// Collect values we want to ignore in the cost model. void collectValuesToIgnore(); /// Split reductions into those that happen in the loop, and those that happen /// outside. In loop reductions are collected into InLoopReductionChains. void collectInLoopReductions(); /// \returns The smallest bitwidth each instruction can be represented with. /// The vector equivalents of these instructions should be truncated to this /// type. const MapVector &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 *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) { 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 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 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(I); if (!Trunc) return false; // Get the source and destination types of the truncate. Type *SrcTy = ToVectorTy(cast(I)->getSrcTy(), VF); Type *DestTy = ToVectorTy(cast(I)->getDestTy(), VF); // If the truncate is free for the given types, return false. Replacing a // free truncate with an induction variable would add an induction variable // update instruction to each iteration of the loop. We exclude from this // check the primary induction variable since it will need an update // instruction regardless. Value *Op = Trunc->getOperand(0); if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy)) return false; // If the truncated value is not an induction variable, return false. return Legal->isInductionPhi(Op); } /// Collects the instructions to scalarize for each predicated instruction in /// the loop. void collectInstsToScalarize(ElementCount VF); /// Collect Uniform and Scalar values for the given \p VF. /// The sets depend on CM decision for Load/Store instructions /// that may be vectorized as interleave, gather-scatter or scalarized. void collectUniformsAndScalars(ElementCount VF) { // Do the analysis once. if (VF.isScalar() || Uniforms.find(VF) != Uniforms.end()) return; setCostBasedWideningDecision(VF); collectLoopUniforms(VF); collectLoopScalars(VF); } /// Returns true if the target machine supports masked store operation /// for the given \p DataType and kind of access to \p Ptr. bool isLegalMaskedStore(Type *DataType, Value *Ptr, Align Alignment) { return Legal->isConsecutivePtr(Ptr) && TTI.isLegalMaskedStore(DataType, Alignment); } /// Returns true if the target machine supports masked load operation /// for the given \p DataType and kind of access to \p Ptr. bool isLegalMaskedLoad(Type *DataType, Value *Ptr, Align Alignment) { return Legal->isConsecutivePtr(Ptr) && TTI.isLegalMaskedLoad(DataType, Alignment); } /// Returns true if the target machine supports masked scatter operation /// for the given \p DataType. bool isLegalMaskedScatter(Type *DataType, Align Alignment) { return TTI.isLegalMaskedScatter(DataType, Alignment); } /// Returns true if the target machine supports masked gather operation /// for the given \p DataType. bool isLegalMaskedGather(Type *DataType, Align Alignment) { return TTI.isLegalMaskedGather(DataType, Alignment); } /// Returns true if the target machine can represent \p V as a masked gather /// or scatter operation. bool isLegalGatherOrScatter(Value *V) { bool LI = isa(V); bool SI = isa(V); if (!LI && !SI) return false; auto *Ty = getMemInstValueType(V); Align Align = getLoadStoreAlignment(V); return (LI && isLegalMaskedGather(Ty, Align)) || (SI && isLegalMaskedScatter(Ty, Align)); } /// Returns true if \p I is an instruction that will be scalarized with /// predication. Such instructions include conditional stores and /// instructions that may divide by zero. /// If a non-zero VF has been calculated, we check if I will be scalarized /// predication for that VF. bool isScalarWithPredication(Instruction *I, ElementCount VF = ElementCount::getFixed(1)); // Returns true if \p I is an instruction that will be predicated either // through scalar predication or masked load/store or masked gather/scatter. // Superset of instructions that return true for isScalarWithPredication. bool isPredicatedInst(Instruction *I) { if (!blockNeedsPredication(I->getParent())) return false; // Loads and stores that need some form of masked operation are predicated // instructions. if (isa(I) || isa(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 * 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() 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 InterleaveInfo.requiresScalarEpilogue(); } /// Returns true if a scalar epilogue is not allowed due to optsize or a /// loop hint annotation. bool isScalarEpilogueAllowed() const { return ScalarEpilogueStatus == CM_ScalarEpilogueAllowed; } /// Returns true if all loop blocks should be masked to fold tail loop. bool foldTailByMasking() const { return FoldTailByMasking; } bool blockNeedsPredication(BasicBlock *BB) { return foldTailByMasking() || Legal->blockNeedsPredication(BB); } /// A SmallMapVector to store the InLoop reduction op chains, mapping phi /// nodes to the chain of instructions representing the reductions. Uses a /// MapVector to ensure deterministic iteration order. using ReductionChainMap = SmallMapVector, 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); /// 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); /// Invalidates decisions already taken by the cost model. void invalidateCostModelingDecisions() { WideningDecisions.clear(); Uniforms.clear(); Scalars.clear(); } private: unsigned NumPredStores = 0; /// \return An upper bound for the vectorization factor, a power-of-2 larger /// than zero. One is returned if vectorization should best be avoided due /// to cost. ElementCount computeFeasibleMaxVF(unsigned ConstTripCount, ElementCount UserVF); /// 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; /// Returns the expected execution cost. The unit of the cost does /// not matter because we use the 'cost' units to compare different /// vector widths. The cost that is returned is *not* normalized by /// the factor width. VectorizationCostTy expectedCost(ElementCount VF); /// Returns the execution time cost of an instruction for a given vector /// width. Vector width of one means scalar. VectorizationCostTy getInstructionCost(Instruction *I, ElementCount VF); /// The cost-computation logic from getInstructionCost which provides /// the vector type as an output parameter. 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. 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); /// 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 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; /// A set containing all BasicBlocks that are known to present after /// vectorization as a predicated block. SmallPtrSet 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 InstsToScalarize; /// Holds the instructions known to be uniform after vectorization. /// The data is collected per VF. DenseMap> Uniforms; /// Holds the instructions known to be scalar after vectorization. /// The data is collected per VF. DenseMap> Scalars; /// Holds the instructions (address computations) that are forced to be /// scalarized. DenseMap> 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 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. 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>; 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(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 filterExtractingOperands(Instruction::op_range Ops, ElementCount VF) { return SmallVector(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 ValuesToIgnore; /// Values to ignore in the cost model when VF > 1. SmallPtrSet VecValuesToIgnore; /// Profitable vector factors. SmallVector ProfitableVFs; }; } // end namespace llvm // Return true if \p OuterLp is an outer loop annotated with hints for explicit // vectorization. The loop needs to be annotated with #pragma omp simd // simdlen(#) or #pragma clang vectorize(enable) vectorize_width(#). If the // vector length information is not provided, vectorization is not considered // explicit. Interleave hints are not allowed either. These limitations will be // relaxed in the future. // Please, note that we are currently forced to abuse the pragma 'clang // vectorize' semantics. This pragma provides *auto-vectorization hints* // (i.e., LV must check that vectorization is legal) whereas pragma 'omp simd' // provides *explicit vectorization hints* (LV can bypass legal checks and // assume that vectorization is legal). However, both hints are implemented // using the same metadata (llvm.loop.vectorize, processed by // LoopVectorizeHints). This will be fixed in the future when the native IR // representation for pragma 'omp simd' is introduced. static bool isExplicitVecOuterLoop(Loop *OuterLp, OptimizationRemarkEmitter *ORE) { assert(!OuterLp->isInnermost() && "This is not an outer loop"); LoopVectorizeHints Hints(OuterLp, true /*DisableInterleaving*/, *ORE); // Only outer loops with an explicit vectorization hint are supported. // Unannotated outer loops are ignored. if (Hints.getForce() == LoopVectorizeHints::FK_Undefined) return false; Function *Fn = OuterLp->getHeader()->getParent(); if (!Hints.allowVectorization(Fn, OuterLp, true /*VectorizeOnlyWhenForced*/)) { LLVM_DEBUG(dbgs() << "LV: Loop hints prevent outer loop vectorization.\n"); return false; } if (Hints.getInterleave() > 1) { // TODO: Interleave support is future work. LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Interleave is not supported for " "outer loops.\n"); Hints.emitRemarkWithHints(); return false; } return true; } static void collectSupportedLoops(Loop &L, LoopInfo *LI, OptimizationRemarkEmitter *ORE, SmallVectorImpl &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(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().getSE(); auto *LI = &getAnalysis().getLoopInfo(); auto *TTI = &getAnalysis().getTTI(F); auto *DT = &getAnalysis().getDomTree(); auto *BFI = &getAnalysis().getBFI(); auto *TLIP = getAnalysisIfAvailable(); auto *TLI = TLIP ? &TLIP->getTLI(F) : nullptr; auto *AA = &getAnalysis().getAAResults(); auto *AC = &getAnalysis().getAssumptionCache(F); auto *LAA = &getAnalysis(); auto *DB = &getAnalysis().getDemandedBits(); auto *ORE = &getAnalysis().getORE(); auto *PSI = &getAnalysis().getPSI(); std::function 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(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); AU.addRequired(); // 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(); AU.addPreserved(); } AU.addPreserved(); AU.addPreserved(); AU.addRequired(); } }; } // 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(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) { assert((isa(EntryVal) || isa(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(EntryVal)) { assert(Start->getType()->isIntegerTy() && "Truncation requires an integer type"); auto *TruncType = cast(EntryVal->getType()); Step = Builder.CreateTrunc(Step, TruncType); Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType); } Value *SplatStart = Builder.CreateVectorSplat(VF, Start); Value *SteppedStart = getStepVector(SplatStart, 0, Step, II.getInductionOpcode()); // We create vector phi nodes for both integer and floating-point induction // variables. Here, we determine the kind of arithmetic we will perform. Instruction::BinaryOps AddOp; Instruction::BinaryOps MulOp; if (Step->getType()->isIntegerTy()) { AddOp = Instruction::Add; MulOp = Instruction::Mul; } else { AddOp = II.getInductionOpcode(); MulOp = Instruction::FMul; } // Multiply the vectorization factor by the step using integer or // floating-point arithmetic as appropriate. Value *ConstVF = getSignedIntOrFpConstant(Step->getType(), VF.getKnownMinValue()); Value *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, Step, ConstVF)); // Create a vector splat to use in the induction update. // // FIXME: If the step is non-constant, we create the vector splat with // IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't // handle a constant vector splat. assert(!VF.isScalable() && "scalable vectors not yet supported."); Value *SplatVF = isa(Mul) ? ConstantVector::getSplat(VF, cast(Mul)) : Builder.CreateVectorSplat(VF, Mul); Builder.restoreIP(CurrIP); // We may need to add the step a number of times, depending on the unroll // factor. The last of those goes into the PHI. PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind", &*LoopVectorBody->getFirstInsertionPt()); VecInd->setDebugLoc(EntryVal->getDebugLoc()); Instruction *LastInduction = VecInd; for (unsigned Part = 0; Part < UF; ++Part) { VectorLoopValueMap.setVectorValue(EntryVal, Part, LastInduction); if (isa(EntryVal)) addMetadata(LastInduction, EntryVal); recordVectorLoopValueForInductionCast(II, EntryVal, LastInduction, Part); LastInduction = cast(addFastMathFlag( Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add"))); LastInduction->setDebugLoc(EntryVal->getDebugLoc()); } // Move the last step to the end of the latch block. This ensures consistent // placement of all induction updates. auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); auto *Br = cast(LoopVectorLatch->getTerminator()); auto *ICmp = cast(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(U); return (OrigLoop->contains(I) && shouldScalarizeInstruction(I)); }; return llvm::any_of(IV->users(), isScalarInst); } void InnerLoopVectorizer::recordVectorLoopValueForInductionCast( const InductionDescriptor &ID, const Instruction *EntryVal, Value *VectorLoopVal, unsigned Part, unsigned Lane) { assert((isa(EntryVal) || isa(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(EntryVal)) return; const SmallVectorImpl &Casts = ID.getCastInsts(); if (Casts.empty()) return; // Only the first Cast instruction in the Casts vector is of interest. // The rest of the Casts (if exist) have no uses outside the // induction update chain itself. Instruction *CastInst = *Casts.begin(); if (Lane < UINT_MAX) VectorLoopValueMap.setScalarValue(CastInst, {Part, Lane}, VectorLoopVal); else VectorLoopValueMap.setVectorValue(CastInst, Part, VectorLoopVal); } void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, Value *Start, TruncInst *Trunc) { assert((IV->getType()->isIntegerTy() || IV != OldInduction) && "Primary induction variable must have an integer type"); auto II = Legal->getInductionVars().find(IV); assert(II != Legal->getInductionVars().end() && "IV is not an induction"); auto ID = II->second; assert(IV->getType() == ID.getStartValue()->getType() && "Types must match"); // The value from the original loop to which we are mapping the new induction // variable. Instruction *EntryVal = Trunc ? cast(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(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(Trunc->getType()); assert(Step->getType()->isIntegerTy() && "Truncation requires an integer step"); ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType); Step = Builder.CreateTrunc(Step, TruncType); } return ScalarIV; }; // Create the vector values from the scalar IV, in the absence of creating a // vector IV. auto CreateSplatIV = [&](Value *ScalarIV, Value *Step) { Value *Broadcasted = getBroadcastInstrs(ScalarIV); for (unsigned Part = 0; Part < UF; ++Part) { assert(!VF.isScalable() && "scalable vectors not yet supported."); Value *EntryPart = getStepVector(Broadcasted, VF.getKnownMinValue() * Part, Step, ID.getInductionOpcode()); VectorLoopValueMap.setVectorValue(EntryVal, Part, EntryPart); if (Trunc) addMetadata(EntryPart, Trunc); recordVectorLoopValueForInductionCast(ID, EntryVal, EntryPart, Part); } }; // Now do the actual transformations, and start with creating the step value. Value *Step = CreateStepValue(ID.getStep()); if (VF.isZero() || VF.isScalar()) { Value *ScalarIV = CreateScalarIV(Step); CreateSplatIV(ScalarIV, Step); return; } // Determine if we want a scalar version of the induction variable. This is // true if the induction variable itself is not widened, or if it has at // least one user in the loop that is not widened. auto NeedsScalarIV = needsScalarInduction(EntryVal); if (!NeedsScalarIV) { createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal); return; } // Try to create a new independent vector induction variable. If we can't // create the phi node, we will splat the scalar induction variable in each // loop iteration. if (!shouldScalarizeInstruction(EntryVal)) { createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal); Value *ScalarIV = CreateScalarIV(Step); // Create scalar steps that can be used by instructions we will later // scalarize. Note that the addition of the scalar steps will not increase // the number of instructions in the loop in the common case prior to // InstCombine. We will be trading one vector extract for each scalar step. buildScalarSteps(ScalarIV, Step, EntryVal, ID); return; } // All IV users are scalar instructions, so only emit a scalar IV, not a // vectorised IV. Except when we tail-fold, then the splat IV feeds the // predicate used by the masked loads/stores. Value *ScalarIV = CreateScalarIV(Step); if (!Cost->isScalarEpilogueAllowed()) CreateSplatIV(ScalarIV, Step); buildScalarSteps(ScalarIV, Step, EntryVal, ID); } Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx, Value *Step, Instruction::BinaryOps BinOp) { // Create and check the types. auto *ValVTy = cast(Val->getType()); int VLen = ValVTy->getNumElements(); Type *STy = Val->getType()->getScalarType(); assert((STy->isIntegerTy() || STy->isFloatingPointTy()) && "Induction Step must be an integer or FP"); assert(Step->getType() == STy && "Step has wrong type"); SmallVector Indices; if (STy->isIntegerTy()) { // Create a vector of consecutive numbers from zero to VF. for (int i = 0; i < VLen; ++i) Indices.push_back(ConstantInt::get(STy, StartIdx + i)); // Add the consecutive indices to the vector value. Constant *Cv = ConstantVector::get(Indices); assert(Cv->getType() == Val->getType() && "Invalid consecutive vec"); Step = Builder.CreateVectorSplat(VLen, Step); assert(Step->getType() == Val->getType() && "Invalid step vec"); // FIXME: The newly created binary instructions should contain nsw/nuw flags, // which can be found from the original scalar operations. Step = Builder.CreateMul(Cv, Step); return Builder.CreateAdd(Val, Step, "induction"); } // Floating point induction. assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) && "Binary Opcode should be specified for FP induction"); // Create a vector of consecutive numbers from zero to VF. for (int i = 0; i < VLen; ++i) Indices.push_back(ConstantFP::get(STy, (double)(StartIdx + i))); // Add the consecutive indices to the vector value. Constant *Cv = ConstantVector::get(Indices); Step = Builder.CreateVectorSplat(VLen, Step); // Floating point operations had to be 'fast' to enable the induction. FastMathFlags Flags; Flags.setFast(); Value *MulOp = Builder.CreateFMul(Cv, Step); if (isa(MulOp)) // Have to check, MulOp may be a constant cast(MulOp)->setFastMathFlags(Flags); Value *BOp = Builder.CreateBinOp(BinOp, Val, MulOp, "induction"); if (isa(BOp)) cast(BOp)->setFastMathFlags(Flags); return BOp; } void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step, Instruction *EntryVal, const InductionDescriptor &ID) { // We shouldn't have to build scalar steps if we aren't vectorizing. assert(VF.isVector() && "VF should be greater than one"); // Get the value type and ensure it and the step have the same integer type. Type *ScalarIVTy = ScalarIV->getType()->getScalarType(); assert(ScalarIVTy == Step->getType() && "Val and Step should have the same type"); // We build scalar steps for both integer and floating-point induction // variables. Here, we determine the kind of arithmetic we will perform. Instruction::BinaryOps AddOp; Instruction::BinaryOps MulOp; if (ScalarIVTy->isIntegerTy()) { AddOp = Instruction::Add; MulOp = Instruction::Mul; } else { AddOp = ID.getInductionOpcode(); MulOp = Instruction::FMul; } // Determine the number of scalars we need to generate for each unroll // iteration. If EntryVal is uniform, we only need to generate the first // lane. Otherwise, we generate all VF values. unsigned Lanes = Cost->isUniformAfterVectorization(cast(EntryVal), VF) ? 1 : VF.getKnownMinValue(); assert((!VF.isScalable() || Lanes == 1) && "Should never scalarize a scalable vector"); // Compute the scalar steps and save the results in VectorLoopValueMap. for (unsigned Part = 0; Part < UF; ++Part) { for (unsigned Lane = 0; Lane < Lanes; ++Lane) { auto *IntStepTy = IntegerType::get(ScalarIVTy->getContext(), ScalarIVTy->getScalarSizeInBits()); Value *StartIdx = createStepForVF(Builder, ConstantInt::get(IntStepTy, Part), VF); if (ScalarIVTy->isFloatingPointTy()) StartIdx = Builder.CreateSIToFP(StartIdx, ScalarIVTy); StartIdx = addFastMathFlag(Builder.CreateBinOp( AddOp, StartIdx, 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(StartIdx)) && "Expected StartIdx to be folded to a constant when VF is not " "scalable"); auto *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, StartIdx, Step)); auto *Add = addFastMathFlag(Builder.CreateBinOp(AddOp, ScalarIV, Mul)); VectorLoopValueMap.setScalarValue(EntryVal, {Part, Lane}, Add); recordVectorLoopValueForInductionCast(ID, EntryVal, Add, Part, Lane); } } } Value *InnerLoopVectorizer::getOrCreateVectorValue(Value *V, unsigned Part) { assert(V != Induction && "The new induction variable should not be used."); assert(!V->getType()->isVectorTy() && "Can't widen a vector"); assert(!V->getType()->isVoidTy() && "Type does not produce a value"); // If we have a stride that is replaced by one, do it here. Defer this for // the VPlan-native path until we start running Legal checks in that path. if (!EnableVPlanNativePath && Legal->hasStride(V)) V = ConstantInt::get(V->getType(), 1); // If we have a vector mapped to this value, return it. if (VectorLoopValueMap.hasVectorValue(V, Part)) return VectorLoopValueMap.getVectorValue(V, Part); // If the value has not been vectorized, check if it has been scalarized // instead. If it has been scalarized, and we actually need the value in // vector form, we will construct the vector values on demand. if (VectorLoopValueMap.hasAnyScalarValue(V)) { Value *ScalarValue = VectorLoopValueMap.getScalarValue(V, {Part, 0}); // If we've scalarized a value, that value should be an instruction. auto *I = cast(V); // If we aren't vectorizing, we can just copy the scalar map values over to // the vector map. if (VF.isScalar()) { VectorLoopValueMap.setVectorValue(V, Part, ScalarValue); return ScalarValue; } // Get the last scalar instruction we generated for V and Part. If the value // is known to be uniform after vectorization, this corresponds to lane zero // of the Part unroll iteration. Otherwise, the last instruction is the one // we created for the last vector lane of the Part unroll iteration. unsigned LastLane = Cost->isUniformAfterVectorization(I, VF) ? 0 : VF.getKnownMinValue() - 1; assert((!VF.isScalable() || LastLane == 0) && "Scalable vectorization can't lead to any scalarized values."); auto *LastInst = cast( VectorLoopValueMap.getScalarValue(V, {Part, LastLane})); // Set the insert point after the last scalarized instruction. This ensures // the insertelement sequence will directly follow the scalar definitions. auto OldIP = Builder.saveIP(); auto NewIP = std::next(BasicBlock::iterator(LastInst)); Builder.SetInsertPoint(&*NewIP); // However, if we are vectorizing, we need to construct the vector values. // If the value is known to be uniform after vectorization, we can just // broadcast the scalar value corresponding to lane zero for each unroll // iteration. Otherwise, we construct the vector values using insertelement // instructions. Since the resulting vectors are stored in // VectorLoopValueMap, we will only generate the insertelements once. Value *VectorValue = nullptr; if (Cost->isUniformAfterVectorization(I, VF)) { VectorValue = getBroadcastInstrs(ScalarValue); VectorLoopValueMap.setVectorValue(V, Part, VectorValue); } else { // Initialize packing with insertelements to start from poison. assert(!VF.isScalable() && "VF is assumed to be non scalable."); Value *Poison = PoisonValue::get(VectorType::get(V->getType(), VF)); VectorLoopValueMap.setVectorValue(V, Part, Poison); for (unsigned Lane = 0; Lane < VF.getKnownMinValue(); ++Lane) packScalarIntoVectorValue(V, {Part, Lane}); VectorValue = VectorLoopValueMap.getVectorValue(V, Part); } Builder.restoreIP(OldIP); return VectorValue; } // If this scalar is unknown, assume that it is a constant or that it is // loop invariant. Broadcast V and save the value for future uses. Value *B = getBroadcastInstrs(V); VectorLoopValueMap.setVectorValue(V, Part, B); return B; } Value * InnerLoopVectorizer::getOrCreateScalarValue(Value *V, const VPIteration &Instance) { // If the value is not an instruction contained in the loop, it should // already be scalar. if (OrigLoop->isLoopInvariant(V)) return V; assert(Instance.Lane > 0 ? !Cost->isUniformAfterVectorization(cast(V), VF) : true && "Uniform values only have lane zero"); // If the value from the original loop has not been vectorized, it is // represented by UF x VF scalar values in the new loop. Return the requested // scalar value. if (VectorLoopValueMap.hasScalarValue(V, Instance)) return VectorLoopValueMap.getScalarValue(V, Instance); // If the value has not been scalarized, get its entry in VectorLoopValueMap // for the given unroll part. If this entry is not a vector type (i.e., the // vectorization factor is one), there is no need to generate an // extractelement instruction. auto *U = getOrCreateVectorValue(V, Instance.Part); if (!U->getType()->isVectorTy()) { assert(VF.isScalar() && "Value not scalarized has non-vector type"); return U; } // Otherwise, the value from the original loop has been vectorized and is // represented by UF vector values. Extract and return the requested scalar // value from the appropriate vector lane. return Builder.CreateExtractElement(U, Builder.getInt32(Instance.Lane)); } void InnerLoopVectorizer::packScalarIntoVectorValue( Value *V, const VPIteration &Instance) { assert(V != Induction && "The new induction variable should not be used."); assert(!V->getType()->isVectorTy() && "Can't pack a vector"); assert(!V->getType()->isVoidTy() && "Type does not produce a value"); Value *ScalarInst = VectorLoopValueMap.getScalarValue(V, Instance); Value *VectorValue = VectorLoopValueMap.getVectorValue(V, Instance.Part); VectorValue = Builder.CreateInsertElement(VectorValue, ScalarInst, Builder.getInt32(Instance.Lane)); VectorLoopValueMap.resetVectorValue(V, Instance.Part, VectorValue); } Value *InnerLoopVectorizer::reverseVector(Value *Vec) { assert(Vec->getType()->isVectorTy() && "Invalid type"); assert(!VF.isScalable() && "Cannot reverse scalable vectors"); SmallVector ShuffleMask; for (unsigned i = 0; i < VF.getKnownMinValue(); ++i) ShuffleMask.push_back(VF.getKnownMinValue() - i - 1); return Builder.CreateShuffleVector(Vec, ShuffleMask, "reverse"); } // Return whether we allow using masked interleave-groups (for dealing with // strided loads/stores that reside in predicated blocks, or for dealing // with gaps). static bool useMaskedInterleavedAccesses(const TargetTransformInfo &TTI) { // If an override option has been passed in for interleaved accesses, use it. if (EnableMaskedInterleavedMemAccesses.getNumOccurrences() > 0) return EnableMaskedInterleavedMemAccesses; return TTI.enableMaskedInterleavedAccessVectorization(); } // Try to vectorize the interleave group that \p Instr belongs to. // // E.g. Translate following interleaved load group (factor = 3): // for (i = 0; i < N; i+=3) { // R = Pic[i]; // Member of index 0 // G = Pic[i+1]; // Member of index 1 // B = Pic[i+2]; // Member of index 2 // ... // do something to R, G, B // } // To: // %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B // %R.vec = shuffle %wide.vec, 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 *Group, ArrayRef VPDefs, VPTransformState &State, VPValue *Addr, ArrayRef 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 = getMemInstValueType(Instr); unsigned InterleaveFactor = Group->getFactor(); assert(!VF.isScalable() && "scalable vectors not yet supported."); auto *VecTy = VectorType::get(ScalarTy, VF * InterleaveFactor); // Prepare for the new pointers. SmallVector AddrParts; unsigned Index = Group->getIndex(Instr); // TODO: extend the masked interleaved-group support to reversed access. assert((!BlockInMask || !Group->isReverse()) && "Reversed masked interleave-group not supported."); // If the group is reverse, adjust the index to refer to the last vector lane // instead of the first. We adjust the index from the first vector lane, // rather than directly getting the pointer for lane VF - 1, because the // pointer operand of the interleaved access is supposed to be uniform. For // uniform instructions, we're only required to generate a value for the // first vector lane in each unroll iteration. assert(!VF.isScalable() && "scalable vector reverse operation is not implemented"); if (Group->isReverse()) Index += (VF.getKnownMinValue() - 1) * Group->getFactor(); for (unsigned Part = 0; Part < UF; Part++) { Value *AddrPart = State.get(Addr, {Part, 0}); setDebugLocFromInst(Builder, AddrPart); // Notice current instruction could be any index. Need to adjust the address // to the member of index 0. // // E.g. a = A[i+1]; // Member of index 1 (Current instruction) // b = A[i]; // Member of index 0 // Current pointer is pointed to A[i+1], adjust it to A[i]. // // E.g. A[i+1] = a; // Member of index 1 // A[i] = b; // Member of index 0 // A[i+2] = c; // Member of index 2 (Current instruction) // Current pointer is pointed to A[i+2], adjust it to A[i]. bool InBounds = false; if (auto *gep = dyn_cast(AddrPart->stripPointerCasts())) InBounds = gep->isInBounds(); AddrPart = Builder.CreateGEP(ScalarTy, AddrPart, Builder.getInt32(-Index)); cast(AddrPart)->setIsInBounds(InBounds); // Cast to the vector pointer type. unsigned AddressSpace = AddrPart->getType()->getPointerAddressSpace(); Type *PtrTy = VecTy->getPointerTo(AddressSpace); AddrParts.push_back(Builder.CreateBitCast(AddrPart, PtrTy)); } setDebugLocFromInst(Builder, Instr); Value *PoisonVec = PoisonValue::get(VecTy); Value *MaskForGaps = nullptr; if (Group->requiresScalarEpilogue() && !Cost->isScalarEpilogueAllowed()) { assert(!VF.isScalable() && "scalable vectors not yet supported."); MaskForGaps = createBitMaskForGaps(Builder, VF.getKnownMinValue(), *Group); assert(MaskForGaps && "Mask for Gaps is required but it is null"); } // Vectorize the interleaved load group. if (isa(Instr)) { // For each unroll part, create a wide load for the group. SmallVector NewLoads; for (unsigned Part = 0; Part < UF; Part++) { Instruction *NewLoad; if (BlockInMask || MaskForGaps) { assert(useMaskedInterleavedAccesses(*TTI) && "masked interleaved groups are not allowed."); Value *GroupMask = MaskForGaps; if (BlockInMask) { Value *BlockInMaskPart = State.get(BlockInMask, Part); assert(!VF.isScalable() && "scalable vectors not yet supported."); Value *ShuffledMask = Builder.CreateShuffleVector( BlockInMaskPart, createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), "interleaved.mask"); GroupMask = MaskForGaps ? Builder.CreateBinOp(Instruction::And, ShuffledMask, MaskForGaps) : ShuffledMask; } NewLoad = Builder.CreateMaskedLoad(AddrParts[Part], Group->getAlign(), GroupMask, 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; assert(!VF.isScalable() && "scalable vectors not yet supported."); auto StrideMask = createStrideMask(I, InterleaveFactor, VF.getKnownMinValue()); for (unsigned Part = 0; Part < UF; Part++) { Value *StridedVec = Builder.CreateShuffleVector( NewLoads[Part], StrideMask, "strided.vec"); // If this member has different type, cast the result type. if (Member->getType() != ScalarTy) { assert(!VF.isScalable() && "VF is assumed to be non scalable."); VectorType *OtherVTy = VectorType::get(Member->getType(), VF); StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL); } if (Group->isReverse()) StridedVec = reverseVector(StridedVec); State.set(VPDefs[J], Member, StridedVec, Part); } ++J; } return; } // The sub vector type for current instruction. assert(!VF.isScalable() && "VF is assumed to be non scalable."); auto *SubVT = VectorType::get(ScalarTy, VF); // Vectorize the interleaved store group. for (unsigned Part = 0; Part < UF; Part++) { // Collect the stored vector from each member. SmallVector StoredVecs; for (unsigned i = 0; i < InterleaveFactor; i++) { // Interleaved store group doesn't allow a gap, so each index has a member assert(Group->getMember(i) && "Fail to get a member from an interleaved store group"); 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. assert(!VF.isScalable() && "scalable vectors not yet supported."); Value *IVec = Builder.CreateShuffleVector( WideVec, createInterleaveMask(VF.getKnownMinValue(), InterleaveFactor), "interleaved.vec"); Instruction *NewStoreInstr; if (BlockInMask) { Value *BlockInMaskPart = State.get(BlockInMask, Part); Value *ShuffledMask = Builder.CreateShuffleVector( BlockInMaskPart, createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), "interleaved.mask"); NewStoreInstr = Builder.CreateMaskedStore( IVec, AddrParts[Part], Group->getAlign(), ShuffledMask); } else NewStoreInstr = Builder.CreateAlignedStore(IVec, AddrParts[Part], Group->getAlign()); Group->addMetadata(NewStoreInstr); } } void InnerLoopVectorizer::vectorizeMemoryInstruction( Instruction *Instr, VPTransformState &State, VPValue *Def, VPValue *Addr, VPValue *StoredValue, VPValue *BlockInMask) { // Attempt to issue a wide load. LoadInst *LI = dyn_cast(Instr); StoreInst *SI = dyn_cast(Instr); assert((LI || SI) && "Invalid Load/Store instruction"); assert((!SI || StoredValue) && "No stored value provided for widened store"); assert((!LI || !StoredValue) && "Stored value provided for widened load"); LoopVectorizationCostModel::InstWidening Decision = Cost->getWideningDecision(Instr, VF); assert((Decision == LoopVectorizationCostModel::CM_Widen || Decision == LoopVectorizationCostModel::CM_Widen_Reverse || Decision == LoopVectorizationCostModel::CM_GatherScatter) && "CM decision is not to widen the memory instruction"); Type *ScalarDataTy = getMemInstValueType(Instr); auto *DataTy = VectorType::get(ScalarDataTy, VF); const Align Alignment = getLoadStoreAlignment(Instr); // Determine if the pointer operand of the access is either consecutive or // reverse consecutive. bool Reverse = (Decision == LoopVectorizationCostModel::CM_Widen_Reverse); bool ConsecutiveStride = Reverse || (Decision == LoopVectorizationCostModel::CM_Widen); bool CreateGatherScatter = (Decision == LoopVectorizationCostModel::CM_GatherScatter); // Either Ptr feeds a vector load/store, or a vector GEP should feed a vector // gather/scatter. Otherwise Decision should have been to Scalarize. assert((ConsecutiveStride || CreateGatherScatter) && "The instruction should be scalarized"); (void)ConsecutiveStride; VectorParts BlockInMaskParts(UF); bool isMaskRequired = BlockInMask; if (isMaskRequired) for (unsigned Part = 0; Part < UF; ++Part) BlockInMaskParts[Part] = State.get(BlockInMask, Part); const auto CreateVecPtr = [&](unsigned Part, Value *Ptr) -> Value * { // Calculate the pointer for the specific unroll-part. GetElementPtrInst *PartPtr = nullptr; bool InBounds = false; if (auto *gep = dyn_cast(Ptr->stripPointerCasts())) InBounds = gep->isInBounds(); if (Reverse) { assert(!VF.isScalable() && "Reversing vectors is not yet supported for scalable vectors."); // If the address is consecutive but reversed, then the // wide store needs to start at the last vector element. PartPtr = cast(Builder.CreateGEP( ScalarDataTy, Ptr, Builder.getInt32(-Part * VF.getKnownMinValue()))); PartPtr->setIsInBounds(InBounds); PartPtr = cast(Builder.CreateGEP( ScalarDataTy, PartPtr, Builder.getInt32(1 - VF.getKnownMinValue()))); PartPtr->setIsInBounds(InBounds); if (isMaskRequired) // Reverse of a null all-one mask is a null mask. BlockInMaskParts[Part] = reverseVector(BlockInMaskParts[Part]); } else { Value *Increment = createStepForVF(Builder, Builder.getInt32(Part), VF); PartPtr = cast( 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(Builder, SI); for (unsigned Part = 0; Part < UF; ++Part) { Instruction *NewSI = nullptr; Value *StoredVal = State.get(StoredValue, Part); if (CreateGatherScatter) { Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; Value *VectorGep = State.get(Addr, Part); NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment, MaskPart); } else { if (Reverse) { // If we store to reverse consecutive memory locations, then we need // to reverse the order of elements in the stored value. StoredVal = reverseVector(StoredVal); // We don't want to update the value in the map as it might be used in // another expression. So don't call resetVectorValue(StoredVal). } auto *VecPtr = CreateVecPtr(Part, State.get(Addr, {0, 0})); if (isMaskRequired) NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment, BlockInMaskParts[Part]); else NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment); } addMetadata(NewSI, SI); } return; } // Handle loads. assert(LI && "Must have a load instruction"); setDebugLocFromInst(Builder, LI); for (unsigned Part = 0; Part < UF; ++Part) { Value *NewLI; if (CreateGatherScatter) { Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; Value *VectorGep = State.get(Addr, Part); NewLI = Builder.CreateMaskedGather(VectorGep, Alignment, MaskPart, nullptr, "wide.masked.gather"); addMetadata(NewLI, LI); } else { auto *VecPtr = CreateVecPtr(Part, State.get(Addr, {0, 0})); if (isMaskRequired) NewLI = Builder.CreateMaskedLoad( VecPtr, Alignment, BlockInMaskParts[Part], 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, Instr, NewLI, Part); } } void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr, VPUser &User, 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(Instr)) if (Instance.Lane != 0 || Instance.Part != 0) return; setDebugLocFromInst(Builder, Instr); // Does this instruction return a value ? bool IsVoidRetTy = Instr->getType()->isVoidTy(); Instruction *Cloned = Instr->clone(); if (!IsVoidRetTy) Cloned->setName(Instr->getName() + ".cloned"); // Replace the operands of the cloned instructions with their scalar // equivalents in the new loop. for (unsigned op = 0, e = User.getNumOperands(); op != e; ++op) { auto *Operand = dyn_cast(Instr->getOperand(op)); auto InputInstance = Instance; if (!Operand || !OrigLoop->contains(Operand) || (Cost->isUniformAfterVectorization(Operand, State.VF))) InputInstance.Lane = 0; auto *NewOp = State.get(User.getOperand(op), InputInstance); Cloned->setOperand(op, NewOp); } addNewMetadata(Cloned, Instr); // Place the cloned scalar in the new loop. Builder.Insert(Cloned); // TODO: Set result for VPValue of VPReciplicateRecipe. This requires // representing scalar values in VPTransformState. Add the cloned scalar to // the scalar map entry. VectorLoopValueMap.setScalarValue(Instr, Instance, Cloned); // If we just cloned a new assumption, add it the assumption cache. if (auto *II = dyn_cast(Cloned)) if (II->getIntrinsicID() == Intrinsic::assume) AC->registerAssumption(II); // End if-block. if (IfPredicateInstr) PredicatedInstructions.push_back(Cloned); } PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start, Value *End, Value *Step, Instruction *DL) { BasicBlock *Header = L->getHeader(); BasicBlock *Latch = L->getLoopLatch(); // As we're just creating this loop, it's possible no latch exists // yet. If so, use the header as this will be a single block loop. if (!Latch) Latch = Header; IRBuilder<> Builder(&*Header->getFirstInsertionPt()); Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction); setDebugLocFromInst(Builder, OldInst); auto *Induction = Builder.CreatePHI(Start->getType(), 2, "index"); Builder.SetInsertPoint(Latch->getTerminator()); setDebugLocFromInst(Builder, OldInst); // Create i+1 and fill the PHINode. Value *Next = Builder.CreateAdd(Induction, Step, "index.next"); Induction->addIncoming(Start, L->getLoopPreheader()); Induction->addIncoming(Next, Latch); // Create the compare. Value *ICmp = Builder.CreateICmpEQ(Next, End); Builder.CreateCondBr(ICmp, L->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(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, ConstantInt::get(Ty, UF), VF); // 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 two cases where we need to ensure (at least) the last iteration // runs in the scalar remainder loop. Thus, if the step evenly divides // the trip count, we set the remainder to be equal to the step. If the step // does not evenly divide the trip count, no adjustment is necessary since // there will already be scalar iterations. Note that the minimum iterations // check ensures that N >= Step. The cases are: // 1) If there is a non-reversed interleaved group that may speculatively // access memory out-of-bounds. // 2) If any instruction may follow a conditionally taken exit. That is, if // the loop contains multiple exiting blocks, or a single exiting block // which is not the latch. if (VF.isVector() && Cost->requiresScalarEpilogue()) { auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0)); R = Builder.CreateSelect(IsZero, Step, R); } VectorTripCount = Builder.CreateSub(TC, R, "n.vec"); return VectorTripCount; } Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy, const DataLayout &DL) { // Verify that V is a vector type with same number of elements as DstVTy. auto *DstFVTy = cast(DstVTy); unsigned VF = DstFVTy->getNumElements(); auto *SrcVecTy = cast(V->getType()); assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match"); Type *SrcElemTy = SrcVecTy->getElementType(); Type *DstElemTy = DstFVTy->getElementType(); assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) && "Vector elements must have same size"); // Do a direct cast if element types are castable. if (CastInst::isBitOrNoopPointerCastable(SrcElemTy, DstElemTy, DL)) { return Builder.CreateBitOrPointerCast(V, DstFVTy); } // V cannot be directly casted to desired vector type. // May happen when V is a floating point vector but DstVTy is a vector of // pointers or vice-versa. Handle this using a two-step bitcast using an // intermediate Integer type for the bitcast i.e. Ptr <-> Int <-> Float. assert((DstElemTy->isPointerTy() != SrcElemTy->isPointerTy()) && "Only one type should be a pointer type"); assert((DstElemTy->isFloatingPointTy() != SrcElemTy->isFloatingPointTy()) && "Only one type should be a floating point type"); Type *IntTy = IntegerType::getIntNTy(V->getContext(), DL.getTypeSizeInBits(SrcElemTy)); auto *VecIntTy = FixedVectorType::get(IntTy, VF); Value *CastVal = Builder.CreateBitOrPointerCast(V, VecIntTy); return Builder.CreateBitOrPointerCast(CastVal, DstFVTy); } void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass) { Value *Count = getOrCreateTripCount(L); // Reuse existing vector loop preheader for TC checks. // Note that new preheader block is generated for vector loop. BasicBlock *const TCCheckBlock = LoopVectorPreHeader; IRBuilder<> Builder(TCCheckBlock->getTerminator()); // Generate code to check if the loop's trip count is less than VF * UF, or // equal to it in case a scalar epilogue is required; this implies that the // vector trip count is zero. This check also covers the case where adding one // to the backedge-taken count overflowed leading to an incorrect trip count // of zero. In this case we will also jump to the scalar loop. auto P = Cost->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE : ICmpInst::ICMP_ULT; // If tail is to be folded, vector loop takes care of all iterations. Value *CheckMinIters = Builder.getFalse(); if (!Cost->foldTailByMasking()) { Value *Step = createStepForVF(Builder, ConstantInt::get(Count->getType(), UF), VF); 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. DT->changeImmediateDominator(Bypass, TCCheckBlock); DT->changeImmediateDominator(LoopExitBlock, TCCheckBlock); ReplaceInstWithInst( TCCheckBlock->getTerminator(), BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); LoopBypassBlocks.push_back(TCCheckBlock); } void InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) { // Reuse existing vector loop preheader for SCEV checks. // Note that new preheader block is generated for vector loop. BasicBlock *const SCEVCheckBlock = LoopVectorPreHeader; // Generate the code to check that the SCEV assumptions that we made. // We want the new basic block to start at the first instruction in a // sequence of instructions that form a check. SCEVExpander Exp(*PSE.getSE(), Bypass->getModule()->getDataLayout(), "scev.check"); Value *SCEVCheck = Exp.expandCodeForPredicate( &PSE.getUnionPredicate(), SCEVCheckBlock->getTerminator()); if (auto *C = dyn_cast(SCEVCheck)) if (C->isZero()) return; assert(!(SCEVCheckBlock->getParent()->hasOptSize() || (OptForSizeBasedOnProfile && Cost->Hints->getForce() != LoopVectorizeHints::FK_Enabled)) && "Cannot SCEV check stride or overflow when optimizing for size"); SCEVCheckBlock->setName("vector.scevcheck"); // Create new preheader for vector loop. LoopVectorPreHeader = SplitBlock(SCEVCheckBlock, SCEVCheckBlock->getTerminator(), DT, LI, nullptr, "vector.ph"); // Update dominator only if this is first RT check. if (LoopBypassBlocks.empty()) { DT->changeImmediateDominator(Bypass, SCEVCheckBlock); DT->changeImmediateDominator(LoopExitBlock, SCEVCheckBlock); } ReplaceInstWithInst( SCEVCheckBlock->getTerminator(), BranchInst::Create(Bypass, LoopVectorPreHeader, SCEVCheck)); LoopBypassBlocks.push_back(SCEVCheckBlock); AddedSafetyChecks = true; } void InnerLoopVectorizer::emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass) { // VPlan-native path does not do any analysis for runtime checks currently. if (EnableVPlanNativePath) return; // Reuse existing vector loop preheader for runtime memory checks. // Note that new preheader block is generated for vector loop. BasicBlock *const MemCheckBlock = L->getLoopPreheader(); // Generate the code that checks in runtime if arrays overlap. We put the // checks into a separate block to make the more common case of few elements // faster. auto *LAI = Legal->getLAI(); const auto &RtPtrChecking = *LAI->getRuntimePointerChecking(); if (!RtPtrChecking.Need) return; if (MemCheckBlock->getParent()->hasOptSize() || OptForSizeBasedOnProfile) { assert(Cost->Hints->getForce() == LoopVectorizeHints::FK_Enabled && "Cannot emit memory checks when optimizing for size, unless forced " "to vectorize."); ORE->emit([&]() { return OptimizationRemarkAnalysis(DEBUG_TYPE, "VectorizationCodeSize", L->getStartLoc(), L->getHeader()) << "Code-size may be reduced by not forcing " "vectorization, or by source-code modifications " "eliminating the need for runtime checks " "(e.g., adding 'restrict')."; }); } MemCheckBlock->setName("vector.memcheck"); // Create new preheader for vector loop. LoopVectorPreHeader = SplitBlock(MemCheckBlock, MemCheckBlock->getTerminator(), DT, LI, nullptr, "vector.ph"); auto *CondBranch = cast( Builder.CreateCondBr(Builder.getTrue(), Bypass, LoopVectorPreHeader)); ReplaceInstWithInst(MemCheckBlock->getTerminator(), CondBranch); LoopBypassBlocks.push_back(MemCheckBlock); AddedSafetyChecks = true; // Update dominator only if this is first RT check. if (LoopBypassBlocks.empty()) { DT->changeImmediateDominator(Bypass, MemCheckBlock); DT->changeImmediateDominator(LoopExitBlock, MemCheckBlock); } Instruction *FirstCheckInst; Instruction *MemRuntimeCheck; std::tie(FirstCheckInst, MemRuntimeCheck) = addRuntimeChecks(MemCheckBlock->getTerminator(), OrigLoop, RtPtrChecking.getChecks(), RtPtrChecking.getSE()); assert(MemRuntimeCheck && "no RT checks generated although RtPtrChecking " "claimed checks are required"); CondBranch->setCondition(MemRuntimeCheck); // We currently don't use LoopVersioning for the actual loop cloning but we // still use it to add the noalias metadata. LVer = std::make_unique( *Legal->getLAI(), Legal->getLAI()->getRuntimePointerChecking()->getChecks(), OrigLoop, LI, DT, PSE.getSE()); LVer->prepareNoAliasMetadata(); } Value *InnerLoopVectorizer::emitTransformedIndex( IRBuilder<> &B, Value *Index, ScalarEvolution *SE, const DataLayout &DL, const InductionDescriptor &ID) const { SCEVExpander Exp(*SE, DL, "induction"); auto Step = ID.getStep(); auto StartValue = ID.getStartValue(); assert(Index->getType() == Step->getType() && "Index type does not match StepValue type"); // Note: the IR at this point is broken. We cannot use SE to create any new // SCEV and then expand it, hoping that SCEV's simplification will give us // a more optimal code. Unfortunately, attempt of doing so on invalid IR may // lead to various SCEV crashes. So all we can do is to use builder and rely // on InstCombine for future simplifications. Here we handle some trivial // cases only. auto CreateAdd = [&B](Value *X, Value *Y) { assert(X->getType() == Y->getType() && "Types don't match!"); if (auto *CX = dyn_cast(X)) if (CX->isZero()) return Y; if (auto *CY = dyn_cast(Y)) if (CY->isZero()) return X; return B.CreateAdd(X, Y); }; auto CreateMul = [&B](Value *X, Value *Y) { assert(X->getType() == Y->getType() && "Types don't match!"); if (auto *CX = dyn_cast(X)) if (CX->isOne()) return Y; if (auto *CY = dyn_cast(Y)) if (CY->isOne()) return X; return B.CreateMul(X, Y); }; // Get a suitable insert point for SCEV expansion. For blocks in the vector // loop, choose the end of the vector loop header (=LoopVectorBody), because // the DomTree is not kept up-to-date for additional blocks generated in the // vector loop. By using the header as insertion point, we guarantee that the // expanded instructions dominate all their uses. auto GetInsertPoint = [this, &B]() { BasicBlock *InsertBB = B.GetInsertPoint()->getParent(); if (InsertBB != LoopVectorBody && LI->getLoopFor(LoopVectorBody) == LI->getLoopFor(InsertBB)) return LoopVectorBody->getTerminator(); return &*B.GetInsertPoint(); }; switch (ID.getKind()) { case InductionDescriptor::IK_IntInduction: { assert(Index->getType() == StartValue->getType() && "Index type does not match StartValue type"); if (ID.getConstIntStepValue() && ID.getConstIntStepValue()->isMinusOne()) return B.CreateSub(StartValue, Index); auto *Offset = CreateMul( Index, Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint())); return CreateAdd(StartValue, Offset); } case InductionDescriptor::IK_PtrInduction: { assert(isa(Step) && "Expected constant step for pointer induction"); return B.CreateGEP( StartValue->getType()->getPointerElementType(), StartValue, CreateMul(Index, Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint()))); } case InductionDescriptor::IK_FpInduction: { assert(Step->getType()->isFloatingPointTy() && "Expected FP Step value"); auto InductionBinOp = ID.getInductionBinOp(); assert(InductionBinOp && (InductionBinOp->getOpcode() == Instruction::FAdd || InductionBinOp->getOpcode() == Instruction::FSub) && "Original bin op should be defined for FP induction"); Value *StepValue = cast(Step)->getValue(); // Floating point operations had to be 'fast' to enable the induction. FastMathFlags Flags; Flags.setFast(); Value *MulExp = B.CreateFMul(StepValue, Index); if (isa(MulExp)) // We have to check, the MulExp may be a constant. cast(MulExp)->setFastMathFlags(Flags); Value *BOp = B.CreateBinOp(InductionBinOp->getOpcode(), StartValue, MulExp, "induction"); if (isa(BOp)) cast(BOp)->setFastMathFlags(Flags); return BOp; } case InductionDescriptor::IK_NoInduction: return nullptr; } llvm_unreachable("invalid enum"); } Loop *InnerLoopVectorizer::createVectorLoopSkeleton(StringRef Prefix) { LoopScalarBody = OrigLoop->getHeader(); LoopVectorPreHeader = OrigLoop->getLoopPreheader(); LoopExitBlock = OrigLoop->getUniqueExitBlock(); assert(LoopExitBlock && "Must have an exit block"); assert(LoopVectorPreHeader && "Invalid loop structure"); LoopMiddleBlock = SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, LI, nullptr, Twine(Prefix) + "middle.block"); LoopScalarPreHeader = SplitBlock(LoopMiddleBlock, LoopMiddleBlock->getTerminator(), DT, LI, nullptr, Twine(Prefix) + "scalar.ph"); // Set up branch from middle block to the exit and scalar preheader blocks. // completeLoopSkeleton will update the condition to use an iteration check, // if required to decide whether to execute the remainder. BranchInst *BrInst = BranchInst::Create(LoopExitBlock, LoopScalarPreHeader, Builder.getTrue()); auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); 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. 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 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()); 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. // If (N - N%VF) == N, then we *don't* need to run the remainder. // If tail is to be folded, we know we don't need to run the remainder. if (!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(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 VectorizedLoopID = makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll, LLVMLoopVectorizeFollowupVectorized}); if (VectorizedLoopID.hasValue()) { L->setLoopID(VectorizedLoopID.getValue()); // Do not setAlreadyVectorized if loop attributes have been defined // explicitly. return LoopVectorPreHeader; } // Keep all loop hints from the original loop on the vector loop (we'll // replace the vectorizer-specific hints below). if (MDNode *LID = OrigLoop->getLoopID()) L->setLoopID(LID); LoopVectorizeHints Hints(L, true, *ORE); Hints.setAlreadyVectorized(); #ifdef EXPENSIVE_CHECKS assert(DT->verify(DominatorTree::VerificationLevel::Fast)); LI->verify(*DT); #endif return LoopVectorPreHeader; } BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() { /* In this function we generate a new loop. The new loop will contain the vectorized instructions while the old loop will continue to run the scalar remainder. [ ] <-- loop iteration number check. / | / v | [ ] <-- vector loop bypass (may consist of multiple blocks). | / | | / v || [ ] <-- vector pre header. |/ | | v | [ ] \ | [ ]_| <-- vector loop. | | | v | -[ ] <--- middle-block. | / | | / v -|- >[ ] <--- new preheader. | | | v | [ ] \ | [ ]_| <-- old scalar loop to handle remainder. \ | \ v >[ ] <-- exit block. ... */ // Get the metadata of the original loop before it gets modified. MDNode *OrigLoopID = OrigLoop->getLoopID(); // Create an empty vector loop, and prepare basic blocks for the runtime // checks. Loop *Lp = createVectorLoopSkeleton(""); // Now, compare the new count to zero. If it is zero skip the vector loop and // jump to the scalar loop. This check also covers the case where the // backedge-taken count is uint##_max: adding one to it will overflow leading // to an incorrect trip count of zero. In this (rare) case we will also jump // to the scalar loop. emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader); // Generate the code to check any assumptions that we've made for SCEV // expressions. emitSCEVChecks(Lp, LoopScalarPreHeader); // Generate the code that checks in runtime if arrays overlap. We put the // checks into a separate block to make the more common case of few elements // faster. emitMemRuntimeChecks(Lp, LoopScalarPreHeader); // Some loops have a single integer induction variable, while other loops // don't. One example is c++ iterators that often have multiple pointer // induction variables. In the code below we also support a case where we // don't have a single induction variable. // // We try to obtain an induction variable from the original loop as hard // as possible. However if we don't find one that: // - is an integer // - counts from zero, stepping by one // - is the size of the widest induction variable type // then we create a new one. OldInduction = Legal->getPrimaryInduction(); Type *IdxTy = Legal->getWidestInductionType(); Value *StartIdx = ConstantInt::get(IdxTy, 0); // The loop step is equal to the vectorization factor (num of SIMD elements) // times the unroll factor (num of SIMD instructions). Builder.SetInsertPoint(&*Lp->getHeader()->getFirstInsertionPt()); Value *Step = createStepForVF(Builder, ConstantInt::get(IdxTy, UF), VF); 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 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(U); if (!OrigLoop->contains(UI)) { assert(isa(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(U); if (!OrigLoop->contains(UI)) { const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); assert(isa(UI) && "Expected LCSSA form"); IRBuilder<> B(MiddleBlock->getTerminator()); Value *CountMinusOne = B.CreateSub( CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1)); Value *CMO = !II.getStep()->getType()->isIntegerTy() ? B.CreateCast(Instruction::SIToFP, CountMinusOne, II.getStep()->getType()) : B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType()); CMO->setName("cast.cmo"); Value *Escape = emitTransformedIndex(B, CMO, PSE.getSE(), DL, II); Escape->setName("ind.escape"); MissingVals[UI] = Escape; } } for (auto &I : MissingVals) { PHINode *PHI = cast(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(I) || isa(I) || isa(I) || isa(I); } static inline Instruction *getEmptyKey() { return DenseMapInfo::getEmptyKey(); } static inline Instruction *getTombstoneKey() { return DenseMapInfo::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 CSEMap; for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E;) { Instruction *In = &*I++; if (!CSEDenseMapInfo::canHandle(In)) continue; // Check if we can replace this instruction with any of the // visited instructions. if (Instruction *V = CSEMap.lookup(In)) { In->replaceAllUsesWith(V); In->eraseFromParent(); continue; } CSEMap[In] = In; } } InstructionCost LoopVectorizationCostModel::getVectorCallCost(CallInst *CI, ElementCount VF, bool &NeedToScalarize) { assert(!VF.isScalable() && "scalable vectors not yet supported."); Function *F = CI->getCalledFunction(); Type *ScalarRetTy = CI->getType(); SmallVector Tys, ScalarTys; for (auto &ArgOp : CI->arg_operands()) ScalarTys.push_back(ArgOp->getType()); // Estimate cost of scalarized vector call. The source operands are assumed // to be vectors, so we need to extract individual elements from there, // execute VF scalar calls, and then gather the result into the vector return // value. 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; } InstructionCost LoopVectorizationCostModel::getVectorIntrinsicCost(CallInst *CI, ElementCount VF) { Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); assert(ID && "Expected intrinsic call!"); IntrinsicCostAttributes CostAttrs(ID, *CI, VF); return TTI.getIntrinsicInstrCost(CostAttrs, TargetTransformInfo::TCK_RecipThroughput); } static Type *smallestIntegerVectorType(Type *T1, Type *T2) { auto *I1 = cast(cast(T1)->getElementType()); auto *I2 = cast(cast(T2)->getElementType()); return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2; } static Type *largestIntegerVectorType(Type *T1, Type *T2) { auto *I1 = cast(cast(T1)->getElementType()); auto *I2 = cast(cast(T2)->getElementType()); return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2; } void InnerLoopVectorizer::truncateToMinimalBitwidths() { // For every instruction `I` in MinBWs, truncate the operands, create a // truncated version of `I` and reextend its result. InstCombine runs // later and will remove any ext/trunc pairs. SmallPtrSet Erased; for (const auto &KV : Cost->getMinimalBitwidths()) { // If the value wasn't vectorized, we must maintain the original scalar // type. The absence of the value from VectorLoopValueMap indicates that it // wasn't vectorized. if (!VectorLoopValueMap.hasAnyVectorValue(KV.first)) continue; for (unsigned Part = 0; Part < UF; ++Part) { Value *I = getOrCreateVectorValue(KV.first, Part); if (Erased.count(I) || I->use_empty() || !isa(I)) continue; Type *OriginalTy = I->getType(); Type *ScalarTruncatedTy = IntegerType::get(OriginalTy->getContext(), KV.second); auto *TruncatedTy = FixedVectorType::get( ScalarTruncatedTy, cast(OriginalTy)->getNumElements()); if (TruncatedTy == OriginalTy) continue; IRBuilder<> B(cast(I)); auto ShrinkOperand = [&](Value *V) -> Value * { if (auto *ZI = dyn_cast(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(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(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false); } else if (auto *CI = dyn_cast(I)) { NewI = B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)), ShrinkOperand(CI->getOperand(1))); } else if (auto *SI = dyn_cast(I)) { NewI = B.CreateSelect(SI->getCondition(), ShrinkOperand(SI->getTrueValue()), ShrinkOperand(SI->getFalseValue())); } else if (auto *CI = dyn_cast(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(I)) { auto Elements0 = cast(SI->getOperand(0)->getType()) ->getNumElements(); auto *O0 = B.CreateZExtOrTrunc( SI->getOperand(0), FixedVectorType::get(ScalarTruncatedTy, Elements0)); auto Elements1 = cast(SI->getOperand(1)->getType()) ->getNumElements(); auto *O1 = B.CreateZExtOrTrunc( SI->getOperand(1), FixedVectorType::get(ScalarTruncatedTy, Elements1)); NewI = B.CreateShuffleVector(O0, O1, SI->getShuffleMask()); } else if (isa(I) || isa(I)) { // Don't do anything with the operands, just extend the result. continue; } else if (auto *IE = dyn_cast(I)) { auto Elements = cast(IE->getOperand(0)->getType()) ->getNumElements(); auto *O0 = B.CreateZExtOrTrunc( IE->getOperand(0), FixedVectorType::get(ScalarTruncatedTy, Elements)); auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy); NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2)); } else if (auto *EE = dyn_cast(I)) { auto Elements = cast(EE->getOperand(0)->getType()) ->getNumElements(); auto *O0 = B.CreateZExtOrTrunc( EE->getOperand(0), FixedVectorType::get(ScalarTruncatedTy, Elements)); NewI = B.CreateExtractElement(O0, EE->getOperand(2)); } else { // If we don't know what to do, be conservative and don't do anything. continue; } // Lastly, extend the result. NewI->takeName(cast(I)); Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy); I->replaceAllUsesWith(Res); cast(I)->eraseFromParent(); Erased.insert(I); VectorLoopValueMap.resetVectorValue(KV.first, Part, Res); } } // We'll have created a bunch of ZExts that are now parentless. Clean up. for (const auto &KV : Cost->getMinimalBitwidths()) { // If the value wasn't vectorized, we must maintain the original scalar // type. The absence of the value from VectorLoopValueMap indicates that it // wasn't vectorized. if (!VectorLoopValueMap.hasAnyVectorValue(KV.first)) continue; for (unsigned Part = 0; Part < UF; ++Part) { Value *I = getOrCreateVectorValue(KV.first, Part); ZExtInst *Inst = dyn_cast(I); if (Inst && Inst->use_empty()) { Value *NewI = Inst->getOperand(0); Inst->eraseFromParent(); VectorLoopValueMap.resetVectorValue(KV.first, Part, NewI); } } } } void InnerLoopVectorizer::fixVectorizedLoop() { // Insert truncates and extends for any truncated instructions as hints to // InstCombine. if (VF.isVector()) truncateToMinimalBitwidths(); // Fix widened non-induction PHIs by setting up the PHI operands. if (OrigPHIsToFix.size()) { assert(EnableVPlanNativePath && "Unexpected non-induction PHIs for fixup in non VPlan-native path"); fixNonInductionPHIs(); } // At this point every instruction in the original loop is widened to a // vector form. Now we need to fix the recurrences in the loop. These PHI // nodes are currently empty because we did not want to introduce cycles. // This is the second stage of vectorizing recurrences. fixCrossIterationPHIs(); // Forget the original basic block. PSE.getSE()->forgetLoop(OrigLoop); // Fix-up external users of the induction variables. for (auto &Entry : Legal->getInductionVars()) fixupIVUsers(Entry.first, Entry.second, getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)), IVEndValues[Entry.first], LoopMiddleBlock); fixLCSSAPHIs(); for (Instruction *PI : PredicatedInstructions) sinkScalarOperands(&*PI); // Remove redundant induction instructions. cse(LoopVectorBody); // Set/update profile weights for the vector and remainder loops as original // loop iterations are now distributed among them. Note that original loop // represented by LoopScalarBody becomes remainder loop after vectorization. // // For cases like foldTailByMasking() and requiresScalarEpiloque() we may // end up getting slightly roughened result but that should be OK since // profile is not inherently precise anyway. Note also possible bypass of // vector code caused by legality checks is ignored, assigning all the weight // to the vector loop, optimistically. // // 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() { // In order to support recurrences we need to be able to vectorize Phi nodes. // Phi nodes have cycles, so we need to vectorize them in two stages. This is // stage #2: We now need to fix the recurrences by adding incoming edges to // the currently empty PHI nodes. At this point every instruction in the // original loop is widened to a vector form so we can use them to construct // the incoming edges. for (PHINode &Phi : OrigLoop->getHeader()->phis()) { // Handle first-order recurrences and reductions that need to be fixed. if (Legal->isFirstOrderRecurrence(&Phi)) fixFirstOrderRecurrence(&Phi); else if (Legal->isReductionVariable(&Phi)) fixReduction(&Phi); } } void InnerLoopVectorizer::fixFirstOrderRecurrence(PHINode *Phi) { // This is the second phase of vectorizing first-order recurrences. An // overview of the transformation is described below. Suppose we have the // following loop. // // for (int i = 0; i < n; ++i) // b[i] = a[i] - a[i - 1]; // // There is a first-order recurrence on "a". For this loop, the shorthand // scalar IR looks like: // // scalar.ph: // s_init = a[-1] // br scalar.body // // scalar.body: // i = phi [0, scalar.ph], [i+1, scalar.body] // s1 = phi [s_init, scalar.ph], [s2, scalar.body] // s2 = a[i] // b[i] = s2 - s1 // br cond, scalar.body, ... // // In this example, s1 is a recurrence because it's value depends on the // previous iteration. In the first phase of vectorization, we created a // temporary value for s1. We now complete the vectorization and produce the // shorthand vector IR shown below (for VF = 4, UF = 1). // // vector.ph: // v_init = vector(..., ..., ..., a[-1]) // br vector.body // // vector.body // i = phi [0, vector.ph], [i+4, vector.body] // v1 = phi [v_init, vector.ph], [v2, vector.body] // v2 = a[i, i+1, i+2, i+3]; // v3 = vector(v1(3), v2(0, 1, 2)) // b[i, i+1, i+2, i+3] = v2 - v3 // br cond, vector.body, middle.block // // middle.block: // x = v2(3) // br scalar.ph // // scalar.ph: // s_init = phi [x, middle.block], [a[-1], otherwise] // br scalar.body // // After execution completes the vector loop, we extract the next value of // the recurrence (x) to use as the initial value in the scalar loop. // Get the original loop preheader and single loop latch. auto *Preheader = OrigLoop->getLoopPreheader(); auto *Latch = OrigLoop->getLoopLatch(); // Get the initial and previous values of the scalar recurrence. auto *ScalarInit = Phi->getIncomingValueForBlock(Preheader); auto *Previous = Phi->getIncomingValueForBlock(Latch); // Create a vector from the initial value. auto *VectorInit = ScalarInit; if (VF.isVector()) { Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); assert(!VF.isScalable() && "VF is assumed to be non scalable."); VectorInit = Builder.CreateInsertElement( PoisonValue::get(VectorType::get(VectorInit->getType(), VF)), VectorInit, Builder.getInt32(VF.getKnownMinValue() - 1), "vector.recur.init"); } // We constructed a temporary phi node in the first phase of vectorization. // This phi node will eventually be deleted. Builder.SetInsertPoint( cast(VectorLoopValueMap.getVectorValue(Phi, 0))); // Create a phi node for the new recurrence. The current value will either be // the initial value inserted into a vector or loop-varying vector value. auto *VecPhi = Builder.CreatePHI(VectorInit->getType(), 2, "vector.recur"); VecPhi->addIncoming(VectorInit, LoopVectorPreHeader); // Get the vectorized previous value of the last part UF - 1. It appears last // among all unrolled iterations, due to the order of their construction. Value *PreviousLastPart = getOrCreateVectorValue(Previous, UF - 1); // Find and set the insertion point after the previous value if it is an // instruction. BasicBlock::iterator InsertPt; // Note that the previous value may have been constant-folded so it is not // guaranteed to be an instruction in the vector loop. // FIXME: Loop invariant values do not form recurrences. We should deal with // them earlier. if (LI->getLoopFor(LoopVectorBody)->isLoopInvariant(PreviousLastPart)) InsertPt = LoopVectorBody->getFirstInsertionPt(); else { Instruction *PreviousInst = cast(PreviousLastPart); if (isa(PreviousLastPart)) // If the previous value is a phi node, we should insert after all the phi // nodes in the block containing the PHI to avoid breaking basic block // verification. Note that the basic block may be different to // LoopVectorBody, in case we predicate the loop. InsertPt = PreviousInst->getParent()->getFirstInsertionPt(); else InsertPt = ++PreviousInst->getIterator(); } Builder.SetInsertPoint(&*InsertPt); // We will construct a vector for the recurrence by combining the values for // the current and previous iterations. This is the required shuffle mask. assert(!VF.isScalable()); SmallVector ShuffleMask(VF.getKnownMinValue()); ShuffleMask[0] = VF.getKnownMinValue() - 1; for (unsigned I = 1; I < VF.getKnownMinValue(); ++I) ShuffleMask[I] = I + VF.getKnownMinValue() - 1; // The vector from which to take the initial value for the current iteration // (actual or unrolled). Initially, this is the vector phi node. Value *Incoming = VecPhi; // Shuffle the current and previous vector and update the vector parts. for (unsigned Part = 0; Part < UF; ++Part) { Value *PreviousPart = getOrCreateVectorValue(Previous, Part); Value *PhiPart = VectorLoopValueMap.getVectorValue(Phi, Part); auto *Shuffle = VF.isVector() ? Builder.CreateShuffleVector(Incoming, PreviousPart, ShuffleMask) : Incoming; PhiPart->replaceAllUsesWith(Shuffle); cast(PhiPart)->eraseFromParent(); VectorLoopValueMap.resetVectorValue(Phi, Part, Shuffle); Incoming = PreviousPart; } // Fix the latch value of the new recurrence in the vector loop. VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch()); // Extract the last vector element in the middle block. This will be the // initial value for the recurrence when jumping to the scalar loop. auto *ExtractForScalar = Incoming; if (VF.isVector()) { Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); ExtractForScalar = Builder.CreateExtractElement( ExtractForScalar, Builder.getInt32(VF.getKnownMinValue() - 1), "vector.recur.extract"); } // Extract the second last element in the middle block if the // Phi is used outside the loop. We need to extract the phi itself // and not the last element (the phi update in the current iteration). This // will be the value when jumping to the exit block from the LoopMiddleBlock, // when the scalar loop is not run at all. Value *ExtractForPhiUsedOutsideLoop = nullptr; if (VF.isVector()) ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement( Incoming, Builder.getInt32(VF.getKnownMinValue() - 2), "vector.recur.extract.for.phi"); // When loop is unrolled without vectorizing, initialize // ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value of // `Incoming`. This is analogous to the vectorized case above: extracting the // second last element when VF > 1. else if (UF > 1) ExtractForPhiUsedOutsideLoop = getOrCreateVectorValue(Previous, UF - 2); // Fix the initial value of the original recurrence in the scalar loop. Builder.SetInsertPoint(&*LoopScalarPreHeader->begin()); auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init"); for (auto *BB : predecessors(LoopScalarPreHeader)) { auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit; Start->addIncoming(Incoming, BB); } Phi->setIncomingValueForBlock(LoopScalarPreHeader, Start); Phi->setName("scalar.recur"); // Finally, fix users of the recurrence outside the loop. The users will need // either the last value of the scalar recurrence or the last value of the // vector recurrence we extracted in the middle block. Since the loop is in // LCSSA form, we just need to find all the phi nodes for the original scalar // recurrence in the exit block, and then add an edge for the middle block. // 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, the exiting path through middle will be // dynamically dead and the value picked for the phi doesn't matter. for (PHINode &LCSSAPhi : LoopExitBlock->phis()) if (any_of(LCSSAPhi.incoming_values(), [Phi](Value *V) { return V == Phi; })) LCSSAPhi.addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock); } void InnerLoopVectorizer::fixReduction(PHINode *Phi) { // Get it's reduction variable descriptor. assert(Legal->isReductionVariable(Phi) && "Unable to find the reduction variable"); RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[Phi]; RecurKind RK = RdxDesc.getRecurrenceKind(); TrackingVH ReductionStartValue = RdxDesc.getRecurrenceStartValue(); Instruction *LoopExitInst = RdxDesc.getLoopExitInstr(); setDebugLocFromInst(Builder, ReductionStartValue); bool IsInLoopReductionPhi = Cost->isInLoopReduction(Phi); // This is the vector-clone of the value that leaves the loop. Type *VecTy = getOrCreateVectorValue(LoopExitInst, 0)->getType(); // Wrap flags are in general invalid after vectorization, clear them. clearReductionWrapFlags(RdxDesc); // Fix the vector-loop phi. // Reductions do not have to start at zero. They can start with // any loop invariant values. BasicBlock *Latch = OrigLoop->getLoopLatch(); Value *LoopVal = Phi->getIncomingValueForBlock(Latch); for (unsigned Part = 0; Part < UF; ++Part) { Value *VecRdxPhi = getOrCreateVectorValue(Phi, Part); Value *Val = getOrCreateVectorValue(LoopVal, Part); cast(VecRdxPhi) ->addIncoming(Val, LI->getLoopFor(LoopVectorBody)->getLoopLatch()); } // Before each round, move the insertion point right between // the PHIs and the values we are going to write. // This allows us to write both PHINodes and the extractelement // instructions. Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); setDebugLocFromInst(Builder, LoopExitInst); // If tail is folded by masking, the vector value to leave the loop should be // a Select choosing between the vectorized LoopExitInst and vectorized Phi, // instead of the former. For an inloop reduction the reduction will already // be predicated, and does not need to be handled here. if (Cost->foldTailByMasking() && !IsInLoopReductionPhi) { for (unsigned Part = 0; Part < UF; ++Part) { Value *VecLoopExitInst = VectorLoopValueMap.getVectorValue(LoopExitInst, Part); Value *Sel = nullptr; for (User *U : VecLoopExitInst->users()) { if (isa(U)) { assert(!Sel && "Reduction exit feeding two selects"); Sel = U; } else assert(isa(U) && "Reduction exit must feed Phi's or select"); } assert(Sel && "Reduction exit feeds no select"); VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, Sel); // If the target can create a predicated operator for the reduction at no // extra cost in the loop (for example a predicated vadd), it can be // cheaper for the select to remain in the loop than be sunk out of it, // and so use the select value for the phi instead of the old // LoopExitValue. RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[Phi]; if (PreferPredicatedReductionSelect || TTI->preferPredicatedReductionSelect( RdxDesc.getOpcode(), Phi->getType(), TargetTransformInfo::ReductionFlags())) { auto *VecRdxPhi = cast(getOrCreateVectorValue(Phi, Part)); VecRdxPhi->setIncomingValueForBlock( LI->getLoopFor(LoopVectorBody)->getLoopLatch(), Sel); } } } // If the vector reduction can be performed in a smaller type, we truncate // then extend the loop exit value to enable InstCombine to evaluate the // entire expression in the smaller type. if (VF.isVector() && Phi->getType() != RdxDesc.getRecurrenceType()) { assert(!IsInLoopReductionPhi && "Unexpected truncated inloop reduction!"); assert(!VF.isScalable() && "scalable vectors not yet supported."); Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF); Builder.SetInsertPoint( LI->getLoopFor(LoopVectorBody)->getLoopLatch()->getTerminator()); VectorParts RdxParts(UF); for (unsigned Part = 0; Part < UF; ++Part) { RdxParts[Part] = VectorLoopValueMap.getVectorValue(LoopExitInst, Part); Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy) : Builder.CreateZExt(Trunc, VecTy); for (Value::user_iterator UI = RdxParts[Part]->user_begin(); UI != RdxParts[Part]->user_end();) if (*UI != Trunc) { (*UI++)->replaceUsesOfWith(RdxParts[Part], Extnd); RdxParts[Part] = Extnd; } else { ++UI; } } Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); for (unsigned Part = 0; Part < UF; ++Part) { RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, RdxParts[Part]); } } // Reduce all of the unrolled parts into a single vector. Value *ReducedPartRdx = VectorLoopValueMap.getVectorValue(LoopExitInst, 0); unsigned Op = RecurrenceDescriptor::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(Builder, LoopMiddleBlock->getTerminator()); for (unsigned Part = 1; Part < UF; ++Part) { Value *RdxPart = VectorLoopValueMap.getVectorValue(LoopExitInst, Part); if (Op != Instruction::ICmp && Op != Instruction::FCmp) // Floating point operations had to be 'fast' to enable the reduction. ReducedPartRdx = addFastMathFlag( Builder.CreateBinOp((Instruction::BinaryOps)Op, RdxPart, ReducedPartRdx, "bin.rdx"), RdxDesc.getFastMathFlags()); else ReducedPartRdx = createMinMaxOp(Builder, 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() && !IsInLoopReductionPhi) { ReducedPartRdx = createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx); // If the reduction can be performed in a smaller type, we need to extend // the reduction to the wider type before we branch to the original loop. if (Phi->getType() != RdxDesc.getRecurrenceType()) ReducedPartRdx = RdxDesc.isSigned() ? Builder.CreateSExt(ReducedPartRdx, Phi->getType()) : Builder.CreateZExt(ReducedPartRdx, Phi->getType()); } // Create a phi node that merges control-flow from the backedge-taken check // block and the middle block. PHINode *BCBlockPhi = PHINode::Create(Phi->getType(), 2, "bc.merge.rdx", LoopScalarPreHeader->getTerminator()); for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]); BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock); // Now, we need to fix the users of the reduction variable // inside and outside of the scalar remainder loop. // We know that the loop is in LCSSA form. We need to update the PHI nodes // in the exit blocks. See comment on analogous loop in // fixFirstOrderRecurrence for a more complete explaination of the logic. for (PHINode &LCSSAPhi : LoopExitBlock->phis()) if (any_of(LCSSAPhi.incoming_values(), [LoopExitInst](Value *V) { return V == 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 = Phi->getBasicBlockIndex(OrigLoop->getLoopLatch()); assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index"); // Pick the other block. int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1); Phi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi); Phi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst); } void InnerLoopVectorizer::clearReductionWrapFlags( RecurrenceDescriptor &RdxDesc) { RecurKind RK = RdxDesc.getRecurrenceKind(); if (RK != RecurKind::Add && RK != RecurKind::Mul) return; Instruction *LoopExitInstr = RdxDesc.getLoopExitInstr(); assert(LoopExitInstr && "null loop exit instruction"); SmallVector Worklist; SmallPtrSet Visited; Worklist.push_back(LoopExitInstr); Visited.insert(LoopExitInstr); while (!Worklist.empty()) { Instruction *Cur = Worklist.pop_back_val(); if (isa(Cur)) for (unsigned Part = 0; Part < UF; ++Part) { Value *V = getOrCreateVectorValue(Cur, Part); cast(V)->dropPoisonGeneratingFlags(); } for (User *U : Cur->users()) { Instruction *UI = cast(U); if ((Cur != LoopExitInstr || OrigLoop->contains(UI->getParent())) && Visited.insert(UI).second) Worklist.push_back(UI); } } } void InnerLoopVectorizer::fixLCSSAPHIs() { 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. unsigned LastLane = 0; if (isa(IncomingValue)) LastLane = Cost->isUniformAfterVectorization( cast(IncomingValue), VF) ? 0 : VF.getKnownMinValue() - 1; assert((!VF.isScalable() || LastLane == 0) && "scalable vectors dont support non-uniform scalars yet"); // Can be a loop invariant incoming value or the last scalar value to be // extracted from the vectorized loop. Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); Value *lastIncomingValue = getOrCreateScalarValue(IncomingValue, { UF - 1, LastLane }); LCSSAPhi.addIncoming(lastIncomingValue, LoopMiddleBlock); } } void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) { // The basic block and loop containing the predicated instruction. auto *PredBB = PredInst->getParent(); auto *VectorLoop = LI->getLoopFor(PredBB); // Initialize a worklist with the operands of the predicated instruction. SetVector 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 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(U.getUser()); BasicBlock *BB = I->getParent(); if (auto *Phi = dyn_cast(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(Worklist.pop_back_val()); // We can't sink an instruction if it is a phi node, is already in the // predicated block, is not in the loop, or may have side effects. if (!I || isa(I) || I->getParent() == PredBB || !VectorLoop->contains(I) || I->mayHaveSideEffects()) continue; // It's legal to sink the instruction if all its uses occur in the // predicated block. Otherwise, there's nothing to do yet, and we may // need to reanalyze the instruction. if (!llvm::all_of(I->uses(), isBlockOfUsePredicated)) { InstsToReanalyze.push_back(I); continue; } // Move the instruction to the beginning of the predicated block, and add // it's operands to the worklist. I->moveBefore(&*PredBB->getFirstInsertionPt()); Worklist.insert(I->op_begin(), I->op_end()); // The sinking may have enabled other instructions to be sunk, so we will // need to iterate. Changed = true; } } while (Changed); } void InnerLoopVectorizer::fixNonInductionPHIs() { for (PHINode *OrigPhi : OrigPHIsToFix) { PHINode *NewPhi = cast(VectorLoopValueMap.getVectorValue(OrigPhi, 0)); unsigned NumIncomingValues = OrigPhi->getNumIncomingValues(); SmallVector ScalarBBPredecessors( predecessors(OrigPhi->getParent())); SmallVector VectorBBPredecessors( predecessors(NewPhi->getParent())); assert(ScalarBBPredecessors.size() == VectorBBPredecessors.size() && "Scalar and Vector BB should have the same number of predecessors"); // The insertion point in Builder may be invalidated by the time we get // here. Force the Builder insertion point to something valid so that we do // not run into issues during insertion point restore in // getOrCreateVectorValue calls below. Builder.SetInsertPoint(NewPhi); // The predecessor order is preserved and we can rely on mapping between // scalar and vector block predecessors. for (unsigned i = 0; i < NumIncomingValues; ++i) { BasicBlock *NewPredBB = VectorBBPredecessors[i]; // When looking up the new scalar/vector values to fix up, use incoming // values from original phi. Value *ScIncV = OrigPhi->getIncomingValueForBlock(ScalarBBPredecessors[i]); // Scalar incoming value may need a broadcast Value *NewIncV = getOrCreateVectorValue(ScIncV, 0); NewPhi->addIncoming(NewIncV, NewPredBB); } } } void InnerLoopVectorizer::widenGEP(GetElementPtrInst *GEP, VPValue *VPDef, VPUser &Operands, unsigned UF, ElementCount VF, bool IsPtrLoopInvariant, SmallBitVector &IsIndexLoopInvariant, VPTransformState &State) { // Construct a vector GEP by widening the operands of the scalar GEP as // necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP // results in a vector of pointers when at least one operand of the GEP // is vector-typed. Thus, to keep the representation compact, we only use // vector-typed operands for loop-varying values. if (VF.isVector() && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) { // If we are vectorizing, but the GEP has only loop-invariant operands, // the GEP we build (by only using vector-typed operands for // loop-varying values) would be a scalar pointer. Thus, to ensure we // produce a vector of pointers, we need to either arbitrarily pick an // operand to broadcast, or broadcast a clone of the original GEP. // Here, we broadcast a clone of the original. // // TODO: If at some point we decide to scalarize instructions having // loop-invariant operands, this special case will no longer be // required. We would add the scalarization decision to // collectLoopScalars() and teach getVectorValue() to broadcast // the lane-zero scalar value. auto *Clone = Builder.Insert(GEP->clone()); for (unsigned Part = 0; Part < UF; ++Part) { Value *EntryPart = Builder.CreateVectorSplat(VF, Clone); State.set(VPDef, GEP, EntryPart, Part); addMetadata(EntryPart, GEP); } } else { // If the GEP has at least one loop-varying operand, we are sure to // produce a vector of pointers. But if we are only unrolling, we want // to produce a scalar GEP for each unroll part. Thus, the GEP we // produce with the code below will be scalar (if VF == 1) or vector // (otherwise). Note that for the unroll-only case, we still maintain // values in the vector mapping with initVector, as we do for other // instructions. for (unsigned Part = 0; Part < UF; ++Part) { // The pointer operand of the new GEP. If it's loop-invariant, we // won't broadcast it. auto *Ptr = IsPtrLoopInvariant ? State.get(Operands.getOperand(0), {0, 0}) : State.get(Operands.getOperand(0), Part); // Collect all the indices for the new GEP. If any index is // loop-invariant, we won't broadcast it. SmallVector Indices; for (unsigned I = 1, E = Operands.getNumOperands(); I < E; I++) { VPValue *Operand = Operands.getOperand(I); if (IsIndexLoopInvariant[I - 1]) Indices.push_back(State.get(Operand, {0, 0})); else Indices.push_back(State.get(Operand, Part)); } // Create the new GEP. Note that this GEP may be a scalar if VF == 1, // but it should be a vector, otherwise. auto *NewGEP = GEP->isInBounds() ? Builder.CreateInBoundsGEP(GEP->getSourceElementType(), Ptr, Indices) : Builder.CreateGEP(GEP->getSourceElementType(), Ptr, Indices); assert((VF.isScalar() || NewGEP->getType()->isVectorTy()) && "NewGEP is not a pointer vector"); State.set(VPDef, GEP, NewGEP, Part); addMetadata(NewGEP, GEP); } } } void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, RecurrenceDescriptor *RdxDesc, Value *StartV, unsigned UF, ElementCount VF) { assert(!VF.isScalable() && "scalable vectors not yet supported."); PHINode *P = cast(PN); if (EnableVPlanNativePath) { // Currently we enter here in the VPlan-native path for non-induction // PHIs where all control flow is uniform. We simply widen these PHIs. // Create a vector phi with no operands - the vector phi operands will be // set at the end of vector code generation. Type *VecTy = (VF.isScalar()) ? PN->getType() : VectorType::get(PN->getType(), VF); Value *VecPhi = Builder.CreatePHI(VecTy, PN->getNumOperands(), "vec.phi"); VectorLoopValueMap.setVectorValue(P, 0, VecPhi); OrigPHIsToFix.push_back(P); return; } assert(PN->getParent() == OrigLoop->getHeader() && "Non-header phis should have been handled elsewhere"); // In order to support recurrences we need to be able to vectorize Phi nodes. // Phi nodes have cycles, so we need to vectorize them in two stages. This is // stage #1: We create a new vector PHI node with no incoming edges. We'll use // this value when we vectorize all of the instructions that use the PHI. if (RdxDesc || Legal->isFirstOrderRecurrence(P)) { Value *Iden = nullptr; bool ScalarPHI = (VF.isScalar()) || Cost->isInLoopReduction(cast(PN)); Type *VecTy = ScalarPHI ? PN->getType() : VectorType::get(PN->getType(), VF); if (RdxDesc) { assert(Legal->isReductionVariable(P) && StartV && "RdxDesc should only be set for reduction variables; in that case " "a StartV is also required"); RecurKind RK = RdxDesc->getRecurrenceKind(); if (RecurrenceDescriptor::isMinMaxRecurrenceKind(RK)) { // MinMax reduction have the start value as their identify. if (ScalarPHI) { Iden = StartV; } else { IRBuilderBase::InsertPointGuard IPBuilder(Builder); Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); StartV = Iden = Builder.CreateVectorSplat(VF, StartV, "minmax.ident"); } } else { Constant *IdenC = RecurrenceDescriptor::getRecurrenceIdentity( RK, VecTy->getScalarType()); Iden = IdenC; if (!ScalarPHI) { Iden = ConstantVector::getSplat(VF, IdenC); IRBuilderBase::InsertPointGuard IPBuilder(Builder); Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); Constant *Zero = Builder.getInt32(0); StartV = Builder.CreateInsertElement(Iden, StartV, Zero); } } } for (unsigned Part = 0; Part < UF; ++Part) { // This is phase one of vectorizing PHIs. Value *EntryPart = PHINode::Create( VecTy, 2, "vec.phi", &*LoopVectorBody->getFirstInsertionPt()); VectorLoopValueMap.setVectorValue(P, Part, EntryPart); if (StartV) { // Make sure to add the reduction start value only to the // first unroll part. Value *StartVal = (Part == 0) ? StartV : Iden; cast(EntryPart)->addIncoming(StartVal, LoopVectorPreHeader); } } return; } assert(!Legal->isReductionVariable(P) && "reductions should be handled above"); setDebugLocFromInst(Builder, P); // This PHINode must be an induction variable. // Make sure that we know about it. assert(Legal->getInductionVars().count(P) && "Not an induction variable"); InductionDescriptor II = Legal->getInductionVars().lookup(P); const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); // FIXME: The newly created binary instructions should contain nsw/nuw flags, // which can be found from the original scalar operations. switch (II.getKind()) { case InductionDescriptor::IK_NoInduction: llvm_unreachable("Unknown induction"); case InductionDescriptor::IK_IntInduction: case InductionDescriptor::IK_FpInduction: llvm_unreachable("Integer/fp induction is handled elsewhere."); case InductionDescriptor::IK_PtrInduction: { // Handle the pointer induction variable case. assert(P->getType()->isPointerTy() && "Unexpected type."); if (Cost->isScalarAfterVectorization(P, VF)) { // This is the normalized GEP that starts counting at zero. Value *PtrInd = Builder.CreateSExtOrTrunc(Induction, II.getStep()->getType()); // Determine the number of scalars we need to generate for each unroll // iteration. If the instruction is uniform, we only need to generate the // first lane. Otherwise, we generate all VF values. unsigned Lanes = Cost->isUniformAfterVectorization(P, VF) ? 1 : VF.getKnownMinValue(); for (unsigned Part = 0; Part < UF; ++Part) { for (unsigned Lane = 0; Lane < Lanes; ++Lane) { Constant *Idx = ConstantInt::get(PtrInd->getType(), Lane + Part * VF.getKnownMinValue()); Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx); Value *SclrGep = emitTransformedIndex(Builder, GlobalIdx, PSE.getSE(), DL, II); SclrGep->setName("next.gep"); VectorLoopValueMap.setScalarValue(P, {Part, Lane}, SclrGep); } } return; } assert(isa(II.getStep()) && "Induction step not a SCEV constant!"); Type *PhiType = II.getStep()->getType(); // Build a pointer phi Value *ScalarStartValue = II.getStartValue(); Type *ScStValueType = ScalarStartValue->getType(); PHINode *NewPointerPhi = PHINode::Create(ScStValueType, 2, "pointer.phi", Induction); NewPointerPhi->addIncoming(ScalarStartValue, LoopVectorPreHeader); // A pointer induction, performed by using a gep BasicBlock *LoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); Instruction *InductionLoc = LoopLatch->getTerminator(); const SCEV *ScalarStep = II.getStep(); SCEVExpander Exp(*PSE.getSE(), DL, "induction"); Value *ScalarStepValue = Exp.expandCodeFor(ScalarStep, PhiType, InductionLoc); Value *InductionGEP = GetElementPtrInst::Create( ScStValueType->getPointerElementType(), NewPointerPhi, Builder.CreateMul( ScalarStepValue, ConstantInt::get(PhiType, VF.getKnownMinValue() * UF)), "ptr.ind", InductionLoc); NewPointerPhi->addIncoming(InductionGEP, LoopLatch); // Create UF many actual address geps that use the pointer // phi as base and a vectorized version of the step value // () as offset. for (unsigned Part = 0; Part < UF; ++Part) { SmallVector Indices; // Create a vector of consecutive numbers from zero to VF. for (unsigned i = 0; i < VF.getKnownMinValue(); ++i) Indices.push_back( ConstantInt::get(PhiType, i + Part * VF.getKnownMinValue())); Constant *StartOffset = ConstantVector::get(Indices); Value *GEP = Builder.CreateGEP( ScStValueType->getPointerElementType(), NewPointerPhi, Builder.CreateMul( StartOffset, Builder.CreateVectorSplat(VF.getKnownMinValue(), ScalarStepValue), "vector.gep")); VectorLoopValueMap.setVectorValue(P, Part, GEP); } } } } /// A helper function for checking whether an integer division-related /// instruction may divide by zero (in which case it must be predicated if /// executed conditionally in the scalar code). /// TODO: It may be worthwhile to generalize and check isKnownNonZero(). /// Non-zero divisors that are non compile-time constants will not be /// converted into multiplication, so we will still end up scalarizing /// the division, but can do so w/o predication. static bool mayDivideByZero(Instruction &I) { assert((I.getOpcode() == Instruction::UDiv || I.getOpcode() == Instruction::SDiv || I.getOpcode() == Instruction::URem || I.getOpcode() == Instruction::SRem) && "Unexpected instruction"); Value *Divisor = I.getOperand(1); auto *CInt = dyn_cast(Divisor); return !CInt || CInt->isZero(); } void InnerLoopVectorizer::widenInstruction(Instruction &I, VPValue *Def, VPUser &User, VPTransformState &State) { switch (I.getOpcode()) { case Instruction::Call: case Instruction::Br: case Instruction::PHI: case Instruction::GetElementPtr: case Instruction::Select: llvm_unreachable("This instruction is handled by a different recipe."); case Instruction::UDiv: case Instruction::SDiv: case Instruction::SRem: case Instruction::URem: case Instruction::Add: case Instruction::FAdd: case Instruction::Sub: case Instruction::FSub: case Instruction::FNeg: case Instruction::Mul: case Instruction::FMul: case Instruction::FDiv: case Instruction::FRem: case Instruction::Shl: case Instruction::LShr: case Instruction::AShr: case Instruction::And: case Instruction::Or: case Instruction::Xor: { // Just widen unops and binops. setDebugLocFromInst(Builder, &I); for (unsigned Part = 0; Part < UF; ++Part) { SmallVector Ops; for (VPValue *VPOp : User.operands()) Ops.push_back(State.get(VPOp, Part)); Value *V = Builder.CreateNAryOp(I.getOpcode(), Ops); if (auto *VecOp = dyn_cast(V)) VecOp->copyIRFlags(&I); // Use this vector value for all users of the original instruction. State.set(Def, &I, V, Part); addMetadata(V, &I); } break; } case Instruction::ICmp: case Instruction::FCmp: { // Widen compares. Generate vector compares. bool FCmp = (I.getOpcode() == Instruction::FCmp); auto *Cmp = cast(&I); setDebugLocFromInst(Builder, Cmp); for (unsigned Part = 0; Part < UF; ++Part) { Value *A = State.get(User.getOperand(0), Part); Value *B = State.get(User.getOperand(1), Part); Value *C = nullptr; if (FCmp) { // Propagate fast math flags. IRBuilder<>::FastMathFlagGuard FMFG(Builder); Builder.setFastMathFlags(Cmp->getFastMathFlags()); C = Builder.CreateFCmp(Cmp->getPredicate(), A, B); } else { C = Builder.CreateICmp(Cmp->getPredicate(), A, B); } State.set(Def, &I, C, Part); 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(&I); setDebugLocFromInst(Builder, CI); /// Vectorize casts. Type *DestTy = (VF.isScalar()) ? CI->getType() : VectorType::get(CI->getType(), VF); for (unsigned Part = 0; Part < UF; ++Part) { Value *A = State.get(User.getOperand(0), Part); Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy); State.set(Def, &I, Cast, Part); addMetadata(Cast, &I); } break; } default: // This instruction is not vectorized by simple widening. LLVM_DEBUG(dbgs() << "LV: Found an unhandled instruction: " << I); llvm_unreachable("Unhandled instruction!"); } // end of switch. } void InnerLoopVectorizer::widenCallInstruction(CallInst &I, VPValue *Def, VPUser &ArgOperands, VPTransformState &State) { assert(!isa(I) && "DbgInfoIntrinsic should have been dropped during VPlan construction"); setDebugLocFromInst(Builder, &I); Module *M = I.getParent()->getParent()->getParent(); auto *CI = cast(&I); SmallVector Tys; for (Value *ArgOperand : CI->arg_operands()) Tys.push_back(ToVectorTy(ArgOperand->getType(), VF.getKnownMinValue())); Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); // The flag shows whether we use Intrinsic or a usual Call for vectorized // version of the instruction. // Is it beneficial to perform intrinsic call compared to lib call? bool NeedToScalarize = false; 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() && "Cannot have invalid costs while widening"); for (unsigned Part = 0; Part < UF; ++Part) { SmallVector Args; for (auto &I : enumerate(ArgOperands.operands())) { // Some intrinsics have a scalar argument - don't replace it with a // vector. Value *Arg; if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, I.index())) Arg = State.get(I.value(), Part); else Arg = State.get(I.value(), {0, 0}); Args.push_back(Arg); } Function *VectorF; if (UseVectorIntrinsic) { // Use vector version of the intrinsic. Type *TysForDecl[] = {CI->getType()}; if (VF.isVector()) { assert(!VF.isScalable() && "VF is assumed to be non scalable."); TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF); } VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl); assert(VectorF && "Can't retrieve vector intrinsic."); } else { // Use vector version of the function call. const VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); #ifndef NDEBUG assert(VFDatabase(*CI).getVectorizedFunction(Shape) != nullptr && "Can't create vector function."); #endif VectorF = VFDatabase(*CI).getVectorizedFunction(Shape); } SmallVector OpBundles; CI->getOperandBundlesAsDefs(OpBundles); CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles); if (isa(V)) V->copyFastMathFlags(CI); State.set(Def, &I, V, Part); addMetadata(V, &I); } } void InnerLoopVectorizer::widenSelectInstruction(SelectInst &I, VPValue *VPDef, VPUser &Operands, bool InvariantCond, VPTransformState &State) { setDebugLocFromInst(Builder, &I); // The condition can be loop invariant but still defined inside the // loop. This means that we can't just use the original 'cond' value. // We have to take the 'vectorized' value and pick the first lane. // Instcombine will make this a no-op. auto *InvarCond = InvariantCond ? State.get(Operands.getOperand(0), {0, 0}) : nullptr; for (unsigned Part = 0; Part < UF; ++Part) { Value *Cond = InvarCond ? InvarCond : State.get(Operands.getOperand(0), Part); Value *Op0 = State.get(Operands.getOperand(1), Part); Value *Op1 = State.get(Operands.getOperand(2), Part); Value *Sel = Builder.CreateSelect(Cond, Op0, Op1); State.set(VPDef, &I, Sel, Part); addMetadata(Sel, &I); } } void LoopVectorizationCostModel::collectLoopScalars(ElementCount VF) { // We should not collect Scalars more than once per VF. Right now, this // function is called from collectUniformsAndScalars(), which already does // this check. Collecting Scalars for VF=1 does not make any sense. assert(VF.isVector() && Scalars.find(VF) == Scalars.end() && "This function should not be visited twice for the same VF"); SmallSetVector Worklist; // These sets are used to seed the analysis with pointers used by memory // accesses that will remain scalar. SmallSetVector ScalarPtrs; SmallPtrSet 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(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(V) && V->getType()->isPointerTy()) || isa(V)) && !TheLoop->isLoopInvariant(V); }; auto isScalarPtrInduction = [&](Instruction *MemAccess, Value *Ptr) { if (!isa(Ptr) || !Legal->getInductionVars().count(cast(Ptr))) return false; auto &Induction = Legal->getInductionVars()[cast(Ptr)]; if (Induction.getKind() != InductionDescriptor::IK_PtrInduction) return false; return isScalarUse(MemAccess, Ptr); }; // A helper that evaluates a memory access's use of a pointer. If the // pointer is actually the pointer induction of a loop, it is being // inserted into Worklist. If the use will be a scalar use, and the // pointer is only used by memory accesses, we place the pointer in // ScalarPtrs. Otherwise, the pointer is placed in PossibleNonScalarPtrs. auto evaluatePtrUse = [&](Instruction *MemAccess, Value *Ptr) { if (isScalarPtrInduction(MemAccess, Ptr)) { Worklist.insert(cast(Ptr)); Instruction *Update = cast( cast(Ptr)->getIncomingValueForBlock(Latch)); Worklist.insert(Update); LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Ptr << "\n"); LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Update << "\n"); return; } // We only care about bitcast and getelementptr instructions contained in // the loop. if (!isLoopVaryingBitCastOrGEP(Ptr)) return; // If the pointer has already been identified as scalar (e.g., if it was // also identified as uniform), there's nothing to do. auto *I = cast(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(U) || isa(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(&I)) { evaluatePtrUse(Load, Load->getPointerOperand()); } else if (auto *Store = dyn_cast(&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(Dst->getOperand(0)); if (llvm::all_of(Src->users(), [&](User *U) -> bool { auto *J = cast(U); return !TheLoop->contains(J) || Worklist.count(J) || ((isa(J) || isa(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(Ind->getIncomingValueForBlock(Latch)); // If tail-folding is applied, the primary induction variable will be used // to feed a vector compare. if (Ind == Legal->getPrimaryInduction() && foldTailByMasking()) continue; // Determine if all users of the induction variable are scalar after // vectorization. auto ScalarInd = llvm::all_of(Ind->users(), [&](User *U) -> bool { auto *I = cast(U); return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I); }); if (!ScalarInd) continue; // Determine if all users of the induction variable update instruction are // scalar after vectorization. auto ScalarIndUpdate = llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { auto *I = cast(U); return I == Ind || !TheLoop->contains(I) || Worklist.count(I); }); if (!ScalarIndUpdate) continue; // The induction variable and its update instruction will remain scalar. Worklist.insert(Ind); Worklist.insert(IndUpdate); LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n"); LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate << "\n"); } Scalars[VF].insert(Worklist.begin(), Worklist.end()); } bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I, ElementCount VF) { if (!blockNeedsPredication(I->getParent())) return false; switch(I->getOpcode()) { default: break; case Instruction::Load: case Instruction::Store: { if (!Legal->isMaskRequired(I)) return false; auto *Ptr = getLoadStorePointerOperand(I); auto *Ty = getMemInstValueType(I); // We have already decided how to vectorize this instruction, get that // result. if (VF.isVector()) { InstWidening WideningDecision = getWideningDecision(I, VF); assert(WideningDecision != CM_Unknown && "Widening decision should be ready at this moment"); return WideningDecision == CM_Scalarize; } const Align Alignment = getLoadStoreAlignment(I); return isa(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) || isLegalMaskedGather(Ty, Alignment)) : !(isLegalMaskedStore(Ty, Ptr, Alignment) || isLegalMaskedScatter(Ty, Alignment)); } case Instruction::UDiv: case Instruction::SDiv: case Instruction::SRem: case Instruction::URem: return mayDivideByZero(*I); } return false; } bool LoopVectorizationCostModel::interleavedAccessCanBeWidened( Instruction *I, ElementCount VF) { assert(isAccessInterleaved(I) && "Expecting interleaved access."); assert(getWideningDecision(I, VF) == CM_Unknown && "Decision should not be set yet."); auto *Group = getInterleavedAccessGroup(I); assert(Group && "Must have a group."); // If the instruction's allocated size doesn't equal it's type size, it // requires padding and will be scalarized. auto &DL = I->getModule()->getDataLayout(); auto *ScalarTy = getMemInstValueType(I); if (hasIrregularType(ScalarTy, DL)) return false; // Check if masking is required. // A Group may need masking for one of two reasons: it resides in a block that // needs predication, or it was decided to use masking to deal with gaps. bool PredicatedAccessRequiresMasking = Legal->blockNeedsPredication(I->getParent()) && Legal->isMaskRequired(I); bool AccessWithGapsRequiresMasking = Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed(); if (!PredicatedAccessRequiresMasking && !AccessWithGapsRequiresMasking) return true; // If masked interleaving is required, we expect that the user/target had // enabled it, because otherwise it either wouldn't have been created or // it should have been invalidated by the CostModel. assert(useMaskedInterleavedAccesses(TTI) && "Masked interleave-groups for predicated accesses are not enabled."); auto *Ty = getMemInstValueType(I); const Align Alignment = getLoadStoreAlignment(I); return isa(I) ? TTI.isLegalMaskedLoad(Ty, Alignment) : TTI.isLegalMaskedStore(Ty, Alignment); } bool LoopVectorizationCostModel::memoryInstructionCanBeWidened( Instruction *I, ElementCount VF) { // Get and ensure we have a valid memory instruction. LoadInst *LI = dyn_cast(I); StoreInst *SI = dyn_cast(I); assert((LI || SI) && "Invalid memory instruction"); auto *Ptr = getLoadStorePointerOperand(I); // In order to be widened, the pointer should be consecutive, first of all. if (!Legal->isConsecutivePtr(Ptr)) return false; // If the instruction is a store located in a predicated block, it will be // scalarized. if (isScalarWithPredication(I)) return false; // If the instruction's allocated size doesn't equal it's type size, it // requires padding and will be scalarized. auto &DL = I->getModule()->getDataLayout(); auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType(); if (hasIrregularType(ScalarTy, DL)) 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(V); return (!I || !TheLoop->contains(I)); }; SetVector Worklist; BasicBlock *Latch = TheLoop->getLoopLatch(); // Instructions that are scalar with predication must not be considered // uniform after vectorization, because that would create an erroneous // replicating region where only a single instance out of VF should be formed. // TODO: optimize such seldom cases if found important, see PR40816. auto addToWorklistIfAllowed = [&](Instruction *I) -> void { if (isOutOfScope(I)) { LLVM_DEBUG(dbgs() << "LV: Found not uniform due to scope: " << *I << "\n"); return; } if (isScalarWithPredication(I, VF)) { LLVM_DEBUG(dbgs() << "LV: Found not uniform being ScalarWithPredication: " << *I << "\n"); return; } LLVM_DEBUG(dbgs() << "LV: Found uniform instruction: " << *I << "\n"); Worklist.insert(I); }; // Start with the conditional branch. If the branch condition is an // instruction contained in the loop that is only used by the branch, it is // uniform. auto *Cmp = dyn_cast(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(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) SmallPtrSet 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 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(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(V); auto UsersAreMemAccesses = llvm::all_of(I->users(), [&](User *U) -> bool { return isVectorizedMemAccessUse(cast(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(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(OV); if (llvm::all_of(OI->users(), [&](User *U) -> bool { auto *J = cast(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(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(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(U); return I == Ind || !TheLoop->contains(I) || Worklist.count(I) || isVectorizedMemAccessUse(I, IndUpdate); }); if (!UniformIndUpdate) continue; // The induction variable and its update instruction will remain uniform. addToWorklistIfAllowed(Ind); addToWorklistIfAllowed(IndUpdate); } Uniforms[VF].insert(Worklist.begin(), Worklist.end()); } bool LoopVectorizationCostModel::runtimeChecksRequired() { LLVM_DEBUG(dbgs() << "LV: Performing code size checks.\n"); if (Legal->getRuntimePointerChecking()->Need) { reportVectorizationFailure("Runtime ptr check is required with -Os/-Oz", "runtime pointer checks needed. Enable vectorization of this " "loop with '#pragma clang loop vectorize(enable)' when " "compiling with -Os/-Oz", "CantVersionLoopWithOptForSize", ORE, TheLoop); return true; } if (!PSE.getUnionPredicate().getPredicates().empty()) { reportVectorizationFailure("Runtime SCEV check is required with -Os/-Oz", "runtime SCEV checks needed. Enable vectorization of this " "loop with '#pragma clang loop vectorize(enable)' when " "compiling with -Os/-Oz", "CantVersionLoopWithOptForSize", ORE, TheLoop); return true; } // FIXME: Avoid specializing for stride==1 instead of bailing out. if (!Legal->getLAI()->getSymbolicStrides().empty()) { reportVectorizationFailure("Runtime stride check for small trip count", "runtime stride == 1 checks needed. Enable vectorization of " "this loop without such check by compiling with -Os/-Oz", "CantVersionLoopWithOptForSize", ORE, TheLoop); return true; } return false; } Optional 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 None; } unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop); LLVM_DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n'); if (TC == 1) { reportVectorizationFailure("Single iteration (non) loop", "loop trip count is one, irrelevant for vectorization", "SingleIterationLoop", ORE, TheLoop); return None; } switch (ScalarEpilogueStatus) { case CM_ScalarEpilogueAllowed: return 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 None; 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 None; } // 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(); } ElementCount MaxVF = computeFeasibleMaxVF(TC, UserVF); assert(!MaxVF.isScalable() && "Scalable vectors do not yet support tail folding"); assert((UserVF.isNonZero() || isPowerOf2_32(MaxVF.getFixedValue())) && "MaxVF must be a power of 2"); unsigned MaxVFtimesIC = UserIC ? MaxVF.getFixedValue() * UserIC : MaxVF.getFixedValue(); // Avoid tail folding if the trip count is known to be a multiple of any VF we // chose. ScalarEvolution *SE = PSE.getSE(); const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount(); const SCEV *ExitCount = SE->getAddExpr( BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType())); const SCEV *Rem = SE->getURemExpr( ExitCount, SE->getConstant(BackedgeTakenCount->getType(), MaxVFtimesIC)); if (Rem->isZero()) { // Accept MaxVF if we do not have a tail. LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n"); return MaxVF; } // If we don't know the precise trip count, or if the trip count that we // found modulo the vectorization factor is not zero, try to fold the tail // by masking. // FIXME: look for a smaller MaxVF that does divide TC rather than masking. if (Legal->prepareToFoldTailByMasking()) { FoldTailByMasking = true; return MaxVF; } // If there was a tail-folding hint/switch, but we can't fold the tail by // masking, fallback to a vectorization with a scalar epilogue. if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) { LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a " "scalar epilogue instead.\n"); ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; return MaxVF; } if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedUsePredicate) { LLVM_DEBUG(dbgs() << "LV: Can't fold tail by masking: don't vectorize\n"); return None; } if (TC == 0) { reportVectorizationFailure( "Unable to calculate the loop count due to complex control flow", "unable to calculate the loop count due to complex control flow", "UnknownLoopCountComplexCFG", ORE, TheLoop); return None; } reportVectorizationFailure( "Cannot optimize for size and vectorize at the same time.", "cannot optimize for size and vectorize at the same time. " "Enable vectorization of this loop with '#pragma clang loop " "vectorize(enable)' when compiling with -Os/-Oz", "NoTailLoopWithOptForSize", ORE, TheLoop); return None; } ElementCount LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount, ElementCount UserVF) { bool IgnoreScalableUserVF = UserVF.isScalable() && !TTI.supportsScalableVectors() && !ForceTargetSupportsScalableVectors; if (IgnoreScalableUserVF) { LLVM_DEBUG( dbgs() << "LV: Ignoring VF=" << UserVF << " because target does not support scalable vectors.\n"); ORE->emit([&]() { return OptimizationRemarkAnalysis(DEBUG_TYPE, "IgnoreScalableUserVF", TheLoop->getStartLoc(), TheLoop->getHeader()) << "Ignoring VF=" << ore::NV("UserVF", UserVF) << " because target does not support scalable vectors."; }); } // Beyond this point two scenarios are handled. If UserVF isn't specified // then a suitable VF is chosen. If UserVF is specified and there are // dependencies, check if it's legal. However, if a UserVF is specified and // there are no dependencies, then there's nothing to do. if (UserVF.isNonZero() && !IgnoreScalableUserVF && Legal->isSafeForAnyVectorWidth()) return UserVF; MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI); unsigned SmallestType, WidestType; std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes(); unsigned WidestRegister = TTI.getRegisterBitWidth(true); // Get the maximum safe dependence distance in bits computed by LAA. // It is computed by MaxVF * sizeOf(type) * 8, where type is taken from // the memory accesses that is most restrictive (involved in the smallest // dependence distance). unsigned MaxSafeVectorWidthInBits = Legal->getMaxSafeVectorWidthInBits(); // If the user vectorization factor is legally unsafe, clamp it to a safe // value. Otherwise, return as is. if (UserVF.isNonZero() && !IgnoreScalableUserVF) { unsigned MaxSafeElements = PowerOf2Floor(MaxSafeVectorWidthInBits / WidestType); ElementCount MaxSafeVF = ElementCount::getFixed(MaxSafeElements); if (UserVF.isScalable()) { Optional MaxVScale = TTI.getMaxVScale(); // Scale VF by vscale before checking if it's safe. MaxSafeVF = ElementCount::getScalable( MaxVScale ? (MaxSafeElements / MaxVScale.getValue()) : 0); if (MaxSafeVF.isZero()) { // The dependence distance is too small to use scalable vectors, // fallback on fixed. LLVM_DEBUG( dbgs() << "LV: Max legal vector width too small, scalable vectorization " "unfeasible. Using fixed-width vectorization instead.\n"); ORE->emit([&]() { return OptimizationRemarkAnalysis(DEBUG_TYPE, "ScalableVFUnfeasible", TheLoop->getStartLoc(), TheLoop->getHeader()) << "Max legal vector width too small, scalable vectorization " << "unfeasible. Using fixed-width vectorization instead."; }); return computeFeasibleMaxVF( ConstTripCount, ElementCount::getFixed(UserVF.getKnownMinValue())); } } LLVM_DEBUG(dbgs() << "LV: The max safe VF is: " << MaxSafeVF << ".\n"); if (ElementCount::isKnownLE(UserVF, MaxSafeVF)) return UserVF; LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVF << " is unsafe, clamping to max safe VF=" << MaxSafeVF << ".\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", MaxSafeVF); }); return MaxSafeVF; } WidestRegister = std::min(WidestRegister, MaxSafeVectorWidthInBits); // Ensure MaxVF is a power of 2; the dependence distance bound may not be. // Note that both WidestRegister and WidestType may not be a powers of 2. unsigned MaxVectorSize = PowerOf2Floor(WidestRegister / WidestType); LLVM_DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestType << " / " << WidestType << " bits.\n"); LLVM_DEBUG(dbgs() << "LV: The Widest register safe to use is: " << WidestRegister << " bits.\n"); assert(MaxVectorSize <= WidestRegister && "Did not expect to pack so many elements" " into one vector!"); if (MaxVectorSize == 0) { LLVM_DEBUG(dbgs() << "LV: The target has no vector registers.\n"); MaxVectorSize = 1; return ElementCount::getFixed(MaxVectorSize); } else if (ConstTripCount && ConstTripCount < MaxVectorSize && isPowerOf2_32(ConstTripCount)) { // We need to clamp the VF to be the ConstTripCount. There is no point in // choosing a higher viable VF as done in the loop below. LLVM_DEBUG(dbgs() << "LV: Clamping the MaxVF to the constant trip count: " << ConstTripCount << "\n"); MaxVectorSize = ConstTripCount; return ElementCount::getFixed(MaxVectorSize); } unsigned MaxVF = MaxVectorSize; if (TTI.shouldMaximizeVectorBandwidth(!isScalarEpilogueAllowed()) || (MaximizeBandwidth && isScalarEpilogueAllowed())) { // Collect all viable vectorization factors larger than the default MaxVF // (i.e. MaxVectorSize). SmallVector VFs; unsigned NewMaxVectorSize = WidestRegister / SmallestType; for (unsigned VS = MaxVectorSize * 2; VS <= NewMaxVectorSize; VS *= 2) VFs.push_back(ElementCount::getFixed(VS)); // For each VF calculate its register usage. auto RUs = calculateRegisterUsage(VFs); // Select the largest VF which doesn't require more registers than existing // ones. for (int i = RUs.size() - 1; i >= 0; --i) { bool Selected = true; for (auto& pair : RUs[i].MaxLocalUsers) { unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first); if (pair.second > TargetNumRegisters) Selected = false; } if (Selected) { MaxVF = VFs[i].getKnownMinValue(); break; } } if (unsigned MinVF = TTI.getMinimumVF(SmallestType)) { if (MaxVF < MinVF) { LLVM_DEBUG(dbgs() << "LV: Overriding calculated MaxVF(" << MaxVF << ") with target's minimum: " << MinVF << '\n'); MaxVF = MinVF; } } } return ElementCount::getFixed(MaxVF); } VectorizationFactor LoopVectorizationCostModel::selectVectorizationFactor(ElementCount MaxVF) { // FIXME: This can be fixed for scalable vectors later, because at this stage // the LoopVectorizer will only consider vectorizing a loop with scalable // vectors when the loop has a hint to enable vectorization for a given VF. assert(!MaxVF.isScalable() && "scalable vectors not yet supported"); 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"); unsigned Width = 1; const float ScalarCost = *ExpectedCost.getValue(); float Cost = ScalarCost; bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled; if (ForceVectorization && MaxVF.isVector()) { // Ignore scalar width, because the user explicitly wants vectorization. // Initialize cost to max so that VF = 2 is, at least, chosen during cost // evaluation. Cost = std::numeric_limits::max(); } for (unsigned i = 2; i <= MaxVF.getFixedValue(); i *= 2) { // Notice that the vector loop needs to be executed less times, so // we need to divide the cost of the vector loops by the width of // the vector elements. VectorizationCostTy C = expectedCost(ElementCount::getFixed(i)); assert(C.first.isValid() && "Unexpected invalid cost for vector loop"); float VectorCost = *C.first.getValue() / (float)i; LLVM_DEBUG(dbgs() << "LV: Vector loop of width " << i << " costs: " << (int)VectorCost << ".\n"); if (!C.second && !ForceVectorization) { LLVM_DEBUG( dbgs() << "LV: Not considering vector loop of width " << i << " because it will not generate any vector instructions.\n"); continue; } // If profitable add it to ProfitableVF list. if (VectorCost < ScalarCost) { ProfitableVFs.push_back(VectorizationFactor( {ElementCount::getFixed(i), (unsigned)VectorCost})); } if (VectorCost < Cost) { Cost = VectorCost; Width = i; } } if (!EnableCondStoresVectorization && NumPredStores) { reportVectorizationFailure("There are conditional stores.", "store that is conditionally executed prevents vectorization", "ConditionalStore", ORE, TheLoop); Width = 1; Cost = ScalarCost; } LLVM_DEBUG(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs() << "LV: Vectorization seems to be not beneficial, " << "but was forced by a user.\n"); LLVM_DEBUG(dbgs() << "LV: Selecting VF: " << Width << ".\n"); VectorizationFactor Factor = {ElementCount::getFixed(Width), (unsigned)(Width * Cost)}; return Factor; } 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(U))) return false; // Look for uses of penultimate value of the induction. for (User *U : Entry.first->users()) if (!L.contains(cast(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; 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; } // FIXME: This can be fixed for scalable vectors later, because at this stage // the LoopVectorizer will only consider vectorizing a loop with scalable // vectors when the loop has a hint to enable vectorization for a given VF. if (MainLoopVF.isScalable()) { LLVM_DEBUG(dbgs() << "LEV: Epilogue vectorization for scalable vectors not " "yet supported.\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";); if (LVP.hasPlanWithVFs( {MainLoopVF, ElementCount::getFixed(EpilogueVectorizationForceVF)})) return {ElementCount::getFixed(EpilogueVectorizationForceVF), 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; } if (!isEpilogueVectorizationProfitable(MainLoopVF)) return Result; for (auto &NextVF : ProfitableVFs) if (ElementCount::isKnownLT(NextVF.Width, MainLoopVF) && (Result.Width.getFixedValue() == 1 || NextVF.Cost < Result.Cost) && LVP.hasPlanWithVFs({MainLoopVF, 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 LoopVectorizationCostModel::getSmallestAndWidestTypes() { unsigned MinWidth = -1U; unsigned MaxWidth = 8; const DataLayout &DL = TheFunction->getParent()->getDataLayout(); // For each block. for (BasicBlock *BB : TheLoop->blocks()) { // For each instruction in the loop. for (Instruction &I : BB->instructionsWithoutDebug()) { Type *T = I.getType(); // Skip ignored values. if (ValuesToIgnore.count(&I)) continue; // Only examine Loads, Stores and PHINodes. if (!isa(I) && !isa(I) && !isa(I)) continue; // Examine PHI nodes that are reduction variables. Update the type to // account for the recurrence type. if (auto *PN = dyn_cast(&I)) { if (!Legal->isReductionVariable(PN)) continue; RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[PN]; if (PreferInLoopReductions || TTI.preferInLoopReduction(RdxDesc.getOpcode(), RdxDesc.getRecurrenceType(), TargetTransformInfo::ReductionFlags())) continue; T = RdxDesc.getRecurrenceType(); } // Examine the stored values. if (auto *ST = dyn_cast(&I)) T = ST->getValueOperand()->getType(); // Ignore loaded pointer types and stored pointer types that are not // vectorizable. // // FIXME: The check here attempts to predict whether a load or store will // be vectorized. We only know this for certain after a VF has // been selected. Here, we assume that if an access can be // vectorized, it will be. We should also look at extending this // optimization to non-pointer types. // if (T->isPointerTy() && !isConsecutiveLoadOrStore(&I) && !isAccessInterleaved(&I) && !isLegalGatherOrScatter(&I)) continue; MinWidth = std::min(MinWidth, (unsigned)DL.getTypeSizeInBits(T->getScalarType())); MaxWidth = std::max(MaxWidth, (unsigned)DL.getTypeSizeInBits(T->getScalarType())); } } return {MinWidth, MaxWidth}; } unsigned LoopVectorizationCostModel::selectInterleaveCount(ElementCount VF, unsigned LoopCost) { // -- The interleave heuristics -- // We interleave the loop in order to expose ILP and reduce the loop overhead. // There are many micro-architectural considerations that we can't predict // at this level. For example, frontend pressure (on decode or fetch) due to // code size, or the number and capabilities of the execution ports. // // We use the following heuristics to select the interleave count: // 1. If the code has reductions, then we interleave to break the cross // iteration dependency. // 2. If the loop is really small, then we interleave to reduce the loop // overhead. // 3. We don't interleave if we think that we will spill registers to memory // due to the increased register pressure. if (!isScalarEpilogueAllowed()) return 1; // We used the distance for the interleave count. if (Legal->getMaxSafeDepDistBytes() != -1U) return 1; auto BestKnownTC = getSmallBestKnownTC(*PSE.getSE(), TheLoop); const bool HasReductions = !Legal->getReductionVars().empty(); // Do not interleave loops with a relatively small known or estimated trip // count. But we will interleave when InterleaveSmallLoopScalarReduction is // enabled, and the code has scalar reductions(HasReductions && VF = 1), // because with the above conditions interleaving can expose ILP and break // cross iteration dependences for reductions. if (BestKnownTC && (*BestKnownTC < TinyTripCountInterleaveThreshold) && !(InterleaveSmallLoopScalarReduction && HasReductions && VF.isScalar())) return 1; RegisterUsage R = calculateRegisterUsage({VF})[0]; // We divide by these constants so assume that we have at least one // instruction that uses at least one register. for (auto& pair : R.MaxLocalUsers) { pair.second = std::max(pair.second, 1U); } // We calculate the interleave count using the following formula. // Subtract the number of loop invariants from the number of available // registers. These registers are used by all of the interleaved instances. // Next, divide the remaining registers by the number of registers that is // required by the loop, in order to estimate how many parallel instances // fit without causing spills. All of this is rounded down if necessary to be // a power of two. We want power of two interleave count to simplify any // addressing operations or alignment considerations. // We also want power of two interleave counts to ensure that the induction // variable of the vector loop wraps to zero, when tail is folded by masking; // this currently happens when OptForSize, in which case IC is set to 1 above. unsigned IC = UINT_MAX; for (auto& pair : R.MaxLocalUsers) { unsigned TargetNumRegisters = TTI.getNumberOfRegisters(pair.first); LLVM_DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters << " registers of " << TTI.getRegisterClassName(pair.first) << " register class\n"); if (VF.isScalar()) { if (ForceTargetNumScalarRegs.getNumOccurrences() > 0) TargetNumRegisters = ForceTargetNumScalarRegs; } else { if (ForceTargetNumVectorRegs.getNumOccurrences() > 0) TargetNumRegisters = ForceTargetNumVectorRegs; } unsigned MaxLocalUsers = pair.second; unsigned LoopInvariantRegs = 0; if (R.LoopInvariantRegs.find(pair.first) != R.LoopInvariantRegs.end()) LoopInvariantRegs = R.LoopInvariantRegs[pair.first]; unsigned TmpIC = PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs) / MaxLocalUsers); // Don't count the induction variable as interleaved. if (EnableIndVarRegisterHeur) { TmpIC = PowerOf2Floor((TargetNumRegisters - LoopInvariantRegs - 1) / std::max(1U, (MaxLocalUsers - 1))); } IC = std::min(IC, TmpIC); } // Clamp the interleave ranges to reasonable counts. 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) { assert(expectedCost(VF).first.isValid() && "Expected a valid cost"); LoopCost = *expectedCost(VF).first.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); // If we have a scalar reduction (vector reductions are already dealt with // by this point), we can increase the critical path length if the loop // we're interleaving is inside another loop. Limit, by default to 2, so the // critical path only gets increased by one reduction operation. if (HasReductions && TheLoop->getLoopDepth() > 1) { unsigned F = static_cast(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::calculateRegisterUsage(ArrayRef 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; // Maps instruction to its index. SmallVector IdxToInstr; // Marks the end of each interval. IntervalMap EndPoint; // Saves the list of instruction indices that are used in the loop. SmallPtrSet Ends; // Saves the list of values that are used in the loop but are // defined outside the loop, such as arguments and constants. SmallPtrSet 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(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; DenseMap 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 OpenIntervals; SmallVector RUs(VFs.size()); SmallVector, 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) { if (Ty->isTokenTy() || !VectorType::isValidElementType(Ty)) return 0U; return TTICapture.getRegUsageForType(VectorType::get(Ty, VF)); }; for (unsigned int i = 0, s = IdxToInstr.size(); i < s; ++i) { Instruction *I = IdxToInstr[i]; // Remove all of the instructions that end at this location. InstrList &List = TransposeEnds[i]; for (Instruction *ToRemove : List) OpenIntervals.erase(ToRemove); // Ignore instructions that are never used within the loop. if (!Ends.count(I)) continue; // Skip ignored values. if (ValuesToIgnore.count(I)) continue; // 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 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 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(I) || (isa(I) && NumPredStores > NumberOfStoresToPredicate); } void LoopVectorizationCostModel::collectInstsToScalarize(ElementCount VF) { // If we aren't vectorizing the loop, or if we've already collected the // instructions to scalarize, there's nothing to do. Collection may already // have occurred if we have a user-selected VF and are now computing the // expected cost for interleaving. if (VF.isScalar() || VF.isZero() || InstsToScalarize.find(VF) != InstsToScalarize.end()) return; // Initialize a mapping for VF in InstsToScalalarize. If we find that it's // not profitable to scalarize any instructions, the presence of VF in the // map will indicate that we've analyzed it already. ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF]; // Find all the instructions that are scalar with predication in the loop and // determine if it would be better to not if-convert the blocks they are in. // If so, we also record the instructions to scalarize. for (BasicBlock *BB : TheLoop->blocks()) { if (!blockNeedsPredication(BB)) continue; for (Instruction &I : *BB) if (isScalarWithPredication(&I)) { ScalarCostsTy ScalarCosts; // Do not apply discount logic if hacked cost is needed // for emulated masked memrefs. if (!useEmulatedMaskMemRefHack(&I) && computePredInstDiscount(&I, ScalarCosts, VF) >= 0) ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end()); // Remember that BB will remain after vectorization. PredicatedBBsAfterVectorization.insert(BB); } } } int LoopVectorizationCostModel::computePredInstDiscount( Instruction *PredInst, 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 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(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. assert(!VF.isScalable() && "scalable vectors not yet supported."); InstructionCost ScalarCost = VF.getKnownMinValue() * getInstructionCost(I, ElementCount::getFixed(1)).first; // Compute the scalarization overhead of needed insertelement instructions // and phi nodes. if (isScalarWithPredication(I) && !I->getType()->isVoidTy()) { ScalarCost += TTI.getScalarizationOverhead( cast(ToVectorTy(I->getType(), VF)), APInt::getAllOnesValue(VF.getKnownMinValue()), true, false); assert(!VF.isScalable() && "scalable vectors not yet supported."); ScalarCost += VF.getKnownMinValue() * TTI.getCFInstrCost(Instruction::PHI, TTI::TCK_RecipThroughput); } // Compute the scalarization overhead of needed extractelement // instructions. For each of the instruction's operands, if the operand can // be scalarized, add it to the worklist; otherwise, account for the // overhead. for (Use &U : I->operands()) if (auto *J = dyn_cast(U.get())) { assert(VectorType::isValidElementType(J->getType()) && "Instruction has non-scalar type"); if (canBeScalarized(J)) Worklist.push_back(J); else if (needsExtract(J, VF)) { assert(!VF.isScalable() && "scalable vectors not yet supported."); ScalarCost += TTI.getScalarizationOverhead( cast(ToVectorTy(J->getType(), VF)), APInt::getAllOnesValue(VF.getKnownMinValue()), false, true); } } // Scale the total scalar cost by block probability. ScalarCost /= getReciprocalPredBlockProb(); // Compute the discount. A non-negative discount means the vector version // of the instruction costs more, and scalarizing would be beneficial. Discount += VectorCost - ScalarCost; ScalarCosts[I] = ScalarCost; } return *Discount.getValue(); } LoopVectorizationCostModel::VectorizationCostTy LoopVectorizationCostModel::expectedCost(ElementCount VF) { VectorizationCostTy Cost; // For each block. for (BasicBlock *BB : TheLoop->blocks()) { VectorizationCostTy BlockCost; // For each instruction in the old loop. for (Instruction &I : BB->instructionsWithoutDebug()) { // Skip ignored values. if (ValuesToIgnore.count(&I) || (VF.isVector() && VecValuesToIgnore.count(&I))) continue; VectorizationCostTy C = getInstructionCost(&I, VF); // Check if we should override the cost. if (ForceTargetInstructionCost.getNumOccurrences() > 0) C.first = InstructionCost(ForceTargetInstructionCost); BlockCost.first += C.first; BlockCost.second |= C.second; LLVM_DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first << " for VF " << VF << " For instruction: " << I << '\n'); } // If we are vectorizing a predicated block, it will have been // if-converted. This means that the block's instructions (aside from // stores and instructions that may divide by zero) will now be // unconditionally executed. For the scalar case, we may not always execute // the predicated block, 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(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."); assert(!VF.isScalable() && "scalable vectors not yet supported."); Type *ValTy = getMemInstValueType(I); auto SE = PSE.getSE(); unsigned AS = getLoadStoreAddressSpace(I); Value *Ptr = getLoadStorePointerOperand(I); Type *PtrTy = ToVectorTy(Ptr->getType(), VF); // Figure out whether the access is strided and get the stride value // if it's known in compile time const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, Legal, PSE, TheLoop); // Get the cost of the scalar memory instruction and address computation. 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 store, it may not be executed for each vector // lane. Scale the cost by the probability of executing the predicated // block. if (isPredicatedInst(I)) { Cost /= getReciprocalPredBlockProb(); if (useEmulatedMaskMemRefHack(I)) // Artificially setting to a high enough value to practically disable // vectorization with such operations. Cost = 3000000; } return Cost; } InstructionCost LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I, ElementCount VF) { Type *ValTy = getMemInstValueType(I); auto *VectorTy = cast(ToVectorTy(ValTy, VF)); Value *Ptr = getLoadStorePointerOperand(I); unsigned AS = getLoadStoreAddressSpace(I); int ConsecutiveStride = Legal->isConsecutivePtr(Ptr); enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput; assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) && "Stride should be 1 or -1 for consecutive memory access"); const Align Alignment = getLoadStoreAlignment(I); 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, 0); return Cost; } InstructionCost LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I, ElementCount VF) { assert(Legal->isUniformMemOp(*I)); Type *ValTy = getMemInstValueType(I); auto *VectorTy = cast(ToVectorTy(ValTy, VF)); const Align Alignment = getLoadStoreAlignment(I); unsigned AS = getLoadStoreAddressSpace(I); enum TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput; if (isa(I)) { return TTI.getAddressComputationCost(ValTy) + TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS, CostKind) + TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy); } StoreInst *SI = cast(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 = getMemInstValueType(I); auto *VectorTy = cast(ToVectorTy(ValTy, VF)); const Align Alignment = getLoadStoreAlignment(I); const Value *Ptr = getLoadStorePointerOperand(I); return TTI.getAddressComputationCost(VectorTy) + TTI.getGatherScatterOpCost( I->getOpcode(), VectorTy, Ptr, Legal->isMaskRequired(I), Alignment, TargetTransformInfo::TCK_RecipThroughput, I); } InstructionCost LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I, ElementCount VF) { Type *ValTy = getMemInstValueType(I); auto *VectorTy = cast(ToVectorTy(ValTy, VF)); unsigned AS = getLoadStoreAddressSpace(I); auto Group = getInterleavedAccessGroup(I); assert(Group && "Fail to get an interleaved access group."); unsigned InterleaveFactor = Group->getFactor(); assert(!VF.isScalable() && "scalable vectors not yet supported."); auto *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor); // Holds the indices of existing members in an interleaved load group. // An interleaved store group doesn't need this as it doesn't allow gaps. SmallVector Indices; if (isa(I)) { for (unsigned i = 0; i < InterleaveFactor; i++) if (Group->getMember(i)) Indices.push_back(i); } // Calculate the cost of the whole interleaved group. bool UseMaskForGaps = Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed(); 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, 0); } return Cost; } InstructionCost LoopVectorizationCostModel::getReductionPatternCost( Instruction *I, ElementCount VF, Type *Ty, TTI::TargetCostKind CostKind) { // Early exit for no inloop reductions if (InLoopReductionChains.empty() || VF.isScalar() || !isa(Ty)) return InstructionCost::getInvalid(); auto *VectorTy = cast(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 ((RetI->getOpcode() == Instruction::SExt || RetI->getOpcode() == Instruction::ZExt)) { if (!RetI->hasOneUser()) return InstructionCost::getInvalid(); RetI = RetI->user_back(); } if (RetI->getOpcode() == Instruction::Mul && RetI->user_back()->getOpcode() == Instruction::Add) { if (!RetI->hasOneUser()) return InstructionCost::getInvalid(); 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 InstructionCost::getInvalid(); // 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(ReductionPhi)) ReductionPhi = InLoopReductionImmediateChains[ReductionPhi]; RecurrenceDescriptor RdxDesc = Legal->getReductionVars()[cast(ReductionPhi)]; unsigned BaseCost = TTI.getArithmeticReductionCost(RdxDesc.getOpcode(), VectorTy, false, CostKind); // 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(RetI->getOperand(0)) : dyn_cast(RetI->getOperand(1)); VectorTy = VectorType::get(I->getOperand(0)->getType(), VectorTy); if (RedOp && (isa(RedOp) || isa(RedOp)) && !TheLoop->isLoopInvariant(RedOp)) { bool IsUnsigned = isa(RedOp); auto *ExtType = VectorType::get(RedOp->getOperand(0)->getType(), VectorTy); InstructionCost RedCost = TTI.getExtendedAddReductionCost( /*IsMLA=*/false, IsUnsigned, RdxDesc.getRecurrenceType(), ExtType, CostKind); unsigned ExtCost = TTI.getCastInstrCost(RedOp->getOpcode(), VectorTy, ExtType, TTI::CastContextHint::None, CostKind, RedOp); if (RedCost.isValid() && RedCost < BaseCost + ExtCost) return I == RetI ? *RedCost.getValue() : 0; } else if (RedOp && RedOp->getOpcode() == Instruction::Mul) { Instruction *Mul = RedOp; Instruction *Op0 = dyn_cast(Mul->getOperand(0)); Instruction *Op1 = dyn_cast(Mul->getOperand(1)); if (Op0 && Op1 && (isa(Op0) || isa(Op0)) && Op0->getOpcode() == Op1->getOpcode() && Op0->getOperand(0)->getType() == Op1->getOperand(0)->getType() && !TheLoop->isLoopInvariant(Op0) && !TheLoop->isLoopInvariant(Op1)) { bool IsUnsigned = isa(Op0); auto *ExtType = VectorType::get(Op0->getOperand(0)->getType(), VectorTy); // reduce(mul(ext, ext)) unsigned ExtCost = TTI.getCastInstrCost(Op0->getOpcode(), VectorTy, ExtType, TTI::CastContextHint::None, CostKind, Op0); unsigned MulCost = TTI.getArithmeticInstrCost(Mul->getOpcode(), 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.getValue() : 0; } else { unsigned MulCost = TTI.getArithmeticInstrCost(Mul->getOpcode(), VectorTy, CostKind); InstructionCost RedCost = TTI.getExtendedAddReductionCost( /*IsMLA=*/true, true, RdxDesc.getRecurrenceType(), VectorTy, CostKind); if (RedCost.isValid() && RedCost < MulCost + BaseCost) return I == RetI ? *RedCost.getValue() : 0; } } return I == RetI ? BaseCost : InstructionCost::getInvalid(); } InstructionCost LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I, ElementCount VF) { // Calculate scalar cost only. Vectorization cost should be ready at this // moment. if (VF.isScalar()) { Type *ValTy = getMemInstValueType(I); const Align Alignment = getLoadStoreAlignment(I); unsigned AS = getLoadStoreAddressSpace(I); return TTI.getAddressComputationCost(ValTy) + TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS, TTI::TCK_RecipThroughput, I); } return getWideningCost(I, VF); } LoopVectorizationCostModel::VectorizationCostTy LoopVectorizationCostModel::getInstructionCost(Instruction *I, ElementCount VF) { // 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 = VF.isVector() && VectorTy->isVectorTy() && TTI.getNumberOfParts(VectorTy) < VF.getKnownMinValue(); return VectorizationCostTy(C, TypeNotScalarized); } InstructionCost LoopVectorizationCostModel::getScalarizationOverhead(Instruction *I, ElementCount VF) { assert(!VF.isScalable() && "cannot compute scalarization overhead for scalable vectorization"); if (VF.isScalar()) return 0; InstructionCost Cost = 0; Type *RetTy = ToVectorTy(I->getType(), VF); if (!RetTy->isVoidTy() && (!isa(I) || !TTI.supportsEfficientVectorElementLoadStore())) Cost += TTI.getScalarizationOverhead( cast(RetTy), APInt::getAllOnesValue(VF.getKnownMinValue()), true, false); // Some targets keep addresses scalar. if (isa(I) && !TTI.prefersVectorizedAddressing()) return Cost; // Some targets support efficient element stores. if (isa(I) && TTI.supportsEfficientVectorElementLoadStore()) return Cost; // Collect operands to consider. CallInst *CI = dyn_cast(I); Instruction::op_range Ops = CI ? CI->arg_operands() : I->operands(); // Skip operands that do not require extraction/scalarization and do not incur // any overhead. return Cost + TTI.getOperandsScalarizationOverhead( filterExtractingOperands(Ops, VF), VF.getKnownMinValue()); } void LoopVectorizationCostModel::setCostBasedWideningDecision(ElementCount VF) { 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(&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 = 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(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 = std::numeric_limits::max(); unsigned NumAccesses = 1; if (isAccessInterleaved(&I)) { auto Group = getInterleavedAccessGroup(&I); assert(Group && "Fail to get an interleaved access group."); // Make one decision for the whole group. if (getWideningDecision(&I, VF) != CM_Unknown) continue; NumAccesses = Group->getNumMembers(); if (interleavedAccessCanBeWidened(&I, VF)) InterleaveCost = getInterleaveGroupCost(&I, VF); } InstructionCost GatherScatterCost = isLegalGatherOrScatter(&I) ? getGatherScatterCost(&I, VF) * NumAccesses : std::numeric_limits::max(); 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 AddrDefs; for (BasicBlock *BB : TheLoop->blocks()) for (Instruction &I : *BB) { Instruction *PtrDef = dyn_cast_or_null(getLoadStorePointerOperand(&I)); if (PtrDef && TheLoop->contains(PtrDef) && getWideningDecision(&I, VF) != CM_GatherScatter) AddrDefs.insert(PtrDef); } // Add all instructions used to generate the addresses. SmallVector Worklist; append_range(Worklist, AddrDefs); while (!Worklist.empty()) { Instruction *I = Worklist.pop_back_val(); for (auto &Op : I->operands()) if (auto *InstOp = dyn_cast(Op)) if ((InstOp->getParent() == I->getParent()) && !isa(InstOp) && AddrDefs.insert(InstOp).second) Worklist.push_back(InstOp); } for (auto *I : AddrDefs) { if (isa(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]); VectorTy = isScalarAfterVectorization(I, VF) ? RetTy : ToVectorTy(RetTy, VF); auto SE = PSE.getSE(); TTI::TargetCostKind CostKind = TTI::TCK_RecipThroughput; // TODO: We need to estimate the cost of intrinsic calls. switch (I->getOpcode()) { case Instruction::GetElementPtr: // We mark this instruction as zero-cost because the cost of GEPs in // vectorized code depends on whether the corresponding memory instruction // is scalarized or not. Therefore, we handle GEPs with the memory // instruction cost. return 0; case Instruction::Br: { // In cases of scalarized and predicated instructions, there will be VF // predicated blocks in the vectorized loop. Each branch around these // blocks requires also an extract of its vector compare i1 element. bool ScalarPredicatedBB = false; BranchInst *BI = cast(I); if (VF.isVector() && BI->isConditional() && (PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) || PredicatedBBsAfterVectorization.count(BI->getSuccessor(1)))) ScalarPredicatedBB = true; if (ScalarPredicatedBB) { // Return cost for branches around scalarized and predicated blocks. assert(!VF.isScalable() && "scalable vectors not yet supported."); auto *Vec_i1Ty = VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF); return (TTI.getScalarizationOverhead( Vec_i1Ty, APInt::getAllOnesValue(VF.getKnownMinValue()), false, true) + (TTI.getCFInstrCost(Instruction::Br, CostKind) * VF.getKnownMinValue())); } else if (I->getParent() == TheLoop->getLoopLatch() || VF.isScalar()) // The back-edge branch will remain, as will all scalar branches. return TTI.getCFInstrCost(Instruction::Br, CostKind); else // This branch will be eliminated by if-conversion. return 0; // Note: We currently assume zero cost for an unconditional branch inside // a predicated block since it will become a fall-through, although we // may decide in the future to call TTI for all branches. } case Instruction::PHI: { auto *Phi = cast(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(VectorTy), VF.getKnownMinValue() - 1, FixedVectorType::get(RetTy, 1)); // Phi nodes in non-header blocks (not inductions, reductions, etc.) are // converted into select instructions. We require N - 1 selects per phi // node, where N is the number of incoming values. if (VF.isVector() && Phi->getParent() != TheLoop->getHeader()) return (Phi->getNumIncomingValues() - 1) * TTI.getCmpSelInstrCost( Instruction::Select, ToVectorTy(Phi->getType(), VF), ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF), 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 InstructionCost RedCost; if ((RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind)) .isValid()) 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 Operands(I->operand_values()); unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1; return N * TTI.getArithmeticInstrCost( I->getOpcode(), VectorTy, CostKind, TargetTransformInfo::OK_AnyValue, Op2VK, TargetTransformInfo::OP_None, Op2VP, Operands, I); } case Instruction::FNeg: { assert(!VF.isScalable() && "VF is assumed to be non scalable."); unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1; return N * TTI.getArithmeticInstrCost( I->getOpcode(), VectorTy, CostKind, TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OK_AnyValue, TargetTransformInfo::OP_None, TargetTransformInfo::OP_None, I->getOperand(0), I); } case Instruction::Select: { SelectInst *SI = cast(I); const SCEV *CondSCEV = SE->getSCEV(SI->getCondition()); bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop)); Type *CondTy = SI->getCondition()->getType(); if (!ScalarCond) CondTy = VectorType::get(CondTy, VF); return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy, CmpInst::BAD_ICMP_PREDICATE, CostKind, I); } case Instruction::ICmp: case Instruction::FCmp: { Type *ValTy = I->getOperand(0)->getType(); Instruction *Op0AsInstruction = dyn_cast(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(getMemInstValueType(I), Width); return getMemoryInstructionCost(I, VF); } case Instruction::ZExt: case Instruction::SExt: case Instruction::FPToUI: case Instruction::FPToSI: case Instruction::FPExt: case Instruction::PtrToInt: case Instruction::IntToPtr: case Instruction::SIToFP: case Instruction::UIToFP: case Instruction::Trunc: case Instruction::FPTrunc: case Instruction::BitCast: { // Computes the CastContextHint from a Load/Store instruction. auto ComputeCCH = [&](Instruction *I) -> TTI::CastContextHint { assert((isa(I) || isa(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(*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(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(I); return TTI.getCastInstrCost(Instruction::Trunc, Trunc->getDestTy(), Trunc->getSrcTy(), CCH, CostKind, Trunc); } // Detect reduction patterns InstructionCost RedCost; if ((RedCost = getReductionPatternCost(I, VF, VectorTy, CostKind)) .isValid()) 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); } } assert(!VF.isScalable() && "VF is assumed to be non scalable"); unsigned N = isScalarAfterVectorization(I, VF) ? VF.getKnownMinValue() : 1; return N * TTI.getCastInstrCost(Opcode, VectorTy, SrcVecTy, CCH, CostKind, I); } case Instruction::Call: { bool NeedToScalarize; CallInst *CI = cast(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); default: // The cost of executing VF copies of the scalar instruction. This opcode // is unknown. Assume that it is the same as 'mul'. return VF.getKnownMinValue() * TTI.getArithmeticInstrCost( Instruction::Mul, VectorTy, CostKind) + getScalarizationOverhead(I, VF); } // end of switch. } char LoopVectorize::ID = 0; static const char lv_name[] = "Loop Vectorization"; INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false) INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass) INITIALIZE_PASS_DEPENDENCY(BasicAAWrapperPass) INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass) INITIALIZE_PASS_DEPENDENCY(GlobalsAAWrapperPass) INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker) INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass) INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass) INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass) INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass) INITIALIZE_PASS_DEPENDENCY(LoopAccessLegacyAnalysis) INITIALIZE_PASS_DEPENDENCY(DemandedBitsWrapperPass) INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass) INITIALIZE_PASS_DEPENDENCY(ProfileSummaryInfoWrapperPass) INITIALIZE_PASS_DEPENDENCY(InjectTLIMappingsLegacy) INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false) namespace llvm { Pass *createLoopVectorizePass() { return new LoopVectorize(); } Pass *createLoopVectorizePass(bool InterleaveOnlyWhenForced, bool VectorizeOnlyWhenForced) { return new LoopVectorize(InterleaveOnlyWhenForced, VectorizeOnlyWhenForced); } } // end namespace llvm bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) { // Check if the pointer operand of a load or store instruction is // consecutive. if (auto *Ptr = getLoadStorePointerOperand(Inst)) return Legal->isConsecutivePtr(Ptr); return false; } void LoopVectorizationCostModel::collectValuesToIgnore() { // Ignore ephemeral values. CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore); // Ignore type-promoting instructions we identified during reduction // detection. for (auto &Reduction : Legal->getReductionVars()) { RecurrenceDescriptor &RedDes = Reduction.second; const SmallPtrSetImpl &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 &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 && !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 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(true /* Vector*/), CM)); LLVM_DEBUG(dbgs() << "LV: VPlan computed VF " << VF << ".\n"); // Make sure we have a VF > 1 for stress testing. if (VPlanBuildStressTest && (VF.isScalar() || VF.isZero())) { LLVM_DEBUG(dbgs() << "LV: VPlan stress testing: " << "overriding computed VF.\n"); VF = ElementCount::getFixed(4); } } assert(EnableVPlanNativePath && "VPlan-native path is not enabled."); assert(isPowerOf2_32(VF.getKnownMinValue()) && "VF needs to be a power of two"); LLVM_DEBUG(dbgs() << "LV: Using " << (!UserVF.isZero() ? "user " : "") << "VF " << VF << " to build VPlans.\n"); buildVPlans(VF, 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 LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) { assert(OrigLoop->isInnermost() && "Inner loop expected."); Optional MaybeMaxVF = CM.computeMaxVF(UserVF, UserIC); if (!MaybeMaxVF) // Cases that should not to be vectorized nor interleaved. return None; // Invalidate interleave groups if all blocks of loop will be predicated. if (CM.blockNeedsPredication(OrigLoop->getHeader()) && !useMaskedInterleavedAccesses(*TTI)) { LLVM_DEBUG( dbgs() << "LV: Invalidate all interleaved groups due to fold-tail by masking " "which requires masked-interleaved support.\n"); if (CM.InterleaveInfo.invalidateGroups()) // Invalidating interleave groups also requires invalidating all decisions // based on them, which includes widening decisions and uniform and scalar // values. CM.invalidateCostModelingDecisions(); } ElementCount MaxVF = MaybeMaxVF.getValue(); assert(MaxVF.isNonZero() && "MaxVF is zero."); bool UserVFIsLegal = ElementCount::isKnownLE(UserVF, MaxVF); if (!UserVF.isZero() && (UserVFIsLegal || (UserVF.isScalable() && MaxVF.isScalable()))) { // FIXME: MaxVF is temporarily used inplace of UserVF for illegal scalable // VFs here, this should be reverted to only use legal UserVFs once the // loop below supports scalable VFs. ElementCount VF = UserVFIsLegal ? UserVF : MaxVF; LLVM_DEBUG(dbgs() << "LV: Using " << (UserVFIsLegal ? "user" : "max") << " VF " << VF << ".\n"); assert(isPowerOf2_32(VF.getKnownMinValue()) && "VF needs to be a power of two"); // Collect the instructions (and their associated costs) that will be more // profitable to scalarize. CM.selectUserVectorizationFactor(VF); CM.collectInLoopReductions(); buildVPlansWithVPRecipes(VF, VF); LLVM_DEBUG(printPlans(dbgs())); return {{VF, 0}}; } assert(!MaxVF.isScalable() && "Scalable vectors not yet supported beyond this point"); for (ElementCount VF = ElementCount::getFixed(1); ElementCount::isKnownLE(VF, MaxVF); VF *= 2) { // 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), MaxVF); LLVM_DEBUG(printPlans(dbgs())); if (MaxVF.isScalar()) return VectorizationFactor::Disabled(); // Select the optimal vectorization factor. return CM.selectVectorizationFactor(MaxVF); } void LoopVectorizationPlanner::setBestPlan(ElementCount VF, unsigned UF) { LLVM_DEBUG(dbgs() << "Setting best plan to VF=" << VF << ", UF=" << UF << '\n'); BestVF = VF; BestUF = UF; erase_if(VPlans, [VF](const VPlanPtr &Plan) { return !Plan->hasVF(VF); }); assert(VPlans.size() == 1 && "Best VF has not a single VPlan."); } void LoopVectorizationPlanner::executePlan(InnerLoopVectorizer &ILV, DominatorTree *DT) { // Perform the actual loop transformation. // 1. Create a new empty loop. Unlink the old loop and connect the new one. VPCallbackILV CallbackILV(ILV); assert(BestVF.hasValue() && "Vectorization Factor is missing"); VPTransformState State{*BestVF, BestUF, OrigLoop, LI, DT, ILV.Builder, ILV.VectorLoopValueMap, &ILV, CallbackILV}; State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton(); State.TripCount = ILV.getOrCreateTripCount(nullptr); State.CanonicalIV = ILV.Induction; 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. assert(VPlans.size() == 1 && "Not a single VPlan to execute."); VPlans.front()->execute(&State); // 3. Fix the vectorized code: take care of header phi's, live-outs, // predication, updating analyses. ILV.fixVectorizedLoop(); ILV.printDebugTracesAtEnd(); } void LoopVectorizationPlanner::collectTriviallyDeadInstructions( SmallPtrSetImpl &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 ExitingBlocks; OrigLoop->getExitingBlocks(ExitingBlocks); for (auto *BB : ExitingBlocks) { auto *Cmp = dyn_cast(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(Op) && Op->hasOneUse()) DeadInstructions.insert(cast(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(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(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 &Casts = IndDes.getCastInsts(); DeadInstructions.insert(Casts.begin(), Casts.end()); } } Value *InnerLoopUnroller::reverseVector(Value *Vec) { return Vec; } Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) { return V; } Value *InnerLoopUnroller::getStepVector(Value *Val, int StartIdx, Value *Step, Instruction::BinaryOps BinOp) { // When unrolling and the VF is 1, we only need to add a simple scalar. Type *Ty = Val->getType(); assert(!Ty->isVectorTy() && "Val must be a scalar"); if (Ty->isFloatingPointTy()) { Constant *C = ConstantFP::get(Ty, (double)StartIdx); // Floating point operations had to be 'fast' to enable the unrolling. Value *MulOp = addFastMathFlag(Builder.CreateFMul(C, Step)); return addFastMathFlag(Builder.CreateBinOp(BinOp, Val, MulOp)); } Constant *C = ConstantInt::get(Ty, StartIdx); return Builder.CreateAdd(Val, Builder.CreateMul(C, Step), "induction"); } static void AddRuntimeUnrollDisableMetaData(Loop *L) { SmallVector 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(LoopID->getOperand(i)); if (MD) { const auto *S = dyn_cast(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 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. BasicBlock *SavedPreHeader = LoopVectorPreHeader; emitSCEVChecks(Lp, LoopScalarPreHeader); // If a safety check was generated save it. if (SavedPreHeader != LoopVectorPreHeader) EPI.SCEVSafetyCheck = SavedPreHeader; // 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. SavedPreHeader = LoopVectorPreHeader; emitMemRuntimeChecks(Lp, LoopScalarPreHeader); // If a safety check was generated save/overwite it. if (SavedPreHeader != LoopVectorPreHeader) EPI.MemSafetyCheck = SavedPreHeader; // 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); Constant *Step = ConstantInt::get(IdxTy, VF.getKnownMinValue() * 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.getKnownMinValue() << ", Main Loop UF:" << EPI.MainLoopUF << ", Epilogue Loop VF:" << EPI.EpilogueVF.getKnownMinValue() << ", Epilogue Loop UF:" << EPI.EpilogueUF << "\n"; }); } void EpilogueVectorizerMainLoop::printDebugTracesAtEnd() { DEBUG_WITH_TYPE(VerboseDebug, { dbgs() << "intermediate fn:\n" << *Induction->getFunction() << "\n"; }); } BasicBlock *EpilogueVectorizerMainLoop::emitMinimumIterationCountCheck( Loop *L, BasicBlock *Bypass, bool ForEpilogue) { assert(L && "Expected valid Loop."); assert(Bypass && "Expected valid bypass basic block."); unsigned VFactor = ForEpilogue ? EPI.EpilogueVF.getKnownMinValue() : VF.getKnownMinValue(); 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() ? ICmpInst::ICMP_ULE : ICmpInst::ICMP_ULT; Value *CheckMinIters = Builder.CreateICmp( P, Count, ConstantInt::get(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); 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); 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(EPI.TripCount) || DT->dominates(cast(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() ? ICmpInst::ICMP_ULE : ICmpInst::ICMP_ULT; Value *CheckMinIters = Builder.CreateICmp( P, Count, ConstantInt::get(Count->getType(), EPI.EpilogueVF.getKnownMinValue() * 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" << "Main Loop VF:" << EPI.MainLoopVF.getKnownMinValue() << ", Main Loop UF:" << EPI.MainLoopUF << ", Epilogue Loop VF:" << EPI.EpilogueVF.getKnownMinValue() << ", Epilogue Loop UF:" << EPI.EpilogueUF << "\n"; }); } void EpilogueVectorizerEpilogueLoop::printDebugTracesAtEnd() { DEBUG_WITH_TYPE(VerboseDebug, { dbgs() << "final fn:\n" << *Induction->getFunction() << "\n"; }); } bool LoopVectorizationPlanner::getDecisionAndClampRange( const std::function &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 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(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.blockNeedsPredication(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->getVPValue(); } VPValue *BTC = Plan->getOrCreateBackedgeTakenCount(); bool TailFolded = !CM.isScalarEpilogueAllowed(); if (TailFolded && CM.TTI.emitGetActiveLaneMask()) { // While ActiveLaneMask is a binary op that consumes the loop tripcount // as a second argument, we only pass the IV here and extract the // tripcount from the transform state where codegen of the VP instructions // happen. BlockMask = Builder.createNaryOp(VPInstruction::ActiveLaneMask, {IV}); } else { BlockMask = Builder.createNaryOp(VPInstruction::ICmpULE, {IV, BTC}); } return BlockMaskCache[BB] = BlockMask; } // This is the block mask. We OR all incoming edges. for (auto *Predecessor : predecessors(BB)) { VPValue *EdgeMask = createEdgeMask(Predecessor, BB, Plan); if (!EdgeMask) // Mask of predecessor is all-one so mask of block is too. return BlockMaskCache[BB] = EdgeMask; if (!BlockMask) { // BlockMask has its initialized nullptr value. BlockMask = EdgeMask; continue; } BlockMask = Builder.createOr(BlockMask, EdgeMask); } return BlockMaskCache[BB] = BlockMask; } VPRecipeBase *VPRecipeBuilder::tryToWidenMemory(Instruction *I, VFRange &Range, VPlanPtr &Plan) { assert((isa(I) || isa(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); VPValue *Addr = Plan->getOrAddVPValue(getLoadStorePointerOperand(I)); if (LoadInst *Load = dyn_cast(I)) return new VPWidenMemoryInstructionRecipe(*Load, Addr, Mask); StoreInst *Store = cast(I); VPValue *StoredValue = Plan->getOrAddVPValue(Store->getValueOperand()); return new VPWidenMemoryInstructionRecipe(*Store, Addr, StoredValue, Mask); } VPWidenIntOrFpInductionRecipe * VPRecipeBuilder::tryToOptimizeInductionPHI(PHINode *Phi, VPlan &Plan) 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) { VPValue *Start = Plan.getOrAddVPValue(II.getStartValue()); return new VPWidenIntOrFpInductionRecipe(Phi, Start); } return nullptr; } VPWidenIntOrFpInductionRecipe * VPRecipeBuilder::tryToOptimizeInductionTruncate(TruncInst *I, 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 { return [=](ElementCount VF) -> bool { return CM.isOptimizableIVTruncate(K, VF); }; }; if (LoopVectorizationPlanner::getDecisionAndClampRange( isOptimizableIVTruncate(I), Range)) { InductionDescriptor II = Legal->getInductionVars().lookup(cast(I->getOperand(0))); VPValue *Start = Plan.getOrAddVPValue(II.getStartValue()); return new VPWidenIntOrFpInductionRecipe(cast(I->getOperand(0)), Start, I); } return nullptr; } VPBlendRecipe *VPRecipeBuilder::tryToBlend(PHINode *Phi, VPlanPtr &Plan) { // We know that all PHIs in non-header blocks are converted into selects, so // we don't have to worry about the insertion order and we can just use the // builder. At this point we generate the predication tree. There may be // duplications since this is a simple recursive scan, but future // optimizations will clean it up. SmallVector Operands; unsigned NumIncoming = Phi->getNumIncomingValues(); for (unsigned In = 0; In < NumIncoming; In++) { VPValue *EdgeMask = createEdgeMask(Phi->getIncomingBlock(In), Phi->getParent(), Plan); assert((EdgeMask || NumIncoming == 1) && "Multiple predecessors with one having a full mask"); Operands.push_back(Plan->getOrAddVPValue(Phi->getIncomingValue(In))); if (EdgeMask) Operands.push_back(EdgeMask); } return new VPBlendRecipe(Phi, Operands); } VPWidenCallRecipe *VPRecipeBuilder::tryToWidenCall(CallInst *CI, VFRange &Range, VPlan &Plan) const { bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange( [this, CI](ElementCount VF) { return CM.isScalarWithPredication(CI, VF); }, Range); if (IsPredicated) return nullptr; Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end || ID == Intrinsic::lifetime_start || ID == Intrinsic::sideeffect || 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; assert(IntrinsicCost.isValid() && CallCost.isValid() && "Cannot have invalid costs while widening"); return UseVectorIntrinsic || !NeedToScalarize; }; if (!LoopVectorizationPlanner::getDecisionAndClampRange(willWiden, Range)) return nullptr; return new VPWidenCallRecipe(*CI, Plan.mapToVPValues(CI->arg_operands())); } bool VPRecipeBuilder::shouldWiden(Instruction *I, VFRange &Range) const { assert(!isa(I) && !isa(I) && !isa(I) && !isa(I) && "Instruction should have been handled earlier"); // Instruction should be widened, unless it is scalar after vectorization, // scalarization is profitable or it is predicated. auto WillScalarize = [this, I](ElementCount VF) -> bool { return CM.isScalarAfterVectorization(I, VF) || CM.isProfitableToScalarize(I, VF) || CM.isScalarWithPredication(I, VF); }; return !LoopVectorizationPlanner::getDecisionAndClampRange(WillScalarize, Range); } VPWidenRecipe *VPRecipeBuilder::tryToWiden(Instruction *I, VPlan &Plan) const { auto IsVectorizableOpcode = [](unsigned Opcode) { switch (Opcode) { case Instruction::Add: case Instruction::And: case Instruction::AShr: case Instruction::BitCast: case Instruction::FAdd: case Instruction::FCmp: case Instruction::FDiv: case Instruction::FMul: case Instruction::FNeg: case Instruction::FPExt: case Instruction::FPToSI: case Instruction::FPToUI: case Instruction::FPTrunc: case Instruction::FRem: case Instruction::FSub: case Instruction::ICmp: case Instruction::IntToPtr: case Instruction::LShr: case Instruction::Mul: case Instruction::Or: case Instruction::PtrToInt: case Instruction::SDiv: case Instruction::Select: case Instruction::SExt: case Instruction::Shl: case Instruction::SIToFP: case Instruction::SRem: case Instruction::Sub: case Instruction::Trunc: case Instruction::UDiv: case Instruction::UIToFP: case Instruction::URem: case Instruction::Xor: case Instruction::ZExt: return true; } return false; }; if (!IsVectorizableOpcode(I->getOpcode())) return nullptr; // Success: widen this instruction. return new VPWidenRecipe(*I, Plan.mapToVPValues(I->operands())); } VPBasicBlock *VPRecipeBuilder::handleReplication( Instruction *I, VFRange &Range, VPBasicBlock *VPBB, DenseMap &PredInst2Recipe, VPlanPtr &Plan) { bool IsUniform = LoopVectorizationPlanner::getDecisionAndClampRange( [&](ElementCount VF) { return CM.isUniformAfterVectorization(I, VF); }, Range); bool IsPredicated = LoopVectorizationPlanner::getDecisionAndClampRange( [&](ElementCount VF) { return CM.isScalarWithPredication(I, VF); }, Range); auto *Recipe = new VPReplicateRecipe(I, Plan->mapToVPValues(I->operands()), IsUniform, IsPredicated); setRecipe(I, Recipe); 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 (auto &Op : I->operands()) if (auto *PredInst = dyn_cast(Op)) if (PredInst2Recipe.find(PredInst) != PredInst2Recipe.end()) PredInst2Recipe[PredInst]->setAlsoPack(false); // Finalize the recipe for Instr, first if it is not predicated. if (!IsPredicated) { LLVM_DEBUG(dbgs() << "LV: Scalarizing:" << *I << "\n"); VPBB->appendRecipe(Recipe); return VPBB; } LLVM_DEBUG(dbgs() << "LV: Scalarizing and predicating:" << *I << "\n"); assert(VPBB->getSuccessors().empty() && "VPBB has successors when handling predicated replication."); // Record predicated instructions for above packing optimizations. PredInst2Recipe[I] = Recipe; VPBlockBase *Region = createReplicateRegion(I, Recipe, Plan); VPBlockUtils::insertBlockAfter(Region, VPBB); auto *RegSucc = new VPBasicBlock(); VPBlockUtils::insertBlockAfter(RegSucc, Region); return RegSucc; } VPRegionBlock *VPRecipeBuilder::createReplicateRegion(Instruction *Instr, VPRecipeBase *PredRecipe, VPlanPtr &Plan) { // Instructions marked for predication are replicated and placed under an // if-then construct to prevent side-effects. // Generate recipes to compute the block mask for this region. VPValue *BlockInMask = createBlockInMask(Instr->getParent(), Plan); // Build the triangular if-then region. std::string RegionName = (Twine("pred.") + Instr->getOpcodeName()).str(); assert(Instr->getParent() && "Predicated instruction not in any basic block"); auto *BOMRecipe = new VPBranchOnMaskRecipe(BlockInMask); auto *Entry = new VPBasicBlock(Twine(RegionName) + ".entry", BOMRecipe); auto *PHIRecipe = Instr->getType()->isVoidTy() ? nullptr : new VPPredInstPHIRecipe(Plan->getOrAddVPValue(Instr)); auto *Exit = new VPBasicBlock(Twine(RegionName) + ".continue", PHIRecipe); auto *Pred = new VPBasicBlock(Twine(RegionName) + ".if", PredRecipe); VPRegionBlock *Region = new VPRegionBlock(Entry, Exit, RegionName, true); // Note: first set Entry as region entry and then connect successors starting // from it in order, to propagate the "parent" of each VPBasicBlock. VPBlockUtils::insertTwoBlocksAfter(Pred, Exit, BlockInMask, Entry); VPBlockUtils::connectBlocks(Pred, Exit); return Region; } VPRecipeBase *VPRecipeBuilder::tryToCreateWidenRecipe(Instruction *Instr, VFRange &Range, VPlanPtr &Plan) { // First, check for specific widening recipes that deal with calls, memory // operations, inductions and Phi nodes. if (auto *CI = dyn_cast(Instr)) return tryToWidenCall(CI, Range, *Plan); if (isa(Instr) || isa(Instr)) return tryToWidenMemory(Instr, Range, Plan); VPRecipeBase *Recipe; if (auto Phi = dyn_cast(Instr)) { if (Phi->getParent() != OrigLoop->getHeader()) return tryToBlend(Phi, Plan); if ((Recipe = tryToOptimizeInductionPHI(Phi, *Plan))) return Recipe; if (Legal->isReductionVariable(Phi)) { RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi]; VPValue *StartV = Plan->getOrAddVPValue(RdxDesc.getRecurrenceStartValue()); return new VPWidenPHIRecipe(Phi, RdxDesc, *StartV); } return new VPWidenPHIRecipe(Phi); } if (isa(Instr) && (Recipe = tryToOptimizeInductionTruncate( cast(Instr), Range, *Plan))) return Recipe; if (!shouldWiden(Instr, Range)) return nullptr; if (auto GEP = dyn_cast(Instr)) return new VPWidenGEPRecipe(GEP, Plan->mapToVPValues(GEP->operands()), OrigLoop); if (auto *SI = dyn_cast(Instr)) { bool InvariantCond = PSE.getSE()->isLoopInvariant(PSE.getSCEV(SI->getOperand(0)), OrigLoop); return new VPWidenSelectRecipe(*SI, Plan->mapToVPValues(SI->operands()), InvariantCond); } return tryToWiden(Instr, *Plan); } void LoopVectorizationPlanner::buildVPlansWithVPRecipes(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 DeadInstructions; collectTriviallyDeadInstructions(DeadInstructions); // Add assume instructions we need to drop to DeadInstructions, to prevent // them from being added to the VPlan. // TODO: We only need to drop assumes in blocks that get flattend. If the // control flow is preserved, we should keep them. auto &ConditionalAssumes = Legal->getConditionalAssumes(); DeadInstructions.insert(ConditionalAssumes.begin(), ConditionalAssumes.end()); DenseMap &SinkAfter = Legal->getSinkAfter(); // Dead instructions do not need sinking. Remove them from SinkAfter. for (Instruction *I : DeadInstructions) SinkAfter.erase(I); 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 &DeadInstructions, const DenseMap &SinkAfter) { // Hold a mapping from predicated instructions to their recipes, in order to // fix their AlsoPack behavior if a user is determined to replicate and use a // scalar instead of vector value. DenseMap PredInst2Recipe; SmallPtrSet *, 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 &ReductionOperations = Reduction.second; RecipeBuilder.recordRecipeOf(Phi); for (auto &R : ReductionOperations) { RecipeBuilder.recordRecipeOf(R); // For min/max reducitons, where we have a pair of icmp/select, we also // need to record the ICmp recipe, so it can be removed later. if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) RecipeBuilder.recordRecipeOf(cast(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 *IG : IAI.getInterleaveGroups()) { auto applyIG = [IG, this](ElementCount VF) -> bool { return (VF.isVector() && // Query is illegal for VF == 1 CM.getWideningDecision(IG->getInsertPos(), VF) == LoopVectorizationCostModel::CM_Interleave); }; if (!getDecisionAndClampRange(applyIG, Range)) continue; InterleaveGroups.insert(IG); for (unsigned i = 0; i < IG->getFactor(); i++) if (Instruction *Member = IG->getMember(i)) RecipeBuilder.recordRecipeOf(Member); }; // --------------------------------------------------------------------------- // Build initial VPlan: Scan the body of the loop in a topological order to // visit each basic block after having visited its predecessor basic blocks. // --------------------------------------------------------------------------- // Create a dummy pre-entry VPBasicBlock to start building the VPlan. auto Plan = std::make_unique(); VPBasicBlock *VPBB = new VPBasicBlock("Pre-Entry"); Plan->setEntry(VPBB); // Scan the body of the loop in a topological order to visit each basic block // after having visited its predecessor basic blocks. LoopBlocksDFS DFS(OrigLoop); DFS.perform(LI); for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) { // Relevant instructions from basic block BB will be grouped into VPRecipe // ingredients and fill a new VPBasicBlock. unsigned VPBBsForBB = 0; auto *FirstVPBBForBB = new VPBasicBlock(BB->getName()); VPBlockUtils::insertBlockAfter(FirstVPBBForBB, VPBB); VPBB = FirstVPBBForBB; Builder.setInsertPoint(VPBB); // Introduce each ingredient into VPlan. // TODO: Model and preserve debug instrinsics in VPlan. for (Instruction &I : BB->instructionsWithoutDebug()) { Instruction *Instr = &I; // First filter out irrelevant instructions, to ensure no recipes are // built for them. if (isa(Instr) || DeadInstructions.count(Instr)) continue; if (auto Recipe = RecipeBuilder.tryToCreateWidenRecipe(Instr, Range, Plan)) { for (auto *Def : Recipe->definedValues()) { auto *UV = Def->getUnderlyingValue(); Plan->addVPValue(UV, Def); } RecipeBuilder.setRecipe(Instr, Recipe); VPBB->appendRecipe(Recipe); continue; } // Otherwise, if all widening options failed, Instruction is to be // replicated. This may create a successor for VPBB. VPBasicBlock *NextVPBB = RecipeBuilder.handleReplication( Instr, Range, VPBB, PredInst2Recipe, Plan); if (NextVPBB != VPBB) { VPBB = NextVPBB; VPBB->setName(BB->hasName() ? BB->getName() + "." + Twine(VPBBsForBB++) : ""); } } } // Discard empty dummy pre-entry VPBasicBlock. Note that other VPBasicBlocks // may also be empty, such as the last one VPBB, reflecting original // basic-blocks with no recipes. VPBasicBlock *PreEntry = cast(Plan->getEntry()); assert(PreEntry->empty() && "Expecting empty pre-entry block."); VPBlockBase *Entry = Plan->setEntry(PreEntry->getSingleSuccessor()); VPBlockUtils::disconnectBlocks(PreEntry, Entry); delete PreEntry; // --------------------------------------------------------------------------- // Transform initial VPlan: Apply previously taken decisions, in order, to // bring the VPlan to its final state. // --------------------------------------------------------------------------- // Apply Sink-After legal constraints. for (auto &Entry : SinkAfter) { VPRecipeBase *Sink = RecipeBuilder.getRecipe(Entry.first); VPRecipeBase *Target = RecipeBuilder.getRecipe(Entry.second); // If the target is in a replication region, make sure to move Sink to the // block after it, not into the replication region itself. if (auto *Region = dyn_cast_or_null(Target->getParent()->getParent())) { if (Region->isReplicator()) { assert(Region->getNumSuccessors() == 1 && "Expected SESE region!"); VPBasicBlock *NextBlock = cast(Region->getSuccessors().front()); Sink->moveBefore(*NextBlock, NextBlock->getFirstNonPhi()); continue; } } Sink->moveAfter(Target); } // Interleave memory: for each Interleave Group we marked earlier as relevant // for this VPlan, replace the Recipes widening its memory instructions with a // single VPInterleaveRecipe at its insertion point. for (auto IG : InterleaveGroups) { auto *Recipe = cast( RecipeBuilder.getRecipe(IG->getInsertPos())); SmallVector StoredValues; for (unsigned i = 0; i < IG->getFactor(); ++i) if (auto *SI = dyn_cast_or_null(IG->getMember(i))) StoredValues.push_back(Plan->getOrAddVPValue(SI->getOperand(0))); 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(); } } // Adjust the recipes for any inloop reductions. if (Range.Start.isVector()) adjustRecipesForInLoopReductions(Plan, RecipeBuilder); // Finally, if tail is folded by masking, introduce selects between the phi // and the live-out instruction of each reduction, at the end of the latch. if (CM.foldTailByMasking() && !Legal->getReductionVars().empty()) { Builder.setInsertPoint(VPBB); auto *Cond = RecipeBuilder.createBlockInMask(OrigLoop->getHeader(), Plan); for (auto &Reduction : Legal->getReductionVars()) { if (CM.isInLoopReduction(Reduction.first)) continue; VPValue *Phi = Plan->getOrAddVPValue(Reduction.first); VPValue *Red = Plan->getOrAddVPValue(Reduction.second.getLoopExitInstr()); Builder.createNaryOp(Instruction::Select, {Cond, Red, Phi}); } } 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); 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(); // 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 DeadInstructions; VPlanTransforms::VPInstructionsToVPRecipes( OrigLoop, Plan, Legal->getInductionVars(), DeadInstructions); return Plan; } // Adjust the recipes for any inloop reductions. The chain of instructions // leading from the loop exit instr to the phi need to be converted to // reductions, with one operand being vector and the other being the scalar // reduction chain. void LoopVectorizationPlanner::adjustRecipesForInLoopReductions( VPlanPtr &Plan, VPRecipeBuilder &RecipeBuilder) { for (auto &Reduction : CM.getInLoopReductionChains()) { PHINode *Phi = Reduction.first; RecurrenceDescriptor &RdxDesc = Legal->getReductionVars()[Phi]; const SmallVector &ReductionOperations = Reduction.second; // ReductionOperations are orders top-down from the phi's use to the // LoopExitValue. We keep a track of the previous item (the Chain) to tell // which of the two operands will remain scalar and which will be reduced. // For minmax the chain will be the select instructions. Instruction *Chain = Phi; for (Instruction *R : ReductionOperations) { VPRecipeBase *WidenRecipe = RecipeBuilder.getRecipe(R); RecurKind Kind = RdxDesc.getRecurrenceKind(); VPValue *ChainOp = Plan->getVPValue(Chain); unsigned FirstOpId; if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { assert(isa(WidenRecipe) && "Expected to replace a VPWidenSelectSC"); FirstOpId = 1; } else { assert(isa(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; VPReductionRecipe *RedRecipe = new VPReductionRecipe( &RdxDesc, R, ChainOp, VecOp, CondOp, Legal->hasFunNoNaNAttr(), TTI); WidenRecipe->getVPValue()->replaceAllUsesWith(RedRecipe); Plan->removeVPValueFor(R); Plan->addVPValue(R, RedRecipe); WidenRecipe->getParent()->insert(RedRecipe, WidenRecipe->getIterator()); WidenRecipe->getVPValue()->replaceAllUsesWith(RedRecipe); WidenRecipe->eraseFromParent(); if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { VPRecipeBase *CompareRecipe = RecipeBuilder.getRecipe(cast(R->getOperand(0))); assert(isa(CompareRecipe) && "Expected to replace a VPWidenSC"); assert(cast(CompareRecipe)->getNumUsers() == 0 && "Expected no remaining users"); CompareRecipe->eraseFromParent(); } Chain = R; } } } Value* LoopVectorizationPlanner::VPCallbackILV:: getOrCreateVectorValues(Value *V, unsigned Part) { return ILV.getOrCreateVectorValue(V, Part); } Value *LoopVectorizationPlanner::VPCallbackILV::getOrCreateScalarValue( Value *V, const VPIteration &Instance) { return ILV.getOrCreateScalarValue(V, Instance); } void VPInterleaveRecipe::print(raw_ostream &O, const Twine &Indent, VPSlotTracker &SlotTracker) const { O << "\"INTERLEAVE-GROUP with factor " << IG->getFactor() << " at "; IG->getInsertPos()->printAsOperand(O, false); O << ", "; getAddr()->printAsOperand(O, SlotTracker); VPValue *Mask = getMask(); if (Mask) { O << ", "; Mask->printAsOperand(O, SlotTracker); } for (unsigned i = 0; i < IG->getFactor(); ++i) if (Instruction *I = IG->getMember(i)) O << "\\l\" +\n" << Indent << "\" " << VPlanIngredient(I) << " " << i; } void VPWidenCallRecipe::execute(VPTransformState &State) { State.ILV->widenCallInstruction(*cast(getUnderlyingInstr()), this, *this, State); } void VPWidenSelectRecipe::execute(VPTransformState &State) { State.ILV->widenSelectInstruction(*cast(getUnderlyingInstr()), this, *this, InvariantCond, State); } void VPWidenRecipe::execute(VPTransformState &State) { State.ILV->widenInstruction(*getUnderlyingInstr(), this, *this, State); } void VPWidenGEPRecipe::execute(VPTransformState &State) { State.ILV->widenGEP(cast(getUnderlyingInstr()), this, *this, State.UF, State.VF, IsPtrLoopInvariant, IsIndexLoopInvariant, State); } void VPWidenIntOrFpInductionRecipe::execute(VPTransformState &State) { assert(!State.Instance && "Int or FP induction being replicated."); State.ILV->widenIntOrFpInduction(IV, getStartValue()->getLiveInIRValue(), Trunc); } void VPWidenPHIRecipe::execute(VPTransformState &State) { Value *StartV = getStartValue() ? getStartValue()->getLiveInIRValue() : nullptr; State.ILV->widenPHIInstruction(Phi, RdxDesc, StartV, State.UF, State.VF); } void VPBlendRecipe::execute(VPTransformState &State) { State.ILV->setDebugLocFromInst(State.Builder, Phi); // We know that all PHIs in non-header blocks are converted into // selects, so we don't have to worry about the insertion order and we // can just use the builder. // At this point we generate the predication tree. There may be // duplications since this is a simple recursive scan, but future // optimizations will clean it up. unsigned NumIncoming = getNumIncomingValues(); // Generate a sequence of selects of the form: // SELECT(Mask3, In3, // SELECT(Mask2, In2, // SELECT(Mask1, In1, // In0))) // Note that Mask0 is never used: lanes for which no path reaches this phi and // are essentially undef are taken from In0. InnerLoopVectorizer::VectorParts Entry(State.UF); for (unsigned In = 0; In < NumIncoming; ++In) { for (unsigned Part = 0; Part < State.UF; ++Part) { // We might have single edge PHIs (blocks) - use an identity // 'select' for the first PHI operand. Value *In0 = State.get(getIncomingValue(In), Part); if (In == 0) Entry[Part] = In0; // Initialize with the first incoming value. else { // Select between the current value and the previous incoming edge // based on the incoming mask. Value *Cond = State.get(getMask(In), Part); Entry[Part] = State.Builder.CreateSelect(Cond, In0, Entry[Part], "predphi"); } } } for (unsigned Part = 0; Part < State.UF; ++Part) State.ValueMap.setVectorValue(Phi, Part, Entry[Part]); } void VPInterleaveRecipe::execute(VPTransformState &State) { assert(!State.Instance && "Interleave group being replicated."); State.ILV->vectorizeInterleaveGroup(IG, definedValues(), State, getAddr(), getStoredValues(), getMask()); } void VPReductionRecipe::execute(VPTransformState &State) { assert(!State.Instance && "Reduction being replicated."); for (unsigned Part = 0; Part < State.UF; ++Part) { RecurKind Kind = RdxDesc->getRecurrenceKind(); Value *NewVecOp = State.get(getVecOp(), Part); if (VPValue *Cond = getCondOp()) { Value *NewCond = State.get(Cond, Part); VectorType *VecTy = cast(NewVecOp->getType()); Constant *Iden = RecurrenceDescriptor::getRecurrenceIdentity( Kind, VecTy->getElementType()); Constant *IdenVec = ConstantVector::getSplat(VecTy->getElementCount(), Iden); Value *Select = State.Builder.CreateSelect(NewCond, NewVecOp, IdenVec); NewVecOp = Select; } Value *NewRed = createTargetReduction(State.Builder, TTI, *RdxDesc, NewVecOp); Value *PrevInChain = State.get(getChainOp(), Part); Value *NextInChain; if (RecurrenceDescriptor::isMinMaxRecurrenceKind(Kind)) { NextInChain = createMinMaxOp(State.Builder, RdxDesc->getRecurrenceKind(), NewRed, PrevInChain); } else { NextInChain = State.Builder.CreateBinOp( (Instruction::BinaryOps)getUnderlyingInstr()->getOpcode(), NewRed, PrevInChain); } State.set(this, getUnderlyingInstr(), 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 == 0) { assert(!State.VF.isScalable() && "VF is assumed to be non scalable."); Value *Poison = PoisonValue::get( VectorType::get(getUnderlyingValue()->getType(), State.VF)); State.ValueMap.setVectorValue(getUnderlyingInstr(), State.Instance->Part, Poison); } State.ILV->packScalarIntoVectorValue(getUnderlyingInstr(), *State.Instance); } return; } // Generate scalar instances for all VF lanes of all UF parts, unless the // instruction is uniform inwhich case generate only the first lane for each // of the UF parts. unsigned EndLane = IsUniform ? 1 : State.VF.getKnownMinValue(); 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, {Part, Lane}, IsPredicated, State); } void VPBranchOnMaskRecipe::execute(VPTransformState &State) { assert(State.Instance && "Branch on Mask works only on single instance."); unsigned Part = State.Instance->Part; unsigned Lane = State.Instance->Lane; Value *ConditionBit = nullptr; VPValue *BlockInMask = getMask(); if (BlockInMask) { ConditionBit = State.get(BlockInMask, Part); if (ConditionBit->getType()->isVectorTy()) ConditionBit = State.Builder.CreateExtractElement( ConditionBit, State.Builder.getInt32(Lane)); } else // Block in mask is all-one. ConditionBit = State.Builder.getTrue(); // Replace the temporary unreachable terminator with a new conditional branch, // whose two destinations will be set later when they are created. auto *CurrentTerminator = State.CFG.PrevBB->getTerminator(); assert(isa(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(State.get(getOperand(0), *State.Instance)); BasicBlock *PredicatedBB = ScalarPredInst->getParent(); BasicBlock *PredicatingBB = PredicatedBB->getSinglePredecessor(); assert(PredicatingBB && "Predicated block has no single predecessor."); // By current pack/unpack logic we need to generate only a single phi node: if // a vector value for the predicated instruction exists at this point it means // the instruction has vector users only, and a phi for the vector value is // needed. In this case the recipe of the predicated instruction is marked to // also do that packing, thereby "hoisting" the insert-element sequence. // Otherwise, a phi node for the scalar value is needed. unsigned Part = State.Instance->Part; Instruction *PredInst = cast(getOperand(0)->getUnderlyingValue()); if (State.ValueMap.hasVectorValue(PredInst, Part)) { Value *VectorValue = State.ValueMap.getVectorValue(PredInst, Part); InsertElementInst *IEI = cast(VectorValue); PHINode *VPhi = State.Builder.CreatePHI(IEI->getType(), 2); VPhi->addIncoming(IEI->getOperand(0), PredicatingBB); // Unmodified vector. VPhi->addIncoming(IEI, PredicatedBB); // New vector with inserted element. State.ValueMap.resetVectorValue(PredInst, Part, VPhi); // Update cache. } else { Type *PredInstType = PredInst->getType(); PHINode *Phi = State.Builder.CreatePHI(PredInstType, 2); Phi->addIncoming(PoisonValue::get(ScalarPredInst->getType()), PredicatingBB); Phi->addIncoming(ScalarPredInst, PredicatedBB); State.ValueMap.resetScalarValue(PredInst, *State.Instance, Phi); } } void VPWidenMemoryInstructionRecipe::execute(VPTransformState &State) { VPValue *StoredValue = isStore() ? getStoredValue() : nullptr; State.ILV->vectorizeMemoryInstruction(&Ingredient, State, StoredValue ? nullptr : getVPValue(), getAddr(), StoredValue, getMask()); } // Determine how to lower the scalar epilogue, which depends on 1) optimising // for minimum code-size, 2) predicate compiler options, 3) loop hints forcing // predication, and 4) a TTI hook that analyses whether the loop is suitable // for predication. static ScalarEpilogueLowering getScalarEpilogueLowering( Function *F, Loop *L, LoopVectorizeHints &Hints, ProfileSummaryInfo *PSI, BlockFrequencyInfo *BFI, TargetTransformInfo *TTI, TargetLibraryInfo *TLI, AssumptionCache *AC, LoopInfo *LI, ScalarEvolution *SE, DominatorTree *DT, LoopVectorizationLegality &LVL) { // 1) OptSize takes precedence over all other options, i.e. if this is set, // don't look at hints or options, and don't request a scalar epilogue. // (For PGSO, as shouldOptimizeForSize isn't currently accessible from // LoopAccessInfo (due to code dependency and not being able to reliably get // PSI/BFI from a loop analysis under NPM), we cannot suppress the collection // of strides in LoopAccessInfo::analyzeLoop() and vectorize without // versioning when the vectorization is forced, unlike hasOptSize. So revert // back to the old way and vectorize with versioning when forced. See D81345.) if (F->hasOptSize() || (llvm::shouldOptimizeForSize(L->getHeader(), PSI, BFI, PGSOQueryType::IRPass) && Hints.getForce() != LoopVectorizeHints::FK_Enabled)) return CM_ScalarEpilogueNotAllowedOptSize; // 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; } void VPTransformState::set(VPValue *Def, Value *IRDef, Value *V, unsigned Part) { set(Def, V, Part); ILV->setVectorValue(IRDef, Part, V); } // Process the loop in the VPlan-native vectorization path. This path builds // VPlan upfront in the vectorization pipeline, which allows to apply // VPlan-to-VPlan transformations from the very beginning without modifying the // input LLVM IR. static bool processLoopInVPlanNativePath( Loop *L, PredicatedScalarEvolution &PSE, LoopInfo *LI, DominatorTree *DT, LoopVectorizationLegality *LVL, TargetTransformInfo *TTI, TargetLibraryInfo *TLI, DemandedBits *DB, AssumptionCache *AC, OptimizationRemarkEmitter *ORE, BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI, LoopVectorizeHints &Hints) { if (isa(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); // Get user vectorization factor. ElementCount UserVF = Hints.getWidth(); // 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; LVP.setBestPlan(VF.Width, 1); InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, 1, LVL, &CM, BFI, PSI); LLVM_DEBUG(dbgs() << "Vectorizing outer loop in \"" << L->getHeader()->getParent()->getName() << "\"\n"); LVP.executePlan(LB, DT); // Mark the loop as already vectorized to avoid vectorizing again. Hints.setAlreadyVectorized(); assert(!verifyFunction(*L->getHeader()->getParent(), &dbgs())); return true; } LoopVectorizePass::LoopVectorizePass(LoopVectorizeOptions Opts) : InterleaveOnlyWhenForced(Opts.InterleaveOnlyWhenForced || !EnableLoopInterleaving), VectorizeOnlyWhenForced(Opts.VectorizeOnlyWhenForced || !EnableLoopVectorization) {} bool LoopVectorizePass::processLoop(Loop *L) { assert((EnableVPlanNativePath || L->isInnermost()) && "VPlan-native path is not enabled. Only process inner loops."); #ifndef NDEBUG const std::string DebugLocStr = getDebugLocString(L); #endif /* NDEBUG */ LLVM_DEBUG(dbgs() << "\nLV: Checking a loop in \"" << L->getHeader()->getParent()->getName() << "\" from " << DebugLocStr << "\n"); LoopVectorizeHints Hints(L, InterleaveOnlyWhenForced, *ORE); LLVM_DEBUG( dbgs() << "LV: Loop hints:" << " force=" << (Hints.getForce() == LoopVectorizeHints::FK_Disabled ? "disabled" : (Hints.getForce() == LoopVectorizeHints::FK_Enabled ? "enabled" : "?")) << " width=" << Hints.getWidth() << " unroll=" << Hints.getInterleave() << "\n"); // Function containing loop Function *F = L->getHeader()->getParent(); // Looking at the diagnostic output is the only way to determine if a loop // was vectorized (other than looking at the IR or machine code), so it // is important to generate an optimization remark for each loop. Most of // these messages are generated as OptimizationRemarkAnalysis. Remarks // generated as OptimizationRemark and OptimizationRemarkMissed are // less verbose reporting vectorized loops and unvectorized loops that may // benefit from vectorization, respectively. if (!Hints.allowVectorization(F, L, VectorizeOnlyWhenForced)) { LLVM_DEBUG(dbgs() << "LV: Loop hints prevent vectorization.\n"); return false; } PredicatedScalarEvolution PSE(*SE, *L); // Check if it is legal to vectorize the loop. LoopVectorizationRequirements Requirements(*ORE); LoopVectorizationLegality LVL(L, PSE, DT, TTI, TLI, AA, F, GetLAA, LI, ORE, &Requirements, &Hints, DB, AC, BFI, PSI); if (!LVL.canVectorize(EnableVPlanNativePath)) { LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n"); Hints.emitRemarkWithHints(); return false; } // Check the function attributes and profiles to find out if this function // should be optimized for size. ScalarEpilogueLowering SEL = getScalarEpilogueLowering( F, L, Hints, PSI, BFI, TTI, TLI, AC, LI, PSE.getSE(), DT, LVL); // Entrance to the VPlan-native vectorization path. Outer loops are processed // here. They may require CFG and instruction level transformations before // even evaluating whether vectorization is profitable. Since we cannot modify // the incoming IR, we need to build VPlan upfront in the vectorization // pipeline. if (!L->isInnermost()) return processLoopInVPlanNativePath(L, PSE, LI, DT, &LVL, TTI, TLI, DB, AC, ORE, BFI, PSI, Hints); assert(L->isInnermost() && "Inner loop expected."); // Check the loop for a trip count threshold: vectorize loops with a tiny trip // count by optimizing for size, to minimize overheads. auto ExpectedTC = getSmallBestKnownTC(*SE, L); if (ExpectedTC && *ExpectedTC < TinyTripCountVectorThreshold) { LLVM_DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " << "This loop is worth vectorizing only if no scalar " << "iteration overheads are incurred."); if (Hints.getForce() == LoopVectorizeHints::FK_Enabled) LLVM_DEBUG(dbgs() << " But vectorizing was explicitly forced.\n"); else { LLVM_DEBUG(dbgs() << "\n"); SEL = CM_ScalarEpilogueNotAllowedLowTripLoop; } } // Check the function attributes to see if implicit floats are allowed. // FIXME: This check doesn't seem possibly correct -- what if the loop is // an integer loop and the vector instructions selected are purely integer // vector instructions? if (F->hasFnAttribute(Attribute::NoImplicitFloat)) { reportVectorizationFailure( "Can't vectorize when the NoImplicitFloat attribute is used", "loop not vectorized due to NoImplicitFloat attribute", "NoImplicitFloat", ORE, L); Hints.emitRemarkWithHints(); return false; } // Check if the target supports potentially unsafe FP vectorization. // FIXME: Add a check for the type of safety issue (denormal, signaling) // for the target we're vectorizing for, to make sure none of the // additional fp-math flags can help. if (Hints.isPotentiallyUnsafe() && TTI->isFPVectorizationPotentiallyUnsafe()) { reportVectorizationFailure( "Potentially unsafe FP op prevents vectorization", "loop not vectorized due to unsafe FP support.", "UnsafeFP", ORE, L); Hints.emitRemarkWithHints(); return false; } bool UseInterleaved = TTI->enableInterleavedAccessVectorization(); InterleavedAccessInfo IAI(PSE, L, DT, LI, LVL.getLAI()); // If an override option has been passed in for interleaved accesses, use it. if (EnableInterleavedMemAccesses.getNumOccurrences() > 0) UseInterleaved = EnableInterleavedMemAccesses; // Analyze interleaved memory accesses. if (UseInterleaved) { IAI.analyzeInterleaving(useMaskedInterleavedAccesses(*TTI)); } // Use the cost model. LoopVectorizationCostModel CM(SEL, L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE, F, &Hints, IAI); CM.collectValuesToIgnore(); // Use the planner for vectorization. LoopVectorizationPlanner LVP(L, LI, TLI, TTI, &LVL, CM, IAI, PSE); // Get user vectorization factor and interleave count. ElementCount UserVF = Hints.getWidth(); unsigned UserIC = Hints.getInterleave(); // Plan how to best vectorize, return the best VF and its cost. Optional 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); } // Identify the diagnostic messages that should be produced. std::pair VecDiagMsg, IntDiagMsg; bool VectorizeLoop = true, InterleaveLoop = true; if (Requirements.doesNotMeet(F, L, Hints)) { LLVM_DEBUG(dbgs() << "LV: Not vectorizing: loop did not meet vectorization " "requirements.\n"); Hints.emitRemarkWithHints(); return false; } if (VF.Width.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'); } LVP.setBestPlan(VF.Width, IC); using namespace ore; bool DisableRuntimeUnroll = false; MDNode *OrigLoopID = L->getLoopID(); if (!VectorizeLoop) { assert(IC > 1 && "interleave count should not be 1 or 0"); // If we decided that it is not legal to vectorize the loop, then // interleave it. InnerLoopUnroller Unroller(L, PSE, LI, DT, TLI, TTI, AC, ORE, IC, &LVL, &CM, BFI, PSI); LVP.executePlan(Unroller, DT); ORE->emit([&]() { return OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(), L->getHeader()) << "interleaved loop (interleaved count: " << NV("InterleaveCount", IC) << ")"; }); } else { // If we decided that it is *legal* to vectorize the loop, then do it. // 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.getKnownMinValue(), IC, EpilogueVF.Width.getKnownMinValue(), 1); EpilogueVectorizerMainLoop MainILV(L, PSE, LI, DT, TLI, TTI, AC, ORE, EPI, &LVL, &CM, BFI, PSI); LVP.setBestPlan(EPI.MainLoopVF, EPI.MainLoopUF); LVP.executePlan(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. LVP.setBestPlan(EPI.EpilogueVF, EPI.EpilogueUF); EPI.MainLoopVF = EPI.EpilogueVF; EPI.MainLoopUF = EPI.EpilogueUF; EpilogueVectorizerEpilogueLoop EpilogILV(L, PSE, LI, DT, TLI, TTI, AC, ORE, EPI, &LVL, &CM, BFI, PSI); LVP.executePlan(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); LVP.executePlan(LB, DT); ++LoopsVectorized; // Add metadata to disable runtime unrolling a scalar loop when there are // no runtime checks about strides and memory. A scalar loop that is // rarely used is not worth unrolling. if (!LB.areSafetyChecksAdded()) DisableRuntimeUnroll = true; } // Report the vectorization decision. ORE->emit([&]() { return OptimizationRemark(LV_NAME, "Vectorized", L->getStartLoc(), L->getHeader()) << "vectorized loop (vectorization width: " << NV("VectorizationFactor", VF.Width) << ", interleaved count: " << NV("InterleaveCount", IC) << ")"; }); } Optional 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 &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 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(F); auto &LI = AM.getResult(F); auto &TTI = AM.getResult(F); auto &DT = AM.getResult(F); auto &BFI = AM.getResult(F); auto &TLI = AM.getResult(F); auto &AA = AM.getResult(F); auto &AC = AM.getResult(F); auto &DB = AM.getResult(F); auto &ORE = AM.getResult(F); MemorySSA *MSSA = EnableMSSALoopDependency ? &AM.getResult(F).getMSSA() : nullptr; auto &LAM = AM.getResult(F).getManager(); std::function GetLAA = [&](Loop &L) -> const LoopAccessInfo & { LoopStandardAnalysisResults AR = {AA, AC, DT, LI, SE, TLI, TTI, nullptr, MSSA}; return LAM.getResult(L, AR); }; auto &MAMProxy = AM.getResult(F); ProfileSummaryInfo *PSI = MAMProxy.getCachedResult(*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(); PA.preserve(); } PA.preserve(); PA.preserve(); if (!Result.MadeCFGChange) PA.preserveSet(); return PA; }