1 //===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===//
3 // The LLVM Compiler Infrastructure
5 // This file is distributed under the University of Illinois Open Source
6 // License. See LICENSE.TXT for details.
8 //===----------------------------------------------------------------------===//
10 // This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops
11 // and generates target-independent LLVM-IR.
12 // The vectorizer uses the TargetTransformInfo analysis to estimate the costs
13 // of instructions in order to estimate the profitability of vectorization.
15 // The loop vectorizer combines consecutive loop iterations into a single
16 // 'wide' iteration. After this transformation the index is incremented
17 // by the SIMD vector width, and not by one.
19 // This pass has three parts:
20 // 1. The main loop pass that drives the different parts.
21 // 2. LoopVectorizationLegality - A unit that checks for the legality
22 // of the vectorization.
23 // 3. InnerLoopVectorizer - A unit that performs the actual
24 // widening of instructions.
25 // 4. LoopVectorizationCostModel - A unit that checks for the profitability
26 // of vectorization. It decides on the optimal vector width, which
27 // can be one, if vectorization is not profitable.
29 //===----------------------------------------------------------------------===//
31 // The reduction-variable vectorization is based on the paper:
32 // D. Nuzman and R. Henderson. Multi-platform Auto-vectorization.
34 // Variable uniformity checks are inspired by:
35 // Karrenberg, R. and Hack, S. Whole Function Vectorization.
37 // The interleaved access vectorization is based on the paper:
38 // Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved
41 // Other ideas/concepts are from:
42 // A. Zaks and D. Nuzman. Autovectorization in GCC-two years later.
44 // S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of
45 // Vectorizing Compilers.
47 //===----------------------------------------------------------------------===//
49 #include "llvm/Transforms/Vectorize/LoopVectorize.h"
50 #include "llvm/ADT/DenseMap.h"
51 #include "llvm/ADT/Hashing.h"
52 #include "llvm/ADT/MapVector.h"
53 #include "llvm/ADT/Optional.h"
54 #include "llvm/ADT/SCCIterator.h"
55 #include "llvm/ADT/SetVector.h"
56 #include "llvm/ADT/SmallPtrSet.h"
57 #include "llvm/ADT/SmallSet.h"
58 #include "llvm/ADT/SmallVector.h"
59 #include "llvm/ADT/Statistic.h"
60 #include "llvm/ADT/StringExtras.h"
61 #include "llvm/Analysis/CodeMetrics.h"
62 #include "llvm/Analysis/GlobalsModRef.h"
63 #include "llvm/Analysis/LoopInfo.h"
64 #include "llvm/Analysis/LoopIterator.h"
65 #include "llvm/Analysis/LoopPass.h"
66 #include "llvm/Analysis/ScalarEvolutionExpander.h"
67 #include "llvm/Analysis/ScalarEvolutionExpressions.h"
68 #include "llvm/Analysis/ValueTracking.h"
69 #include "llvm/Analysis/VectorUtils.h"
70 #include "llvm/IR/Constants.h"
71 #include "llvm/IR/DataLayout.h"
72 #include "llvm/IR/DebugInfo.h"
73 #include "llvm/IR/DerivedTypes.h"
74 #include "llvm/IR/DiagnosticInfo.h"
75 #include "llvm/IR/Dominators.h"
76 #include "llvm/IR/Function.h"
77 #include "llvm/IR/IRBuilder.h"
78 #include "llvm/IR/Instructions.h"
79 #include "llvm/IR/IntrinsicInst.h"
80 #include "llvm/IR/LLVMContext.h"
81 #include "llvm/IR/Module.h"
82 #include "llvm/IR/PatternMatch.h"
83 #include "llvm/IR/Type.h"
84 #include "llvm/IR/User.h"
85 #include "llvm/IR/Value.h"
86 #include "llvm/IR/ValueHandle.h"
87 #include "llvm/IR/Verifier.h"
88 #include "llvm/Pass.h"
89 #include "llvm/Support/BranchProbability.h"
90 #include "llvm/Support/CommandLine.h"
91 #include "llvm/Support/Debug.h"
92 #include "llvm/Support/raw_ostream.h"
93 #include "llvm/Transforms/Scalar.h"
94 #include "llvm/Transforms/Utils/BasicBlockUtils.h"
95 #include "llvm/Transforms/Utils/Local.h"
96 #include "llvm/Transforms/Utils/LoopSimplify.h"
97 #include "llvm/Transforms/Utils/LoopUtils.h"
98 #include "llvm/Transforms/Utils/LoopVersioning.h"
99 #include "llvm/Transforms/Vectorize.h"
104 using namespace llvm;
105 using namespace llvm::PatternMatch;
107 #define LV_NAME "loop-vectorize"
108 #define DEBUG_TYPE LV_NAME
110 STATISTIC(LoopsVectorized, "Number of loops vectorized");
111 STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization");
114 EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden,
115 cl::desc("Enable if-conversion during vectorization."));
117 /// Loops with a known constant trip count below this number are vectorized only
118 /// if no scalar iteration overheads are incurred.
119 static cl::opt<unsigned> TinyTripCountVectorThreshold(
120 "vectorizer-min-trip-count", cl::init(16), cl::Hidden,
121 cl::desc("Loops with a constant trip count that is smaller than this "
122 "value are vectorized only if no scalar iteration overheads "
125 static cl::opt<bool> MaximizeBandwidth(
126 "vectorizer-maximize-bandwidth", cl::init(false), cl::Hidden,
127 cl::desc("Maximize bandwidth when selecting vectorization factor which "
128 "will be determined by the smallest type in loop."));
130 static cl::opt<bool> EnableInterleavedMemAccesses(
131 "enable-interleaved-mem-accesses", cl::init(false), cl::Hidden,
132 cl::desc("Enable vectorization on interleaved memory accesses in a loop"));
134 /// Maximum factor for an interleaved memory access.
135 static cl::opt<unsigned> MaxInterleaveGroupFactor(
136 "max-interleave-group-factor", cl::Hidden,
137 cl::desc("Maximum factor for an interleaved access group (default = 8)"),
140 /// We don't interleave loops with a known constant trip count below this
142 static const unsigned TinyTripCountInterleaveThreshold = 128;
144 static cl::opt<unsigned> ForceTargetNumScalarRegs(
145 "force-target-num-scalar-regs", cl::init(0), cl::Hidden,
146 cl::desc("A flag that overrides the target's number of scalar registers."));
148 static cl::opt<unsigned> ForceTargetNumVectorRegs(
149 "force-target-num-vector-regs", cl::init(0), cl::Hidden,
150 cl::desc("A flag that overrides the target's number of vector registers."));
152 /// Maximum vectorization interleave count.
153 static const unsigned MaxInterleaveFactor = 16;
155 static cl::opt<unsigned> ForceTargetMaxScalarInterleaveFactor(
156 "force-target-max-scalar-interleave", cl::init(0), cl::Hidden,
157 cl::desc("A flag that overrides the target's max interleave factor for "
160 static cl::opt<unsigned> ForceTargetMaxVectorInterleaveFactor(
161 "force-target-max-vector-interleave", cl::init(0), cl::Hidden,
162 cl::desc("A flag that overrides the target's max interleave factor for "
163 "vectorized loops."));
165 static cl::opt<unsigned> ForceTargetInstructionCost(
166 "force-target-instruction-cost", cl::init(0), cl::Hidden,
167 cl::desc("A flag that overrides the target's expected cost for "
168 "an instruction to a single constant value. Mostly "
169 "useful for getting consistent testing."));
171 static cl::opt<unsigned> SmallLoopCost(
172 "small-loop-cost", cl::init(20), cl::Hidden,
174 "The cost of a loop that is considered 'small' by the interleaver."));
176 static cl::opt<bool> LoopVectorizeWithBlockFrequency(
177 "loop-vectorize-with-block-frequency", cl::init(false), cl::Hidden,
178 cl::desc("Enable the use of the block frequency analysis to access PGO "
179 "heuristics minimizing code growth in cold regions and being more "
180 "aggressive in hot regions."));
182 // Runtime interleave loops for load/store throughput.
183 static cl::opt<bool> EnableLoadStoreRuntimeInterleave(
184 "enable-loadstore-runtime-interleave", cl::init(true), cl::Hidden,
186 "Enable runtime interleaving until load/store ports are saturated"));
188 /// The number of stores in a loop that are allowed to need predication.
189 static cl::opt<unsigned> NumberOfStoresToPredicate(
190 "vectorize-num-stores-pred", cl::init(1), cl::Hidden,
191 cl::desc("Max number of stores to be predicated behind an if."));
193 static cl::opt<bool> EnableIndVarRegisterHeur(
194 "enable-ind-var-reg-heur", cl::init(true), cl::Hidden,
195 cl::desc("Count the induction variable only once when interleaving"));
197 static cl::opt<bool> EnableCondStoresVectorization(
198 "enable-cond-stores-vec", cl::init(true), cl::Hidden,
199 cl::desc("Enable if predication of stores during vectorization."));
201 static cl::opt<unsigned> MaxNestedScalarReductionIC(
202 "max-nested-scalar-reduction-interleave", cl::init(2), cl::Hidden,
203 cl::desc("The maximum interleave count to use when interleaving a scalar "
204 "reduction in a nested loop."));
206 static cl::opt<unsigned> PragmaVectorizeMemoryCheckThreshold(
207 "pragma-vectorize-memory-check-threshold", cl::init(128), cl::Hidden,
208 cl::desc("The maximum allowed number of runtime memory checks with a "
209 "vectorize(enable) pragma."));
211 static cl::opt<unsigned> VectorizeSCEVCheckThreshold(
212 "vectorize-scev-check-threshold", cl::init(16), cl::Hidden,
213 cl::desc("The maximum number of SCEV checks allowed."));
215 static cl::opt<unsigned> PragmaVectorizeSCEVCheckThreshold(
216 "pragma-vectorize-scev-check-threshold", cl::init(128), cl::Hidden,
217 cl::desc("The maximum number of SCEV checks allowed with a "
218 "vectorize(enable) pragma"));
220 /// Create an analysis remark that explains why vectorization failed
222 /// \p PassName is the name of the pass (e.g. can be AlwaysPrint). \p
223 /// RemarkName is the identifier for the remark. If \p I is passed it is an
224 /// instruction that prevents vectorization. Otherwise \p TheLoop is used for
225 /// the location of the remark. \return the remark object that can be
227 static OptimizationRemarkAnalysis
228 createMissedAnalysis(const char *PassName, StringRef RemarkName, Loop *TheLoop,
229 Instruction *I = nullptr) {
230 Value *CodeRegion = TheLoop->getHeader();
231 DebugLoc DL = TheLoop->getStartLoc();
234 CodeRegion = I->getParent();
235 // If there is no debug location attached to the instruction, revert back to
237 if (I->getDebugLoc())
238 DL = I->getDebugLoc();
241 OptimizationRemarkAnalysis R(PassName, RemarkName, DL, CodeRegion);
242 R << "loop not vectorized: ";
248 // Forward declarations.
249 class LoopVectorizeHints;
250 class LoopVectorizationLegality;
251 class LoopVectorizationCostModel;
252 class LoopVectorizationRequirements;
254 /// Returns true if the given loop body has a cycle, excluding the loop
256 static bool hasCyclesInLoopBody(const Loop &L) {
260 for (const auto &SCC :
261 make_range(scc_iterator<Loop, LoopBodyTraits>::begin(L),
262 scc_iterator<Loop, LoopBodyTraits>::end(L))) {
263 if (SCC.size() > 1) {
264 DEBUG(dbgs() << "LVL: Detected a cycle in the loop body:\n");
272 /// A helper function for converting Scalar types to vector types.
273 /// If the incoming type is void, we return void. If the VF is 1, we return
275 static Type *ToVectorTy(Type *Scalar, unsigned VF) {
276 if (Scalar->isVoidTy() || VF == 1)
278 return VectorType::get(Scalar, VF);
281 // FIXME: The following helper functions have multiple implementations
282 // in the project. They can be effectively organized in a common Load/Store
285 /// A helper function that returns the pointer operand of a load or store
287 static Value *getPointerOperand(Value *I) {
288 if (auto *LI = dyn_cast<LoadInst>(I))
289 return LI->getPointerOperand();
290 if (auto *SI = dyn_cast<StoreInst>(I))
291 return SI->getPointerOperand();
295 /// A helper function that returns the type of loaded or stored value.
296 static Type *getMemInstValueType(Value *I) {
297 assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
298 "Expected Load or Store instruction");
299 if (auto *LI = dyn_cast<LoadInst>(I))
300 return LI->getType();
301 return cast<StoreInst>(I)->getValueOperand()->getType();
304 /// A helper function that returns the alignment of load or store instruction.
305 static unsigned getMemInstAlignment(Value *I) {
306 assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
307 "Expected Load or Store instruction");
308 if (auto *LI = dyn_cast<LoadInst>(I))
309 return LI->getAlignment();
310 return cast<StoreInst>(I)->getAlignment();
313 /// A helper function that returns the address space of the pointer operand of
314 /// load or store instruction.
315 static unsigned getMemInstAddressSpace(Value *I) {
316 assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
317 "Expected Load or Store instruction");
318 if (auto *LI = dyn_cast<LoadInst>(I))
319 return LI->getPointerAddressSpace();
320 return cast<StoreInst>(I)->getPointerAddressSpace();
323 /// A helper function that returns true if the given type is irregular. The
324 /// type is irregular if its allocated size doesn't equal the store size of an
325 /// element of the corresponding vector type at the given vectorization factor.
326 static bool hasIrregularType(Type *Ty, const DataLayout &DL, unsigned VF) {
328 // Determine if an array of VF elements of type Ty is "bitcast compatible"
329 // with a <VF x Ty> vector.
331 auto *VectorTy = VectorType::get(Ty, VF);
332 return VF * DL.getTypeAllocSize(Ty) != DL.getTypeStoreSize(VectorTy);
335 // If the vectorization factor is one, we just check if an array of type Ty
336 // requires padding between elements.
337 return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty);
340 /// A helper function that returns the reciprocal of the block probability of
341 /// predicated blocks. If we return X, we are assuming the predicated block
342 /// will execute once for for every X iterations of the loop header.
344 /// TODO: We should use actual block probability here, if available. Currently,
345 /// we always assume predicated blocks have a 50% chance of executing.
346 static unsigned getReciprocalPredBlockProb() { return 2; }
348 /// A helper function that adds a 'fast' flag to floating-point operations.
349 static Value *addFastMathFlag(Value *V) {
350 if (isa<FPMathOperator>(V)) {
352 Flags.setUnsafeAlgebra();
353 cast<Instruction>(V)->setFastMathFlags(Flags);
358 /// A helper function that returns an integer or floating-point constant with
360 static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) {
361 return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C)
362 : ConstantFP::get(Ty, C);
365 /// InnerLoopVectorizer vectorizes loops which contain only one basic
366 /// block to a specified vectorization factor (VF).
367 /// This class performs the widening of scalars into vectors, or multiple
368 /// scalars. This class also implements the following features:
369 /// * It inserts an epilogue loop for handling loops that don't have iteration
370 /// counts that are known to be a multiple of the vectorization factor.
371 /// * It handles the code generation for reduction variables.
372 /// * Scalarization (implementation using scalars) of un-vectorizable
374 /// InnerLoopVectorizer does not perform any vectorization-legality
375 /// checks, and relies on the caller to check for the different legality
376 /// aspects. The InnerLoopVectorizer relies on the
377 /// LoopVectorizationLegality class to provide information about the induction
378 /// and reduction variables that were found to a given vectorization factor.
379 class InnerLoopVectorizer {
381 InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
382 LoopInfo *LI, DominatorTree *DT,
383 const TargetLibraryInfo *TLI,
384 const TargetTransformInfo *TTI, AssumptionCache *AC,
385 OptimizationRemarkEmitter *ORE, unsigned VecWidth,
386 unsigned UnrollFactor, LoopVectorizationLegality *LVL,
387 LoopVectorizationCostModel *CM)
388 : OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI),
389 AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor),
390 Builder(PSE.getSE()->getContext()), Induction(nullptr),
391 OldInduction(nullptr), VectorLoopValueMap(UnrollFactor, VecWidth),
392 TripCount(nullptr), VectorTripCount(nullptr), Legal(LVL), Cost(CM),
393 AddedSafetyChecks(false) {}
395 /// Create a new empty loop. Unlink the old loop and connect the new one.
396 void createVectorizedLoopSkeleton();
398 /// Vectorize a single instruction within the innermost loop.
399 void vectorizeInstruction(Instruction &I);
401 /// Fix the vectorized code, taking care of header phi's, live-outs, and more.
402 void fixVectorizedLoop();
404 // Return true if any runtime check is added.
405 bool areSafetyChecksAdded() { return AddedSafetyChecks; }
407 virtual ~InnerLoopVectorizer() {}
410 /// A small list of PHINodes.
411 typedef SmallVector<PHINode *, 4> PhiVector;
413 /// A type for vectorized values in the new loop. Each value from the
414 /// original loop, when vectorized, is represented by UF vector values in the
415 /// new unrolled loop, where UF is the unroll factor.
416 typedef SmallVector<Value *, 2> VectorParts;
418 /// A type for scalarized values in the new loop. Each value from the
419 /// original loop, when scalarized, is represented by UF x VF scalar values
420 /// in the new unrolled loop, where UF is the unroll factor and VF is the
421 /// vectorization factor.
422 typedef SmallVector<SmallVector<Value *, 4>, 2> ScalarParts;
424 // When we if-convert we need to create edge masks. We have to cache values
425 // so that we don't end up with exponential recursion/IR.
426 typedef DenseMap<std::pair<BasicBlock *, BasicBlock *>, VectorParts>
428 typedef DenseMap<BasicBlock *, VectorParts> BlockMaskCacheTy;
430 /// Set up the values of the IVs correctly when exiting the vector loop.
431 void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II,
432 Value *CountRoundDown, Value *EndValue,
433 BasicBlock *MiddleBlock);
435 /// Create a new induction variable inside L.
436 PHINode *createInductionVariable(Loop *L, Value *Start, Value *End,
437 Value *Step, Instruction *DL);
439 /// Handle all cross-iteration phis in the header.
440 void fixCrossIterationPHIs();
442 /// Fix a first-order recurrence. This is the second phase of vectorizing
444 void fixFirstOrderRecurrence(PHINode *Phi);
446 /// Fix a reduction cross-iteration phi. This is the second phase of
447 /// vectorizing this phi node.
448 void fixReduction(PHINode *Phi);
450 /// \brief The Loop exit block may have single value PHI nodes with some
451 /// incoming value. While vectorizing we only handled real values
452 /// that were defined inside the loop and we should have one value for
453 /// each predecessor of its parent basic block. See PR14725.
456 /// Iteratively sink the scalarized operands of a predicated instruction into
457 /// the block that was created for it.
458 void sinkScalarOperands(Instruction *PredInst);
460 /// Predicate conditional instructions that require predication on their
461 /// respective conditions.
462 void predicateInstructions();
464 /// Shrinks vector element sizes to the smallest bitwidth they can be legally
466 void truncateToMinimalBitwidths();
468 /// A helper function that computes the predicate of the block BB, assuming
469 /// that the header block of the loop is set to True. It returns the *entry*
470 /// mask for the block BB.
471 VectorParts createBlockInMask(BasicBlock *BB);
472 /// A helper function that computes the predicate of the edge between SRC
474 VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst);
476 /// Vectorize a single PHINode in a block. This method handles the induction
477 /// variable canonicalization. It supports both VF = 1 for unrolled loops and
478 /// arbitrary length vectors.
479 void widenPHIInstruction(Instruction *PN, unsigned UF, unsigned VF);
481 /// Insert the new loop to the loop hierarchy and pass manager
482 /// and update the analysis passes.
483 void updateAnalysis();
485 /// This instruction is un-vectorizable. Implement it as a sequence
486 /// of scalars. If \p IfPredicateInstr is true we need to 'hide' each
487 /// scalarized instruction behind an if block predicated on the control
488 /// dependence of the instruction.
489 void scalarizeInstruction(Instruction *Instr, bool IfPredicateInstr = false);
491 /// Vectorize Load and Store instructions,
492 virtual void vectorizeMemoryInstruction(Instruction *Instr);
494 /// Create a broadcast instruction. This method generates a broadcast
495 /// instruction (shuffle) for loop invariant values and for the induction
496 /// value. If this is the induction variable then we extend it to N, N+1, ...
497 /// this is needed because each iteration in the loop corresponds to a SIMD
499 virtual Value *getBroadcastInstrs(Value *V);
501 /// This function adds (StartIdx, StartIdx + Step, StartIdx + 2*Step, ...)
502 /// to each vector element of Val. The sequence starts at StartIndex.
503 /// \p Opcode is relevant for FP induction variable.
504 virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step,
505 Instruction::BinaryOps Opcode =
506 Instruction::BinaryOpsEnd);
508 /// Compute scalar induction steps. \p ScalarIV is the scalar induction
509 /// variable on which to base the steps, \p Step is the size of the step, and
510 /// \p EntryVal is the value from the original loop that maps to the steps.
511 /// Note that \p EntryVal doesn't have to be an induction variable (e.g., it
512 /// can be a truncate instruction).
513 void buildScalarSteps(Value *ScalarIV, Value *Step, Value *EntryVal,
514 const InductionDescriptor &ID);
516 /// Create a vector induction phi node based on an existing scalar one. \p
517 /// EntryVal is the value from the original loop that maps to the vector phi
518 /// node, and \p Step is the loop-invariant step. If \p EntryVal is a
519 /// truncate instruction, instead of widening the original IV, we widen a
520 /// version of the IV truncated to \p EntryVal's type.
521 void createVectorIntOrFpInductionPHI(const InductionDescriptor &II,
522 Value *Step, Instruction *EntryVal);
524 /// Widen an integer or floating-point induction variable \p IV. If \p Trunc
525 /// is provided, the integer induction variable will first be truncated to
526 /// the corresponding type.
527 void widenIntOrFpInduction(PHINode *IV, TruncInst *Trunc = nullptr);
529 /// Returns true if an instruction \p I should be scalarized instead of
530 /// vectorized for the chosen vectorization factor.
531 bool shouldScalarizeInstruction(Instruction *I) const;
533 /// Returns true if we should generate a scalar version of \p IV.
534 bool needsScalarInduction(Instruction *IV) const;
536 /// getOrCreateVectorValue and getOrCreateScalarValue coordinate to generate a
537 /// vector or scalar value on-demand if one is not yet available. When
538 /// vectorizing a loop, we visit the definition of an instruction before its
539 /// uses. When visiting the definition, we either vectorize or scalarize the
540 /// instruction, creating an entry for it in the corresponding map. (In some
541 /// cases, such as induction variables, we will create both vector and scalar
542 /// entries.) Then, as we encounter uses of the definition, we derive values
543 /// for each scalar or vector use unless such a value is already available.
544 /// For example, if we scalarize a definition and one of its uses is vector,
545 /// we build the required vector on-demand with an insertelement sequence
546 /// when visiting the use. Otherwise, if the use is scalar, we can use the
547 /// existing scalar definition.
549 /// Return a value in the new loop corresponding to \p V from the original
550 /// loop at unroll index \p Part. If the value has already been vectorized,
551 /// the corresponding vector entry in VectorLoopValueMap is returned. If,
552 /// however, the value has a scalar entry in VectorLoopValueMap, we construct
553 /// a new vector value on-demand by inserting the scalar values into a vector
554 /// with an insertelement sequence. If the value has been neither vectorized
555 /// nor scalarized, it must be loop invariant, so we simply broadcast the
556 /// value into a vector.
557 Value *getOrCreateVectorValue(Value *V, unsigned Part);
559 /// Return a value in the new loop corresponding to \p V from the original
560 /// loop at unroll index \p Part and vector index \p Lane. If the value has
561 /// been vectorized but not scalarized, the necessary extractelement
562 /// instruction will be generated.
563 Value *getOrCreateScalarValue(Value *V, unsigned Part, unsigned Lane);
565 /// Try to vectorize the interleaved access group that \p Instr belongs to.
566 void vectorizeInterleaveGroup(Instruction *Instr);
568 /// Generate a shuffle sequence that will reverse the vector Vec.
569 virtual Value *reverseVector(Value *Vec);
571 /// Returns (and creates if needed) the original loop trip count.
572 Value *getOrCreateTripCount(Loop *NewLoop);
574 /// Returns (and creates if needed) the trip count of the widened loop.
575 Value *getOrCreateVectorTripCount(Loop *NewLoop);
577 /// Emit a bypass check to see if the vector trip count is zero, including if
579 void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass);
580 /// Emit a bypass check to see if all of the SCEV assumptions we've
581 /// had to make are correct.
582 void emitSCEVChecks(Loop *L, BasicBlock *Bypass);
583 /// Emit bypass checks to check any memory assumptions we may have made.
584 void emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass);
586 /// Add additional metadata to \p To that was not present on \p Orig.
588 /// Currently this is used to add the noalias annotations based on the
589 /// inserted memchecks. Use this for instructions that are *cloned* into the
591 void addNewMetadata(Instruction *To, const Instruction *Orig);
593 /// Add metadata from one instruction to another.
595 /// This includes both the original MDs from \p From and additional ones (\see
596 /// addNewMetadata). Use this for *newly created* instructions in the vector
598 void addMetadata(Instruction *To, Instruction *From);
600 /// \brief Similar to the previous function but it adds the metadata to a
601 /// vector of instructions.
602 void addMetadata(ArrayRef<Value *> To, Instruction *From);
604 /// \brief Set the debug location in the builder using the debug location in
606 void setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr);
608 /// This is a helper class for maintaining vectorization state. It's used for
609 /// mapping values from the original loop to their corresponding values in
610 /// the new loop. Two mappings are maintained: one for vectorized values and
611 /// one for scalarized values. Vectorized values are represented with UF
612 /// vector values in the new loop, and scalarized values are represented with
613 /// UF x VF scalar values in the new loop. UF and VF are the unroll and
614 /// vectorization factors, respectively.
616 /// Entries can be added to either map with setVectorValue and setScalarValue,
617 /// which assert that an entry was not already added before. If an entry is to
618 /// replace an existing one, call resetVectorValue. This is currently needed
619 /// to modify the mapped values during "fix-up" operations that occur once the
620 /// first phase of widening is complete. These operations include type
621 /// truncation and the second phase of recurrence widening.
623 /// Entries from either map can be retrieved using the getVectorValue and
624 /// getScalarValue functions, which assert that the desired value exists.
628 /// Construct an empty map with the given unroll and vectorization factors.
629 ValueMap(unsigned UF, unsigned VF) : UF(UF), VF(VF) {}
631 /// \return True if the map has any vector entry for \p Key.
632 bool hasAnyVectorValue(Value *Key) const {
633 return VectorMapStorage.count(Key);
636 /// \return True if the map has a vector entry for \p Key and \p Part.
637 bool hasVectorValue(Value *Key, unsigned Part) const {
638 assert(Part < UF && "Queried Vector Part is too large.");
639 if (!hasAnyVectorValue(Key))
641 const VectorParts &Entry = VectorMapStorage.find(Key)->second;
642 assert(Entry.size() == UF && "VectorParts has wrong dimensions.");
643 return Entry[Part] != nullptr;
646 /// \return True if the map has any scalar entry for \p Key.
647 bool hasAnyScalarValue(Value *Key) const {
648 return ScalarMapStorage.count(Key);
651 /// \return True if the map has a scalar entry for \p Key, \p Part and
653 bool hasScalarValue(Value *Key, unsigned Part, unsigned Lane) const {
654 assert(Part < UF && "Queried Scalar Part is too large.");
655 assert(Lane < VF && "Queried Scalar Lane is too large.");
656 if (!hasAnyScalarValue(Key))
658 const ScalarParts &Entry = ScalarMapStorage.find(Key)->second;
659 assert(Entry.size() == UF && "ScalarParts has wrong dimensions.");
660 assert(Entry[Part].size() == VF && "ScalarParts has wrong dimensions.");
661 return Entry[Part][Lane] != nullptr;
664 /// Retrieve the existing vector value that corresponds to \p Key and
666 Value *getVectorValue(Value *Key, unsigned Part) {
667 assert(hasVectorValue(Key, Part) && "Getting non-existent value.");
668 return VectorMapStorage[Key][Part];
671 /// Retrieve the existing scalar value that corresponds to \p Key, \p Part
673 Value *getScalarValue(Value *Key, unsigned Part, unsigned Lane) {
674 assert(hasScalarValue(Key, Part, Lane) && "Getting non-existent value.");
675 return ScalarMapStorage[Key][Part][Lane];
678 /// Set a vector value associated with \p Key and \p Part. Assumes such a
679 /// value is not already set. If it is, use resetVectorValue() instead.
680 void setVectorValue(Value *Key, unsigned Part, Value *Vector) {
681 assert(!hasVectorValue(Key, Part) && "Vector value already set for part");
682 if (!VectorMapStorage.count(Key)) {
683 VectorParts Entry(UF);
684 VectorMapStorage[Key] = Entry;
686 VectorMapStorage[Key][Part] = Vector;
689 /// Set a scalar value associated with \p Key for \p Part and \p Lane.
690 /// Assumes such a value is not already set.
691 void setScalarValue(Value *Key, unsigned Part, unsigned Lane,
693 assert(!hasScalarValue(Key, Part, Lane) && "Scalar value already set");
694 if (!ScalarMapStorage.count(Key)) {
695 ScalarParts Entry(UF);
696 for (unsigned Part = 0; Part < UF; ++Part)
697 Entry[Part].resize(VF, nullptr);
698 // TODO: Consider storing uniform values only per-part, as they occupy
699 // lane 0 only, keeping the other VF-1 redundant entries null.
700 ScalarMapStorage[Key] = Entry;
702 ScalarMapStorage[Key][Part][Lane] = Scalar;
705 /// Reset the vector value associated with \p Key for the given \p Part.
706 /// This function can be used to update values that have already been
707 /// vectorized. This is the case for "fix-up" operations including type
708 /// truncation and the second phase of recurrence vectorization.
709 void resetVectorValue(Value *Key, unsigned Part, Value *Vector) {
710 assert(hasVectorValue(Key, Part) && "Vector value not set for part");
711 VectorMapStorage[Key][Part] = Vector;
715 /// The unroll factor. Each entry in the vector map contains UF vector
719 /// The vectorization factor. Each entry in the scalar map contains UF x VF
723 /// The vector and scalar map storage. We use std::map and not DenseMap
724 /// because insertions to DenseMap invalidate its iterators.
725 std::map<Value *, VectorParts> VectorMapStorage;
726 std::map<Value *, ScalarParts> ScalarMapStorage;
729 /// The original loop.
731 /// A wrapper around ScalarEvolution used to add runtime SCEV checks. Applies
732 /// dynamic knowledge to simplify SCEV expressions and converts them to a
733 /// more usable form.
734 PredicatedScalarEvolution &PSE;
741 /// Target Library Info.
742 const TargetLibraryInfo *TLI;
743 /// Target Transform Info.
744 const TargetTransformInfo *TTI;
745 /// Assumption Cache.
747 /// Interface to emit optimization remarks.
748 OptimizationRemarkEmitter *ORE;
750 /// \brief LoopVersioning. It's only set up (non-null) if memchecks were
753 /// This is currently only used to add no-alias metadata based on the
754 /// memchecks. The actually versioning is performed manually.
755 std::unique_ptr<LoopVersioning> LVer;
757 /// The vectorization SIMD factor to use. Each vector will have this many
762 /// The vectorization unroll factor to use. Each scalar is vectorized to this
763 /// many different vector instructions.
766 /// The builder that we use
769 // --- Vectorization state ---
771 /// The vector-loop preheader.
772 BasicBlock *LoopVectorPreHeader;
773 /// The scalar-loop preheader.
774 BasicBlock *LoopScalarPreHeader;
775 /// Middle Block between the vector and the scalar.
776 BasicBlock *LoopMiddleBlock;
777 /// The ExitBlock of the scalar loop.
778 BasicBlock *LoopExitBlock;
779 /// The vector loop body.
780 BasicBlock *LoopVectorBody;
781 /// The scalar loop body.
782 BasicBlock *LoopScalarBody;
783 /// A list of all bypass blocks. The first block is the entry of the loop.
784 SmallVector<BasicBlock *, 4> LoopBypassBlocks;
786 /// The new Induction variable which was added to the new block.
788 /// The induction variable of the old basic block.
789 PHINode *OldInduction;
791 /// Maps values from the original loop to their corresponding values in the
792 /// vectorized loop. A key value can map to either vector values, scalar
793 /// values or both kinds of values, depending on whether the key was
794 /// vectorized and scalarized.
795 ValueMap VectorLoopValueMap;
797 /// Store instructions that should be predicated, as a pair
798 /// <StoreInst, Predicate>
799 SmallVector<std::pair<Instruction *, Value *>, 4> PredicatedInstructions;
800 EdgeMaskCacheTy EdgeMaskCache;
801 BlockMaskCacheTy BlockMaskCache;
802 /// Trip count of the original loop.
804 /// Trip count of the widened loop (TripCount - TripCount % (VF*UF))
805 Value *VectorTripCount;
807 /// The legality analysis.
808 LoopVectorizationLegality *Legal;
810 /// The profitablity analysis.
811 LoopVectorizationCostModel *Cost;
813 // Record whether runtime checks are added.
814 bool AddedSafetyChecks;
816 // Holds the end values for each induction variable. We save the end values
817 // so we can later fix-up the external users of the induction variables.
818 DenseMap<PHINode *, Value *> IVEndValues;
821 class InnerLoopUnroller : public InnerLoopVectorizer {
823 InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
824 LoopInfo *LI, DominatorTree *DT,
825 const TargetLibraryInfo *TLI,
826 const TargetTransformInfo *TTI, AssumptionCache *AC,
827 OptimizationRemarkEmitter *ORE, unsigned UnrollFactor,
828 LoopVectorizationLegality *LVL,
829 LoopVectorizationCostModel *CM)
830 : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, 1,
831 UnrollFactor, LVL, CM) {}
834 void vectorizeMemoryInstruction(Instruction *Instr) override;
835 Value *getBroadcastInstrs(Value *V) override;
836 Value *getStepVector(Value *Val, int StartIdx, Value *Step,
837 Instruction::BinaryOps Opcode =
838 Instruction::BinaryOpsEnd) override;
839 Value *reverseVector(Value *Vec) override;
842 /// \brief Look for a meaningful debug location on the instruction or it's
844 static Instruction *getDebugLocFromInstOrOperands(Instruction *I) {
849 if (I->getDebugLoc() != Empty)
852 for (User::op_iterator OI = I->op_begin(), OE = I->op_end(); OI != OE; ++OI) {
853 if (Instruction *OpInst = dyn_cast<Instruction>(*OI))
854 if (OpInst->getDebugLoc() != Empty)
861 void InnerLoopVectorizer::setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) {
862 if (const Instruction *Inst = dyn_cast_or_null<Instruction>(Ptr)) {
863 const DILocation *DIL = Inst->getDebugLoc();
864 if (DIL && Inst->getFunction()->isDebugInfoForProfiling())
865 B.SetCurrentDebugLocation(DIL->cloneWithDuplicationFactor(UF * VF));
867 B.SetCurrentDebugLocation(DIL);
869 B.SetCurrentDebugLocation(DebugLoc());
873 /// \return string containing a file name and a line # for the given loop.
874 static std::string getDebugLocString(const Loop *L) {
877 raw_string_ostream OS(Result);
878 if (const DebugLoc LoopDbgLoc = L->getStartLoc())
879 LoopDbgLoc.print(OS);
881 // Just print the module name.
882 OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier();
889 void InnerLoopVectorizer::addNewMetadata(Instruction *To,
890 const Instruction *Orig) {
891 // If the loop was versioned with memchecks, add the corresponding no-alias
893 if (LVer && (isa<LoadInst>(Orig) || isa<StoreInst>(Orig)))
894 LVer->annotateInstWithNoAlias(To, Orig);
897 void InnerLoopVectorizer::addMetadata(Instruction *To,
899 propagateMetadata(To, From);
900 addNewMetadata(To, From);
903 void InnerLoopVectorizer::addMetadata(ArrayRef<Value *> To,
905 for (Value *V : To) {
906 if (Instruction *I = dyn_cast<Instruction>(V))
907 addMetadata(I, From);
911 /// \brief The group of interleaved loads/stores sharing the same stride and
912 /// close to each other.
914 /// Each member in this group has an index starting from 0, and the largest
915 /// index should be less than interleaved factor, which is equal to the absolute
916 /// value of the access's stride.
918 /// E.g. An interleaved load group of factor 4:
919 /// for (unsigned i = 0; i < 1024; i+=4) {
920 /// a = A[i]; // Member of index 0
921 /// b = A[i+1]; // Member of index 1
922 /// d = A[i+3]; // Member of index 3
926 /// An interleaved store group of factor 4:
927 /// for (unsigned i = 0; i < 1024; i+=4) {
929 /// A[i] = a; // Member of index 0
930 /// A[i+1] = b; // Member of index 1
931 /// A[i+2] = c; // Member of index 2
932 /// A[i+3] = d; // Member of index 3
935 /// Note: the interleaved load group could have gaps (missing members), but
936 /// the interleaved store group doesn't allow gaps.
937 class InterleaveGroup {
939 InterleaveGroup(Instruction *Instr, int Stride, unsigned Align)
940 : Align(Align), SmallestKey(0), LargestKey(0), InsertPos(Instr) {
941 assert(Align && "The alignment should be non-zero");
943 Factor = std::abs(Stride);
944 assert(Factor > 1 && "Invalid interleave factor");
946 Reverse = Stride < 0;
950 bool isReverse() const { return Reverse; }
951 unsigned getFactor() const { return Factor; }
952 unsigned getAlignment() const { return Align; }
953 unsigned getNumMembers() const { return Members.size(); }
955 /// \brief Try to insert a new member \p Instr with index \p Index and
956 /// alignment \p NewAlign. The index is related to the leader and it could be
957 /// negative if it is the new leader.
959 /// \returns false if the instruction doesn't belong to the group.
960 bool insertMember(Instruction *Instr, int Index, unsigned NewAlign) {
961 assert(NewAlign && "The new member's alignment should be non-zero");
963 int Key = Index + SmallestKey;
965 // Skip if there is already a member with the same index.
966 if (Members.count(Key))
969 if (Key > LargestKey) {
970 // The largest index is always less than the interleave factor.
971 if (Index >= static_cast<int>(Factor))
975 } else if (Key < SmallestKey) {
976 // The largest index is always less than the interleave factor.
977 if (LargestKey - Key >= static_cast<int>(Factor))
983 // It's always safe to select the minimum alignment.
984 Align = std::min(Align, NewAlign);
985 Members[Key] = Instr;
989 /// \brief Get the member with the given index \p Index
991 /// \returns nullptr if contains no such member.
992 Instruction *getMember(unsigned Index) const {
993 int Key = SmallestKey + Index;
994 if (!Members.count(Key))
997 return Members.find(Key)->second;
1000 /// \brief Get the index for the given member. Unlike the key in the member
1001 /// map, the index starts from 0.
1002 unsigned getIndex(Instruction *Instr) const {
1003 for (auto I : Members)
1004 if (I.second == Instr)
1005 return I.first - SmallestKey;
1007 llvm_unreachable("InterleaveGroup contains no such member");
1010 Instruction *getInsertPos() const { return InsertPos; }
1011 void setInsertPos(Instruction *Inst) { InsertPos = Inst; }
1014 unsigned Factor; // Interleave Factor.
1017 DenseMap<int, Instruction *> Members;
1021 // To avoid breaking dependences, vectorized instructions of an interleave
1022 // group should be inserted at either the first load or the last store in
1025 // E.g. %even = load i32 // Insert Position
1026 // %add = add i32 %even // Use of %even
1030 // %odd = add i32 // Def of %odd
1031 // store i32 %odd // Insert Position
1032 Instruction *InsertPos;
1035 /// \brief Drive the analysis of interleaved memory accesses in the loop.
1037 /// Use this class to analyze interleaved accesses only when we can vectorize
1038 /// a loop. Otherwise it's meaningless to do analysis as the vectorization
1039 /// on interleaved accesses is unsafe.
1041 /// The analysis collects interleave groups and records the relationships
1042 /// between the member and the group in a map.
1043 class InterleavedAccessInfo {
1045 InterleavedAccessInfo(PredicatedScalarEvolution &PSE, Loop *L,
1046 DominatorTree *DT, LoopInfo *LI)
1047 : PSE(PSE), TheLoop(L), DT(DT), LI(LI), LAI(nullptr),
1048 RequiresScalarEpilogue(false) {}
1050 ~InterleavedAccessInfo() {
1051 SmallSet<InterleaveGroup *, 4> DelSet;
1052 // Avoid releasing a pointer twice.
1053 for (auto &I : InterleaveGroupMap)
1054 DelSet.insert(I.second);
1055 for (auto *Ptr : DelSet)
1059 /// \brief Analyze the interleaved accesses and collect them in interleave
1060 /// groups. Substitute symbolic strides using \p Strides.
1061 void analyzeInterleaving(const ValueToValueMap &Strides);
1063 /// \brief Check if \p Instr belongs to any interleave group.
1064 bool isInterleaved(Instruction *Instr) const {
1065 return InterleaveGroupMap.count(Instr);
1068 /// \brief Return the maximum interleave factor of all interleaved groups.
1069 unsigned getMaxInterleaveFactor() const {
1070 unsigned MaxFactor = 1;
1071 for (auto &Entry : InterleaveGroupMap)
1072 MaxFactor = std::max(MaxFactor, Entry.second->getFactor());
1076 /// \brief Get the interleave group that \p Instr belongs to.
1078 /// \returns nullptr if doesn't have such group.
1079 InterleaveGroup *getInterleaveGroup(Instruction *Instr) const {
1080 if (InterleaveGroupMap.count(Instr))
1081 return InterleaveGroupMap.find(Instr)->second;
1085 /// \brief Returns true if an interleaved group that may access memory
1086 /// out-of-bounds requires a scalar epilogue iteration for correctness.
1087 bool requiresScalarEpilogue() const { return RequiresScalarEpilogue; }
1089 /// \brief Initialize the LoopAccessInfo used for dependence checking.
1090 void setLAI(const LoopAccessInfo *Info) { LAI = Info; }
1093 /// A wrapper around ScalarEvolution, used to add runtime SCEV checks.
1094 /// Simplifies SCEV expressions in the context of existing SCEV assumptions.
1095 /// The interleaved access analysis can also add new predicates (for example
1096 /// by versioning strides of pointers).
1097 PredicatedScalarEvolution &PSE;
1101 const LoopAccessInfo *LAI;
1103 /// True if the loop may contain non-reversed interleaved groups with
1104 /// out-of-bounds accesses. We ensure we don't speculatively access memory
1105 /// out-of-bounds by executing at least one scalar epilogue iteration.
1106 bool RequiresScalarEpilogue;
1108 /// Holds the relationships between the members and the interleave group.
1109 DenseMap<Instruction *, InterleaveGroup *> InterleaveGroupMap;
1111 /// Holds dependences among the memory accesses in the loop. It maps a source
1112 /// access to a set of dependent sink accesses.
1113 DenseMap<Instruction *, SmallPtrSet<Instruction *, 2>> Dependences;
1115 /// \brief The descriptor for a strided memory access.
1116 struct StrideDescriptor {
1117 StrideDescriptor(int64_t Stride, const SCEV *Scev, uint64_t Size,
1119 : Stride(Stride), Scev(Scev), Size(Size), Align(Align) {}
1121 StrideDescriptor() = default;
1123 // The access's stride. It is negative for a reverse access.
1125 const SCEV *Scev = nullptr; // The scalar expression of this access
1126 uint64_t Size = 0; // The size of the memory object.
1127 unsigned Align = 0; // The alignment of this access.
1130 /// \brief A type for holding instructions and their stride descriptors.
1131 typedef std::pair<Instruction *, StrideDescriptor> StrideEntry;
1133 /// \brief Create a new interleave group with the given instruction \p Instr,
1134 /// stride \p Stride and alignment \p Align.
1136 /// \returns the newly created interleave group.
1137 InterleaveGroup *createInterleaveGroup(Instruction *Instr, int Stride,
1139 assert(!InterleaveGroupMap.count(Instr) &&
1140 "Already in an interleaved access group");
1141 InterleaveGroupMap[Instr] = new InterleaveGroup(Instr, Stride, Align);
1142 return InterleaveGroupMap[Instr];
1145 /// \brief Release the group and remove all the relationships.
1146 void releaseGroup(InterleaveGroup *Group) {
1147 for (unsigned i = 0; i < Group->getFactor(); i++)
1148 if (Instruction *Member = Group->getMember(i))
1149 InterleaveGroupMap.erase(Member);
1154 /// \brief Collect all the accesses with a constant stride in program order.
1155 void collectConstStrideAccesses(
1156 MapVector<Instruction *, StrideDescriptor> &AccessStrideInfo,
1157 const ValueToValueMap &Strides);
1159 /// \brief Returns true if \p Stride is allowed in an interleaved group.
1160 static bool isStrided(int Stride) {
1161 unsigned Factor = std::abs(Stride);
1162 return Factor >= 2 && Factor <= MaxInterleaveGroupFactor;
1165 /// \brief Returns true if \p BB is a predicated block.
1166 bool isPredicated(BasicBlock *BB) const {
1167 return LoopAccessInfo::blockNeedsPredication(BB, TheLoop, DT);
1170 /// \brief Returns true if LoopAccessInfo can be used for dependence queries.
1171 bool areDependencesValid() const {
1172 return LAI && LAI->getDepChecker().getDependences();
1175 /// \brief Returns true if memory accesses \p A and \p B can be reordered, if
1176 /// necessary, when constructing interleaved groups.
1178 /// \p A must precede \p B in program order. We return false if reordering is
1179 /// not necessary or is prevented because \p A and \p B may be dependent.
1180 bool canReorderMemAccessesForInterleavedGroups(StrideEntry *A,
1181 StrideEntry *B) const {
1183 // Code motion for interleaved accesses can potentially hoist strided loads
1184 // and sink strided stores. The code below checks the legality of the
1185 // following two conditions:
1187 // 1. Potentially moving a strided load (B) before any store (A) that
1190 // 2. Potentially moving a strided store (A) after any load or store (B)
1193 // It's legal to reorder A and B if we know there isn't a dependence from A
1194 // to B. Note that this determination is conservative since some
1195 // dependences could potentially be reordered safely.
1197 // A is potentially the source of a dependence.
1198 auto *Src = A->first;
1199 auto SrcDes = A->second;
1201 // B is potentially the sink of a dependence.
1202 auto *Sink = B->first;
1203 auto SinkDes = B->second;
1205 // Code motion for interleaved accesses can't violate WAR dependences.
1206 // Thus, reordering is legal if the source isn't a write.
1207 if (!Src->mayWriteToMemory())
1210 // At least one of the accesses must be strided.
1211 if (!isStrided(SrcDes.Stride) && !isStrided(SinkDes.Stride))
1214 // If dependence information is not available from LoopAccessInfo,
1215 // conservatively assume the instructions can't be reordered.
1216 if (!areDependencesValid())
1219 // If we know there is a dependence from source to sink, assume the
1220 // instructions can't be reordered. Otherwise, reordering is legal.
1221 return !Dependences.count(Src) || !Dependences.lookup(Src).count(Sink);
1224 /// \brief Collect the dependences from LoopAccessInfo.
1226 /// We process the dependences once during the interleaved access analysis to
1227 /// enable constant-time dependence queries.
1228 void collectDependences() {
1229 if (!areDependencesValid())
1231 auto *Deps = LAI->getDepChecker().getDependences();
1232 for (auto Dep : *Deps)
1233 Dependences[Dep.getSource(*LAI)].insert(Dep.getDestination(*LAI));
1237 /// Utility class for getting and setting loop vectorizer hints in the form
1238 /// of loop metadata.
1239 /// This class keeps a number of loop annotations locally (as member variables)
1240 /// and can, upon request, write them back as metadata on the loop. It will
1241 /// initially scan the loop for existing metadata, and will update the local
1242 /// values based on information in the loop.
1243 /// We cannot write all values to metadata, as the mere presence of some info,
1244 /// for example 'force', means a decision has been made. So, we need to be
1245 /// careful NOT to add them if the user hasn't specifically asked so.
1246 class LoopVectorizeHints {
1247 enum HintKind { HK_WIDTH, HK_UNROLL, HK_FORCE };
1249 /// Hint - associates name and validation with the hint value.
1252 unsigned Value; // This may have to change for non-numeric values.
1255 Hint(const char *Name, unsigned Value, HintKind Kind)
1256 : Name(Name), Value(Value), Kind(Kind) {}
1258 bool validate(unsigned Val) {
1261 return isPowerOf2_32(Val) && Val <= VectorizerParams::MaxVectorWidth;
1263 return isPowerOf2_32(Val) && Val <= MaxInterleaveFactor;
1271 /// Vectorization width.
1273 /// Vectorization interleave factor.
1275 /// Vectorization forced
1278 /// Return the loop metadata prefix.
1279 static StringRef Prefix() { return "llvm.loop."; }
1281 /// True if there is any unsafe math in the loop.
1282 bool PotentiallyUnsafe;
1286 FK_Undefined = -1, ///< Not selected.
1287 FK_Disabled = 0, ///< Forcing disabled.
1288 FK_Enabled = 1, ///< Forcing enabled.
1291 LoopVectorizeHints(const Loop *L, bool DisableInterleaving,
1292 OptimizationRemarkEmitter &ORE)
1293 : Width("vectorize.width", VectorizerParams::VectorizationFactor,
1295 Interleave("interleave.count", DisableInterleaving, HK_UNROLL),
1296 Force("vectorize.enable", FK_Undefined, HK_FORCE),
1297 PotentiallyUnsafe(false), TheLoop(L), ORE(ORE) {
1298 // Populate values with existing loop metadata.
1299 getHintsFromMetadata();
1301 // force-vector-interleave overrides DisableInterleaving.
1302 if (VectorizerParams::isInterleaveForced())
1303 Interleave.Value = VectorizerParams::VectorizationInterleave;
1305 DEBUG(if (DisableInterleaving && Interleave.Value == 1) dbgs()
1306 << "LV: Interleaving disabled by the pass manager\n");
1309 /// Mark the loop L as already vectorized by setting the width to 1.
1310 void setAlreadyVectorized() {
1311 Width.Value = Interleave.Value = 1;
1312 Hint Hints[] = {Width, Interleave};
1313 writeHintsToMetadata(Hints);
1316 bool allowVectorization(Function *F, Loop *L, bool AlwaysVectorize) const {
1317 if (getForce() == LoopVectorizeHints::FK_Disabled) {
1318 DEBUG(dbgs() << "LV: Not vectorizing: #pragma vectorize disable.\n");
1319 emitRemarkWithHints();
1323 if (!AlwaysVectorize && getForce() != LoopVectorizeHints::FK_Enabled) {
1324 DEBUG(dbgs() << "LV: Not vectorizing: No #pragma vectorize enable.\n");
1325 emitRemarkWithHints();
1329 if (getWidth() == 1 && getInterleave() == 1) {
1330 // FIXME: Add a separate metadata to indicate when the loop has already
1331 // been vectorized instead of setting width and count to 1.
1332 DEBUG(dbgs() << "LV: Not vectorizing: Disabled/already vectorized.\n");
1333 // FIXME: Add interleave.disable metadata. This will allow
1334 // vectorize.disable to be used without disabling the pass and errors
1335 // to differentiate between disabled vectorization and a width of 1.
1336 ORE.emit(OptimizationRemarkAnalysis(vectorizeAnalysisPassName(),
1337 "AllDisabled", L->getStartLoc(),
1339 << "loop not vectorized: vectorization and interleaving are "
1340 "explicitly disabled, or vectorize width and interleave "
1341 "count are both set to 1");
1348 /// Dumps all the hint information.
1349 void emitRemarkWithHints() const {
1350 using namespace ore;
1351 if (Force.Value == LoopVectorizeHints::FK_Disabled)
1352 ORE.emit(OptimizationRemarkMissed(LV_NAME, "MissedExplicitlyDisabled",
1353 TheLoop->getStartLoc(),
1354 TheLoop->getHeader())
1355 << "loop not vectorized: vectorization is explicitly disabled");
1357 OptimizationRemarkMissed R(LV_NAME, "MissedDetails",
1358 TheLoop->getStartLoc(), TheLoop->getHeader());
1359 R << "loop not vectorized";
1360 if (Force.Value == LoopVectorizeHints::FK_Enabled) {
1361 R << " (Force=" << NV("Force", true);
1362 if (Width.Value != 0)
1363 R << ", Vector Width=" << NV("VectorWidth", Width.Value);
1364 if (Interleave.Value != 0)
1365 R << ", Interleave Count=" << NV("InterleaveCount", Interleave.Value);
1372 unsigned getWidth() const { return Width.Value; }
1373 unsigned getInterleave() const { return Interleave.Value; }
1374 enum ForceKind getForce() const { return (ForceKind)Force.Value; }
1376 /// \brief If hints are provided that force vectorization, use the AlwaysPrint
1377 /// pass name to force the frontend to print the diagnostic.
1378 const char *vectorizeAnalysisPassName() const {
1379 if (getWidth() == 1)
1381 if (getForce() == LoopVectorizeHints::FK_Disabled)
1383 if (getForce() == LoopVectorizeHints::FK_Undefined && getWidth() == 0)
1385 return OptimizationRemarkAnalysis::AlwaysPrint;
1388 bool allowReordering() const {
1389 // When enabling loop hints are provided we allow the vectorizer to change
1390 // the order of operations that is given by the scalar loop. This is not
1391 // enabled by default because can be unsafe or inefficient. For example,
1392 // reordering floating-point operations will change the way round-off
1393 // error accumulates in the loop.
1394 return getForce() == LoopVectorizeHints::FK_Enabled || getWidth() > 1;
1397 bool isPotentiallyUnsafe() const {
1398 // Avoid FP vectorization if the target is unsure about proper support.
1399 // This may be related to the SIMD unit in the target not handling
1400 // IEEE 754 FP ops properly, or bad single-to-double promotions.
1401 // Otherwise, a sequence of vectorized loops, even without reduction,
1402 // could lead to different end results on the destination vectors.
1403 return getForce() != LoopVectorizeHints::FK_Enabled && PotentiallyUnsafe;
1406 void setPotentiallyUnsafe() { PotentiallyUnsafe = true; }
1409 /// Find hints specified in the loop metadata and update local values.
1410 void getHintsFromMetadata() {
1411 MDNode *LoopID = TheLoop->getLoopID();
1415 // First operand should refer to the loop id itself.
1416 assert(LoopID->getNumOperands() > 0 && "requires at least one operand");
1417 assert(LoopID->getOperand(0) == LoopID && "invalid loop id");
1419 for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
1420 const MDString *S = nullptr;
1421 SmallVector<Metadata *, 4> Args;
1423 // The expected hint is either a MDString or a MDNode with the first
1424 // operand a MDString.
1425 if (const MDNode *MD = dyn_cast<MDNode>(LoopID->getOperand(i))) {
1426 if (!MD || MD->getNumOperands() == 0)
1428 S = dyn_cast<MDString>(MD->getOperand(0));
1429 for (unsigned i = 1, ie = MD->getNumOperands(); i < ie; ++i)
1430 Args.push_back(MD->getOperand(i));
1432 S = dyn_cast<MDString>(LoopID->getOperand(i));
1433 assert(Args.size() == 0 && "too many arguments for MDString");
1439 // Check if the hint starts with the loop metadata prefix.
1440 StringRef Name = S->getString();
1441 if (Args.size() == 1)
1442 setHint(Name, Args[0]);
1446 /// Checks string hint with one operand and set value if valid.
1447 void setHint(StringRef Name, Metadata *Arg) {
1448 if (!Name.startswith(Prefix()))
1450 Name = Name.substr(Prefix().size(), StringRef::npos);
1452 const ConstantInt *C = mdconst::dyn_extract<ConstantInt>(Arg);
1455 unsigned Val = C->getZExtValue();
1457 Hint *Hints[] = {&Width, &Interleave, &Force};
1458 for (auto H : Hints) {
1459 if (Name == H->Name) {
1460 if (H->validate(Val))
1463 DEBUG(dbgs() << "LV: ignoring invalid hint '" << Name << "'\n");
1469 /// Create a new hint from name / value pair.
1470 MDNode *createHintMetadata(StringRef Name, unsigned V) const {
1471 LLVMContext &Context = TheLoop->getHeader()->getContext();
1472 Metadata *MDs[] = {MDString::get(Context, Name),
1473 ConstantAsMetadata::get(
1474 ConstantInt::get(Type::getInt32Ty(Context), V))};
1475 return MDNode::get(Context, MDs);
1478 /// Matches metadata with hint name.
1479 bool matchesHintMetadataName(MDNode *Node, ArrayRef<Hint> HintTypes) {
1480 MDString *Name = dyn_cast<MDString>(Node->getOperand(0));
1484 for (auto H : HintTypes)
1485 if (Name->getString().endswith(H.Name))
1490 /// Sets current hints into loop metadata, keeping other values intact.
1491 void writeHintsToMetadata(ArrayRef<Hint> HintTypes) {
1492 if (HintTypes.size() == 0)
1495 // Reserve the first element to LoopID (see below).
1496 SmallVector<Metadata *, 4> MDs(1);
1497 // If the loop already has metadata, then ignore the existing operands.
1498 MDNode *LoopID = TheLoop->getLoopID();
1500 for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
1501 MDNode *Node = cast<MDNode>(LoopID->getOperand(i));
1502 // If node in update list, ignore old value.
1503 if (!matchesHintMetadataName(Node, HintTypes))
1504 MDs.push_back(Node);
1508 // Now, add the missing hints.
1509 for (auto H : HintTypes)
1510 MDs.push_back(createHintMetadata(Twine(Prefix(), H.Name).str(), H.Value));
1512 // Replace current metadata node with new one.
1513 LLVMContext &Context = TheLoop->getHeader()->getContext();
1514 MDNode *NewLoopID = MDNode::get(Context, MDs);
1515 // Set operand 0 to refer to the loop id itself.
1516 NewLoopID->replaceOperandWith(0, NewLoopID);
1518 TheLoop->setLoopID(NewLoopID);
1521 /// The loop these hints belong to.
1522 const Loop *TheLoop;
1524 /// Interface to emit optimization remarks.
1525 OptimizationRemarkEmitter &ORE;
1528 static void emitMissedWarning(Function *F, Loop *L,
1529 const LoopVectorizeHints &LH,
1530 OptimizationRemarkEmitter *ORE) {
1531 LH.emitRemarkWithHints();
1533 if (LH.getForce() == LoopVectorizeHints::FK_Enabled) {
1534 if (LH.getWidth() != 1)
1535 ORE->emit(DiagnosticInfoOptimizationFailure(
1536 DEBUG_TYPE, "FailedRequestedVectorization",
1537 L->getStartLoc(), L->getHeader())
1538 << "loop not vectorized: "
1539 << "failed explicitly specified loop vectorization");
1540 else if (LH.getInterleave() != 1)
1541 ORE->emit(DiagnosticInfoOptimizationFailure(
1542 DEBUG_TYPE, "FailedRequestedInterleaving", L->getStartLoc(),
1544 << "loop not interleaved: "
1545 << "failed explicitly specified loop interleaving");
1549 /// LoopVectorizationLegality checks if it is legal to vectorize a loop, and
1550 /// to what vectorization factor.
1551 /// This class does not look at the profitability of vectorization, only the
1552 /// legality. This class has two main kinds of checks:
1553 /// * Memory checks - The code in canVectorizeMemory checks if vectorization
1554 /// will change the order of memory accesses in a way that will change the
1555 /// correctness of the program.
1556 /// * Scalars checks - The code in canVectorizeInstrs and canVectorizeMemory
1557 /// checks for a number of different conditions, such as the availability of a
1558 /// single induction variable, that all types are supported and vectorize-able,
1559 /// etc. This code reflects the capabilities of InnerLoopVectorizer.
1560 /// This class is also used by InnerLoopVectorizer for identifying
1561 /// induction variable and the different reduction variables.
1562 class LoopVectorizationLegality {
1564 LoopVectorizationLegality(
1565 Loop *L, PredicatedScalarEvolution &PSE, DominatorTree *DT,
1566 TargetLibraryInfo *TLI, AliasAnalysis *AA, Function *F,
1567 const TargetTransformInfo *TTI,
1568 std::function<const LoopAccessInfo &(Loop &)> *GetLAA, LoopInfo *LI,
1569 OptimizationRemarkEmitter *ORE, LoopVectorizationRequirements *R,
1570 LoopVectorizeHints *H)
1571 : NumPredStores(0), TheLoop(L), PSE(PSE), TLI(TLI), TTI(TTI), DT(DT),
1572 GetLAA(GetLAA), LAI(nullptr), ORE(ORE), InterleaveInfo(PSE, L, DT, LI),
1573 PrimaryInduction(nullptr), WidestIndTy(nullptr), HasFunNoNaNAttr(false),
1574 Requirements(R), Hints(H) {}
1576 /// ReductionList contains the reduction descriptors for all
1577 /// of the reductions that were found in the loop.
1578 typedef DenseMap<PHINode *, RecurrenceDescriptor> ReductionList;
1580 /// InductionList saves induction variables and maps them to the
1581 /// induction descriptor.
1582 typedef MapVector<PHINode *, InductionDescriptor> InductionList;
1584 /// RecurrenceSet contains the phi nodes that are recurrences other than
1585 /// inductions and reductions.
1586 typedef SmallPtrSet<const PHINode *, 8> RecurrenceSet;
1588 /// Returns true if it is legal to vectorize this loop.
1589 /// This does not mean that it is profitable to vectorize this
1590 /// loop, only that it is legal to do so.
1591 bool canVectorize();
1593 /// Returns the primary induction variable.
1594 PHINode *getPrimaryInduction() { return PrimaryInduction; }
1596 /// Returns the reduction variables found in the loop.
1597 ReductionList *getReductionVars() { return &Reductions; }
1599 /// Returns the induction variables found in the loop.
1600 InductionList *getInductionVars() { return &Inductions; }
1602 /// Return the first-order recurrences found in the loop.
1603 RecurrenceSet *getFirstOrderRecurrences() { return &FirstOrderRecurrences; }
1605 /// Return the set of instructions to sink to handle first-order recurrences.
1606 DenseMap<Instruction *, Instruction *> &getSinkAfter() { return SinkAfter; }
1608 /// Returns the widest induction type.
1609 Type *getWidestInductionType() { return WidestIndTy; }
1611 /// Returns True if V is an induction variable in this loop.
1612 bool isInductionVariable(const Value *V);
1614 /// Returns True if PN is a reduction variable in this loop.
1615 bool isReductionVariable(PHINode *PN) { return Reductions.count(PN); }
1617 /// Returns True if Phi is a first-order recurrence in this loop.
1618 bool isFirstOrderRecurrence(const PHINode *Phi);
1620 /// Return true if the block BB needs to be predicated in order for the loop
1621 /// to be vectorized.
1622 bool blockNeedsPredication(BasicBlock *BB);
1624 /// Check if this pointer is consecutive when vectorizing. This happens
1625 /// when the last index of the GEP is the induction variable, or that the
1626 /// pointer itself is an induction variable.
1627 /// This check allows us to vectorize A[idx] into a wide load/store.
1629 /// 0 - Stride is unknown or non-consecutive.
1630 /// 1 - Address is consecutive.
1631 /// -1 - Address is consecutive, and decreasing.
1632 int isConsecutivePtr(Value *Ptr);
1634 /// Returns true if the value V is uniform within the loop.
1635 bool isUniform(Value *V);
1637 /// Returns the information that we collected about runtime memory check.
1638 const RuntimePointerChecking *getRuntimePointerChecking() const {
1639 return LAI->getRuntimePointerChecking();
1642 const LoopAccessInfo *getLAI() const { return LAI; }
1644 /// \brief Check if \p Instr belongs to any interleaved access group.
1645 bool isAccessInterleaved(Instruction *Instr) {
1646 return InterleaveInfo.isInterleaved(Instr);
1649 /// \brief Return the maximum interleave factor of all interleaved groups.
1650 unsigned getMaxInterleaveFactor() const {
1651 return InterleaveInfo.getMaxInterleaveFactor();
1654 /// \brief Get the interleaved access group that \p Instr belongs to.
1655 const InterleaveGroup *getInterleavedAccessGroup(Instruction *Instr) {
1656 return InterleaveInfo.getInterleaveGroup(Instr);
1659 /// \brief Returns true if an interleaved group requires a scalar iteration
1660 /// to handle accesses with gaps.
1661 bool requiresScalarEpilogue() const {
1662 return InterleaveInfo.requiresScalarEpilogue();
1665 unsigned getMaxSafeDepDistBytes() { return LAI->getMaxSafeDepDistBytes(); }
1667 bool hasStride(Value *V) { return LAI->hasStride(V); }
1669 /// Returns true if the target machine supports masked store operation
1670 /// for the given \p DataType and kind of access to \p Ptr.
1671 bool isLegalMaskedStore(Type *DataType, Value *Ptr) {
1672 return isConsecutivePtr(Ptr) && TTI->isLegalMaskedStore(DataType);
1674 /// Returns true if the target machine supports masked load operation
1675 /// for the given \p DataType and kind of access to \p Ptr.
1676 bool isLegalMaskedLoad(Type *DataType, Value *Ptr) {
1677 return isConsecutivePtr(Ptr) && TTI->isLegalMaskedLoad(DataType);
1679 /// Returns true if the target machine supports masked scatter operation
1680 /// for the given \p DataType.
1681 bool isLegalMaskedScatter(Type *DataType) {
1682 return TTI->isLegalMaskedScatter(DataType);
1684 /// Returns true if the target machine supports masked gather operation
1685 /// for the given \p DataType.
1686 bool isLegalMaskedGather(Type *DataType) {
1687 return TTI->isLegalMaskedGather(DataType);
1689 /// Returns true if the target machine can represent \p V as a masked gather
1690 /// or scatter operation.
1691 bool isLegalGatherOrScatter(Value *V) {
1692 auto *LI = dyn_cast<LoadInst>(V);
1693 auto *SI = dyn_cast<StoreInst>(V);
1696 auto *Ptr = getPointerOperand(V);
1697 auto *Ty = cast<PointerType>(Ptr->getType())->getElementType();
1698 return (LI && isLegalMaskedGather(Ty)) || (SI && isLegalMaskedScatter(Ty));
1701 /// Returns true if vector representation of the instruction \p I
1703 bool isMaskRequired(const Instruction *I) { return (MaskedOp.count(I) != 0); }
1704 unsigned getNumStores() const { return LAI->getNumStores(); }
1705 unsigned getNumLoads() const { return LAI->getNumLoads(); }
1706 unsigned getNumPredStores() const { return NumPredStores; }
1708 /// Returns true if \p I is an instruction that will be scalarized with
1709 /// predication. Such instructions include conditional stores and
1710 /// instructions that may divide by zero.
1711 bool isScalarWithPredication(Instruction *I);
1713 /// Returns true if \p I is a memory instruction with consecutive memory
1714 /// access that can be widened.
1715 bool memoryInstructionCanBeWidened(Instruction *I, unsigned VF = 1);
1717 // Returns true if the NoNaN attribute is set on the function.
1718 bool hasFunNoNaNAttr() const { return HasFunNoNaNAttr; }
1721 /// Check if a single basic block loop is vectorizable.
1722 /// At this point we know that this is a loop with a constant trip count
1723 /// and we only need to check individual instructions.
1724 bool canVectorizeInstrs();
1726 /// When we vectorize loops we may change the order in which
1727 /// we read and write from memory. This method checks if it is
1728 /// legal to vectorize the code, considering only memory constrains.
1729 /// Returns true if the loop is vectorizable
1730 bool canVectorizeMemory();
1732 /// Return true if we can vectorize this loop using the IF-conversion
1734 bool canVectorizeWithIfConvert();
1736 /// Return true if all of the instructions in the block can be speculatively
1737 /// executed. \p SafePtrs is a list of addresses that are known to be legal
1738 /// and we know that we can read from them without segfault.
1739 bool blockCanBePredicated(BasicBlock *BB, SmallPtrSetImpl<Value *> &SafePtrs);
1741 /// Updates the vectorization state by adding \p Phi to the inductions list.
1742 /// This can set \p Phi as the main induction of the loop if \p Phi is a
1743 /// better choice for the main induction than the existing one.
1744 void addInductionPhi(PHINode *Phi, const InductionDescriptor &ID,
1745 SmallPtrSetImpl<Value *> &AllowedExit);
1747 /// Create an analysis remark that explains why vectorization failed
1749 /// \p RemarkName is the identifier for the remark. If \p I is passed it is
1750 /// an instruction that prevents vectorization. Otherwise the loop is used
1751 /// for the location of the remark. \return the remark object that can be
1753 OptimizationRemarkAnalysis
1754 createMissedAnalysis(StringRef RemarkName, Instruction *I = nullptr) const {
1755 return ::createMissedAnalysis(Hints->vectorizeAnalysisPassName(),
1756 RemarkName, TheLoop, I);
1759 /// \brief If an access has a symbolic strides, this maps the pointer value to
1760 /// the stride symbol.
1761 const ValueToValueMap *getSymbolicStrides() {
1762 // FIXME: Currently, the set of symbolic strides is sometimes queried before
1763 // it's collected. This happens from canVectorizeWithIfConvert, when the
1764 // pointer is checked to reference consecutive elements suitable for a
1766 return LAI ? &LAI->getSymbolicStrides() : nullptr;
1769 unsigned NumPredStores;
1771 /// The loop that we evaluate.
1773 /// A wrapper around ScalarEvolution used to add runtime SCEV checks.
1774 /// Applies dynamic knowledge to simplify SCEV expressions in the context
1775 /// of existing SCEV assumptions. The analysis will also add a minimal set
1776 /// of new predicates if this is required to enable vectorization and
1778 PredicatedScalarEvolution &PSE;
1779 /// Target Library Info.
1780 TargetLibraryInfo *TLI;
1781 /// Target Transform Info
1782 const TargetTransformInfo *TTI;
1785 // LoopAccess analysis.
1786 std::function<const LoopAccessInfo &(Loop &)> *GetLAA;
1787 // And the loop-accesses info corresponding to this loop. This pointer is
1788 // null until canVectorizeMemory sets it up.
1789 const LoopAccessInfo *LAI;
1790 /// Interface to emit optimization remarks.
1791 OptimizationRemarkEmitter *ORE;
1793 /// The interleave access information contains groups of interleaved accesses
1794 /// with the same stride and close to each other.
1795 InterleavedAccessInfo InterleaveInfo;
1797 // --- vectorization state --- //
1799 /// Holds the primary induction variable. This is the counter of the
1801 PHINode *PrimaryInduction;
1802 /// Holds the reduction variables.
1803 ReductionList Reductions;
1804 /// Holds all of the induction variables that we found in the loop.
1805 /// Notice that inductions don't need to start at zero and that induction
1806 /// variables can be pointers.
1807 InductionList Inductions;
1808 /// Holds the phi nodes that are first-order recurrences.
1809 RecurrenceSet FirstOrderRecurrences;
1810 /// Holds instructions that need to sink past other instructions to handle
1811 /// first-order recurrences.
1812 DenseMap<Instruction *, Instruction *> SinkAfter;
1813 /// Holds the widest induction type encountered.
1816 /// Allowed outside users. This holds the induction and reduction
1817 /// vars which can be accessed from outside the loop.
1818 SmallPtrSet<Value *, 4> AllowedExit;
1820 /// Can we assume the absence of NaNs.
1821 bool HasFunNoNaNAttr;
1823 /// Vectorization requirements that will go through late-evaluation.
1824 LoopVectorizationRequirements *Requirements;
1826 /// Used to emit an analysis of any legality issues.
1827 LoopVectorizeHints *Hints;
1829 /// While vectorizing these instructions we have to generate a
1830 /// call to the appropriate masked intrinsic
1831 SmallPtrSet<const Instruction *, 8> MaskedOp;
1834 /// LoopVectorizationCostModel - estimates the expected speedups due to
1836 /// In many cases vectorization is not profitable. This can happen because of
1837 /// a number of reasons. In this class we mainly attempt to predict the
1838 /// expected speedup/slowdowns due to the supported instruction set. We use the
1839 /// TargetTransformInfo to query the different backends for the cost of
1840 /// different operations.
1841 class LoopVectorizationCostModel {
1843 LoopVectorizationCostModel(Loop *L, PredicatedScalarEvolution &PSE,
1844 LoopInfo *LI, LoopVectorizationLegality *Legal,
1845 const TargetTransformInfo &TTI,
1846 const TargetLibraryInfo *TLI, DemandedBits *DB,
1847 AssumptionCache *AC,
1848 OptimizationRemarkEmitter *ORE, const Function *F,
1849 const LoopVectorizeHints *Hints)
1850 : TheLoop(L), PSE(PSE), LI(LI), Legal(Legal), TTI(TTI), TLI(TLI), DB(DB),
1851 AC(AC), ORE(ORE), TheFunction(F), Hints(Hints) {}
1853 /// \return An upper bound for the vectorization factor, or None if
1854 /// vectorization should be avoided up front.
1855 Optional<unsigned> computeMaxVF(bool OptForSize);
1857 /// Information about vectorization costs
1858 struct VectorizationFactor {
1859 unsigned Width; // Vector width with best cost
1860 unsigned Cost; // Cost of the loop with that width
1862 /// \return The most profitable vectorization factor and the cost of that VF.
1863 /// This method checks every power of two up to MaxVF. If UserVF is not ZERO
1864 /// then this vectorization factor will be selected if vectorization is
1866 VectorizationFactor selectVectorizationFactor(unsigned MaxVF);
1868 /// Setup cost-based decisions for user vectorization factor.
1869 void selectUserVectorizationFactor(unsigned UserVF) {
1870 collectUniformsAndScalars(UserVF);
1871 collectInstsToScalarize(UserVF);
1874 /// \return The size (in bits) of the smallest and widest types in the code
1875 /// that needs to be vectorized. We ignore values that remain scalar such as
1876 /// 64 bit loop indices.
1877 std::pair<unsigned, unsigned> getSmallestAndWidestTypes();
1879 /// \return The desired interleave count.
1880 /// If interleave count has been specified by metadata it will be returned.
1881 /// Otherwise, the interleave count is computed and returned. VF and LoopCost
1882 /// are the selected vectorization factor and the cost of the selected VF.
1883 unsigned selectInterleaveCount(bool OptForSize, unsigned VF,
1886 /// Memory access instruction may be vectorized in more than one way.
1887 /// Form of instruction after vectorization depends on cost.
1888 /// This function takes cost-based decisions for Load/Store instructions
1889 /// and collects them in a map. This decisions map is used for building
1890 /// the lists of loop-uniform and loop-scalar instructions.
1891 /// The calculated cost is saved with widening decision in order to
1892 /// avoid redundant calculations.
1893 void setCostBasedWideningDecision(unsigned VF);
1895 /// \brief A struct that represents some properties of the register usage
1897 struct RegisterUsage {
1898 /// Holds the number of loop invariant values that are used in the loop.
1899 unsigned LoopInvariantRegs;
1900 /// Holds the maximum number of concurrent live intervals in the loop.
1901 unsigned MaxLocalUsers;
1902 /// Holds the number of instructions in the loop.
1903 unsigned NumInstructions;
1906 /// \return Returns information about the register usages of the loop for the
1907 /// given vectorization factors.
1908 SmallVector<RegisterUsage, 8> calculateRegisterUsage(ArrayRef<unsigned> VFs);
1910 /// Collect values we want to ignore in the cost model.
1911 void collectValuesToIgnore();
1913 /// \returns The smallest bitwidth each instruction can be represented with.
1914 /// The vector equivalents of these instructions should be truncated to this
1916 const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const {
1920 /// \returns True if it is more profitable to scalarize instruction \p I for
1921 /// vectorization factor \p VF.
1922 bool isProfitableToScalarize(Instruction *I, unsigned VF) const {
1923 auto Scalars = InstsToScalarize.find(VF);
1924 assert(Scalars != InstsToScalarize.end() &&
1925 "VF not yet analyzed for scalarization profitability");
1926 return Scalars->second.count(I);
1929 /// Returns true if \p I is known to be uniform after vectorization.
1930 bool isUniformAfterVectorization(Instruction *I, unsigned VF) const {
1933 assert(Uniforms.count(VF) && "VF not yet analyzed for uniformity");
1934 auto UniformsPerVF = Uniforms.find(VF);
1935 return UniformsPerVF->second.count(I);
1938 /// Returns true if \p I is known to be scalar after vectorization.
1939 bool isScalarAfterVectorization(Instruction *I, unsigned VF) const {
1942 assert(Scalars.count(VF) && "Scalar values are not calculated for VF");
1943 auto ScalarsPerVF = Scalars.find(VF);
1944 return ScalarsPerVF->second.count(I);
1947 /// \returns True if instruction \p I can be truncated to a smaller bitwidth
1948 /// for vectorization factor \p VF.
1949 bool canTruncateToMinimalBitwidth(Instruction *I, unsigned VF) const {
1950 return VF > 1 && MinBWs.count(I) && !isProfitableToScalarize(I, VF) &&
1951 !isScalarAfterVectorization(I, VF);
1954 /// Decision that was taken during cost calculation for memory instruction.
1963 /// Save vectorization decision \p W and \p Cost taken by the cost model for
1964 /// instruction \p I and vector width \p VF.
1965 void setWideningDecision(Instruction *I, unsigned VF, InstWidening W,
1967 assert(VF >= 2 && "Expected VF >=2");
1968 WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
1971 /// Save vectorization decision \p W and \p Cost taken by the cost model for
1972 /// interleaving group \p Grp and vector width \p VF.
1973 void setWideningDecision(const InterleaveGroup *Grp, unsigned VF,
1974 InstWidening W, unsigned Cost) {
1975 assert(VF >= 2 && "Expected VF >=2");
1976 /// Broadcast this decicion to all instructions inside the group.
1977 /// But the cost will be assigned to one instruction only.
1978 for (unsigned i = 0; i < Grp->getFactor(); ++i) {
1979 if (auto *I = Grp->getMember(i)) {
1980 if (Grp->getInsertPos() == I)
1981 WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
1983 WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0);
1988 /// Return the cost model decision for the given instruction \p I and vector
1989 /// width \p VF. Return CM_Unknown if this instruction did not pass
1990 /// through the cost modeling.
1991 InstWidening getWideningDecision(Instruction *I, unsigned VF) {
1992 assert(VF >= 2 && "Expected VF >=2");
1993 std::pair<Instruction *, unsigned> InstOnVF = std::make_pair(I, VF);
1994 auto Itr = WideningDecisions.find(InstOnVF);
1995 if (Itr == WideningDecisions.end())
1997 return Itr->second.first;
2000 /// Return the vectorization cost for the given instruction \p I and vector
2002 unsigned getWideningCost(Instruction *I, unsigned VF) {
2003 assert(VF >= 2 && "Expected VF >=2");
2004 std::pair<Instruction *, unsigned> InstOnVF = std::make_pair(I, VF);
2005 assert(WideningDecisions.count(InstOnVF) && "The cost is not calculated");
2006 return WideningDecisions[InstOnVF].second;
2009 /// Return True if instruction \p I is an optimizable truncate whose operand
2010 /// is an induction variable. Such a truncate will be removed by adding a new
2011 /// induction variable with the destination type.
2012 bool isOptimizableIVTruncate(Instruction *I, unsigned VF) {
2014 // If the instruction is not a truncate, return false.
2015 auto *Trunc = dyn_cast<TruncInst>(I);
2019 // Get the source and destination types of the truncate.
2020 Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF);
2021 Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF);
2023 // If the truncate is free for the given types, return false. Replacing a
2024 // free truncate with an induction variable would add an induction variable
2025 // update instruction to each iteration of the loop. We exclude from this
2026 // check the primary induction variable since it will need an update
2027 // instruction regardless.
2028 Value *Op = Trunc->getOperand(0);
2029 if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy))
2032 // If the truncated value is not an induction variable, return false.
2033 return Legal->isInductionVariable(Op);
2037 /// \return An upper bound for the vectorization factor, larger than zero.
2038 /// One is returned if vectorization should best be avoided due to cost.
2039 unsigned computeFeasibleMaxVF(bool OptForSize);
2041 /// The vectorization cost is a combination of the cost itself and a boolean
2042 /// indicating whether any of the contributing operations will actually
2044 /// vector values after type legalization in the backend. If this latter value
2046 /// false, then all operations will be scalarized (i.e. no vectorization has
2047 /// actually taken place).
2048 typedef std::pair<unsigned, bool> VectorizationCostTy;
2050 /// Returns the expected execution cost. The unit of the cost does
2051 /// not matter because we use the 'cost' units to compare different
2052 /// vector widths. The cost that is returned is *not* normalized by
2053 /// the factor width.
2054 VectorizationCostTy expectedCost(unsigned VF);
2056 /// Returns the execution time cost of an instruction for a given vector
2057 /// width. Vector width of one means scalar.
2058 VectorizationCostTy getInstructionCost(Instruction *I, unsigned VF);
2060 /// The cost-computation logic from getInstructionCost which provides
2061 /// the vector type as an output parameter.
2062 unsigned getInstructionCost(Instruction *I, unsigned VF, Type *&VectorTy);
2064 /// Calculate vectorization cost of memory instruction \p I.
2065 unsigned getMemoryInstructionCost(Instruction *I, unsigned VF);
2067 /// The cost computation for scalarized memory instruction.
2068 unsigned getMemInstScalarizationCost(Instruction *I, unsigned VF);
2070 /// The cost computation for interleaving group of memory instructions.
2071 unsigned getInterleaveGroupCost(Instruction *I, unsigned VF);
2073 /// The cost computation for Gather/Scatter instruction.
2074 unsigned getGatherScatterCost(Instruction *I, unsigned VF);
2076 /// The cost computation for widening instruction \p I with consecutive
2078 unsigned getConsecutiveMemOpCost(Instruction *I, unsigned VF);
2080 /// The cost calculation for Load instruction \p I with uniform pointer -
2081 /// scalar load + broadcast.
2082 unsigned getUniformMemOpCost(Instruction *I, unsigned VF);
2084 /// Returns whether the instruction is a load or store and will be a emitted
2085 /// as a vector operation.
2086 bool isConsecutiveLoadOrStore(Instruction *I);
2088 /// Create an analysis remark that explains why vectorization failed
2090 /// \p RemarkName is the identifier for the remark. \return the remark object
2091 /// that can be streamed to.
2092 OptimizationRemarkAnalysis createMissedAnalysis(StringRef RemarkName) {
2093 return ::createMissedAnalysis(Hints->vectorizeAnalysisPassName(),
2094 RemarkName, TheLoop);
2097 /// Map of scalar integer values to the smallest bitwidth they can be legally
2098 /// represented as. The vector equivalents of these values should be truncated
2100 MapVector<Instruction *, uint64_t> MinBWs;
2102 /// A type representing the costs for instructions if they were to be
2103 /// scalarized rather than vectorized. The entries are Instruction-Cost
2105 typedef DenseMap<Instruction *, unsigned> ScalarCostsTy;
2107 /// A set containing all BasicBlocks that are known to present after
2108 /// vectorization as a predicated block.
2109 SmallPtrSet<BasicBlock *, 4> PredicatedBBsAfterVectorization;
2111 /// A map holding scalar costs for different vectorization factors. The
2112 /// presence of a cost for an instruction in the mapping indicates that the
2113 /// instruction will be scalarized when vectorizing with the associated
2114 /// vectorization factor. The entries are VF-ScalarCostTy pairs.
2115 DenseMap<unsigned, ScalarCostsTy> InstsToScalarize;
2117 /// Holds the instructions known to be uniform after vectorization.
2118 /// The data is collected per VF.
2119 DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> Uniforms;
2121 /// Holds the instructions known to be scalar after vectorization.
2122 /// The data is collected per VF.
2123 DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> Scalars;
2125 /// Holds the instructions (address computations) that are forced to be
2127 DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> ForcedScalars;
2129 /// Returns the expected difference in cost from scalarizing the expression
2130 /// feeding a predicated instruction \p PredInst. The instructions to
2131 /// scalarize and their scalar costs are collected in \p ScalarCosts. A
2132 /// non-negative return value implies the expression will be scalarized.
2133 /// Currently, only single-use chains are considered for scalarization.
2134 int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts,
2137 /// Collects the instructions to scalarize for each predicated instruction in
2139 void collectInstsToScalarize(unsigned VF);
2141 /// Collect the instructions that are uniform after vectorization. An
2142 /// instruction is uniform if we represent it with a single scalar value in
2143 /// the vectorized loop corresponding to each vector iteration. Examples of
2144 /// uniform instructions include pointer operands of consecutive or
2145 /// interleaved memory accesses. Note that although uniformity implies an
2146 /// instruction will be scalar, the reverse is not true. In general, a
2147 /// scalarized instruction will be represented by VF scalar values in the
2148 /// vectorized loop, each corresponding to an iteration of the original
2150 void collectLoopUniforms(unsigned VF);
2152 /// Collect the instructions that are scalar after vectorization. An
2153 /// instruction is scalar if it is known to be uniform or will be scalarized
2154 /// during vectorization. Non-uniform scalarized instructions will be
2155 /// represented by VF values in the vectorized loop, each corresponding to an
2156 /// iteration of the original scalar loop.
2157 void collectLoopScalars(unsigned VF);
2159 /// Collect Uniform and Scalar values for the given \p VF.
2160 /// The sets depend on CM decision for Load/Store instructions
2161 /// that may be vectorized as interleave, gather-scatter or scalarized.
2162 void collectUniformsAndScalars(unsigned VF) {
2163 // Do the analysis once.
2164 if (VF == 1 || Uniforms.count(VF))
2166 setCostBasedWideningDecision(VF);
2167 collectLoopUniforms(VF);
2168 collectLoopScalars(VF);
2171 /// Keeps cost model vectorization decision and cost for instructions.
2172 /// Right now it is used for memory instructions only.
2173 typedef DenseMap<std::pair<Instruction *, unsigned>,
2174 std::pair<InstWidening, unsigned>>
2177 DecisionList WideningDecisions;
2180 /// The loop that we evaluate.
2182 /// Predicated scalar evolution analysis.
2183 PredicatedScalarEvolution &PSE;
2184 /// Loop Info analysis.
2186 /// Vectorization legality.
2187 LoopVectorizationLegality *Legal;
2188 /// Vector target information.
2189 const TargetTransformInfo &TTI;
2190 /// Target Library Info.
2191 const TargetLibraryInfo *TLI;
2192 /// Demanded bits analysis.
2194 /// Assumption cache.
2195 AssumptionCache *AC;
2196 /// Interface to emit optimization remarks.
2197 OptimizationRemarkEmitter *ORE;
2199 const Function *TheFunction;
2200 /// Loop Vectorize Hint.
2201 const LoopVectorizeHints *Hints;
2202 /// Values to ignore in the cost model.
2203 SmallPtrSet<const Value *, 16> ValuesToIgnore;
2204 /// Values to ignore in the cost model when VF > 1.
2205 SmallPtrSet<const Value *, 16> VecValuesToIgnore;
2208 /// LoopVectorizationPlanner - drives the vectorization process after having
2209 /// passed Legality checks.
2210 class LoopVectorizationPlanner {
2212 LoopVectorizationPlanner(Loop *OrigLoop, LoopInfo *LI,
2213 LoopVectorizationLegality *Legal,
2214 LoopVectorizationCostModel &CM)
2215 : OrigLoop(OrigLoop), LI(LI), Legal(Legal), CM(CM) {}
2217 ~LoopVectorizationPlanner() {}
2219 /// Plan how to best vectorize, return the best VF and its cost.
2220 LoopVectorizationCostModel::VectorizationFactor plan(bool OptForSize,
2223 /// Generate the IR code for the vectorized loop.
2224 void executePlan(InnerLoopVectorizer &ILV);
2227 /// Collect the instructions from the original loop that would be trivially
2228 /// dead in the vectorized loop if generated.
2229 void collectTriviallyDeadInstructions(
2230 SmallPtrSetImpl<Instruction *> &DeadInstructions);
2233 /// The loop that we evaluate.
2236 /// Loop Info analysis.
2239 /// The legality analysis.
2240 LoopVectorizationLegality *Legal;
2242 /// The profitablity analysis.
2243 LoopVectorizationCostModel &CM;
2246 /// \brief This holds vectorization requirements that must be verified late in
2247 /// the process. The requirements are set by legalize and costmodel. Once
2248 /// vectorization has been determined to be possible and profitable the
2249 /// requirements can be verified by looking for metadata or compiler options.
2250 /// For example, some loops require FP commutativity which is only allowed if
2251 /// vectorization is explicitly specified or if the fast-math compiler option
2252 /// has been provided.
2253 /// Late evaluation of these requirements allows helpful diagnostics to be
2254 /// composed that tells the user what need to be done to vectorize the loop. For
2255 /// example, by specifying #pragma clang loop vectorize or -ffast-math. Late
2256 /// evaluation should be used only when diagnostics can generated that can be
2257 /// followed by a non-expert user.
2258 class LoopVectorizationRequirements {
2260 LoopVectorizationRequirements(OptimizationRemarkEmitter &ORE)
2261 : NumRuntimePointerChecks(0), UnsafeAlgebraInst(nullptr), ORE(ORE) {}
2263 void addUnsafeAlgebraInst(Instruction *I) {
2264 // First unsafe algebra instruction.
2265 if (!UnsafeAlgebraInst)
2266 UnsafeAlgebraInst = I;
2269 void addRuntimePointerChecks(unsigned Num) { NumRuntimePointerChecks = Num; }
2271 bool doesNotMeet(Function *F, Loop *L, const LoopVectorizeHints &Hints) {
2272 const char *PassName = Hints.vectorizeAnalysisPassName();
2273 bool Failed = false;
2274 if (UnsafeAlgebraInst && !Hints.allowReordering()) {
2276 OptimizationRemarkAnalysisFPCommute(PassName, "CantReorderFPOps",
2277 UnsafeAlgebraInst->getDebugLoc(),
2278 UnsafeAlgebraInst->getParent())
2279 << "loop not vectorized: cannot prove it is safe to reorder "
2280 "floating-point operations");
2284 // Test if runtime memcheck thresholds are exceeded.
2285 bool PragmaThresholdReached =
2286 NumRuntimePointerChecks > PragmaVectorizeMemoryCheckThreshold;
2287 bool ThresholdReached =
2288 NumRuntimePointerChecks > VectorizerParams::RuntimeMemoryCheckThreshold;
2289 if ((ThresholdReached && !Hints.allowReordering()) ||
2290 PragmaThresholdReached) {
2291 ORE.emit(OptimizationRemarkAnalysisAliasing(PassName, "CantReorderMemOps",
2294 << "loop not vectorized: cannot prove it is safe to reorder "
2295 "memory operations");
2296 DEBUG(dbgs() << "LV: Too many memory checks needed.\n");
2304 unsigned NumRuntimePointerChecks;
2305 Instruction *UnsafeAlgebraInst;
2307 /// Interface to emit optimization remarks.
2308 OptimizationRemarkEmitter &ORE;
2311 static void addAcyclicInnerLoop(Loop &L, SmallVectorImpl<Loop *> &V) {
2313 if (!hasCyclesInLoopBody(L))
2317 for (Loop *InnerL : L)
2318 addAcyclicInnerLoop(*InnerL, V);
2321 /// The LoopVectorize Pass.
2322 struct LoopVectorize : public FunctionPass {
2323 /// Pass identification, replacement for typeid
2326 explicit LoopVectorize(bool NoUnrolling = false, bool AlwaysVectorize = true)
2327 : FunctionPass(ID) {
2328 Impl.DisableUnrolling = NoUnrolling;
2329 Impl.AlwaysVectorize = AlwaysVectorize;
2330 initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
2333 LoopVectorizePass Impl;
2335 bool runOnFunction(Function &F) override {
2336 if (skipFunction(F))
2339 auto *SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE();
2340 auto *LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
2341 auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
2342 auto *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
2343 auto *BFI = &getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI();
2344 auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>();
2345 auto *TLI = TLIP ? &TLIP->getTLI() : nullptr;
2346 auto *AA = &getAnalysis<AAResultsWrapperPass>().getAAResults();
2347 auto *AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F);
2348 auto *LAA = &getAnalysis<LoopAccessLegacyAnalysis>();
2349 auto *DB = &getAnalysis<DemandedBitsWrapperPass>().getDemandedBits();
2350 auto *ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE();
2352 std::function<const LoopAccessInfo &(Loop &)> GetLAA =
2353 [&](Loop &L) -> const LoopAccessInfo & { return LAA->getInfo(&L); };
2355 return Impl.runImpl(F, *SE, *LI, *TTI, *DT, *BFI, TLI, *DB, *AA, *AC,
2359 void getAnalysisUsage(AnalysisUsage &AU) const override {
2360 AU.addRequired<AssumptionCacheTracker>();
2361 AU.addRequired<BlockFrequencyInfoWrapperPass>();
2362 AU.addRequired<DominatorTreeWrapperPass>();
2363 AU.addRequired<LoopInfoWrapperPass>();
2364 AU.addRequired<ScalarEvolutionWrapperPass>();
2365 AU.addRequired<TargetTransformInfoWrapperPass>();
2366 AU.addRequired<AAResultsWrapperPass>();
2367 AU.addRequired<LoopAccessLegacyAnalysis>();
2368 AU.addRequired<DemandedBitsWrapperPass>();
2369 AU.addRequired<OptimizationRemarkEmitterWrapperPass>();
2370 AU.addPreserved<LoopInfoWrapperPass>();
2371 AU.addPreserved<DominatorTreeWrapperPass>();
2372 AU.addPreserved<BasicAAWrapperPass>();
2373 AU.addPreserved<GlobalsAAWrapperPass>();
2377 } // end anonymous namespace
2379 //===----------------------------------------------------------------------===//
2380 // Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
2381 // LoopVectorizationCostModel and LoopVectorizationPlanner.
2382 //===----------------------------------------------------------------------===//
2384 Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) {
2385 // We need to place the broadcast of invariant variables outside the loop.
2386 Instruction *Instr = dyn_cast<Instruction>(V);
2387 bool NewInstr = (Instr && Instr->getParent() == LoopVectorBody);
2388 bool Invariant = OrigLoop->isLoopInvariant(V) && !NewInstr;
2390 // Place the code for broadcasting invariant variables in the new preheader.
2391 IRBuilder<>::InsertPointGuard Guard(Builder);
2393 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
2395 // Broadcast the scalar into all locations in the vector.
2396 Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast");
2401 void InnerLoopVectorizer::createVectorIntOrFpInductionPHI(
2402 const InductionDescriptor &II, Value *Step, Instruction *EntryVal) {
2403 Value *Start = II.getStartValue();
2405 // Construct the initial value of the vector IV in the vector loop preheader
2406 auto CurrIP = Builder.saveIP();
2407 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
2408 if (isa<TruncInst>(EntryVal)) {
2409 assert(Start->getType()->isIntegerTy() &&
2410 "Truncation requires an integer type");
2411 auto *TruncType = cast<IntegerType>(EntryVal->getType());
2412 Step = Builder.CreateTrunc(Step, TruncType);
2413 Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType);
2415 Value *SplatStart = Builder.CreateVectorSplat(VF, Start);
2416 Value *SteppedStart =
2417 getStepVector(SplatStart, 0, Step, II.getInductionOpcode());
2419 // We create vector phi nodes for both integer and floating-point induction
2420 // variables. Here, we determine the kind of arithmetic we will perform.
2421 Instruction::BinaryOps AddOp;
2422 Instruction::BinaryOps MulOp;
2423 if (Step->getType()->isIntegerTy()) {
2424 AddOp = Instruction::Add;
2425 MulOp = Instruction::Mul;
2427 AddOp = II.getInductionOpcode();
2428 MulOp = Instruction::FMul;
2431 // Multiply the vectorization factor by the step using integer or
2432 // floating-point arithmetic as appropriate.
2433 Value *ConstVF = getSignedIntOrFpConstant(Step->getType(), VF);
2434 Value *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, Step, ConstVF));
2436 // Create a vector splat to use in the induction update.
2438 // FIXME: If the step is non-constant, we create the vector splat with
2439 // IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't
2440 // handle a constant vector splat.
2441 Value *SplatVF = isa<Constant>(Mul)
2442 ? ConstantVector::getSplat(VF, cast<Constant>(Mul))
2443 : Builder.CreateVectorSplat(VF, Mul);
2444 Builder.restoreIP(CurrIP);
2446 // We may need to add the step a number of times, depending on the unroll
2447 // factor. The last of those goes into the PHI.
2448 PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind",
2449 &*LoopVectorBody->getFirstInsertionPt());
2450 Instruction *LastInduction = VecInd;
2451 for (unsigned Part = 0; Part < UF; ++Part) {
2452 VectorLoopValueMap.setVectorValue(EntryVal, Part, LastInduction);
2453 if (isa<TruncInst>(EntryVal))
2454 addMetadata(LastInduction, EntryVal);
2455 LastInduction = cast<Instruction>(addFastMathFlag(
2456 Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add")));
2459 // Move the last step to the end of the latch block. This ensures consistent
2460 // placement of all induction updates.
2461 auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch();
2462 auto *Br = cast<BranchInst>(LoopVectorLatch->getTerminator());
2463 auto *ICmp = cast<Instruction>(Br->getCondition());
2464 LastInduction->moveBefore(ICmp);
2465 LastInduction->setName("vec.ind.next");
2467 VecInd->addIncoming(SteppedStart, LoopVectorPreHeader);
2468 VecInd->addIncoming(LastInduction, LoopVectorLatch);
2471 bool InnerLoopVectorizer::shouldScalarizeInstruction(Instruction *I) const {
2472 return Cost->isScalarAfterVectorization(I, VF) ||
2473 Cost->isProfitableToScalarize(I, VF);
2476 bool InnerLoopVectorizer::needsScalarInduction(Instruction *IV) const {
2477 if (shouldScalarizeInstruction(IV))
2479 auto isScalarInst = [&](User *U) -> bool {
2480 auto *I = cast<Instruction>(U);
2481 return (OrigLoop->contains(I) && shouldScalarizeInstruction(I));
2483 return any_of(IV->users(), isScalarInst);
2486 void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, TruncInst *Trunc) {
2488 assert((IV->getType()->isIntegerTy() || IV != OldInduction) &&
2489 "Primary induction variable must have an integer type");
2491 auto II = Legal->getInductionVars()->find(IV);
2492 assert(II != Legal->getInductionVars()->end() && "IV is not an induction");
2494 auto ID = II->second;
2495 assert(IV->getType() == ID.getStartValue()->getType() && "Types must match");
2497 // The scalar value to broadcast. This will be derived from the canonical
2498 // induction variable.
2499 Value *ScalarIV = nullptr;
2501 // The value from the original loop to which we are mapping the new induction
2503 Instruction *EntryVal = Trunc ? cast<Instruction>(Trunc) : IV;
2505 // True if we have vectorized the induction variable.
2506 auto VectorizedIV = false;
2508 // Determine if we want a scalar version of the induction variable. This is
2509 // true if the induction variable itself is not widened, or if it has at
2510 // least one user in the loop that is not widened.
2511 auto NeedsScalarIV = VF > 1 && needsScalarInduction(EntryVal);
2513 // Generate code for the induction step. Note that induction steps are
2514 // required to be loop-invariant
2515 assert(PSE.getSE()->isLoopInvariant(ID.getStep(), OrigLoop) &&
2516 "Induction step should be loop invariant");
2517 auto &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
2518 Value *Step = nullptr;
2519 if (PSE.getSE()->isSCEVable(IV->getType())) {
2520 SCEVExpander Exp(*PSE.getSE(), DL, "induction");
2521 Step = Exp.expandCodeFor(ID.getStep(), ID.getStep()->getType(),
2522 LoopVectorPreHeader->getTerminator());
2524 Step = cast<SCEVUnknown>(ID.getStep())->getValue();
2527 // Try to create a new independent vector induction variable. If we can't
2528 // create the phi node, we will splat the scalar induction variable in each
2530 if (VF > 1 && !shouldScalarizeInstruction(EntryVal)) {
2531 createVectorIntOrFpInductionPHI(ID, Step, EntryVal);
2532 VectorizedIV = true;
2535 // If we haven't yet vectorized the induction variable, or if we will create
2536 // a scalar one, we need to define the scalar induction variable and step
2537 // values. If we were given a truncation type, truncate the canonical
2538 // induction variable and step. Otherwise, derive these values from the
2539 // induction descriptor.
2540 if (!VectorizedIV || NeedsScalarIV) {
2541 ScalarIV = Induction;
2542 if (IV != OldInduction) {
2543 ScalarIV = IV->getType()->isIntegerTy()
2544 ? Builder.CreateSExtOrTrunc(Induction, IV->getType())
2545 : Builder.CreateCast(Instruction::SIToFP, Induction,
2547 ScalarIV = ID.transform(Builder, ScalarIV, PSE.getSE(), DL);
2548 ScalarIV->setName("offset.idx");
2551 auto *TruncType = cast<IntegerType>(Trunc->getType());
2552 assert(Step->getType()->isIntegerTy() &&
2553 "Truncation requires an integer step");
2554 ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType);
2555 Step = Builder.CreateTrunc(Step, TruncType);
2559 // If we haven't yet vectorized the induction variable, splat the scalar
2560 // induction variable, and build the necessary step vectors.
2561 if (!VectorizedIV) {
2562 Value *Broadcasted = getBroadcastInstrs(ScalarIV);
2563 for (unsigned Part = 0; Part < UF; ++Part) {
2565 getStepVector(Broadcasted, VF * Part, Step, ID.getInductionOpcode());
2566 VectorLoopValueMap.setVectorValue(EntryVal, Part, EntryPart);
2568 addMetadata(EntryPart, Trunc);
2572 // If an induction variable is only used for counting loop iterations or
2573 // calculating addresses, it doesn't need to be widened. Create scalar steps
2574 // that can be used by instructions we will later scalarize. Note that the
2575 // addition of the scalar steps will not increase the number of instructions
2576 // in the loop in the common case prior to InstCombine. We will be trading
2577 // one vector extract for each scalar step.
2579 buildScalarSteps(ScalarIV, Step, EntryVal, ID);
2582 Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx, Value *Step,
2583 Instruction::BinaryOps BinOp) {
2584 // Create and check the types.
2585 assert(Val->getType()->isVectorTy() && "Must be a vector");
2586 int VLen = Val->getType()->getVectorNumElements();
2588 Type *STy = Val->getType()->getScalarType();
2589 assert((STy->isIntegerTy() || STy->isFloatingPointTy()) &&
2590 "Induction Step must be an integer or FP");
2591 assert(Step->getType() == STy && "Step has wrong type");
2593 SmallVector<Constant *, 8> Indices;
2595 if (STy->isIntegerTy()) {
2596 // Create a vector of consecutive numbers from zero to VF.
2597 for (int i = 0; i < VLen; ++i)
2598 Indices.push_back(ConstantInt::get(STy, StartIdx + i));
2600 // Add the consecutive indices to the vector value.
2601 Constant *Cv = ConstantVector::get(Indices);
2602 assert(Cv->getType() == Val->getType() && "Invalid consecutive vec");
2603 Step = Builder.CreateVectorSplat(VLen, Step);
2604 assert(Step->getType() == Val->getType() && "Invalid step vec");
2605 // FIXME: The newly created binary instructions should contain nsw/nuw flags,
2606 // which can be found from the original scalar operations.
2607 Step = Builder.CreateMul(Cv, Step);
2608 return Builder.CreateAdd(Val, Step, "induction");
2611 // Floating point induction.
2612 assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) &&
2613 "Binary Opcode should be specified for FP induction");
2614 // Create a vector of consecutive numbers from zero to VF.
2615 for (int i = 0; i < VLen; ++i)
2616 Indices.push_back(ConstantFP::get(STy, (double)(StartIdx + i)));
2618 // Add the consecutive indices to the vector value.
2619 Constant *Cv = ConstantVector::get(Indices);
2621 Step = Builder.CreateVectorSplat(VLen, Step);
2623 // Floating point operations had to be 'fast' to enable the induction.
2624 FastMathFlags Flags;
2625 Flags.setUnsafeAlgebra();
2627 Value *MulOp = Builder.CreateFMul(Cv, Step);
2628 if (isa<Instruction>(MulOp))
2629 // Have to check, MulOp may be a constant
2630 cast<Instruction>(MulOp)->setFastMathFlags(Flags);
2632 Value *BOp = Builder.CreateBinOp(BinOp, Val, MulOp, "induction");
2633 if (isa<Instruction>(BOp))
2634 cast<Instruction>(BOp)->setFastMathFlags(Flags);
2638 void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step,
2640 const InductionDescriptor &ID) {
2642 // We shouldn't have to build scalar steps if we aren't vectorizing.
2643 assert(VF > 1 && "VF should be greater than one");
2645 // Get the value type and ensure it and the step have the same integer type.
2646 Type *ScalarIVTy = ScalarIV->getType()->getScalarType();
2647 assert(ScalarIVTy == Step->getType() &&
2648 "Val and Step should have the same type");
2650 // We build scalar steps for both integer and floating-point induction
2651 // variables. Here, we determine the kind of arithmetic we will perform.
2652 Instruction::BinaryOps AddOp;
2653 Instruction::BinaryOps MulOp;
2654 if (ScalarIVTy->isIntegerTy()) {
2655 AddOp = Instruction::Add;
2656 MulOp = Instruction::Mul;
2658 AddOp = ID.getInductionOpcode();
2659 MulOp = Instruction::FMul;
2662 // Determine the number of scalars we need to generate for each unroll
2663 // iteration. If EntryVal is uniform, we only need to generate the first
2664 // lane. Otherwise, we generate all VF values.
2666 Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF) ? 1 : VF;
2668 // Compute the scalar steps and save the results in VectorLoopValueMap.
2669 for (unsigned Part = 0; Part < UF; ++Part) {
2670 for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
2671 auto *StartIdx = getSignedIntOrFpConstant(ScalarIVTy, VF * Part + Lane);
2672 auto *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, StartIdx, Step));
2673 auto *Add = addFastMathFlag(Builder.CreateBinOp(AddOp, ScalarIV, Mul));
2674 VectorLoopValueMap.setScalarValue(EntryVal, Part, Lane, Add);
2679 int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) {
2681 const ValueToValueMap &Strides = getSymbolicStrides() ? *getSymbolicStrides() :
2684 int Stride = getPtrStride(PSE, Ptr, TheLoop, Strides, true, false);
2685 if (Stride == 1 || Stride == -1)
2690 bool LoopVectorizationLegality::isUniform(Value *V) {
2691 return LAI->isUniform(V);
2694 Value *InnerLoopVectorizer::getOrCreateVectorValue(Value *V, unsigned Part) {
2695 assert(V != Induction && "The new induction variable should not be used.");
2696 assert(!V->getType()->isVectorTy() && "Can't widen a vector");
2697 assert(!V->getType()->isVoidTy() && "Type does not produce a value");
2699 // If we have a stride that is replaced by one, do it here.
2700 if (Legal->hasStride(V))
2701 V = ConstantInt::get(V->getType(), 1);
2703 // If we have a vector mapped to this value, return it.
2704 if (VectorLoopValueMap.hasVectorValue(V, Part))
2705 return VectorLoopValueMap.getVectorValue(V, Part);
2707 // If the value has not been vectorized, check if it has been scalarized
2708 // instead. If it has been scalarized, and we actually need the value in
2709 // vector form, we will construct the vector values on demand.
2710 if (VectorLoopValueMap.hasAnyScalarValue(V)) {
2712 Value *ScalarValue = VectorLoopValueMap.getScalarValue(V, Part, 0);
2714 // If we've scalarized a value, that value should be an instruction.
2715 auto *I = cast<Instruction>(V);
2717 // If we aren't vectorizing, we can just copy the scalar map values over to
2720 VectorLoopValueMap.setVectorValue(V, Part, ScalarValue);
2724 // Get the last scalar instruction we generated for V and Part. If the value
2725 // is known to be uniform after vectorization, this corresponds to lane zero
2726 // of the Part unroll iteration. Otherwise, the last instruction is the one
2727 // we created for the last vector lane of the Part unroll iteration.
2728 unsigned LastLane = Cost->isUniformAfterVectorization(I, VF) ? 0 : VF - 1;
2730 cast<Instruction>(VectorLoopValueMap.getScalarValue(V, Part, LastLane));
2732 // Set the insert point after the last scalarized instruction. This ensures
2733 // the insertelement sequence will directly follow the scalar definitions.
2734 auto OldIP = Builder.saveIP();
2735 auto NewIP = std::next(BasicBlock::iterator(LastInst));
2736 Builder.SetInsertPoint(&*NewIP);
2738 // However, if we are vectorizing, we need to construct the vector values.
2739 // If the value is known to be uniform after vectorization, we can just
2740 // broadcast the scalar value corresponding to lane zero for each unroll
2741 // iteration. Otherwise, we construct the vector values using insertelement
2742 // instructions. Since the resulting vectors are stored in
2743 // VectorLoopValueMap, we will only generate the insertelements once.
2744 Value *VectorValue = nullptr;
2745 if (Cost->isUniformAfterVectorization(I, VF)) {
2746 VectorValue = getBroadcastInstrs(ScalarValue);
2748 VectorValue = UndefValue::get(VectorType::get(V->getType(), VF));
2749 for (unsigned Lane = 0; Lane < VF; ++Lane)
2750 VectorValue = Builder.CreateInsertElement(
2751 VectorValue, getOrCreateScalarValue(V, Part, Lane),
2752 Builder.getInt32(Lane));
2754 VectorLoopValueMap.setVectorValue(V, Part, VectorValue);
2755 Builder.restoreIP(OldIP);
2759 // If this scalar is unknown, assume that it is a constant or that it is
2760 // loop invariant. Broadcast V and save the value for future uses.
2761 Value *B = getBroadcastInstrs(V);
2762 VectorLoopValueMap.setVectorValue(V, Part, B);
2766 Value *InnerLoopVectorizer::getOrCreateScalarValue(Value *V, unsigned Part,
2769 // If the value is not an instruction contained in the loop, it should
2770 // already be scalar.
2771 if (OrigLoop->isLoopInvariant(V))
2774 assert(Lane > 0 ? !Cost->isUniformAfterVectorization(cast<Instruction>(V), VF)
2775 : true && "Uniform values only have lane zero");
2777 // If the value from the original loop has not been vectorized, it is
2778 // represented by UF x VF scalar values in the new loop. Return the requested
2780 if (VectorLoopValueMap.hasScalarValue(V, Part, Lane))
2781 return VectorLoopValueMap.getScalarValue(V, Part, Lane);
2783 // If the value has not been scalarized, get its entry in VectorLoopValueMap
2784 // for the given unroll part. If this entry is not a vector type (i.e., the
2785 // vectorization factor is one), there is no need to generate an
2786 // extractelement instruction.
2787 auto *U = getOrCreateVectorValue(V, Part);
2788 if (!U->getType()->isVectorTy()) {
2789 assert(VF == 1 && "Value not scalarized has non-vector type");
2793 // Otherwise, the value from the original loop has been vectorized and is
2794 // represented by UF vector values. Extract and return the requested scalar
2795 // value from the appropriate vector lane.
2796 return Builder.CreateExtractElement(U, Builder.getInt32(Lane));
2799 Value *InnerLoopVectorizer::reverseVector(Value *Vec) {
2800 assert(Vec->getType()->isVectorTy() && "Invalid type");
2801 SmallVector<Constant *, 8> ShuffleMask;
2802 for (unsigned i = 0; i < VF; ++i)
2803 ShuffleMask.push_back(Builder.getInt32(VF - i - 1));
2805 return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()),
2806 ConstantVector::get(ShuffleMask),
2810 // Try to vectorize the interleave group that \p Instr belongs to.
2812 // E.g. Translate following interleaved load group (factor = 3):
2813 // for (i = 0; i < N; i+=3) {
2814 // R = Pic[i]; // Member of index 0
2815 // G = Pic[i+1]; // Member of index 1
2816 // B = Pic[i+2]; // Member of index 2
2817 // ... // do something to R, G, B
2820 // %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B
2821 // %R.vec = shuffle %wide.vec, undef, <0, 3, 6, 9> ; R elements
2822 // %G.vec = shuffle %wide.vec, undef, <1, 4, 7, 10> ; G elements
2823 // %B.vec = shuffle %wide.vec, undef, <2, 5, 8, 11> ; B elements
2825 // Or translate following interleaved store group (factor = 3):
2826 // for (i = 0; i < N; i+=3) {
2827 // ... do something to R, G, B
2828 // Pic[i] = R; // Member of index 0
2829 // Pic[i+1] = G; // Member of index 1
2830 // Pic[i+2] = B; // Member of index 2
2833 // %R_G.vec = shuffle %R.vec, %G.vec, <0, 1, 2, ..., 7>
2834 // %B_U.vec = shuffle %B.vec, undef, <0, 1, 2, 3, u, u, u, u>
2835 // %interleaved.vec = shuffle %R_G.vec, %B_U.vec,
2836 // <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> ; Interleave R,G,B elements
2837 // store <12 x i32> %interleaved.vec ; Write 4 tuples of R,G,B
2838 void InnerLoopVectorizer::vectorizeInterleaveGroup(Instruction *Instr) {
2839 const InterleaveGroup *Group = Legal->getInterleavedAccessGroup(Instr);
2840 assert(Group && "Fail to get an interleaved access group.");
2842 // Skip if current instruction is not the insert position.
2843 if (Instr != Group->getInsertPos())
2846 Value *Ptr = getPointerOperand(Instr);
2848 // Prepare for the vector type of the interleaved load/store.
2849 Type *ScalarTy = getMemInstValueType(Instr);
2850 unsigned InterleaveFactor = Group->getFactor();
2851 Type *VecTy = VectorType::get(ScalarTy, InterleaveFactor * VF);
2852 Type *PtrTy = VecTy->getPointerTo(getMemInstAddressSpace(Instr));
2854 // Prepare for the new pointers.
2855 setDebugLocFromInst(Builder, Ptr);
2856 SmallVector<Value *, 2> NewPtrs;
2857 unsigned Index = Group->getIndex(Instr);
2859 // If the group is reverse, adjust the index to refer to the last vector lane
2860 // instead of the first. We adjust the index from the first vector lane,
2861 // rather than directly getting the pointer for lane VF - 1, because the
2862 // pointer operand of the interleaved access is supposed to be uniform. For
2863 // uniform instructions, we're only required to generate a value for the
2864 // first vector lane in each unroll iteration.
2865 if (Group->isReverse())
2866 Index += (VF - 1) * Group->getFactor();
2868 for (unsigned Part = 0; Part < UF; Part++) {
2869 Value *NewPtr = getOrCreateScalarValue(Ptr, Part, 0);
2871 // Notice current instruction could be any index. Need to adjust the address
2872 // to the member of index 0.
2874 // E.g. a = A[i+1]; // Member of index 1 (Current instruction)
2875 // b = A[i]; // Member of index 0
2876 // Current pointer is pointed to A[i+1], adjust it to A[i].
2878 // E.g. A[i+1] = a; // Member of index 1
2879 // A[i] = b; // Member of index 0
2880 // A[i+2] = c; // Member of index 2 (Current instruction)
2881 // Current pointer is pointed to A[i+2], adjust it to A[i].
2882 NewPtr = Builder.CreateGEP(NewPtr, Builder.getInt32(-Index));
2884 // Cast to the vector pointer type.
2885 NewPtrs.push_back(Builder.CreateBitCast(NewPtr, PtrTy));
2888 setDebugLocFromInst(Builder, Instr);
2889 Value *UndefVec = UndefValue::get(VecTy);
2891 // Vectorize the interleaved load group.
2892 if (isa<LoadInst>(Instr)) {
2894 // For each unroll part, create a wide load for the group.
2895 SmallVector<Value *, 2> NewLoads;
2896 for (unsigned Part = 0; Part < UF; Part++) {
2897 auto *NewLoad = Builder.CreateAlignedLoad(
2898 NewPtrs[Part], Group->getAlignment(), "wide.vec");
2899 addMetadata(NewLoad, Instr);
2900 NewLoads.push_back(NewLoad);
2903 // For each member in the group, shuffle out the appropriate data from the
2905 for (unsigned I = 0; I < InterleaveFactor; ++I) {
2906 Instruction *Member = Group->getMember(I);
2908 // Skip the gaps in the group.
2912 Constant *StrideMask = createStrideMask(Builder, I, InterleaveFactor, VF);
2913 for (unsigned Part = 0; Part < UF; Part++) {
2914 Value *StridedVec = Builder.CreateShuffleVector(
2915 NewLoads[Part], UndefVec, StrideMask, "strided.vec");
2917 // If this member has different type, cast the result type.
2918 if (Member->getType() != ScalarTy) {
2919 VectorType *OtherVTy = VectorType::get(Member->getType(), VF);
2920 StridedVec = Builder.CreateBitOrPointerCast(StridedVec, OtherVTy);
2923 if (Group->isReverse())
2924 StridedVec = reverseVector(StridedVec);
2926 VectorLoopValueMap.setVectorValue(Member, Part, StridedVec);
2932 // The sub vector type for current instruction.
2933 VectorType *SubVT = VectorType::get(ScalarTy, VF);
2935 // Vectorize the interleaved store group.
2936 for (unsigned Part = 0; Part < UF; Part++) {
2937 // Collect the stored vector from each member.
2938 SmallVector<Value *, 4> StoredVecs;
2939 for (unsigned i = 0; i < InterleaveFactor; i++) {
2940 // Interleaved store group doesn't allow a gap, so each index has a member
2941 Instruction *Member = Group->getMember(i);
2942 assert(Member && "Fail to get a member from an interleaved store group");
2944 Value *StoredVec = getOrCreateVectorValue(
2945 cast<StoreInst>(Member)->getValueOperand(), Part);
2946 if (Group->isReverse())
2947 StoredVec = reverseVector(StoredVec);
2949 // If this member has different type, cast it to an unified type.
2950 if (StoredVec->getType() != SubVT)
2951 StoredVec = Builder.CreateBitOrPointerCast(StoredVec, SubVT);
2953 StoredVecs.push_back(StoredVec);
2956 // Concatenate all vectors into a wide vector.
2957 Value *WideVec = concatenateVectors(Builder, StoredVecs);
2959 // Interleave the elements in the wide vector.
2960 Constant *IMask = createInterleaveMask(Builder, VF, InterleaveFactor);
2961 Value *IVec = Builder.CreateShuffleVector(WideVec, UndefVec, IMask,
2964 Instruction *NewStoreInstr =
2965 Builder.CreateAlignedStore(IVec, NewPtrs[Part], Group->getAlignment());
2966 addMetadata(NewStoreInstr, Instr);
2970 void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr) {
2971 // Attempt to issue a wide load.
2972 LoadInst *LI = dyn_cast<LoadInst>(Instr);
2973 StoreInst *SI = dyn_cast<StoreInst>(Instr);
2975 assert((LI || SI) && "Invalid Load/Store instruction");
2977 LoopVectorizationCostModel::InstWidening Decision =
2978 Cost->getWideningDecision(Instr, VF);
2979 assert(Decision != LoopVectorizationCostModel::CM_Unknown &&
2980 "CM decision should be taken at this point");
2981 if (Decision == LoopVectorizationCostModel::CM_Interleave)
2982 return vectorizeInterleaveGroup(Instr);
2984 Type *ScalarDataTy = getMemInstValueType(Instr);
2985 Type *DataTy = VectorType::get(ScalarDataTy, VF);
2986 Value *Ptr = getPointerOperand(Instr);
2987 unsigned Alignment = getMemInstAlignment(Instr);
2988 // An alignment of 0 means target abi alignment. We need to use the scalar's
2989 // target abi alignment in such a case.
2990 const DataLayout &DL = Instr->getModule()->getDataLayout();
2992 Alignment = DL.getABITypeAlignment(ScalarDataTy);
2993 unsigned AddressSpace = getMemInstAddressSpace(Instr);
2995 // Scalarize the memory instruction if necessary.
2996 if (Decision == LoopVectorizationCostModel::CM_Scalarize)
2997 return scalarizeInstruction(Instr, Legal->isScalarWithPredication(Instr));
2999 // Determine if the pointer operand of the access is either consecutive or
3000 // reverse consecutive.
3001 int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
3002 bool Reverse = ConsecutiveStride < 0;
3003 bool CreateGatherScatter =
3004 (Decision == LoopVectorizationCostModel::CM_GatherScatter);
3006 // Either Ptr feeds a vector load/store, or a vector GEP should feed a vector
3007 // gather/scatter. Otherwise Decision should have been to Scalarize.
3008 assert((ConsecutiveStride || CreateGatherScatter) &&
3009 "The instruction should be scalarized");
3011 // Handle consecutive loads/stores.
3012 if (ConsecutiveStride)
3013 Ptr = getOrCreateScalarValue(Ptr, 0, 0);
3015 VectorParts Mask = createBlockInMask(Instr->getParent());
3018 assert(!Legal->isUniform(SI->getPointerOperand()) &&
3019 "We do not allow storing to uniform addresses");
3020 setDebugLocFromInst(Builder, SI);
3022 for (unsigned Part = 0; Part < UF; ++Part) {
3023 Instruction *NewSI = nullptr;
3024 Value *StoredVal = getOrCreateVectorValue(SI->getValueOperand(), Part);
3025 if (CreateGatherScatter) {
3026 Value *MaskPart = Legal->isMaskRequired(SI) ? Mask[Part] : nullptr;
3027 Value *VectorGep = getOrCreateVectorValue(Ptr, Part);
3028 NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment,
3031 // Calculate the pointer for the specific unroll-part.
3033 Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(Part * VF));
3036 // If we store to reverse consecutive memory locations, then we need
3037 // to reverse the order of elements in the stored value.
3038 StoredVal = reverseVector(StoredVal);
3039 // We don't want to update the value in the map as it might be used in
3040 // another expression. So don't call resetVectorValue(StoredVal).
3042 // If the address is consecutive but reversed, then the
3043 // wide store needs to start at the last vector element.
3045 Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(-Part * VF));
3047 Builder.CreateGEP(nullptr, PartPtr, Builder.getInt32(1 - VF));
3048 Mask[Part] = reverseVector(Mask[Part]);
3052 Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
3054 if (Legal->isMaskRequired(SI))
3055 NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment,
3058 NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment);
3060 addMetadata(NewSI, SI);
3066 assert(LI && "Must have a load instruction");
3067 setDebugLocFromInst(Builder, LI);
3068 for (unsigned Part = 0; Part < UF; ++Part) {
3070 if (CreateGatherScatter) {
3071 Value *MaskPart = Legal->isMaskRequired(LI) ? Mask[Part] : nullptr;
3072 Value *VectorGep = getOrCreateVectorValue(Ptr, Part);
3073 NewLI = Builder.CreateMaskedGather(VectorGep, Alignment, MaskPart,
3074 nullptr, "wide.masked.gather");
3075 addMetadata(NewLI, LI);
3077 // Calculate the pointer for the specific unroll-part.
3079 Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(Part * VF));
3082 // If the address is consecutive but reversed, then the
3083 // wide load needs to start at the last vector element.
3084 PartPtr = Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(-Part * VF));
3085 PartPtr = Builder.CreateGEP(nullptr, PartPtr, Builder.getInt32(1 - VF));
3086 Mask[Part] = reverseVector(Mask[Part]);
3090 Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
3091 if (Legal->isMaskRequired(LI))
3092 NewLI = Builder.CreateMaskedLoad(VecPtr, Alignment, Mask[Part],
3093 UndefValue::get(DataTy),
3094 "wide.masked.load");
3096 NewLI = Builder.CreateAlignedLoad(VecPtr, Alignment, "wide.load");
3098 // Add metadata to the load, but setVectorValue to the reverse shuffle.
3099 addMetadata(NewLI, LI);
3101 NewLI = reverseVector(NewLI);
3103 VectorLoopValueMap.setVectorValue(Instr, Part, NewLI);
3107 void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr,
3108 bool IfPredicateInstr) {
3109 assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
3110 DEBUG(dbgs() << "LV: Scalarizing"
3111 << (IfPredicateInstr ? " and predicating:" : ":") << *Instr
3113 // Holds vector parameters or scalars, in case of uniform vals.
3114 SmallVector<VectorParts, 4> Params;
3116 setDebugLocFromInst(Builder, Instr);
3118 // Does this instruction return a value ?
3119 bool IsVoidRetTy = Instr->getType()->isVoidTy();
3122 if (IfPredicateInstr)
3123 Cond = createBlockInMask(Instr->getParent());
3125 // Determine the number of scalars we need to generate for each unroll
3126 // iteration. If the instruction is uniform, we only need to generate the
3127 // first lane. Otherwise, we generate all VF values.
3128 unsigned Lanes = Cost->isUniformAfterVectorization(Instr, VF) ? 1 : VF;
3130 // For each vector unroll 'part':
3131 for (unsigned Part = 0; Part < UF; ++Part) {
3132 // For each scalar that we create:
3133 for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
3136 Value *Cmp = nullptr;
3137 if (IfPredicateInstr) {
3139 if (Cmp->getType()->isVectorTy())
3140 Cmp = Builder.CreateExtractElement(Cmp, Builder.getInt32(Lane));
3141 Cmp = Builder.CreateICmp(ICmpInst::ICMP_EQ, Cmp,
3142 ConstantInt::get(Cmp->getType(), 1));
3145 Instruction *Cloned = Instr->clone();
3147 Cloned->setName(Instr->getName() + ".cloned");
3149 // Replace the operands of the cloned instructions with their scalar
3150 // equivalents in the new loop.
3151 for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
3152 auto *NewOp = getOrCreateScalarValue(Instr->getOperand(op), Part, Lane);
3153 Cloned->setOperand(op, NewOp);
3155 addNewMetadata(Cloned, Instr);
3157 // Place the cloned scalar in the new loop.
3158 Builder.Insert(Cloned);
3160 // Add the cloned scalar to the scalar map entry.
3161 VectorLoopValueMap.setScalarValue(Instr, Part, Lane, Cloned);
3163 // If we just cloned a new assumption, add it the assumption cache.
3164 if (auto *II = dyn_cast<IntrinsicInst>(Cloned))
3165 if (II->getIntrinsicID() == Intrinsic::assume)
3166 AC->registerAssumption(II);
3169 if (IfPredicateInstr)
3170 PredicatedInstructions.push_back(std::make_pair(Cloned, Cmp));
3175 PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start,
3176 Value *End, Value *Step,
3178 BasicBlock *Header = L->getHeader();
3179 BasicBlock *Latch = L->getLoopLatch();
3180 // As we're just creating this loop, it's possible no latch exists
3181 // yet. If so, use the header as this will be a single block loop.
3185 IRBuilder<> Builder(&*Header->getFirstInsertionPt());
3186 Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction);
3187 setDebugLocFromInst(Builder, OldInst);
3188 auto *Induction = Builder.CreatePHI(Start->getType(), 2, "index");
3190 Builder.SetInsertPoint(Latch->getTerminator());
3191 setDebugLocFromInst(Builder, OldInst);
3193 // Create i+1 and fill the PHINode.
3194 Value *Next = Builder.CreateAdd(Induction, Step, "index.next");
3195 Induction->addIncoming(Start, L->getLoopPreheader());
3196 Induction->addIncoming(Next, Latch);
3197 // Create the compare.
3198 Value *ICmp = Builder.CreateICmpEQ(Next, End);
3199 Builder.CreateCondBr(ICmp, L->getExitBlock(), Header);
3201 // Now we have two terminators. Remove the old one from the block.
3202 Latch->getTerminator()->eraseFromParent();
3207 Value *InnerLoopVectorizer::getOrCreateTripCount(Loop *L) {
3211 IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
3212 // Find the loop boundaries.
3213 ScalarEvolution *SE = PSE.getSE();
3214 const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount();
3215 assert(BackedgeTakenCount != SE->getCouldNotCompute() &&
3216 "Invalid loop count");
3218 Type *IdxTy = Legal->getWidestInductionType();
3220 // The exit count might have the type of i64 while the phi is i32. This can
3221 // happen if we have an induction variable that is sign extended before the
3222 // compare. The only way that we get a backedge taken count is that the
3223 // induction variable was signed and as such will not overflow. In such a case
3224 // truncation is legal.
3225 if (BackedgeTakenCount->getType()->getPrimitiveSizeInBits() >
3226 IdxTy->getPrimitiveSizeInBits())
3227 BackedgeTakenCount = SE->getTruncateOrNoop(BackedgeTakenCount, IdxTy);
3228 BackedgeTakenCount = SE->getNoopOrZeroExtend(BackedgeTakenCount, IdxTy);
3230 // Get the total trip count from the count by adding 1.
3231 const SCEV *ExitCount = SE->getAddExpr(
3232 BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType()));
3234 const DataLayout &DL = L->getHeader()->getModule()->getDataLayout();
3236 // Expand the trip count and place the new instructions in the preheader.
3237 // Notice that the pre-header does not change, only the loop body.
3238 SCEVExpander Exp(*SE, DL, "induction");
3240 // Count holds the overall loop count (N).
3241 TripCount = Exp.expandCodeFor(ExitCount, ExitCount->getType(),
3242 L->getLoopPreheader()->getTerminator());
3244 if (TripCount->getType()->isPointerTy())
3246 CastInst::CreatePointerCast(TripCount, IdxTy, "exitcount.ptrcnt.to.int",
3247 L->getLoopPreheader()->getTerminator());
3252 Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) {
3253 if (VectorTripCount)
3254 return VectorTripCount;
3256 Value *TC = getOrCreateTripCount(L);
3257 IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
3259 // Now we need to generate the expression for the part of the loop that the
3260 // vectorized body will execute. This is equal to N - (N % Step) if scalar
3261 // iterations are not required for correctness, or N - Step, otherwise. Step
3262 // is equal to the vectorization factor (number of SIMD elements) times the
3263 // unroll factor (number of SIMD instructions).
3264 Constant *Step = ConstantInt::get(TC->getType(), VF * UF);
3265 Value *R = Builder.CreateURem(TC, Step, "n.mod.vf");
3267 // If there is a non-reversed interleaved group that may speculatively access
3268 // memory out-of-bounds, we need to ensure that there will be at least one
3269 // iteration of the scalar epilogue loop. Thus, if the step evenly divides
3270 // the trip count, we set the remainder to be equal to the step. If the step
3271 // does not evenly divide the trip count, no adjustment is necessary since
3272 // there will already be scalar iterations. Note that the minimum iterations
3273 // check ensures that N >= Step.
3274 if (VF > 1 && Legal->requiresScalarEpilogue()) {
3275 auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0));
3276 R = Builder.CreateSelect(IsZero, Step, R);
3279 VectorTripCount = Builder.CreateSub(TC, R, "n.vec");
3281 return VectorTripCount;
3284 void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L,
3285 BasicBlock *Bypass) {
3286 Value *Count = getOrCreateTripCount(L);
3287 BasicBlock *BB = L->getLoopPreheader();
3288 IRBuilder<> Builder(BB->getTerminator());
3290 // Generate code to check if the loop's trip count is less than VF * UF, or
3291 // equal to it in case a scalar epilogue is required; this implies that the
3292 // vector trip count is zero. This check also covers the case where adding one
3293 // to the backedge-taken count overflowed leading to an incorrect trip count
3294 // of zero. In this case we will also jump to the scalar loop.
3295 auto P = Legal->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE
3296 : ICmpInst::ICMP_ULT;
3297 Value *CheckMinIters = Builder.CreateICmp(
3298 P, Count, ConstantInt::get(Count->getType(), VF * UF), "min.iters.check");
3300 BasicBlock *NewBB = BB->splitBasicBlock(BB->getTerminator(), "vector.ph");
3301 // Update dominator tree immediately if the generated block is a
3302 // LoopBypassBlock because SCEV expansions to generate loop bypass
3303 // checks may query it before the current function is finished.
3304 DT->addNewBlock(NewBB, BB);
3305 if (L->getParentLoop())
3306 L->getParentLoop()->addBasicBlockToLoop(NewBB, *LI);
3307 ReplaceInstWithInst(BB->getTerminator(),
3308 BranchInst::Create(Bypass, NewBB, CheckMinIters));
3309 LoopBypassBlocks.push_back(BB);
3312 void InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) {
3313 BasicBlock *BB = L->getLoopPreheader();
3315 // Generate the code to check that the SCEV assumptions that we made.
3316 // We want the new basic block to start at the first instruction in a
3317 // sequence of instructions that form a check.
3318 SCEVExpander Exp(*PSE.getSE(), Bypass->getModule()->getDataLayout(),
3321 Exp.expandCodeForPredicate(&PSE.getUnionPredicate(), BB->getTerminator());
3323 if (auto *C = dyn_cast<ConstantInt>(SCEVCheck))
3327 // Create a new block containing the stride check.
3328 BB->setName("vector.scevcheck");
3329 auto *NewBB = BB->splitBasicBlock(BB->getTerminator(), "vector.ph");
3330 // Update dominator tree immediately if the generated block is a
3331 // LoopBypassBlock because SCEV expansions to generate loop bypass
3332 // checks may query it before the current function is finished.
3333 DT->addNewBlock(NewBB, BB);
3334 if (L->getParentLoop())
3335 L->getParentLoop()->addBasicBlockToLoop(NewBB, *LI);
3336 ReplaceInstWithInst(BB->getTerminator(),
3337 BranchInst::Create(Bypass, NewBB, SCEVCheck));
3338 LoopBypassBlocks.push_back(BB);
3339 AddedSafetyChecks = true;
3342 void InnerLoopVectorizer::emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass) {
3343 BasicBlock *BB = L->getLoopPreheader();
3345 // Generate the code that checks in runtime if arrays overlap. We put the
3346 // checks into a separate block to make the more common case of few elements
3348 Instruction *FirstCheckInst;
3349 Instruction *MemRuntimeCheck;
3350 std::tie(FirstCheckInst, MemRuntimeCheck) =
3351 Legal->getLAI()->addRuntimeChecks(BB->getTerminator());
3352 if (!MemRuntimeCheck)
3355 // Create a new block containing the memory check.
3356 BB->setName("vector.memcheck");
3357 auto *NewBB = BB->splitBasicBlock(BB->getTerminator(), "vector.ph");
3358 // Update dominator tree immediately if the generated block is a
3359 // LoopBypassBlock because SCEV expansions to generate loop bypass
3360 // checks may query it before the current function is finished.
3361 DT->addNewBlock(NewBB, BB);
3362 if (L->getParentLoop())
3363 L->getParentLoop()->addBasicBlockToLoop(NewBB, *LI);
3364 ReplaceInstWithInst(BB->getTerminator(),
3365 BranchInst::Create(Bypass, NewBB, MemRuntimeCheck));
3366 LoopBypassBlocks.push_back(BB);
3367 AddedSafetyChecks = true;
3369 // We currently don't use LoopVersioning for the actual loop cloning but we
3370 // still use it to add the noalias metadata.
3371 LVer = llvm::make_unique<LoopVersioning>(*Legal->getLAI(), OrigLoop, LI, DT,
3373 LVer->prepareNoAliasMetadata();
3376 void InnerLoopVectorizer::createVectorizedLoopSkeleton() {
3378 In this function we generate a new loop. The new loop will contain
3379 the vectorized instructions while the old loop will continue to run the
3382 [ ] <-- loop iteration number check.
3385 | [ ] <-- vector loop bypass (may consist of multiple blocks).
3388 || [ ] <-- vector pre header.
3392 | [ ]_| <-- vector loop.
3395 | -[ ] <--- middle-block.
3398 -|- >[ ] <--- new preheader.
3402 | [ ]_| <-- old scalar loop to handle remainder.
3405 >[ ] <-- exit block.
3409 BasicBlock *OldBasicBlock = OrigLoop->getHeader();
3410 BasicBlock *VectorPH = OrigLoop->getLoopPreheader();
3411 BasicBlock *ExitBlock = OrigLoop->getExitBlock();
3412 assert(VectorPH && "Invalid loop structure");
3413 assert(ExitBlock && "Must have an exit block");
3415 // Some loops have a single integer induction variable, while other loops
3416 // don't. One example is c++ iterators that often have multiple pointer
3417 // induction variables. In the code below we also support a case where we
3418 // don't have a single induction variable.
3420 // We try to obtain an induction variable from the original loop as hard
3421 // as possible. However if we don't find one that:
3423 // - counts from zero, stepping by one
3424 // - is the size of the widest induction variable type
3425 // then we create a new one.
3426 OldInduction = Legal->getPrimaryInduction();
3427 Type *IdxTy = Legal->getWidestInductionType();
3429 // Split the single block loop into the two loop structure described above.
3430 BasicBlock *VecBody =
3431 VectorPH->splitBasicBlock(VectorPH->getTerminator(), "vector.body");
3432 BasicBlock *MiddleBlock =
3433 VecBody->splitBasicBlock(VecBody->getTerminator(), "middle.block");
3434 BasicBlock *ScalarPH =
3435 MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(), "scalar.ph");
3437 // Create and register the new vector loop.
3438 Loop *Lp = new Loop();
3439 Loop *ParentLoop = OrigLoop->getParentLoop();
3441 // Insert the new loop into the loop nest and register the new basic blocks
3442 // before calling any utilities such as SCEV that require valid LoopInfo.
3444 ParentLoop->addChildLoop(Lp);
3445 ParentLoop->addBasicBlockToLoop(ScalarPH, *LI);
3446 ParentLoop->addBasicBlockToLoop(MiddleBlock, *LI);
3448 LI->addTopLevelLoop(Lp);
3450 Lp->addBasicBlockToLoop(VecBody, *LI);
3452 // Find the loop boundaries.
3453 Value *Count = getOrCreateTripCount(Lp);
3455 Value *StartIdx = ConstantInt::get(IdxTy, 0);
3457 // Now, compare the new count to zero. If it is zero skip the vector loop and
3458 // jump to the scalar loop. This check also covers the case where the
3459 // backedge-taken count is uint##_max: adding one to it will overflow leading
3460 // to an incorrect trip count of zero. In this (rare) case we will also jump
3461 // to the scalar loop.
3462 emitMinimumIterationCountCheck(Lp, ScalarPH);
3464 // Generate the code to check any assumptions that we've made for SCEV
3466 emitSCEVChecks(Lp, ScalarPH);
3468 // Generate the code that checks in runtime if arrays overlap. We put the
3469 // checks into a separate block to make the more common case of few elements
3471 emitMemRuntimeChecks(Lp, ScalarPH);
3473 // Generate the induction variable.
3474 // The loop step is equal to the vectorization factor (num of SIMD elements)
3475 // times the unroll factor (num of SIMD instructions).
3476 Value *CountRoundDown = getOrCreateVectorTripCount(Lp);
3477 Constant *Step = ConstantInt::get(IdxTy, VF * UF);
3479 createInductionVariable(Lp, StartIdx, CountRoundDown, Step,
3480 getDebugLocFromInstOrOperands(OldInduction));
3482 // We are going to resume the execution of the scalar loop.
3483 // Go over all of the induction variables that we found and fix the
3484 // PHIs that are left in the scalar version of the loop.
3485 // The starting values of PHI nodes depend on the counter of the last
3486 // iteration in the vectorized loop.
3487 // If we come from a bypass edge then we need to start from the original
3490 // This variable saves the new starting index for the scalar loop. It is used
3491 // to test if there are any tail iterations left once the vector loop has
3493 LoopVectorizationLegality::InductionList *List = Legal->getInductionVars();
3494 for (auto &InductionEntry : *List) {
3495 PHINode *OrigPhi = InductionEntry.first;
3496 InductionDescriptor II = InductionEntry.second;
3498 // Create phi nodes to merge from the backedge-taken check block.
3499 PHINode *BCResumeVal = PHINode::Create(
3500 OrigPhi->getType(), 3, "bc.resume.val", ScalarPH->getTerminator());
3501 Value *&EndValue = IVEndValues[OrigPhi];
3502 if (OrigPhi == OldInduction) {
3503 // We know what the end value is.
3504 EndValue = CountRoundDown;
3506 IRBuilder<> B(Lp->getLoopPreheader()->getTerminator());
3507 Type *StepType = II.getStep()->getType();
3508 Instruction::CastOps CastOp =
3509 CastInst::getCastOpcode(CountRoundDown, true, StepType, true);
3510 Value *CRD = B.CreateCast(CastOp, CountRoundDown, StepType, "cast.crd");
3511 const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
3512 EndValue = II.transform(B, CRD, PSE.getSE(), DL);
3513 EndValue->setName("ind.end");
3516 // The new PHI merges the original incoming value, in case of a bypass,
3517 // or the value at the end of the vectorized loop.
3518 BCResumeVal->addIncoming(EndValue, MiddleBlock);
3520 // Fix the scalar body counter (PHI node).
3521 unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH);
3523 // The old induction's phi node in the scalar body needs the truncated
3525 for (BasicBlock *BB : LoopBypassBlocks)
3526 BCResumeVal->addIncoming(II.getStartValue(), BB);
3527 OrigPhi->setIncomingValue(BlockIdx, BCResumeVal);
3530 // Add a check in the middle block to see if we have completed
3531 // all of the iterations in the first vector loop.
3532 // If (N - N%VF) == N, then we *don't* need to run the remainder.
3534 CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, Count,
3535 CountRoundDown, "cmp.n", MiddleBlock->getTerminator());
3536 ReplaceInstWithInst(MiddleBlock->getTerminator(),
3537 BranchInst::Create(ExitBlock, ScalarPH, CmpN));
3539 // Get ready to start creating new instructions into the vectorized body.
3540 Builder.SetInsertPoint(&*VecBody->getFirstInsertionPt());
3543 LoopVectorPreHeader = Lp->getLoopPreheader();
3544 LoopScalarPreHeader = ScalarPH;
3545 LoopMiddleBlock = MiddleBlock;
3546 LoopExitBlock = ExitBlock;
3547 LoopVectorBody = VecBody;
3548 LoopScalarBody = OldBasicBlock;
3550 // Keep all loop hints from the original loop on the vector loop (we'll
3551 // replace the vectorizer-specific hints below).
3552 if (MDNode *LID = OrigLoop->getLoopID())
3555 LoopVectorizeHints Hints(Lp, true, *ORE);
3556 Hints.setAlreadyVectorized();
3559 // Fix up external users of the induction variable. At this point, we are
3560 // in LCSSA form, with all external PHIs that use the IV having one input value,
3561 // coming from the remainder loop. We need those PHIs to also have a correct
3562 // value for the IV when arriving directly from the middle block.
3563 void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi,
3564 const InductionDescriptor &II,
3565 Value *CountRoundDown, Value *EndValue,
3566 BasicBlock *MiddleBlock) {
3567 // There are two kinds of external IV usages - those that use the value
3568 // computed in the last iteration (the PHI) and those that use the penultimate
3569 // value (the value that feeds into the phi from the loop latch).
3570 // We allow both, but they, obviously, have different values.
3572 assert(OrigLoop->getExitBlock() && "Expected a single exit block");
3574 DenseMap<Value *, Value *> MissingVals;
3576 // An external user of the last iteration's value should see the value that
3577 // the remainder loop uses to initialize its own IV.
3578 Value *PostInc = OrigPhi->getIncomingValueForBlock(OrigLoop->getLoopLatch());
3579 for (User *U : PostInc->users()) {
3580 Instruction *UI = cast<Instruction>(U);
3581 if (!OrigLoop->contains(UI)) {
3582 assert(isa<PHINode>(UI) && "Expected LCSSA form");
3583 MissingVals[UI] = EndValue;
3587 // An external user of the penultimate value need to see EndValue - Step.
3588 // The simplest way to get this is to recompute it from the constituent SCEVs,
3589 // that is Start + (Step * (CRD - 1)).
3590 for (User *U : OrigPhi->users()) {
3591 auto *UI = cast<Instruction>(U);
3592 if (!OrigLoop->contains(UI)) {
3593 const DataLayout &DL =
3594 OrigLoop->getHeader()->getModule()->getDataLayout();
3595 assert(isa<PHINode>(UI) && "Expected LCSSA form");
3597 IRBuilder<> B(MiddleBlock->getTerminator());
3598 Value *CountMinusOne = B.CreateSub(
3599 CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1));
3601 !II.getStep()->getType()->isIntegerTy()
3602 ? B.CreateCast(Instruction::SIToFP, CountMinusOne,
3603 II.getStep()->getType())
3604 : B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType());
3605 CMO->setName("cast.cmo");
3606 Value *Escape = II.transform(B, CMO, PSE.getSE(), DL);
3607 Escape->setName("ind.escape");
3608 MissingVals[UI] = Escape;
3612 for (auto &I : MissingVals) {
3613 PHINode *PHI = cast<PHINode>(I.first);
3614 // One corner case we have to handle is two IVs "chasing" each-other,
3615 // that is %IV2 = phi [...], [ %IV1, %latch ]
3616 // In this case, if IV1 has an external use, we need to avoid adding both
3617 // "last value of IV1" and "penultimate value of IV2". So, verify that we
3618 // don't already have an incoming value for the middle block.
3619 if (PHI->getBasicBlockIndex(MiddleBlock) == -1)
3620 PHI->addIncoming(I.second, MiddleBlock);
3625 struct CSEDenseMapInfo {
3626 static bool canHandle(const Instruction *I) {
3627 return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) ||
3628 isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I);
3630 static inline Instruction *getEmptyKey() {
3631 return DenseMapInfo<Instruction *>::getEmptyKey();
3633 static inline Instruction *getTombstoneKey() {
3634 return DenseMapInfo<Instruction *>::getTombstoneKey();
3636 static unsigned getHashValue(const Instruction *I) {
3637 assert(canHandle(I) && "Unknown instruction!");
3638 return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(),
3639 I->value_op_end()));
3641 static bool isEqual(const Instruction *LHS, const Instruction *RHS) {
3642 if (LHS == getEmptyKey() || RHS == getEmptyKey() ||
3643 LHS == getTombstoneKey() || RHS == getTombstoneKey())
3645 return LHS->isIdenticalTo(RHS);
3650 ///\brief Perform cse of induction variable instructions.
3651 static void cse(BasicBlock *BB) {
3652 // Perform simple cse.
3653 SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap;
3654 for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E;) {
3655 Instruction *In = &*I++;
3657 if (!CSEDenseMapInfo::canHandle(In))
3660 // Check if we can replace this instruction with any of the
3661 // visited instructions.
3662 if (Instruction *V = CSEMap.lookup(In)) {
3663 In->replaceAllUsesWith(V);
3664 In->eraseFromParent();
3672 /// \brief Estimate the overhead of scalarizing an instruction. This is a
3673 /// convenience wrapper for the type-based getScalarizationOverhead API.
3674 static unsigned getScalarizationOverhead(Instruction *I, unsigned VF,
3675 const TargetTransformInfo &TTI) {
3680 Type *RetTy = ToVectorTy(I->getType(), VF);
3681 if (!RetTy->isVoidTy() &&
3682 (!isa<LoadInst>(I) ||
3683 !TTI.supportsEfficientVectorElementLoadStore()))
3684 Cost += TTI.getScalarizationOverhead(RetTy, true, false);
3686 if (CallInst *CI = dyn_cast<CallInst>(I)) {
3687 SmallVector<const Value *, 4> Operands(CI->arg_operands());
3688 Cost += TTI.getOperandsScalarizationOverhead(Operands, VF);
3690 else if (!isa<StoreInst>(I) ||
3691 !TTI.supportsEfficientVectorElementLoadStore()) {
3692 SmallVector<const Value *, 4> Operands(I->operand_values());
3693 Cost += TTI.getOperandsScalarizationOverhead(Operands, VF);
3699 // Estimate cost of a call instruction CI if it were vectorized with factor VF.
3700 // Return the cost of the instruction, including scalarization overhead if it's
3701 // needed. The flag NeedToScalarize shows if the call needs to be scalarized -
3702 // i.e. either vector version isn't available, or is too expensive.
3703 static unsigned getVectorCallCost(CallInst *CI, unsigned VF,
3704 const TargetTransformInfo &TTI,
3705 const TargetLibraryInfo *TLI,
3706 bool &NeedToScalarize) {
3707 Function *F = CI->getCalledFunction();
3708 StringRef FnName = CI->getCalledFunction()->getName();
3709 Type *ScalarRetTy = CI->getType();
3710 SmallVector<Type *, 4> Tys, ScalarTys;
3711 for (auto &ArgOp : CI->arg_operands())
3712 ScalarTys.push_back(ArgOp->getType());
3714 // Estimate cost of scalarized vector call. The source operands are assumed
3715 // to be vectors, so we need to extract individual elements from there,
3716 // execute VF scalar calls, and then gather the result into the vector return
3718 unsigned ScalarCallCost = TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys);
3720 return ScalarCallCost;
3722 // Compute corresponding vector type for return value and arguments.
3723 Type *RetTy = ToVectorTy(ScalarRetTy, VF);
3724 for (Type *ScalarTy : ScalarTys)
3725 Tys.push_back(ToVectorTy(ScalarTy, VF));
3727 // Compute costs of unpacking argument values for the scalar calls and
3728 // packing the return values to a vector.
3729 unsigned ScalarizationCost = getScalarizationOverhead(CI, VF, TTI);
3731 unsigned Cost = ScalarCallCost * VF + ScalarizationCost;
3733 // If we can't emit a vector call for this function, then the currently found
3734 // cost is the cost we need to return.
3735 NeedToScalarize = true;
3736 if (!TLI || !TLI->isFunctionVectorizable(FnName, VF) || CI->isNoBuiltin())
3739 // If the corresponding vector cost is cheaper, return its cost.
3740 unsigned VectorCallCost = TTI.getCallInstrCost(nullptr, RetTy, Tys);
3741 if (VectorCallCost < Cost) {
3742 NeedToScalarize = false;
3743 return VectorCallCost;
3748 // Estimate cost of an intrinsic call instruction CI if it were vectorized with
3749 // factor VF. Return the cost of the instruction, including scalarization
3750 // overhead if it's needed.
3751 static unsigned getVectorIntrinsicCost(CallInst *CI, unsigned VF,
3752 const TargetTransformInfo &TTI,
3753 const TargetLibraryInfo *TLI) {
3754 Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
3755 assert(ID && "Expected intrinsic call!");
3758 if (auto *FPMO = dyn_cast<FPMathOperator>(CI))
3759 FMF = FPMO->getFastMathFlags();
3761 SmallVector<Value *, 4> Operands(CI->arg_operands());
3762 return TTI.getIntrinsicInstrCost(ID, CI->getType(), Operands, FMF, VF);
3765 static Type *smallestIntegerVectorType(Type *T1, Type *T2) {
3766 auto *I1 = cast<IntegerType>(T1->getVectorElementType());
3767 auto *I2 = cast<IntegerType>(T2->getVectorElementType());
3768 return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2;
3770 static Type *largestIntegerVectorType(Type *T1, Type *T2) {
3771 auto *I1 = cast<IntegerType>(T1->getVectorElementType());
3772 auto *I2 = cast<IntegerType>(T2->getVectorElementType());
3773 return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2;
3776 void InnerLoopVectorizer::truncateToMinimalBitwidths() {
3777 // For every instruction `I` in MinBWs, truncate the operands, create a
3778 // truncated version of `I` and reextend its result. InstCombine runs
3779 // later and will remove any ext/trunc pairs.
3781 SmallPtrSet<Value *, 4> Erased;
3782 for (const auto &KV : Cost->getMinimalBitwidths()) {
3783 // If the value wasn't vectorized, we must maintain the original scalar
3784 // type. The absence of the value from VectorLoopValueMap indicates that it
3785 // wasn't vectorized.
3786 if (!VectorLoopValueMap.hasAnyVectorValue(KV.first))
3788 for (unsigned Part = 0; Part < UF; ++Part) {
3789 Value *I = getOrCreateVectorValue(KV.first, Part);
3790 if (Erased.count(I) || I->use_empty() || !isa<Instruction>(I))
3792 Type *OriginalTy = I->getType();
3793 Type *ScalarTruncatedTy =
3794 IntegerType::get(OriginalTy->getContext(), KV.second);
3795 Type *TruncatedTy = VectorType::get(ScalarTruncatedTy,
3796 OriginalTy->getVectorNumElements());
3797 if (TruncatedTy == OriginalTy)
3800 IRBuilder<> B(cast<Instruction>(I));
3801 auto ShrinkOperand = [&](Value *V) -> Value * {
3802 if (auto *ZI = dyn_cast<ZExtInst>(V))
3803 if (ZI->getSrcTy() == TruncatedTy)
3804 return ZI->getOperand(0);
3805 return B.CreateZExtOrTrunc(V, TruncatedTy);
3808 // The actual instruction modification depends on the instruction type,
3810 Value *NewI = nullptr;
3811 if (auto *BO = dyn_cast<BinaryOperator>(I)) {
3812 NewI = B.CreateBinOp(BO->getOpcode(), ShrinkOperand(BO->getOperand(0)),
3813 ShrinkOperand(BO->getOperand(1)));
3815 // Any wrapping introduced by shrinking this operation shouldn't be
3816 // considered undefined behavior. So, we can't unconditionally copy
3817 // arithmetic wrapping flags to NewI.
3818 cast<BinaryOperator>(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false);
3819 } else if (auto *CI = dyn_cast<ICmpInst>(I)) {
3821 B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)),
3822 ShrinkOperand(CI->getOperand(1)));
3823 } else if (auto *SI = dyn_cast<SelectInst>(I)) {
3824 NewI = B.CreateSelect(SI->getCondition(),
3825 ShrinkOperand(SI->getTrueValue()),
3826 ShrinkOperand(SI->getFalseValue()));
3827 } else if (auto *CI = dyn_cast<CastInst>(I)) {
3828 switch (CI->getOpcode()) {
3830 llvm_unreachable("Unhandled cast!");
3831 case Instruction::Trunc:
3832 NewI = ShrinkOperand(CI->getOperand(0));
3834 case Instruction::SExt:
3835 NewI = B.CreateSExtOrTrunc(
3837 smallestIntegerVectorType(OriginalTy, TruncatedTy));
3839 case Instruction::ZExt:
3840 NewI = B.CreateZExtOrTrunc(
3842 smallestIntegerVectorType(OriginalTy, TruncatedTy));
3845 } else if (auto *SI = dyn_cast<ShuffleVectorInst>(I)) {
3846 auto Elements0 = SI->getOperand(0)->getType()->getVectorNumElements();
3847 auto *O0 = B.CreateZExtOrTrunc(
3848 SI->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements0));
3849 auto Elements1 = SI->getOperand(1)->getType()->getVectorNumElements();
3850 auto *O1 = B.CreateZExtOrTrunc(
3851 SI->getOperand(1), VectorType::get(ScalarTruncatedTy, Elements1));
3853 NewI = B.CreateShuffleVector(O0, O1, SI->getMask());
3854 } else if (isa<LoadInst>(I)) {
3855 // Don't do anything with the operands, just extend the result.
3857 } else if (auto *IE = dyn_cast<InsertElementInst>(I)) {
3858 auto Elements = IE->getOperand(0)->getType()->getVectorNumElements();
3859 auto *O0 = B.CreateZExtOrTrunc(
3860 IE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements));
3861 auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy);
3862 NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2));
3863 } else if (auto *EE = dyn_cast<ExtractElementInst>(I)) {
3864 auto Elements = EE->getOperand(0)->getType()->getVectorNumElements();
3865 auto *O0 = B.CreateZExtOrTrunc(
3866 EE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements));
3867 NewI = B.CreateExtractElement(O0, EE->getOperand(2));
3869 llvm_unreachable("Unhandled instruction type!");
3872 // Lastly, extend the result.
3873 NewI->takeName(cast<Instruction>(I));
3874 Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy);
3875 I->replaceAllUsesWith(Res);
3876 cast<Instruction>(I)->eraseFromParent();
3878 VectorLoopValueMap.resetVectorValue(KV.first, Part, Res);
3882 // We'll have created a bunch of ZExts that are now parentless. Clean up.
3883 for (const auto &KV : Cost->getMinimalBitwidths()) {
3884 // If the value wasn't vectorized, we must maintain the original scalar
3885 // type. The absence of the value from VectorLoopValueMap indicates that it
3886 // wasn't vectorized.
3887 if (!VectorLoopValueMap.hasAnyVectorValue(KV.first))
3889 for (unsigned Part = 0; Part < UF; ++Part) {
3890 Value *I = getOrCreateVectorValue(KV.first, Part);
3891 ZExtInst *Inst = dyn_cast<ZExtInst>(I);
3892 if (Inst && Inst->use_empty()) {
3893 Value *NewI = Inst->getOperand(0);
3894 Inst->eraseFromParent();
3895 VectorLoopValueMap.resetVectorValue(KV.first, Part, NewI);
3901 void InnerLoopVectorizer::fixVectorizedLoop() {
3902 // Insert truncates and extends for any truncated instructions as hints to
3905 truncateToMinimalBitwidths();
3907 // At this point every instruction in the original loop is widened to a
3908 // vector form. Now we need to fix the recurrences in the loop. These PHI
3909 // nodes are currently empty because we did not want to introduce cycles.
3910 // This is the second stage of vectorizing recurrences.
3911 fixCrossIterationPHIs();
3913 // Update the dominator tree.
3915 // FIXME: After creating the structure of the new loop, the dominator tree is
3916 // no longer up-to-date, and it remains that way until we update it
3917 // here. An out-of-date dominator tree is problematic for SCEV,
3918 // because SCEVExpander uses it to guide code generation. The
3919 // vectorizer use SCEVExpanders in several places. Instead, we should
3920 // keep the dominator tree up-to-date as we go.
3923 // Fix-up external users of the induction variables.
3924 for (auto &Entry : *Legal->getInductionVars())
3925 fixupIVUsers(Entry.first, Entry.second,
3926 getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)),
3927 IVEndValues[Entry.first], LoopMiddleBlock);
3930 predicateInstructions();
3932 // Remove redundant induction instructions.
3933 cse(LoopVectorBody);
3936 void InnerLoopVectorizer::fixCrossIterationPHIs() {
3937 // In order to support recurrences we need to be able to vectorize Phi nodes.
3938 // Phi nodes have cycles, so we need to vectorize them in two stages. This is
3939 // stage #2: We now need to fix the recurrences by adding incoming edges to
3940 // the currently empty PHI nodes. At this point every instruction in the
3941 // original loop is widened to a vector form so we can use them to construct
3942 // the incoming edges.
3943 for (Instruction &I : *OrigLoop->getHeader()) {
3944 PHINode *Phi = dyn_cast<PHINode>(&I);
3947 // Handle first-order recurrences and reductions that need to be fixed.
3948 if (Legal->isFirstOrderRecurrence(Phi))
3949 fixFirstOrderRecurrence(Phi);
3950 else if (Legal->isReductionVariable(Phi))
3955 void InnerLoopVectorizer::fixFirstOrderRecurrence(PHINode *Phi) {
3957 // This is the second phase of vectorizing first-order recurrences. An
3958 // overview of the transformation is described below. Suppose we have the
3961 // for (int i = 0; i < n; ++i)
3962 // b[i] = a[i] - a[i - 1];
3964 // There is a first-order recurrence on "a". For this loop, the shorthand
3965 // scalar IR looks like:
3972 // i = phi [0, scalar.ph], [i+1, scalar.body]
3973 // s1 = phi [s_init, scalar.ph], [s2, scalar.body]
3976 // br cond, scalar.body, ...
3978 // In this example, s1 is a recurrence because it's value depends on the
3979 // previous iteration. In the first phase of vectorization, we created a
3980 // temporary value for s1. We now complete the vectorization and produce the
3981 // shorthand vector IR shown below (for VF = 4, UF = 1).
3984 // v_init = vector(..., ..., ..., a[-1])
3988 // i = phi [0, vector.ph], [i+4, vector.body]
3989 // v1 = phi [v_init, vector.ph], [v2, vector.body]
3990 // v2 = a[i, i+1, i+2, i+3];
3991 // v3 = vector(v1(3), v2(0, 1, 2))
3992 // b[i, i+1, i+2, i+3] = v2 - v3
3993 // br cond, vector.body, middle.block
4000 // s_init = phi [x, middle.block], [a[-1], otherwise]
4003 // After execution completes the vector loop, we extract the next value of
4004 // the recurrence (x) to use as the initial value in the scalar loop.
4006 // Get the original loop preheader and single loop latch.
4007 auto *Preheader = OrigLoop->getLoopPreheader();
4008 auto *Latch = OrigLoop->getLoopLatch();
4010 // Get the initial and previous values of the scalar recurrence.
4011 auto *ScalarInit = Phi->getIncomingValueForBlock(Preheader);
4012 auto *Previous = Phi->getIncomingValueForBlock(Latch);
4014 // Create a vector from the initial value.
4015 auto *VectorInit = ScalarInit;
4017 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
4018 VectorInit = Builder.CreateInsertElement(
4019 UndefValue::get(VectorType::get(VectorInit->getType(), VF)), VectorInit,
4020 Builder.getInt32(VF - 1), "vector.recur.init");
4023 // We constructed a temporary phi node in the first phase of vectorization.
4024 // This phi node will eventually be deleted.
4025 Builder.SetInsertPoint(
4026 cast<Instruction>(VectorLoopValueMap.getVectorValue(Phi, 0)));
4028 // Create a phi node for the new recurrence. The current value will either be
4029 // the initial value inserted into a vector or loop-varying vector value.
4030 auto *VecPhi = Builder.CreatePHI(VectorInit->getType(), 2, "vector.recur");
4031 VecPhi->addIncoming(VectorInit, LoopVectorPreHeader);
4033 // Get the vectorized previous value of the last part UF - 1. It appears last
4034 // among all unrolled iterations, due to the order of their construction.
4035 Value *PreviousLastPart = getOrCreateVectorValue(Previous, UF - 1);
4037 // Set the insertion point after the previous value if it is an instruction.
4038 // Note that the previous value may have been constant-folded so it is not
4039 // guaranteed to be an instruction in the vector loop. Also, if the previous
4040 // value is a phi node, we should insert after all the phi nodes to avoid
4041 // breaking basic block verification.
4042 if (LI->getLoopFor(LoopVectorBody)->isLoopInvariant(PreviousLastPart) ||
4043 isa<PHINode>(PreviousLastPart))
4044 Builder.SetInsertPoint(&*LoopVectorBody->getFirstInsertionPt());
4046 Builder.SetInsertPoint(
4047 &*++BasicBlock::iterator(cast<Instruction>(PreviousLastPart)));
4049 // We will construct a vector for the recurrence by combining the values for
4050 // the current and previous iterations. This is the required shuffle mask.
4051 SmallVector<Constant *, 8> ShuffleMask(VF);
4052 ShuffleMask[0] = Builder.getInt32(VF - 1);
4053 for (unsigned I = 1; I < VF; ++I)
4054 ShuffleMask[I] = Builder.getInt32(I + VF - 1);
4056 // The vector from which to take the initial value for the current iteration
4057 // (actual or unrolled). Initially, this is the vector phi node.
4058 Value *Incoming = VecPhi;
4060 // Shuffle the current and previous vector and update the vector parts.
4061 for (unsigned Part = 0; Part < UF; ++Part) {
4062 Value *PreviousPart = getOrCreateVectorValue(Previous, Part);
4063 Value *PhiPart = VectorLoopValueMap.getVectorValue(Phi, Part);
4065 VF > 1 ? Builder.CreateShuffleVector(Incoming, PreviousPart,
4066 ConstantVector::get(ShuffleMask))
4068 PhiPart->replaceAllUsesWith(Shuffle);
4069 cast<Instruction>(PhiPart)->eraseFromParent();
4070 VectorLoopValueMap.resetVectorValue(Phi, Part, Shuffle);
4071 Incoming = PreviousPart;
4074 // Fix the latch value of the new recurrence in the vector loop.
4075 VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
4077 // Extract the last vector element in the middle block. This will be the
4078 // initial value for the recurrence when jumping to the scalar loop.
4079 auto *ExtractForScalar = Incoming;
4081 Builder.SetInsertPoint(LoopMiddleBlock->getTerminator());
4082 ExtractForScalar = Builder.CreateExtractElement(
4083 ExtractForScalar, Builder.getInt32(VF - 1), "vector.recur.extract");
4085 // Extract the second last element in the middle block if the
4086 // Phi is used outside the loop. We need to extract the phi itself
4087 // and not the last element (the phi update in the current iteration). This
4088 // will be the value when jumping to the exit block from the LoopMiddleBlock,
4089 // when the scalar loop is not run at all.
4090 Value *ExtractForPhiUsedOutsideLoop = nullptr;
4092 ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement(
4093 Incoming, Builder.getInt32(VF - 2), "vector.recur.extract.for.phi");
4094 // When loop is unrolled without vectorizing, initialize
4095 // ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value of
4096 // `Incoming`. This is analogous to the vectorized case above: extracting the
4097 // second last element when VF > 1.
4099 ExtractForPhiUsedOutsideLoop = getOrCreateVectorValue(Previous, UF - 2);
4101 // Fix the initial value of the original recurrence in the scalar loop.
4102 Builder.SetInsertPoint(&*LoopScalarPreHeader->begin());
4103 auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init");
4104 for (auto *BB : predecessors(LoopScalarPreHeader)) {
4105 auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit;
4106 Start->addIncoming(Incoming, BB);
4109 Phi->setIncomingValue(Phi->getBasicBlockIndex(LoopScalarPreHeader), Start);
4110 Phi->setName("scalar.recur");
4112 // Finally, fix users of the recurrence outside the loop. The users will need
4113 // either the last value of the scalar recurrence or the last value of the
4114 // vector recurrence we extracted in the middle block. Since the loop is in
4115 // LCSSA form, we just need to find the phi node for the original scalar
4116 // recurrence in the exit block, and then add an edge for the middle block.
4117 for (auto &I : *LoopExitBlock) {
4118 auto *LCSSAPhi = dyn_cast<PHINode>(&I);
4121 if (LCSSAPhi->getIncomingValue(0) == Phi) {
4122 LCSSAPhi->addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock);
4128 void InnerLoopVectorizer::fixReduction(PHINode *Phi) {
4129 Constant *Zero = Builder.getInt32(0);
4131 // Get it's reduction variable descriptor.
4132 assert(Legal->isReductionVariable(Phi) &&
4133 "Unable to find the reduction variable");
4134 RecurrenceDescriptor RdxDesc = (*Legal->getReductionVars())[Phi];
4136 RecurrenceDescriptor::RecurrenceKind RK = RdxDesc.getRecurrenceKind();
4137 TrackingVH<Value> ReductionStartValue = RdxDesc.getRecurrenceStartValue();
4138 Instruction *LoopExitInst = RdxDesc.getLoopExitInstr();
4139 RecurrenceDescriptor::MinMaxRecurrenceKind MinMaxKind =
4140 RdxDesc.getMinMaxRecurrenceKind();
4141 setDebugLocFromInst(Builder, ReductionStartValue);
4143 // We need to generate a reduction vector from the incoming scalar.
4144 // To do so, we need to generate the 'identity' vector and override
4145 // one of the elements with the incoming scalar reduction. We need
4146 // to do it in the vector-loop preheader.
4147 Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
4149 // This is the vector-clone of the value that leaves the loop.
4150 Type *VecTy = getOrCreateVectorValue(LoopExitInst, 0)->getType();
4152 // Find the reduction identity variable. Zero for addition, or, xor,
4153 // one for multiplication, -1 for And.
4156 if (RK == RecurrenceDescriptor::RK_IntegerMinMax ||
4157 RK == RecurrenceDescriptor::RK_FloatMinMax) {
4158 // MinMax reduction have the start value as their identify.
4160 VectorStart = Identity = ReductionStartValue;
4162 VectorStart = Identity =
4163 Builder.CreateVectorSplat(VF, ReductionStartValue, "minmax.ident");
4166 // Handle other reduction kinds:
4167 Constant *Iden = RecurrenceDescriptor::getRecurrenceIdentity(
4168 RK, VecTy->getScalarType());
4171 // This vector is the Identity vector where the first element is the
4172 // incoming scalar reduction.
4173 VectorStart = ReductionStartValue;
4175 Identity = ConstantVector::getSplat(VF, Iden);
4177 // This vector is the Identity vector where the first element is the
4178 // incoming scalar reduction.
4180 Builder.CreateInsertElement(Identity, ReductionStartValue, Zero);
4184 // Fix the vector-loop phi.
4186 // Reductions do not have to start at zero. They can start with
4187 // any loop invariant values.
4188 BasicBlock *Latch = OrigLoop->getLoopLatch();
4189 Value *LoopVal = Phi->getIncomingValueForBlock(Latch);
4190 for (unsigned Part = 0; Part < UF; ++Part) {
4191 Value *VecRdxPhi = getOrCreateVectorValue(Phi, Part);
4192 Value *Val = getOrCreateVectorValue(LoopVal, Part);
4193 // Make sure to add the reduction stat value only to the
4194 // first unroll part.
4195 Value *StartVal = (Part == 0) ? VectorStart : Identity;
4196 cast<PHINode>(VecRdxPhi)->addIncoming(StartVal, LoopVectorPreHeader);
4197 cast<PHINode>(VecRdxPhi)
4198 ->addIncoming(Val, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
4201 // Before each round, move the insertion point right between
4202 // the PHIs and the values we are going to write.
4203 // This allows us to write both PHINodes and the extractelement
4205 Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt());
4207 setDebugLocFromInst(Builder, LoopExitInst);
4209 // If the vector reduction can be performed in a smaller type, we truncate
4210 // then extend the loop exit value to enable InstCombine to evaluate the
4211 // entire expression in the smaller type.
4212 if (VF > 1 && Phi->getType() != RdxDesc.getRecurrenceType()) {
4213 Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF);
4214 Builder.SetInsertPoint(LoopVectorBody->getTerminator());
4215 VectorParts RdxParts(UF);
4216 for (unsigned Part = 0; Part < UF; ++Part) {
4217 RdxParts[Part] = VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
4218 Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
4219 Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy)
4220 : Builder.CreateZExt(Trunc, VecTy);
4221 for (Value::user_iterator UI = RdxParts[Part]->user_begin();
4222 UI != RdxParts[Part]->user_end();)
4224 (*UI++)->replaceUsesOfWith(RdxParts[Part], Extnd);
4225 RdxParts[Part] = Extnd;
4230 Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt());
4231 for (unsigned Part = 0; Part < UF; ++Part) {
4232 RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
4233 VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, RdxParts[Part]);
4237 // Reduce all of the unrolled parts into a single vector.
4238 Value *ReducedPartRdx = VectorLoopValueMap.getVectorValue(LoopExitInst, 0);
4239 unsigned Op = RecurrenceDescriptor::getRecurrenceBinOp(RK);
4240 setDebugLocFromInst(Builder, ReducedPartRdx);
4241 for (unsigned Part = 1; Part < UF; ++Part) {
4242 Value *RdxPart = VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
4243 if (Op != Instruction::ICmp && Op != Instruction::FCmp)
4244 // Floating point operations had to be 'fast' to enable the reduction.
4245 ReducedPartRdx = addFastMathFlag(
4246 Builder.CreateBinOp((Instruction::BinaryOps)Op, RdxPart,
4247 ReducedPartRdx, "bin.rdx"));
4249 ReducedPartRdx = RecurrenceDescriptor::createMinMaxOp(
4250 Builder, MinMaxKind, ReducedPartRdx, RdxPart);
4254 bool NoNaN = Legal->hasFunNoNaNAttr();
4256 createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx, NoNaN);
4257 // If the reduction can be performed in a smaller type, we need to extend
4258 // the reduction to the wider type before we branch to the original loop.
4259 if (Phi->getType() != RdxDesc.getRecurrenceType())
4262 ? Builder.CreateSExt(ReducedPartRdx, Phi->getType())
4263 : Builder.CreateZExt(ReducedPartRdx, Phi->getType());
4266 // Create a phi node that merges control-flow from the backedge-taken check
4267 // block and the middle block.
4268 PHINode *BCBlockPhi = PHINode::Create(Phi->getType(), 2, "bc.merge.rdx",
4269 LoopScalarPreHeader->getTerminator());
4270 for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
4271 BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]);
4272 BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock);
4274 // Now, we need to fix the users of the reduction variable
4275 // inside and outside of the scalar remainder loop.
4276 // We know that the loop is in LCSSA form. We need to update the
4277 // PHI nodes in the exit blocks.
4278 for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
4279 LEE = LoopExitBlock->end();
4280 LEI != LEE; ++LEI) {
4281 PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
4285 // All PHINodes need to have a single entry edge, or two if
4286 // we already fixed them.
4287 assert(LCSSAPhi->getNumIncomingValues() < 3 && "Invalid LCSSA PHI");
4289 // We found a reduction value exit-PHI. Update it with the
4290 // incoming bypass edge.
4291 if (LCSSAPhi->getIncomingValue(0) == LoopExitInst)
4292 LCSSAPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock);
4293 } // end of the LCSSA phi scan.
4295 // Fix the scalar loop reduction variable with the incoming reduction sum
4296 // from the vector body and from the backedge value.
4297 int IncomingEdgeBlockIdx =
4298 Phi->getBasicBlockIndex(OrigLoop->getLoopLatch());
4299 assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
4300 // Pick the other block.
4301 int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
4302 Phi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi);
4303 Phi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst);
4306 void InnerLoopVectorizer::fixLCSSAPHIs() {
4307 for (Instruction &LEI : *LoopExitBlock) {
4308 auto *LCSSAPhi = dyn_cast<PHINode>(&LEI);
4311 if (LCSSAPhi->getNumIncomingValues() == 1) {
4312 assert(OrigLoop->isLoopInvariant(LCSSAPhi->getIncomingValue(0)) &&
4313 "Incoming value isn't loop invariant");
4314 LCSSAPhi->addIncoming(LCSSAPhi->getIncomingValue(0), LoopMiddleBlock);
4319 void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) {
4321 // The basic block and loop containing the predicated instruction.
4322 auto *PredBB = PredInst->getParent();
4323 auto *VectorLoop = LI->getLoopFor(PredBB);
4325 // Initialize a worklist with the operands of the predicated instruction.
4326 SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end());
4328 // Holds instructions that we need to analyze again. An instruction may be
4329 // reanalyzed if we don't yet know if we can sink it or not.
4330 SmallVector<Instruction *, 8> InstsToReanalyze;
4332 // Returns true if a given use occurs in the predicated block. Phi nodes use
4333 // their operands in their corresponding predecessor blocks.
4334 auto isBlockOfUsePredicated = [&](Use &U) -> bool {
4335 auto *I = cast<Instruction>(U.getUser());
4336 BasicBlock *BB = I->getParent();
4337 if (auto *Phi = dyn_cast<PHINode>(I))
4338 BB = Phi->getIncomingBlock(
4339 PHINode::getIncomingValueNumForOperand(U.getOperandNo()));
4340 return BB == PredBB;
4343 // Iteratively sink the scalarized operands of the predicated instruction
4344 // into the block we created for it. When an instruction is sunk, it's
4345 // operands are then added to the worklist. The algorithm ends after one pass
4346 // through the worklist doesn't sink a single instruction.
4350 // Add the instructions that need to be reanalyzed to the worklist, and
4351 // reset the changed indicator.
4352 Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end());
4353 InstsToReanalyze.clear();
4356 while (!Worklist.empty()) {
4357 auto *I = dyn_cast<Instruction>(Worklist.pop_back_val());
4359 // We can't sink an instruction if it is a phi node, is already in the
4360 // predicated block, is not in the loop, or may have side effects.
4361 if (!I || isa<PHINode>(I) || I->getParent() == PredBB ||
4362 !VectorLoop->contains(I) || I->mayHaveSideEffects())
4365 // It's legal to sink the instruction if all its uses occur in the
4366 // predicated block. Otherwise, there's nothing to do yet, and we may
4367 // need to reanalyze the instruction.
4368 if (!all_of(I->uses(), isBlockOfUsePredicated)) {
4369 InstsToReanalyze.push_back(I);
4373 // Move the instruction to the beginning of the predicated block, and add
4374 // it's operands to the worklist.
4375 I->moveBefore(&*PredBB->getFirstInsertionPt());
4376 Worklist.insert(I->op_begin(), I->op_end());
4378 // The sinking may have enabled other instructions to be sunk, so we will
4385 void InnerLoopVectorizer::predicateInstructions() {
4387 // For each instruction I marked for predication on value C, split I into its
4388 // own basic block to form an if-then construct over C. Since I may be fed by
4389 // an extractelement instruction or other scalar operand, we try to
4390 // iteratively sink its scalar operands into the predicated block. If I feeds
4391 // an insertelement instruction, we try to move this instruction into the
4392 // predicated block as well. For non-void types, a phi node will be created
4393 // for the resulting value (either vector or scalar).
4395 // So for some predicated instruction, e.g. the conditional sdiv in:
4399 // %add = add nsw i32 %mul, %0
4400 // %cmp5 = icmp sgt i32 %2, 7
4401 // br i1 %cmp5, label %if.then, label %if.end
4404 // %div = sdiv i32 %0, %1
4408 // %x.0 = phi i32 [ %div, %if.then ], [ %add, %for.body ]
4410 // the sdiv at this point is scalarized and if-converted using a select.
4411 // The inactive elements in the vector are not used, but the predicated
4412 // instruction is still executed for all vector elements, essentially:
4416 // %17 = add nsw <2 x i32> %16, %wide.load
4417 // %29 = extractelement <2 x i32> %wide.load, i32 0
4418 // %30 = extractelement <2 x i32> %wide.load51, i32 0
4419 // %31 = sdiv i32 %29, %30
4420 // %32 = insertelement <2 x i32> undef, i32 %31, i32 0
4421 // %35 = extractelement <2 x i32> %wide.load, i32 1
4422 // %36 = extractelement <2 x i32> %wide.load51, i32 1
4423 // %37 = sdiv i32 %35, %36
4424 // %38 = insertelement <2 x i32> %32, i32 %37, i32 1
4425 // %predphi = select <2 x i1> %26, <2 x i32> %38, <2 x i32> %17
4427 // Predication will now re-introduce the original control flow to avoid false
4428 // side-effects by the sdiv instructions on the inactive elements, yielding
4433 // %5 = add nsw <2 x i32> %4, %wide.load
4434 // %8 = icmp sgt <2 x i32> %wide.load52, <i32 7, i32 7>
4435 // %9 = extractelement <2 x i1> %8, i32 0
4436 // br i1 %9, label %pred.sdiv.if, label %pred.sdiv.continue
4439 // %10 = extractelement <2 x i32> %wide.load, i32 0
4440 // %11 = extractelement <2 x i32> %wide.load51, i32 0
4441 // %12 = sdiv i32 %10, %11
4442 // %13 = insertelement <2 x i32> undef, i32 %12, i32 0
4443 // br label %pred.sdiv.continue
4445 // pred.sdiv.continue:
4446 // %14 = phi <2 x i32> [ undef, %vector.body ], [ %13, %pred.sdiv.if ]
4447 // %15 = extractelement <2 x i1> %8, i32 1
4448 // br i1 %15, label %pred.sdiv.if54, label %pred.sdiv.continue55
4451 // %16 = extractelement <2 x i32> %wide.load, i32 1
4452 // %17 = extractelement <2 x i32> %wide.load51, i32 1
4453 // %18 = sdiv i32 %16, %17
4454 // %19 = insertelement <2 x i32> %14, i32 %18, i32 1
4455 // br label %pred.sdiv.continue55
4457 // pred.sdiv.continue55:
4458 // %20 = phi <2 x i32> [ %14, %pred.sdiv.continue ], [ %19, %pred.sdiv.if54 ]
4459 // %predphi = select <2 x i1> %8, <2 x i32> %20, <2 x i32> %5
4461 for (auto KV : PredicatedInstructions) {
4462 BasicBlock::iterator I(KV.first);
4463 BasicBlock *Head = I->getParent();
4464 auto *T = SplitBlockAndInsertIfThen(KV.second, &*I, /*Unreachable=*/false,
4465 /*BranchWeights=*/nullptr, DT, LI);
4467 sinkScalarOperands(&*I);
4469 BasicBlock *PredicatedBlock = I->getParent();
4470 Twine BBNamePrefix = Twine("pred.") + I->getOpcodeName();
4471 PredicatedBlock->setName(BBNamePrefix + ".if");
4472 PredicatedBlock->getSingleSuccessor()->setName(BBNamePrefix + ".continue");
4474 // If the instruction is non-void create a Phi node at reconvergence point.
4475 if (!I->getType()->isVoidTy()) {
4476 Value *IncomingTrue = nullptr;
4477 Value *IncomingFalse = nullptr;
4479 if (I->hasOneUse() && isa<InsertElementInst>(*I->user_begin())) {
4480 // If the predicated instruction is feeding an insert-element, move it
4481 // into the Then block; Phi node will be created for the vector.
4482 InsertElementInst *IEI = cast<InsertElementInst>(*I->user_begin());
4484 IncomingTrue = IEI; // the new vector with the inserted element.
4485 IncomingFalse = IEI->getOperand(0); // the unmodified vector
4487 // Phi node will be created for the scalar predicated instruction.
4489 IncomingFalse = UndefValue::get(I->getType());
4492 BasicBlock *PostDom = I->getParent()->getSingleSuccessor();
4493 assert(PostDom && "Then block has multiple successors");
4495 PHINode::Create(IncomingTrue->getType(), 2, "", &PostDom->front());
4496 IncomingTrue->replaceAllUsesWith(Phi);
4497 Phi->addIncoming(IncomingFalse, Head);
4498 Phi->addIncoming(IncomingTrue, I->getParent());
4502 DEBUG(DT->verifyDomTree());
4505 InnerLoopVectorizer::VectorParts
4506 InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) {
4507 assert(is_contained(predecessors(Dst), Src) && "Invalid edge");
4509 // Look for cached value.
4510 std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst);
4511 EdgeMaskCacheTy::iterator ECEntryIt = EdgeMaskCache.find(Edge);
4512 if (ECEntryIt != EdgeMaskCache.end())
4513 return ECEntryIt->second;
4515 VectorParts SrcMask = createBlockInMask(Src);
4517 // The terminator has to be a branch inst!
4518 BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator());
4519 assert(BI && "Unexpected terminator found");
4521 if (BI->isConditional()) {
4523 VectorParts EdgeMask(UF);
4524 for (unsigned Part = 0; Part < UF; ++Part) {
4525 auto *EdgeMaskPart = getOrCreateVectorValue(BI->getCondition(), Part);
4526 if (BI->getSuccessor(0) != Dst)
4527 EdgeMaskPart = Builder.CreateNot(EdgeMaskPart);
4529 EdgeMaskPart = Builder.CreateAnd(EdgeMaskPart, SrcMask[Part]);
4530 EdgeMask[Part] = EdgeMaskPart;
4533 EdgeMaskCache[Edge] = EdgeMask;
4537 EdgeMaskCache[Edge] = SrcMask;
4541 InnerLoopVectorizer::VectorParts
4542 InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) {
4543 assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
4545 // Look for cached value.
4546 BlockMaskCacheTy::iterator BCEntryIt = BlockMaskCache.find(BB);
4547 if (BCEntryIt != BlockMaskCache.end())
4548 return BCEntryIt->second;
4550 VectorParts BlockMask(UF);
4552 // Loop incoming mask is all-one.
4553 if (OrigLoop->getHeader() == BB) {
4554 Value *C = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 1);
4555 for (unsigned Part = 0; Part < UF; ++Part)
4556 BlockMask[Part] = getOrCreateVectorValue(C, Part);
4557 BlockMaskCache[BB] = BlockMask;
4561 // This is the block mask. We OR all incoming edges, and with zero.
4562 Value *Zero = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 0);
4563 for (unsigned Part = 0; Part < UF; ++Part)
4564 BlockMask[Part] = getOrCreateVectorValue(Zero, Part);
4567 for (pred_iterator It = pred_begin(BB), E = pred_end(BB); It != E; ++It) {
4568 VectorParts EM = createEdgeMask(*It, BB);
4569 for (unsigned Part = 0; Part < UF; ++Part)
4570 BlockMask[Part] = Builder.CreateOr(BlockMask[Part], EM[Part]);
4573 BlockMaskCache[BB] = BlockMask;
4577 void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, unsigned UF,
4579 PHINode *P = cast<PHINode>(PN);
4580 // In order to support recurrences we need to be able to vectorize Phi nodes.
4581 // Phi nodes have cycles, so we need to vectorize them in two stages. This is
4582 // stage #1: We create a new vector PHI node with no incoming edges. We'll use
4583 // this value when we vectorize all of the instructions that use the PHI.
4584 if (Legal->isReductionVariable(P) || Legal->isFirstOrderRecurrence(P)) {
4585 for (unsigned Part = 0; Part < UF; ++Part) {
4586 // This is phase one of vectorizing PHIs.
4588 (VF == 1) ? PN->getType() : VectorType::get(PN->getType(), VF);
4589 Value *EntryPart = PHINode::Create(
4590 VecTy, 2, "vec.phi", &*LoopVectorBody->getFirstInsertionPt());
4591 VectorLoopValueMap.setVectorValue(P, Part, EntryPart);
4596 setDebugLocFromInst(Builder, P);
4597 // Check for PHI nodes that are lowered to vector selects.
4598 if (P->getParent() != OrigLoop->getHeader()) {
4599 // We know that all PHIs in non-header blocks are converted into
4600 // selects, so we don't have to worry about the insertion order and we
4601 // can just use the builder.
4602 // At this point we generate the predication tree. There may be
4603 // duplications since this is a simple recursive scan, but future
4604 // optimizations will clean it up.
4606 unsigned NumIncoming = P->getNumIncomingValues();
4608 // Generate a sequence of selects of the form:
4609 // SELECT(Mask3, In3,
4610 // SELECT(Mask2, In2,
4612 VectorParts Entry(UF);
4613 for (unsigned In = 0; In < NumIncoming; In++) {
4615 createEdgeMask(P->getIncomingBlock(In), P->getParent());
4617 for (unsigned Part = 0; Part < UF; ++Part) {
4618 Value *In0 = getOrCreateVectorValue(P->getIncomingValue(In), Part);
4619 // We might have single edge PHIs (blocks) - use an identity
4620 // 'select' for the first PHI operand.
4622 Entry[Part] = Builder.CreateSelect(Cond[Part], In0, In0);
4624 // Select between the current value and the previous incoming edge
4625 // based on the incoming mask.
4626 Entry[Part] = Builder.CreateSelect(Cond[Part], In0, Entry[Part],
4630 for (unsigned Part = 0; Part < UF; ++Part)
4631 VectorLoopValueMap.setVectorValue(P, Part, Entry[Part]);
4635 // This PHINode must be an induction variable.
4636 // Make sure that we know about it.
4637 assert(Legal->getInductionVars()->count(P) && "Not an induction variable");
4639 InductionDescriptor II = Legal->getInductionVars()->lookup(P);
4640 const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
4642 // FIXME: The newly created binary instructions should contain nsw/nuw flags,
4643 // which can be found from the original scalar operations.
4644 switch (II.getKind()) {
4645 case InductionDescriptor::IK_NoInduction:
4646 llvm_unreachable("Unknown induction");
4647 case InductionDescriptor::IK_IntInduction:
4648 case InductionDescriptor::IK_FpInduction:
4649 return widenIntOrFpInduction(P);
4650 case InductionDescriptor::IK_PtrInduction: {
4651 // Handle the pointer induction variable case.
4652 assert(P->getType()->isPointerTy() && "Unexpected type.");
4653 // This is the normalized GEP that starts counting at zero.
4654 Value *PtrInd = Induction;
4655 PtrInd = Builder.CreateSExtOrTrunc(PtrInd, II.getStep()->getType());
4656 // Determine the number of scalars we need to generate for each unroll
4657 // iteration. If the instruction is uniform, we only need to generate the
4658 // first lane. Otherwise, we generate all VF values.
4659 unsigned Lanes = Cost->isUniformAfterVectorization(P, VF) ? 1 : VF;
4660 // These are the scalar results. Notice that we don't generate vector GEPs
4661 // because scalar GEPs result in better code.
4662 for (unsigned Part = 0; Part < UF; ++Part) {
4663 for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
4664 Constant *Idx = ConstantInt::get(PtrInd->getType(), Lane + Part * VF);
4665 Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx);
4666 Value *SclrGep = II.transform(Builder, GlobalIdx, PSE.getSE(), DL);
4667 SclrGep->setName("next.gep");
4668 VectorLoopValueMap.setScalarValue(P, Part, Lane, SclrGep);
4676 /// A helper function for checking whether an integer division-related
4677 /// instruction may divide by zero (in which case it must be predicated if
4678 /// executed conditionally in the scalar code).
4679 /// TODO: It may be worthwhile to generalize and check isKnownNonZero().
4680 /// Non-zero divisors that are non compile-time constants will not be
4681 /// converted into multiplication, so we will still end up scalarizing
4682 /// the division, but can do so w/o predication.
4683 static bool mayDivideByZero(Instruction &I) {
4684 assert((I.getOpcode() == Instruction::UDiv ||
4685 I.getOpcode() == Instruction::SDiv ||
4686 I.getOpcode() == Instruction::URem ||
4687 I.getOpcode() == Instruction::SRem) &&
4688 "Unexpected instruction");
4689 Value *Divisor = I.getOperand(1);
4690 auto *CInt = dyn_cast<ConstantInt>(Divisor);
4691 return !CInt || CInt->isZero();
4694 void InnerLoopVectorizer::vectorizeInstruction(Instruction &I) {
4695 // Scalarize instructions that should remain scalar after vectorization.
4697 !(isa<BranchInst>(&I) || isa<PHINode>(&I) || isa<DbgInfoIntrinsic>(&I)) &&
4698 shouldScalarizeInstruction(&I)) {
4699 scalarizeInstruction(&I, Legal->isScalarWithPredication(&I));
4703 switch (I.getOpcode()) {
4704 case Instruction::Br:
4705 // Nothing to do for PHIs and BR, since we already took care of the
4706 // loop control flow instructions.
4708 case Instruction::PHI: {
4709 // Vectorize PHINodes.
4710 widenPHIInstruction(&I, UF, VF);
4713 case Instruction::GetElementPtr: {
4714 // Construct a vector GEP by widening the operands of the scalar GEP as
4715 // necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP
4716 // results in a vector of pointers when at least one operand of the GEP
4717 // is vector-typed. Thus, to keep the representation compact, we only use
4718 // vector-typed operands for loop-varying values.
4719 auto *GEP = cast<GetElementPtrInst>(&I);
4721 if (VF > 1 && OrigLoop->hasLoopInvariantOperands(GEP)) {
4722 // If we are vectorizing, but the GEP has only loop-invariant operands,
4723 // the GEP we build (by only using vector-typed operands for
4724 // loop-varying values) would be a scalar pointer. Thus, to ensure we
4725 // produce a vector of pointers, we need to either arbitrarily pick an
4726 // operand to broadcast, or broadcast a clone of the original GEP.
4727 // Here, we broadcast a clone of the original.
4729 // TODO: If at some point we decide to scalarize instructions having
4730 // loop-invariant operands, this special case will no longer be
4731 // required. We would add the scalarization decision to
4732 // collectLoopScalars() and teach getVectorValue() to broadcast
4733 // the lane-zero scalar value.
4734 auto *Clone = Builder.Insert(GEP->clone());
4735 for (unsigned Part = 0; Part < UF; ++Part) {
4736 Value *EntryPart = Builder.CreateVectorSplat(VF, Clone);
4737 VectorLoopValueMap.setVectorValue(&I, Part, EntryPart);
4738 addMetadata(EntryPart, GEP);
4741 // If the GEP has at least one loop-varying operand, we are sure to
4742 // produce a vector of pointers. But if we are only unrolling, we want
4743 // to produce a scalar GEP for each unroll part. Thus, the GEP we
4744 // produce with the code below will be scalar (if VF == 1) or vector
4745 // (otherwise). Note that for the unroll-only case, we still maintain
4746 // values in the vector mapping with initVector, as we do for other
4748 for (unsigned Part = 0; Part < UF; ++Part) {
4750 // The pointer operand of the new GEP. If it's loop-invariant, we
4751 // won't broadcast it.
4753 OrigLoop->isLoopInvariant(GEP->getPointerOperand())
4754 ? GEP->getPointerOperand()
4755 : getOrCreateVectorValue(GEP->getPointerOperand(), Part);
4757 // Collect all the indices for the new GEP. If any index is
4758 // loop-invariant, we won't broadcast it.
4759 SmallVector<Value *, 4> Indices;
4760 for (auto &U : make_range(GEP->idx_begin(), GEP->idx_end())) {
4761 if (OrigLoop->isLoopInvariant(U.get()))
4762 Indices.push_back(U.get());
4764 Indices.push_back(getOrCreateVectorValue(U.get(), Part));
4767 // Create the new GEP. Note that this GEP may be a scalar if VF == 1,
4768 // but it should be a vector, otherwise.
4769 auto *NewGEP = GEP->isInBounds()
4770 ? Builder.CreateInBoundsGEP(Ptr, Indices)
4771 : Builder.CreateGEP(Ptr, Indices);
4772 assert((VF == 1 || NewGEP->getType()->isVectorTy()) &&
4773 "NewGEP is not a pointer vector");
4774 VectorLoopValueMap.setVectorValue(&I, Part, NewGEP);
4775 addMetadata(NewGEP, GEP);
4781 case Instruction::UDiv:
4782 case Instruction::SDiv:
4783 case Instruction::SRem:
4784 case Instruction::URem:
4785 // Scalarize with predication if this instruction may divide by zero and
4786 // block execution is conditional, otherwise fallthrough.
4787 if (Legal->isScalarWithPredication(&I)) {
4788 scalarizeInstruction(&I, true);
4792 case Instruction::Add:
4793 case Instruction::FAdd:
4794 case Instruction::Sub:
4795 case Instruction::FSub:
4796 case Instruction::Mul:
4797 case Instruction::FMul:
4798 case Instruction::FDiv:
4799 case Instruction::FRem:
4800 case Instruction::Shl:
4801 case Instruction::LShr:
4802 case Instruction::AShr:
4803 case Instruction::And:
4804 case Instruction::Or:
4805 case Instruction::Xor: {
4806 // Just widen binops.
4807 auto *BinOp = cast<BinaryOperator>(&I);
4808 setDebugLocFromInst(Builder, BinOp);
4810 for (unsigned Part = 0; Part < UF; ++Part) {
4811 Value *A = getOrCreateVectorValue(BinOp->getOperand(0), Part);
4812 Value *B = getOrCreateVectorValue(BinOp->getOperand(1), Part);
4813 Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A, B);
4815 if (BinaryOperator *VecOp = dyn_cast<BinaryOperator>(V))
4816 VecOp->copyIRFlags(BinOp);
4818 // Use this vector value for all users of the original instruction.
4819 VectorLoopValueMap.setVectorValue(&I, Part, V);
4820 addMetadata(V, BinOp);
4825 case Instruction::Select: {
4827 // If the selector is loop invariant we can create a select
4828 // instruction with a scalar condition. Otherwise, use vector-select.
4829 auto *SE = PSE.getSE();
4830 bool InvariantCond =
4831 SE->isLoopInvariant(PSE.getSCEV(I.getOperand(0)), OrigLoop);
4832 setDebugLocFromInst(Builder, &I);
4834 // The condition can be loop invariant but still defined inside the
4835 // loop. This means that we can't just use the original 'cond' value.
4836 // We have to take the 'vectorized' value and pick the first lane.
4837 // Instcombine will make this a no-op.
4839 auto *ScalarCond = getOrCreateScalarValue(I.getOperand(0), 0, 0);
4841 for (unsigned Part = 0; Part < UF; ++Part) {
4842 Value *Cond = getOrCreateVectorValue(I.getOperand(0), Part);
4843 Value *Op0 = getOrCreateVectorValue(I.getOperand(1), Part);
4844 Value *Op1 = getOrCreateVectorValue(I.getOperand(2), Part);
4846 Builder.CreateSelect(InvariantCond ? ScalarCond : Cond, Op0, Op1);
4847 VectorLoopValueMap.setVectorValue(&I, Part, Sel);
4848 addMetadata(Sel, &I);
4854 case Instruction::ICmp:
4855 case Instruction::FCmp: {
4856 // Widen compares. Generate vector compares.
4857 bool FCmp = (I.getOpcode() == Instruction::FCmp);
4858 auto *Cmp = dyn_cast<CmpInst>(&I);
4859 setDebugLocFromInst(Builder, Cmp);
4860 for (unsigned Part = 0; Part < UF; ++Part) {
4861 Value *A = getOrCreateVectorValue(Cmp->getOperand(0), Part);
4862 Value *B = getOrCreateVectorValue(Cmp->getOperand(1), Part);
4865 C = Builder.CreateFCmp(Cmp->getPredicate(), A, B);
4866 cast<FCmpInst>(C)->copyFastMathFlags(Cmp);
4868 C = Builder.CreateICmp(Cmp->getPredicate(), A, B);
4870 VectorLoopValueMap.setVectorValue(&I, Part, C);
4877 case Instruction::Store:
4878 case Instruction::Load:
4879 vectorizeMemoryInstruction(&I);
4881 case Instruction::ZExt:
4882 case Instruction::SExt:
4883 case Instruction::FPToUI:
4884 case Instruction::FPToSI:
4885 case Instruction::FPExt:
4886 case Instruction::PtrToInt:
4887 case Instruction::IntToPtr:
4888 case Instruction::SIToFP:
4889 case Instruction::UIToFP:
4890 case Instruction::Trunc:
4891 case Instruction::FPTrunc:
4892 case Instruction::BitCast: {
4893 auto *CI = dyn_cast<CastInst>(&I);
4894 setDebugLocFromInst(Builder, CI);
4896 // Optimize the special case where the source is a constant integer
4897 // induction variable. Notice that we can only optimize the 'trunc' case
4898 // because (a) FP conversions lose precision, (b) sext/zext may wrap, and
4899 // (c) other casts depend on pointer size.
4900 if (Cost->isOptimizableIVTruncate(CI, VF)) {
4901 widenIntOrFpInduction(cast<PHINode>(CI->getOperand(0)),
4902 cast<TruncInst>(CI));
4906 /// Vectorize casts.
4908 (VF == 1) ? CI->getType() : VectorType::get(CI->getType(), VF);
4910 for (unsigned Part = 0; Part < UF; ++Part) {
4911 Value *A = getOrCreateVectorValue(CI->getOperand(0), Part);
4912 Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy);
4913 VectorLoopValueMap.setVectorValue(&I, Part, Cast);
4914 addMetadata(Cast, &I);
4919 case Instruction::Call: {
4920 // Ignore dbg intrinsics.
4921 if (isa<DbgInfoIntrinsic>(I))
4923 setDebugLocFromInst(Builder, &I);
4925 Module *M = I.getParent()->getParent()->getParent();
4926 auto *CI = cast<CallInst>(&I);
4928 StringRef FnName = CI->getCalledFunction()->getName();
4929 Function *F = CI->getCalledFunction();
4930 Type *RetTy = ToVectorTy(CI->getType(), VF);
4931 SmallVector<Type *, 4> Tys;
4932 for (Value *ArgOperand : CI->arg_operands())
4933 Tys.push_back(ToVectorTy(ArgOperand->getType(), VF));
4935 Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
4936 if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end ||
4937 ID == Intrinsic::lifetime_start)) {
4938 scalarizeInstruction(&I);
4941 // The flag shows whether we use Intrinsic or a usual Call for vectorized
4942 // version of the instruction.
4943 // Is it beneficial to perform intrinsic call compared to lib call?
4944 bool NeedToScalarize;
4945 unsigned CallCost = getVectorCallCost(CI, VF, *TTI, TLI, NeedToScalarize);
4946 bool UseVectorIntrinsic =
4947 ID && getVectorIntrinsicCost(CI, VF, *TTI, TLI) <= CallCost;
4948 if (!UseVectorIntrinsic && NeedToScalarize) {
4949 scalarizeInstruction(&I);
4953 for (unsigned Part = 0; Part < UF; ++Part) {
4954 SmallVector<Value *, 4> Args;
4955 for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) {
4956 Value *Arg = CI->getArgOperand(i);
4957 // Some intrinsics have a scalar argument - don't replace it with a
4959 if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, i))
4960 Arg = getOrCreateVectorValue(CI->getArgOperand(i), Part);
4961 Args.push_back(Arg);
4965 if (UseVectorIntrinsic) {
4966 // Use vector version of the intrinsic.
4967 Type *TysForDecl[] = {CI->getType()};
4969 TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF);
4970 VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl);
4972 // Use vector version of the library call.
4973 StringRef VFnName = TLI->getVectorizedFunction(FnName, VF);
4974 assert(!VFnName.empty() && "Vector function name is empty.");
4975 VectorF = M->getFunction(VFnName);
4977 // Generate a declaration
4978 FunctionType *FTy = FunctionType::get(RetTy, Tys, false);
4980 Function::Create(FTy, Function::ExternalLinkage, VFnName, M);
4981 VectorF->copyAttributesFrom(F);
4984 assert(VectorF && "Can't create vector function.");
4986 SmallVector<OperandBundleDef, 1> OpBundles;
4987 CI->getOperandBundlesAsDefs(OpBundles);
4988 CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles);
4990 if (isa<FPMathOperator>(V))
4991 V->copyFastMathFlags(CI);
4993 VectorLoopValueMap.setVectorValue(&I, Part, V);
5001 // All other instructions are unsupported. Scalarize them.
5002 scalarizeInstruction(&I);
5007 void InnerLoopVectorizer::updateAnalysis() {
5008 // Forget the original basic block.
5009 PSE.getSE()->forgetLoop(OrigLoop);
5011 // Update the dominator tree information.
5012 assert(DT->properlyDominates(LoopBypassBlocks.front(), LoopExitBlock) &&
5013 "Entry does not dominate exit.");
5015 DT->addNewBlock(LI->getLoopFor(LoopVectorBody)->getHeader(),
5016 LoopVectorPreHeader);
5017 DT->addNewBlock(LoopMiddleBlock,
5018 LI->getLoopFor(LoopVectorBody)->getLoopLatch());
5019 DT->addNewBlock(LoopScalarPreHeader, LoopBypassBlocks[0]);
5020 DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader);
5021 DT->changeImmediateDominator(LoopExitBlock, LoopBypassBlocks[0]);
5023 DEBUG(DT->verifyDomTree());
5026 /// \brief Check whether it is safe to if-convert this phi node.
5028 /// Phi nodes with constant expressions that can trap are not safe to if
5030 static bool canIfConvertPHINodes(BasicBlock *BB) {
5031 for (Instruction &I : *BB) {
5032 auto *Phi = dyn_cast<PHINode>(&I);
5035 for (Value *V : Phi->incoming_values())
5036 if (auto *C = dyn_cast<Constant>(V))
5043 bool LoopVectorizationLegality::canVectorizeWithIfConvert() {
5044 if (!EnableIfConversion) {
5045 ORE->emit(createMissedAnalysis("IfConversionDisabled")
5046 << "if-conversion is disabled");
5050 assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable");
5052 // A list of pointers that we can safely read and write to.
5053 SmallPtrSet<Value *, 8> SafePointes;
5055 // Collect safe addresses.
5056 for (BasicBlock *BB : TheLoop->blocks()) {
5057 if (blockNeedsPredication(BB))
5060 for (Instruction &I : *BB)
5061 if (auto *Ptr = getPointerOperand(&I))
5062 SafePointes.insert(Ptr);
5065 // Collect the blocks that need predication.
5066 BasicBlock *Header = TheLoop->getHeader();
5067 for (BasicBlock *BB : TheLoop->blocks()) {
5068 // We don't support switch statements inside loops.
5069 if (!isa<BranchInst>(BB->getTerminator())) {
5070 ORE->emit(createMissedAnalysis("LoopContainsSwitch", BB->getTerminator())
5071 << "loop contains a switch statement");
5075 // We must be able to predicate all blocks that need to be predicated.
5076 if (blockNeedsPredication(BB)) {
5077 if (!blockCanBePredicated(BB, SafePointes)) {
5078 ORE->emit(createMissedAnalysis("NoCFGForSelect", BB->getTerminator())
5079 << "control flow cannot be substituted for a select");
5082 } else if (BB != Header && !canIfConvertPHINodes(BB)) {
5083 ORE->emit(createMissedAnalysis("NoCFGForSelect", BB->getTerminator())
5084 << "control flow cannot be substituted for a select");
5089 // We can if-convert this loop.
5093 bool LoopVectorizationLegality::canVectorize() {
5094 // Store the result and return it at the end instead of exiting early, in case
5095 // allowExtraAnalysis is used to report multiple reasons for not vectorizing.
5097 // We must have a loop in canonical form. Loops with indirectbr in them cannot
5098 // be canonicalized.
5099 if (!TheLoop->getLoopPreheader()) {
5100 ORE->emit(createMissedAnalysis("CFGNotUnderstood")
5101 << "loop control flow is not understood by vectorizer");
5102 if (ORE->allowExtraAnalysis())
5108 // FIXME: The code is currently dead, since the loop gets sent to
5109 // LoopVectorizationLegality is already an innermost loop.
5111 // We can only vectorize innermost loops.
5112 if (!TheLoop->empty()) {
5113 ORE->emit(createMissedAnalysis("NotInnermostLoop")
5114 << "loop is not the innermost loop");
5115 if (ORE->allowExtraAnalysis())
5121 // We must have a single backedge.
5122 if (TheLoop->getNumBackEdges() != 1) {
5123 ORE->emit(createMissedAnalysis("CFGNotUnderstood")
5124 << "loop control flow is not understood by vectorizer");
5125 if (ORE->allowExtraAnalysis())
5131 // We must have a single exiting block.
5132 if (!TheLoop->getExitingBlock()) {
5133 ORE->emit(createMissedAnalysis("CFGNotUnderstood")
5134 << "loop control flow is not understood by vectorizer");
5135 if (ORE->allowExtraAnalysis())
5141 // We only handle bottom-tested loops, i.e. loop in which the condition is
5142 // checked at the end of each iteration. With that we can assume that all
5143 // instructions in the loop are executed the same number of times.
5144 if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) {
5145 ORE->emit(createMissedAnalysis("CFGNotUnderstood")
5146 << "loop control flow is not understood by vectorizer");
5147 if (ORE->allowExtraAnalysis())
5153 // We need to have a loop header.
5154 DEBUG(dbgs() << "LV: Found a loop: " << TheLoop->getHeader()->getName()
5157 // Check if we can if-convert non-single-bb loops.
5158 unsigned NumBlocks = TheLoop->getNumBlocks();
5159 if (NumBlocks != 1 && !canVectorizeWithIfConvert()) {
5160 DEBUG(dbgs() << "LV: Can't if-convert the loop.\n");
5161 if (ORE->allowExtraAnalysis())
5167 // Check if we can vectorize the instructions and CFG in this loop.
5168 if (!canVectorizeInstrs()) {
5169 DEBUG(dbgs() << "LV: Can't vectorize the instructions or CFG\n");
5170 if (ORE->allowExtraAnalysis())
5176 // Go over each instruction and look at memory deps.
5177 if (!canVectorizeMemory()) {
5178 DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n");
5179 if (ORE->allowExtraAnalysis())
5185 DEBUG(dbgs() << "LV: We can vectorize this loop"
5186 << (LAI->getRuntimePointerChecking()->Need
5187 ? " (with a runtime bound check)"
5191 bool UseInterleaved = TTI->enableInterleavedAccessVectorization();
5193 // If an override option has been passed in for interleaved accesses, use it.
5194 if (EnableInterleavedMemAccesses.getNumOccurrences() > 0)
5195 UseInterleaved = EnableInterleavedMemAccesses;
5197 // Analyze interleaved memory accesses.
5199 InterleaveInfo.analyzeInterleaving(*getSymbolicStrides());
5201 unsigned SCEVThreshold = VectorizeSCEVCheckThreshold;
5202 if (Hints->getForce() == LoopVectorizeHints::FK_Enabled)
5203 SCEVThreshold = PragmaVectorizeSCEVCheckThreshold;
5205 if (PSE.getUnionPredicate().getComplexity() > SCEVThreshold) {
5206 ORE->emit(createMissedAnalysis("TooManySCEVRunTimeChecks")
5207 << "Too many SCEV assumptions need to be made and checked "
5209 DEBUG(dbgs() << "LV: Too many SCEV checks needed.\n");
5210 if (ORE->allowExtraAnalysis())
5216 // Okay! We've done all the tests. If any have failed, return false. Otherwise
5217 // we can vectorize, and at this point we don't have any other mem analysis
5218 // which may limit our maximum vectorization factor, so just return true with
5223 static Type *convertPointerToIntegerType(const DataLayout &DL, Type *Ty) {
5224 if (Ty->isPointerTy())
5225 return DL.getIntPtrType(Ty);
5227 // It is possible that char's or short's overflow when we ask for the loop's
5228 // trip count, work around this by changing the type size.
5229 if (Ty->getScalarSizeInBits() < 32)
5230 return Type::getInt32Ty(Ty->getContext());
5235 static Type *getWiderType(const DataLayout &DL, Type *Ty0, Type *Ty1) {
5236 Ty0 = convertPointerToIntegerType(DL, Ty0);
5237 Ty1 = convertPointerToIntegerType(DL, Ty1);
5238 if (Ty0->getScalarSizeInBits() > Ty1->getScalarSizeInBits())
5243 /// \brief Check that the instruction has outside loop users and is not an
5244 /// identified reduction variable.
5245 static bool hasOutsideLoopUser(const Loop *TheLoop, Instruction *Inst,
5246 SmallPtrSetImpl<Value *> &AllowedExit) {
5247 // Reduction and Induction instructions are allowed to have exit users. All
5248 // other instructions must not have external users.
5249 if (!AllowedExit.count(Inst))
5250 // Check that all of the users of the loop are inside the BB.
5251 for (User *U : Inst->users()) {
5252 Instruction *UI = cast<Instruction>(U);
5253 // This user may be a reduction exit value.
5254 if (!TheLoop->contains(UI)) {
5255 DEBUG(dbgs() << "LV: Found an outside user for : " << *UI << '\n');
5262 void LoopVectorizationLegality::addInductionPhi(
5263 PHINode *Phi, const InductionDescriptor &ID,
5264 SmallPtrSetImpl<Value *> &AllowedExit) {
5265 Inductions[Phi] = ID;
5266 Type *PhiTy = Phi->getType();
5267 const DataLayout &DL = Phi->getModule()->getDataLayout();
5269 // Get the widest type.
5270 if (!PhiTy->isFloatingPointTy()) {
5272 WidestIndTy = convertPointerToIntegerType(DL, PhiTy);
5274 WidestIndTy = getWiderType(DL, PhiTy, WidestIndTy);
5277 // Int inductions are special because we only allow one IV.
5278 if (ID.getKind() == InductionDescriptor::IK_IntInduction &&
5279 ID.getConstIntStepValue() &&
5280 ID.getConstIntStepValue()->isOne() &&
5281 isa<Constant>(ID.getStartValue()) &&
5282 cast<Constant>(ID.getStartValue())->isNullValue()) {
5284 // Use the phi node with the widest type as induction. Use the last
5285 // one if there are multiple (no good reason for doing this other
5286 // than it is expedient). We've checked that it begins at zero and
5287 // steps by one, so this is a canonical induction variable.
5288 if (!PrimaryInduction || PhiTy == WidestIndTy)
5289 PrimaryInduction = Phi;
5292 // Both the PHI node itself, and the "post-increment" value feeding
5293 // back into the PHI node may have external users.
5294 // We can allow those uses, except if the SCEVs we have for them rely
5295 // on predicates that only hold within the loop, since allowing the exit
5296 // currently means re-using this SCEV outside the loop.
5297 if (PSE.getUnionPredicate().isAlwaysTrue()) {
5298 AllowedExit.insert(Phi);
5299 AllowedExit.insert(Phi->getIncomingValueForBlock(TheLoop->getLoopLatch()));
5302 DEBUG(dbgs() << "LV: Found an induction variable.\n");
5306 bool LoopVectorizationLegality::canVectorizeInstrs() {
5307 BasicBlock *Header = TheLoop->getHeader();
5309 // Look for the attribute signaling the absence of NaNs.
5310 Function &F = *Header->getParent();
5312 F.getFnAttribute("no-nans-fp-math").getValueAsString() == "true";
5314 // For each block in the loop.
5315 for (BasicBlock *BB : TheLoop->blocks()) {
5316 // Scan the instructions in the block and look for hazards.
5317 for (Instruction &I : *BB) {
5318 if (auto *Phi = dyn_cast<PHINode>(&I)) {
5319 Type *PhiTy = Phi->getType();
5320 // Check that this PHI type is allowed.
5321 if (!PhiTy->isIntegerTy() && !PhiTy->isFloatingPointTy() &&
5322 !PhiTy->isPointerTy()) {
5323 ORE->emit(createMissedAnalysis("CFGNotUnderstood", Phi)
5324 << "loop control flow is not understood by vectorizer");
5325 DEBUG(dbgs() << "LV: Found an non-int non-pointer PHI.\n");
5329 // If this PHINode is not in the header block, then we know that we
5330 // can convert it to select during if-conversion. No need to check if
5331 // the PHIs in this block are induction or reduction variables.
5333 // Check that this instruction has no outside users or is an
5334 // identified reduction value with an outside user.
5335 if (!hasOutsideLoopUser(TheLoop, Phi, AllowedExit))
5337 ORE->emit(createMissedAnalysis("NeitherInductionNorReduction", Phi)
5338 << "value could not be identified as "
5339 "an induction or reduction variable");
5343 // We only allow if-converted PHIs with exactly two incoming values.
5344 if (Phi->getNumIncomingValues() != 2) {
5345 ORE->emit(createMissedAnalysis("CFGNotUnderstood", Phi)
5346 << "control flow not understood by vectorizer");
5347 DEBUG(dbgs() << "LV: Found an invalid PHI.\n");
5351 RecurrenceDescriptor RedDes;
5352 if (RecurrenceDescriptor::isReductionPHI(Phi, TheLoop, RedDes)) {
5353 if (RedDes.hasUnsafeAlgebra())
5354 Requirements->addUnsafeAlgebraInst(RedDes.getUnsafeAlgebraInst());
5355 AllowedExit.insert(RedDes.getLoopExitInstr());
5356 Reductions[Phi] = RedDes;
5360 InductionDescriptor ID;
5361 if (InductionDescriptor::isInductionPHI(Phi, TheLoop, PSE, ID)) {
5362 addInductionPhi(Phi, ID, AllowedExit);
5363 if (ID.hasUnsafeAlgebra() && !HasFunNoNaNAttr)
5364 Requirements->addUnsafeAlgebraInst(ID.getUnsafeAlgebraInst());
5368 if (RecurrenceDescriptor::isFirstOrderRecurrence(Phi, TheLoop,
5370 FirstOrderRecurrences.insert(Phi);
5374 // As a last resort, coerce the PHI to a AddRec expression
5375 // and re-try classifying it a an induction PHI.
5376 if (InductionDescriptor::isInductionPHI(Phi, TheLoop, PSE, ID, true)) {
5377 addInductionPhi(Phi, ID, AllowedExit);
5381 ORE->emit(createMissedAnalysis("NonReductionValueUsedOutsideLoop", Phi)
5382 << "value that could not be identified as "
5383 "reduction is used outside the loop");
5384 DEBUG(dbgs() << "LV: Found an unidentified PHI." << *Phi << "\n");
5386 } // end of PHI handling
5388 // We handle calls that:
5389 // * Are debug info intrinsics.
5390 // * Have a mapping to an IR intrinsic.
5391 // * Have a vector version available.
5392 auto *CI = dyn_cast<CallInst>(&I);
5393 if (CI && !getVectorIntrinsicIDForCall(CI, TLI) &&
5394 !isa<DbgInfoIntrinsic>(CI) &&
5395 !(CI->getCalledFunction() && TLI &&
5396 TLI->isFunctionVectorizable(CI->getCalledFunction()->getName()))) {
5397 ORE->emit(createMissedAnalysis("CantVectorizeCall", CI)
5398 << "call instruction cannot be vectorized");
5399 DEBUG(dbgs() << "LV: Found a non-intrinsic, non-libfunc callsite.\n");
5403 // Intrinsics such as powi,cttz and ctlz are legal to vectorize if the
5404 // second argument is the same (i.e. loop invariant)
5405 if (CI && hasVectorInstrinsicScalarOpd(
5406 getVectorIntrinsicIDForCall(CI, TLI), 1)) {
5407 auto *SE = PSE.getSE();
5408 if (!SE->isLoopInvariant(PSE.getSCEV(CI->getOperand(1)), TheLoop)) {
5409 ORE->emit(createMissedAnalysis("CantVectorizeIntrinsic", CI)
5410 << "intrinsic instruction cannot be vectorized");
5411 DEBUG(dbgs() << "LV: Found unvectorizable intrinsic " << *CI << "\n");
5416 // Check that the instruction return type is vectorizable.
5417 // Also, we can't vectorize extractelement instructions.
5418 if ((!VectorType::isValidElementType(I.getType()) &&
5419 !I.getType()->isVoidTy()) ||
5420 isa<ExtractElementInst>(I)) {
5421 ORE->emit(createMissedAnalysis("CantVectorizeInstructionReturnType", &I)
5422 << "instruction return type cannot be vectorized");
5423 DEBUG(dbgs() << "LV: Found unvectorizable type.\n");
5427 // Check that the stored type is vectorizable.
5428 if (auto *ST = dyn_cast<StoreInst>(&I)) {
5429 Type *T = ST->getValueOperand()->getType();
5430 if (!VectorType::isValidElementType(T)) {
5431 ORE->emit(createMissedAnalysis("CantVectorizeStore", ST)
5432 << "store instruction cannot be vectorized");
5436 // FP instructions can allow unsafe algebra, thus vectorizable by
5437 // non-IEEE-754 compliant SIMD units.
5438 // This applies to floating-point math operations and calls, not memory
5439 // operations, shuffles, or casts, as they don't change precision or
5441 } else if (I.getType()->isFloatingPointTy() && (CI || I.isBinaryOp()) &&
5442 !I.hasUnsafeAlgebra()) {
5443 DEBUG(dbgs() << "LV: Found FP op with unsafe algebra.\n");
5444 Hints->setPotentiallyUnsafe();
5447 // Reduction instructions are allowed to have exit users.
5448 // All other instructions must not have external users.
5449 if (hasOutsideLoopUser(TheLoop, &I, AllowedExit)) {
5450 ORE->emit(createMissedAnalysis("ValueUsedOutsideLoop", &I)
5451 << "value cannot be used outside the loop");
5458 if (!PrimaryInduction) {
5459 DEBUG(dbgs() << "LV: Did not find one integer induction var.\n");
5460 if (Inductions.empty()) {
5461 ORE->emit(createMissedAnalysis("NoInductionVariable")
5462 << "loop induction variable could not be identified");
5467 // Now we know the widest induction type, check if our found induction
5468 // is the same size. If it's not, unset it here and InnerLoopVectorizer
5469 // will create another.
5470 if (PrimaryInduction && WidestIndTy != PrimaryInduction->getType())
5471 PrimaryInduction = nullptr;
5476 void LoopVectorizationCostModel::collectLoopScalars(unsigned VF) {
5478 // We should not collect Scalars more than once per VF. Right now, this
5479 // function is called from collectUniformsAndScalars(), which already does
5480 // this check. Collecting Scalars for VF=1 does not make any sense.
5481 assert(VF >= 2 && !Scalars.count(VF) &&
5482 "This function should not be visited twice for the same VF");
5484 SmallSetVector<Instruction *, 8> Worklist;
5486 // These sets are used to seed the analysis with pointers used by memory
5487 // accesses that will remain scalar.
5488 SmallSetVector<Instruction *, 8> ScalarPtrs;
5489 SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs;
5491 // A helper that returns true if the use of Ptr by MemAccess will be scalar.
5492 // The pointer operands of loads and stores will be scalar as long as the
5493 // memory access is not a gather or scatter operation. The value operand of a
5494 // store will remain scalar if the store is scalarized.
5495 auto isScalarUse = [&](Instruction *MemAccess, Value *Ptr) {
5496 InstWidening WideningDecision = getWideningDecision(MemAccess, VF);
5497 assert(WideningDecision != CM_Unknown &&
5498 "Widening decision should be ready at this moment");
5499 if (auto *Store = dyn_cast<StoreInst>(MemAccess))
5500 if (Ptr == Store->getValueOperand())
5501 return WideningDecision == CM_Scalarize;
5502 assert(Ptr == getPointerOperand(MemAccess) &&
5503 "Ptr is neither a value or pointer operand");
5504 return WideningDecision != CM_GatherScatter;
5507 // A helper that returns true if the given value is a bitcast or
5508 // getelementptr instruction contained in the loop.
5509 auto isLoopVaryingBitCastOrGEP = [&](Value *V) {
5510 return ((isa<BitCastInst>(V) && V->getType()->isPointerTy()) ||
5511 isa<GetElementPtrInst>(V)) &&
5512 !TheLoop->isLoopInvariant(V);
5515 // A helper that evaluates a memory access's use of a pointer. If the use
5516 // will be a scalar use, and the pointer is only used by memory accesses, we
5517 // place the pointer in ScalarPtrs. Otherwise, the pointer is placed in
5518 // PossibleNonScalarPtrs.
5519 auto evaluatePtrUse = [&](Instruction *MemAccess, Value *Ptr) {
5521 // We only care about bitcast and getelementptr instructions contained in
5523 if (!isLoopVaryingBitCastOrGEP(Ptr))
5526 // If the pointer has already been identified as scalar (e.g., if it was
5527 // also identified as uniform), there's nothing to do.
5528 auto *I = cast<Instruction>(Ptr);
5529 if (Worklist.count(I))
5532 // If the use of the pointer will be a scalar use, and all users of the
5533 // pointer are memory accesses, place the pointer in ScalarPtrs. Otherwise,
5534 // place the pointer in PossibleNonScalarPtrs.
5535 if (isScalarUse(MemAccess, Ptr) && all_of(I->users(), [&](User *U) {
5536 return isa<LoadInst>(U) || isa<StoreInst>(U);
5538 ScalarPtrs.insert(I);
5540 PossibleNonScalarPtrs.insert(I);
5543 // We seed the scalars analysis with three classes of instructions: (1)
5544 // instructions marked uniform-after-vectorization, (2) bitcast and
5545 // getelementptr instructions used by memory accesses requiring a scalar use,
5546 // and (3) pointer induction variables and their update instructions (we
5547 // currently only scalarize these).
5549 // (1) Add to the worklist all instructions that have been identified as
5550 // uniform-after-vectorization.
5551 Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end());
5553 // (2) Add to the worklist all bitcast and getelementptr instructions used by
5554 // memory accesses requiring a scalar use. The pointer operands of loads and
5555 // stores will be scalar as long as the memory accesses is not a gather or
5556 // scatter operation. The value operand of a store will remain scalar if the
5557 // store is scalarized.
5558 for (auto *BB : TheLoop->blocks())
5559 for (auto &I : *BB) {
5560 if (auto *Load = dyn_cast<LoadInst>(&I)) {
5561 evaluatePtrUse(Load, Load->getPointerOperand());
5562 } else if (auto *Store = dyn_cast<StoreInst>(&I)) {
5563 evaluatePtrUse(Store, Store->getPointerOperand());
5564 evaluatePtrUse(Store, Store->getValueOperand());
5567 for (auto *I : ScalarPtrs)
5568 if (!PossibleNonScalarPtrs.count(I)) {
5569 DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n");
5573 // (3) Add to the worklist all pointer induction variables and their update
5576 // TODO: Once we are able to vectorize pointer induction variables we should
5577 // no longer insert them into the worklist here.
5578 auto *Latch = TheLoop->getLoopLatch();
5579 for (auto &Induction : *Legal->getInductionVars()) {
5580 auto *Ind = Induction.first;
5581 auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
5582 if (Induction.second.getKind() != InductionDescriptor::IK_PtrInduction)
5584 Worklist.insert(Ind);
5585 Worklist.insert(IndUpdate);
5586 DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n");
5587 DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate << "\n");
5590 // Insert the forced scalars.
5591 // FIXME: Currently widenPHIInstruction() often creates a dead vector
5592 // induction variable when the PHI user is scalarized.
5593 if (ForcedScalars.count(VF))
5594 for (auto *I : ForcedScalars.find(VF)->second)
5597 // Expand the worklist by looking through any bitcasts and getelementptr
5598 // instructions we've already identified as scalar. This is similar to the
5599 // expansion step in collectLoopUniforms(); however, here we're only
5600 // expanding to include additional bitcasts and getelementptr instructions.
5602 while (Idx != Worklist.size()) {
5603 Instruction *Dst = Worklist[Idx++];
5604 if (!isLoopVaryingBitCastOrGEP(Dst->getOperand(0)))
5606 auto *Src = cast<Instruction>(Dst->getOperand(0));
5607 if (all_of(Src->users(), [&](User *U) -> bool {
5608 auto *J = cast<Instruction>(U);
5609 return !TheLoop->contains(J) || Worklist.count(J) ||
5610 ((isa<LoadInst>(J) || isa<StoreInst>(J)) &&
5611 isScalarUse(J, Src));
5613 Worklist.insert(Src);
5614 DEBUG(dbgs() << "LV: Found scalar instruction: " << *Src << "\n");
5618 // An induction variable will remain scalar if all users of the induction
5619 // variable and induction variable update remain scalar.
5620 for (auto &Induction : *Legal->getInductionVars()) {
5621 auto *Ind = Induction.first;
5622 auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
5624 // We already considered pointer induction variables, so there's no reason
5625 // to look at their users again.
5627 // TODO: Once we are able to vectorize pointer induction variables we
5628 // should no longer skip over them here.
5629 if (Induction.second.getKind() == InductionDescriptor::IK_PtrInduction)
5632 // Determine if all users of the induction variable are scalar after
5634 auto ScalarInd = all_of(Ind->users(), [&](User *U) -> bool {
5635 auto *I = cast<Instruction>(U);
5636 return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I);
5641 // Determine if all users of the induction variable update instruction are
5642 // scalar after vectorization.
5643 auto ScalarIndUpdate = all_of(IndUpdate->users(), [&](User *U) -> bool {
5644 auto *I = cast<Instruction>(U);
5645 return I == Ind || !TheLoop->contains(I) || Worklist.count(I);
5647 if (!ScalarIndUpdate)
5650 // The induction variable and its update instruction will remain scalar.
5651 Worklist.insert(Ind);
5652 Worklist.insert(IndUpdate);
5653 DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n");
5654 DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate << "\n");
5657 Scalars[VF].insert(Worklist.begin(), Worklist.end());
5660 bool LoopVectorizationLegality::isScalarWithPredication(Instruction *I) {
5661 if (!blockNeedsPredication(I->getParent()))
5663 switch(I->getOpcode()) {
5666 case Instruction::Store:
5667 return !isMaskRequired(I);
5668 case Instruction::UDiv:
5669 case Instruction::SDiv:
5670 case Instruction::SRem:
5671 case Instruction::URem:
5672 return mayDivideByZero(*I);
5677 bool LoopVectorizationLegality::memoryInstructionCanBeWidened(Instruction *I,
5679 // Get and ensure we have a valid memory instruction.
5680 LoadInst *LI = dyn_cast<LoadInst>(I);
5681 StoreInst *SI = dyn_cast<StoreInst>(I);
5682 assert((LI || SI) && "Invalid memory instruction");
5684 auto *Ptr = getPointerOperand(I);
5686 // In order to be widened, the pointer should be consecutive, first of all.
5687 if (!isConsecutivePtr(Ptr))
5690 // If the instruction is a store located in a predicated block, it will be
5692 if (isScalarWithPredication(I))
5695 // If the instruction's allocated size doesn't equal it's type size, it
5696 // requires padding and will be scalarized.
5697 auto &DL = I->getModule()->getDataLayout();
5698 auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType();
5699 if (hasIrregularType(ScalarTy, DL, VF))
5705 void LoopVectorizationCostModel::collectLoopUniforms(unsigned VF) {
5707 // We should not collect Uniforms more than once per VF. Right now,
5708 // this function is called from collectUniformsAndScalars(), which
5709 // already does this check. Collecting Uniforms for VF=1 does not make any
5712 assert(VF >= 2 && !Uniforms.count(VF) &&
5713 "This function should not be visited twice for the same VF");
5715 // Visit the list of Uniforms. If we'll not find any uniform value, we'll
5716 // not analyze again. Uniforms.count(VF) will return 1.
5717 Uniforms[VF].clear();
5719 // We now know that the loop is vectorizable!
5720 // Collect instructions inside the loop that will remain uniform after
5723 // Global values, params and instructions outside of current loop are out of
5725 auto isOutOfScope = [&](Value *V) -> bool {
5726 Instruction *I = dyn_cast<Instruction>(V);
5727 return (!I || !TheLoop->contains(I));
5730 SetVector<Instruction *> Worklist;
5731 BasicBlock *Latch = TheLoop->getLoopLatch();
5733 // Start with the conditional branch. If the branch condition is an
5734 // instruction contained in the loop that is only used by the branch, it is
5736 auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0));
5737 if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse()) {
5738 Worklist.insert(Cmp);
5739 DEBUG(dbgs() << "LV: Found uniform instruction: " << *Cmp << "\n");
5742 // Holds consecutive and consecutive-like pointers. Consecutive-like pointers
5743 // are pointers that are treated like consecutive pointers during
5744 // vectorization. The pointer operands of interleaved accesses are an
5746 SmallSetVector<Instruction *, 8> ConsecutiveLikePtrs;
5748 // Holds pointer operands of instructions that are possibly non-uniform.
5749 SmallPtrSet<Instruction *, 8> PossibleNonUniformPtrs;
5751 auto isUniformDecision = [&](Instruction *I, unsigned VF) {
5752 InstWidening WideningDecision = getWideningDecision(I, VF);
5753 assert(WideningDecision != CM_Unknown &&
5754 "Widening decision should be ready at this moment");
5756 return (WideningDecision == CM_Widen ||
5757 WideningDecision == CM_Interleave);
5759 // Iterate over the instructions in the loop, and collect all
5760 // consecutive-like pointer operands in ConsecutiveLikePtrs. If it's possible
5761 // that a consecutive-like pointer operand will be scalarized, we collect it
5762 // in PossibleNonUniformPtrs instead. We use two sets here because a single
5763 // getelementptr instruction can be used by both vectorized and scalarized
5764 // memory instructions. For example, if a loop loads and stores from the same
5765 // location, but the store is conditional, the store will be scalarized, and
5766 // the getelementptr won't remain uniform.
5767 for (auto *BB : TheLoop->blocks())
5768 for (auto &I : *BB) {
5770 // If there's no pointer operand, there's nothing to do.
5771 auto *Ptr = dyn_cast_or_null<Instruction>(getPointerOperand(&I));
5775 // True if all users of Ptr are memory accesses that have Ptr as their
5777 auto UsersAreMemAccesses = all_of(Ptr->users(), [&](User *U) -> bool {
5778 return getPointerOperand(U) == Ptr;
5781 // Ensure the memory instruction will not be scalarized or used by
5782 // gather/scatter, making its pointer operand non-uniform. If the pointer
5783 // operand is used by any instruction other than a memory access, we
5784 // conservatively assume the pointer operand may be non-uniform.
5785 if (!UsersAreMemAccesses || !isUniformDecision(&I, VF))
5786 PossibleNonUniformPtrs.insert(Ptr);
5788 // If the memory instruction will be vectorized and its pointer operand
5789 // is consecutive-like, or interleaving - the pointer operand should
5792 ConsecutiveLikePtrs.insert(Ptr);
5795 // Add to the Worklist all consecutive and consecutive-like pointers that
5796 // aren't also identified as possibly non-uniform.
5797 for (auto *V : ConsecutiveLikePtrs)
5798 if (!PossibleNonUniformPtrs.count(V)) {
5799 DEBUG(dbgs() << "LV: Found uniform instruction: " << *V << "\n");
5803 // Expand Worklist in topological order: whenever a new instruction
5804 // is added , its users should be either already inside Worklist, or
5805 // out of scope. It ensures a uniform instruction will only be used
5806 // by uniform instructions or out of scope instructions.
5808 while (idx != Worklist.size()) {
5809 Instruction *I = Worklist[idx++];
5811 for (auto OV : I->operand_values()) {
5812 if (isOutOfScope(OV))
5814 auto *OI = cast<Instruction>(OV);
5815 if (all_of(OI->users(), [&](User *U) -> bool {
5816 auto *J = cast<Instruction>(U);
5817 return !TheLoop->contains(J) || Worklist.count(J) ||
5818 (OI == getPointerOperand(J) && isUniformDecision(J, VF));
5820 Worklist.insert(OI);
5821 DEBUG(dbgs() << "LV: Found uniform instruction: " << *OI << "\n");
5826 // Returns true if Ptr is the pointer operand of a memory access instruction
5827 // I, and I is known to not require scalarization.
5828 auto isVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool {
5829 return getPointerOperand(I) == Ptr && isUniformDecision(I, VF);
5832 // For an instruction to be added into Worklist above, all its users inside
5833 // the loop should also be in Worklist. However, this condition cannot be
5834 // true for phi nodes that form a cyclic dependence. We must process phi
5835 // nodes separately. An induction variable will remain uniform if all users
5836 // of the induction variable and induction variable update remain uniform.
5837 // The code below handles both pointer and non-pointer induction variables.
5838 for (auto &Induction : *Legal->getInductionVars()) {
5839 auto *Ind = Induction.first;
5840 auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
5842 // Determine if all users of the induction variable are uniform after
5844 auto UniformInd = all_of(Ind->users(), [&](User *U) -> bool {
5845 auto *I = cast<Instruction>(U);
5846 return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) ||
5847 isVectorizedMemAccessUse(I, Ind);
5852 // Determine if all users of the induction variable update instruction are
5853 // uniform after vectorization.
5854 auto UniformIndUpdate = all_of(IndUpdate->users(), [&](User *U) -> bool {
5855 auto *I = cast<Instruction>(U);
5856 return I == Ind || !TheLoop->contains(I) || Worklist.count(I) ||
5857 isVectorizedMemAccessUse(I, IndUpdate);
5859 if (!UniformIndUpdate)
5862 // The induction variable and its update instruction will remain uniform.
5863 Worklist.insert(Ind);
5864 Worklist.insert(IndUpdate);
5865 DEBUG(dbgs() << "LV: Found uniform instruction: " << *Ind << "\n");
5866 DEBUG(dbgs() << "LV: Found uniform instruction: " << *IndUpdate << "\n");
5869 Uniforms[VF].insert(Worklist.begin(), Worklist.end());
5872 bool LoopVectorizationLegality::canVectorizeMemory() {
5873 LAI = &(*GetLAA)(*TheLoop);
5874 InterleaveInfo.setLAI(LAI);
5875 const OptimizationRemarkAnalysis *LAR = LAI->getReport();
5877 OptimizationRemarkAnalysis VR(Hints->vectorizeAnalysisPassName(),
5878 "loop not vectorized: ", *LAR);
5881 if (!LAI->canVectorizeMemory())
5884 if (LAI->hasStoreToLoopInvariantAddress()) {
5885 ORE->emit(createMissedAnalysis("CantVectorizeStoreToLoopInvariantAddress")
5886 << "write to a loop invariant address could not be vectorized");
5887 DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n");
5891 Requirements->addRuntimePointerChecks(LAI->getNumRuntimePointerChecks());
5892 PSE.addPredicate(LAI->getPSE().getUnionPredicate());
5897 bool LoopVectorizationLegality::isInductionVariable(const Value *V) {
5898 Value *In0 = const_cast<Value *>(V);
5899 PHINode *PN = dyn_cast_or_null<PHINode>(In0);
5903 return Inductions.count(PN);
5906 bool LoopVectorizationLegality::isFirstOrderRecurrence(const PHINode *Phi) {
5907 return FirstOrderRecurrences.count(Phi);
5910 bool LoopVectorizationLegality::blockNeedsPredication(BasicBlock *BB) {
5911 return LoopAccessInfo::blockNeedsPredication(BB, TheLoop, DT);
5914 bool LoopVectorizationLegality::blockCanBePredicated(
5915 BasicBlock *BB, SmallPtrSetImpl<Value *> &SafePtrs) {
5916 const bool IsAnnotatedParallel = TheLoop->isAnnotatedParallel();
5918 for (Instruction &I : *BB) {
5919 // Check that we don't have a constant expression that can trap as operand.
5920 for (Value *Operand : I.operands()) {
5921 if (auto *C = dyn_cast<Constant>(Operand))
5925 // We might be able to hoist the load.
5926 if (I.mayReadFromMemory()) {
5927 auto *LI = dyn_cast<LoadInst>(&I);
5930 if (!SafePtrs.count(LI->getPointerOperand())) {
5931 if (isLegalMaskedLoad(LI->getType(), LI->getPointerOperand()) ||
5932 isLegalMaskedGather(LI->getType())) {
5933 MaskedOp.insert(LI);
5936 // !llvm.mem.parallel_loop_access implies if-conversion safety.
5937 if (IsAnnotatedParallel)
5943 if (I.mayWriteToMemory()) {
5944 auto *SI = dyn_cast<StoreInst>(&I);
5945 // We only support predication of stores in basic blocks with one
5950 // Build a masked store if it is legal for the target.
5951 if (isLegalMaskedStore(SI->getValueOperand()->getType(),
5952 SI->getPointerOperand()) ||
5953 isLegalMaskedScatter(SI->getValueOperand()->getType())) {
5954 MaskedOp.insert(SI);
5958 bool isSafePtr = (SafePtrs.count(SI->getPointerOperand()) != 0);
5959 bool isSinglePredecessor = SI->getParent()->getSinglePredecessor();
5961 if (++NumPredStores > NumberOfStoresToPredicate || !isSafePtr ||
5962 !isSinglePredecessor)
5972 void InterleavedAccessInfo::collectConstStrideAccesses(
5973 MapVector<Instruction *, StrideDescriptor> &AccessStrideInfo,
5974 const ValueToValueMap &Strides) {
5976 auto &DL = TheLoop->getHeader()->getModule()->getDataLayout();
5978 // Since it's desired that the load/store instructions be maintained in
5979 // "program order" for the interleaved access analysis, we have to visit the
5980 // blocks in the loop in reverse postorder (i.e., in a topological order).
5981 // Such an ordering will ensure that any load/store that may be executed
5982 // before a second load/store will precede the second load/store in
5983 // AccessStrideInfo.
5984 LoopBlocksDFS DFS(TheLoop);
5986 for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO()))
5987 for (auto &I : *BB) {
5988 auto *LI = dyn_cast<LoadInst>(&I);
5989 auto *SI = dyn_cast<StoreInst>(&I);
5993 Value *Ptr = getPointerOperand(&I);
5994 // We don't check wrapping here because we don't know yet if Ptr will be
5995 // part of a full group or a group with gaps. Checking wrapping for all
5996 // pointers (even those that end up in groups with no gaps) will be overly
5997 // conservative. For full groups, wrapping should be ok since if we would
5998 // wrap around the address space we would do a memory access at nullptr
5999 // even without the transformation. The wrapping checks are therefore
6000 // deferred until after we've formed the interleaved groups.
6001 int64_t Stride = getPtrStride(PSE, Ptr, TheLoop, Strides,
6002 /*Assume=*/true, /*ShouldCheckWrap=*/false);
6004 const SCEV *Scev = replaceSymbolicStrideSCEV(PSE, Strides, Ptr);
6005 PointerType *PtrTy = dyn_cast<PointerType>(Ptr->getType());
6006 uint64_t Size = DL.getTypeAllocSize(PtrTy->getElementType());
6008 // An alignment of 0 means target ABI alignment.
6009 unsigned Align = getMemInstAlignment(&I);
6011 Align = DL.getABITypeAlignment(PtrTy->getElementType());
6013 AccessStrideInfo[&I] = StrideDescriptor(Stride, Scev, Size, Align);
6017 // Analyze interleaved accesses and collect them into interleaved load and
6020 // When generating code for an interleaved load group, we effectively hoist all
6021 // loads in the group to the location of the first load in program order. When
6022 // generating code for an interleaved store group, we sink all stores to the
6023 // location of the last store. This code motion can change the order of load
6024 // and store instructions and may break dependences.
6026 // The code generation strategy mentioned above ensures that we won't violate
6027 // any write-after-read (WAR) dependences.
6029 // E.g., for the WAR dependence: a = A[i]; // (1)
6032 // The store group of (2) is always inserted at or below (2), and the load
6033 // group of (1) is always inserted at or above (1). Thus, the instructions will
6034 // never be reordered. All other dependences are checked to ensure the
6035 // correctness of the instruction reordering.
6037 // The algorithm visits all memory accesses in the loop in bottom-up program
6038 // order. Program order is established by traversing the blocks in the loop in
6039 // reverse postorder when collecting the accesses.
6041 // We visit the memory accesses in bottom-up order because it can simplify the
6042 // construction of store groups in the presence of write-after-write (WAW)
6045 // E.g., for the WAW dependence: A[i] = a; // (1)
6047 // A[i + 1] = c; // (3)
6049 // We will first create a store group with (3) and (2). (1) can't be added to
6050 // this group because it and (2) are dependent. However, (1) can be grouped
6051 // with other accesses that may precede it in program order. Note that a
6052 // bottom-up order does not imply that WAW dependences should not be checked.
6053 void InterleavedAccessInfo::analyzeInterleaving(
6054 const ValueToValueMap &Strides) {
6055 DEBUG(dbgs() << "LV: Analyzing interleaved accesses...\n");
6057 // Holds all accesses with a constant stride.
6058 MapVector<Instruction *, StrideDescriptor> AccessStrideInfo;
6059 collectConstStrideAccesses(AccessStrideInfo, Strides);
6061 if (AccessStrideInfo.empty())
6064 // Collect the dependences in the loop.
6065 collectDependences();
6067 // Holds all interleaved store groups temporarily.
6068 SmallSetVector<InterleaveGroup *, 4> StoreGroups;
6069 // Holds all interleaved load groups temporarily.
6070 SmallSetVector<InterleaveGroup *, 4> LoadGroups;
6072 // Search in bottom-up program order for pairs of accesses (A and B) that can
6073 // form interleaved load or store groups. In the algorithm below, access A
6074 // precedes access B in program order. We initialize a group for B in the
6075 // outer loop of the algorithm, and then in the inner loop, we attempt to
6076 // insert each A into B's group if:
6078 // 1. A and B have the same stride,
6079 // 2. A and B have the same memory object size, and
6080 // 3. A belongs in B's group according to its distance from B.
6082 // Special care is taken to ensure group formation will not break any
6084 for (auto BI = AccessStrideInfo.rbegin(), E = AccessStrideInfo.rend();
6086 Instruction *B = BI->first;
6087 StrideDescriptor DesB = BI->second;
6089 // Initialize a group for B if it has an allowable stride. Even if we don't
6090 // create a group for B, we continue with the bottom-up algorithm to ensure
6091 // we don't break any of B's dependences.
6092 InterleaveGroup *Group = nullptr;
6093 if (isStrided(DesB.Stride)) {
6094 Group = getInterleaveGroup(B);
6096 DEBUG(dbgs() << "LV: Creating an interleave group with:" << *B << '\n');
6097 Group = createInterleaveGroup(B, DesB.Stride, DesB.Align);
6099 if (B->mayWriteToMemory())
6100 StoreGroups.insert(Group);
6102 LoadGroups.insert(Group);
6105 for (auto AI = std::next(BI); AI != E; ++AI) {
6106 Instruction *A = AI->first;
6107 StrideDescriptor DesA = AI->second;
6109 // Our code motion strategy implies that we can't have dependences
6110 // between accesses in an interleaved group and other accesses located
6111 // between the first and last member of the group. Note that this also
6112 // means that a group can't have more than one member at a given offset.
6113 // The accesses in a group can have dependences with other accesses, but
6114 // we must ensure we don't extend the boundaries of the group such that
6115 // we encompass those dependent accesses.
6117 // For example, assume we have the sequence of accesses shown below in a
6120 // (1, 2) is a group | A[i] = a; // (1)
6121 // | A[i-1] = b; // (2) |
6122 // A[i-3] = c; // (3)
6123 // A[i] = d; // (4) | (2, 4) is not a group
6125 // Because accesses (2) and (3) are dependent, we can group (2) with (1)
6126 // but not with (4). If we did, the dependent access (3) would be within
6127 // the boundaries of the (2, 4) group.
6128 if (!canReorderMemAccessesForInterleavedGroups(&*AI, &*BI)) {
6130 // If a dependence exists and A is already in a group, we know that A
6131 // must be a store since A precedes B and WAR dependences are allowed.
6132 // Thus, A would be sunk below B. We release A's group to prevent this
6133 // illegal code motion. A will then be free to form another group with
6134 // instructions that precede it.
6135 if (isInterleaved(A)) {
6136 InterleaveGroup *StoreGroup = getInterleaveGroup(A);
6137 StoreGroups.remove(StoreGroup);
6138 releaseGroup(StoreGroup);
6141 // If a dependence exists and A is not already in a group (or it was
6142 // and we just released it), B might be hoisted above A (if B is a
6143 // load) or another store might be sunk below A (if B is a store). In
6144 // either case, we can't add additional instructions to B's group. B
6145 // will only form a group with instructions that it precedes.
6149 // At this point, we've checked for illegal code motion. If either A or B
6150 // isn't strided, there's nothing left to do.
6151 if (!isStrided(DesA.Stride) || !isStrided(DesB.Stride))
6154 // Ignore A if it's already in a group or isn't the same kind of memory
6156 if (isInterleaved(A) || A->mayReadFromMemory() != B->mayReadFromMemory())
6159 // Check rules 1 and 2. Ignore A if its stride or size is different from
6161 if (DesA.Stride != DesB.Stride || DesA.Size != DesB.Size)
6164 // Ignore A if the memory object of A and B don't belong to the same
6166 if (getMemInstAddressSpace(A) != getMemInstAddressSpace(B))
6169 // Calculate the distance from A to B.
6170 const SCEVConstant *DistToB = dyn_cast<SCEVConstant>(
6171 PSE.getSE()->getMinusSCEV(DesA.Scev, DesB.Scev));
6174 int64_t DistanceToB = DistToB->getAPInt().getSExtValue();
6176 // Check rule 3. Ignore A if its distance to B is not a multiple of the
6178 if (DistanceToB % static_cast<int64_t>(DesB.Size))
6181 // Ignore A if either A or B is in a predicated block. Although we
6182 // currently prevent group formation for predicated accesses, we may be
6183 // able to relax this limitation in the future once we handle more
6184 // complicated blocks.
6185 if (isPredicated(A->getParent()) || isPredicated(B->getParent()))
6188 // The index of A is the index of B plus A's distance to B in multiples
6191 Group->getIndex(B) + DistanceToB / static_cast<int64_t>(DesB.Size);
6193 // Try to insert A into B's group.
6194 if (Group->insertMember(A, IndexA, DesA.Align)) {
6195 DEBUG(dbgs() << "LV: Inserted:" << *A << '\n'
6196 << " into the interleave group with" << *B << '\n');
6197 InterleaveGroupMap[A] = Group;
6199 // Set the first load in program order as the insert position.
6200 if (A->mayReadFromMemory())
6201 Group->setInsertPos(A);
6203 } // Iteration over A accesses.
6204 } // Iteration over B accesses.
6206 // Remove interleaved store groups with gaps.
6207 for (InterleaveGroup *Group : StoreGroups)
6208 if (Group->getNumMembers() != Group->getFactor())
6209 releaseGroup(Group);
6211 // Remove interleaved groups with gaps (currently only loads) whose memory
6212 // accesses may wrap around. We have to revisit the getPtrStride analysis,
6213 // this time with ShouldCheckWrap=true, since collectConstStrideAccesses does
6214 // not check wrapping (see documentation there).
6215 // FORNOW we use Assume=false;
6216 // TODO: Change to Assume=true but making sure we don't exceed the threshold
6217 // of runtime SCEV assumptions checks (thereby potentially failing to
6218 // vectorize altogether).
6219 // Additional optional optimizations:
6220 // TODO: If we are peeling the loop and we know that the first pointer doesn't
6221 // wrap then we can deduce that all pointers in the group don't wrap.
6222 // This means that we can forcefully peel the loop in order to only have to
6223 // check the first pointer for no-wrap. When we'll change to use Assume=true
6224 // we'll only need at most one runtime check per interleaved group.
6226 for (InterleaveGroup *Group : LoadGroups) {
6228 // Case 1: A full group. Can Skip the checks; For full groups, if the wide
6229 // load would wrap around the address space we would do a memory access at
6230 // nullptr even without the transformation.
6231 if (Group->getNumMembers() == Group->getFactor())
6234 // Case 2: If first and last members of the group don't wrap this implies
6235 // that all the pointers in the group don't wrap.
6236 // So we check only group member 0 (which is always guaranteed to exist),
6237 // and group member Factor - 1; If the latter doesn't exist we rely on
6238 // peeling (if it is a non-reveresed accsess -- see Case 3).
6239 Value *FirstMemberPtr = getPointerOperand(Group->getMember(0));
6240 if (!getPtrStride(PSE, FirstMemberPtr, TheLoop, Strides, /*Assume=*/false,
6241 /*ShouldCheckWrap=*/true)) {
6242 DEBUG(dbgs() << "LV: Invalidate candidate interleaved group due to "
6243 "first group member potentially pointer-wrapping.\n");
6244 releaseGroup(Group);
6247 Instruction *LastMember = Group->getMember(Group->getFactor() - 1);
6249 Value *LastMemberPtr = getPointerOperand(LastMember);
6250 if (!getPtrStride(PSE, LastMemberPtr, TheLoop, Strides, /*Assume=*/false,
6251 /*ShouldCheckWrap=*/true)) {
6252 DEBUG(dbgs() << "LV: Invalidate candidate interleaved group due to "
6253 "last group member potentially pointer-wrapping.\n");
6254 releaseGroup(Group);
6257 // Case 3: A non-reversed interleaved load group with gaps: We need
6258 // to execute at least one scalar epilogue iteration. This will ensure
6259 // we don't speculatively access memory out-of-bounds. We only need
6260 // to look for a member at index factor - 1, since every group must have
6261 // a member at index zero.
6262 if (Group->isReverse()) {
6263 releaseGroup(Group);
6266 DEBUG(dbgs() << "LV: Interleaved group requires epilogue iteration.\n");
6267 RequiresScalarEpilogue = true;
6272 Optional<unsigned> LoopVectorizationCostModel::computeMaxVF(bool OptForSize) {
6273 if (!EnableCondStoresVectorization && Legal->getNumPredStores()) {
6274 ORE->emit(createMissedAnalysis("ConditionalStore")
6275 << "store that is conditionally executed prevents vectorization");
6276 DEBUG(dbgs() << "LV: No vectorization. There are conditional stores.\n");
6280 if (!OptForSize) // Remaining checks deal with scalar loop when OptForSize.
6281 return computeFeasibleMaxVF(OptForSize);
6283 if (Legal->getRuntimePointerChecking()->Need) {
6284 ORE->emit(createMissedAnalysis("CantVersionLoopWithOptForSize")
6285 << "runtime pointer checks needed. Enable vectorization of this "
6286 "loop with '#pragma clang loop vectorize(enable)' when "
6287 "compiling with -Os/-Oz");
6289 << "LV: Aborting. Runtime ptr check is required with -Os/-Oz.\n");
6293 // If we optimize the program for size, avoid creating the tail loop.
6294 unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
6295 DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n');
6297 // If we don't know the precise trip count, don't try to vectorize.
6300 createMissedAnalysis("UnknownLoopCountComplexCFG")
6301 << "unable to calculate the loop count due to complex control flow");
6302 DEBUG(dbgs() << "LV: Aborting. A tail loop is required with -Os/-Oz.\n");
6306 unsigned MaxVF = computeFeasibleMaxVF(OptForSize);
6308 if (TC % MaxVF != 0) {
6309 // If the trip count that we found modulo the vectorization factor is not
6310 // zero then we require a tail.
6311 // FIXME: look for a smaller MaxVF that does divide TC rather than give up.
6312 // FIXME: return None if loop requiresScalarEpilog(<MaxVF>), or look for a
6313 // smaller MaxVF that does not require a scalar epilog.
6315 ORE->emit(createMissedAnalysis("NoTailLoopWithOptForSize")
6316 << "cannot optimize for size and vectorize at the "
6317 "same time. Enable vectorization of this loop "
6318 "with '#pragma clang loop vectorize(enable)' "
6319 "when compiling with -Os/-Oz");
6320 DEBUG(dbgs() << "LV: Aborting. A tail loop is required with -Os/-Oz.\n");
6327 unsigned LoopVectorizationCostModel::computeFeasibleMaxVF(bool OptForSize) {
6328 MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI);
6329 unsigned SmallestType, WidestType;
6330 std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes();
6331 unsigned WidestRegister = TTI.getRegisterBitWidth(true);
6332 unsigned MaxSafeDepDist = -1U;
6334 // Get the maximum safe dependence distance in bits computed by LAA. If the
6335 // loop contains any interleaved accesses, we divide the dependence distance
6336 // by the maximum interleave factor of all interleaved groups. Note that
6337 // although the division ensures correctness, this is a fairly conservative
6338 // computation because the maximum distance computed by LAA may not involve
6339 // any of the interleaved accesses.
6340 if (Legal->getMaxSafeDepDistBytes() != -1U)
6342 Legal->getMaxSafeDepDistBytes() * 8 / Legal->getMaxInterleaveFactor();
6345 ((WidestRegister < MaxSafeDepDist) ? WidestRegister : MaxSafeDepDist);
6346 unsigned MaxVectorSize = WidestRegister / WidestType;
6348 DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestType << " / "
6349 << WidestType << " bits.\n");
6350 DEBUG(dbgs() << "LV: The Widest register is: " << WidestRegister
6353 if (MaxVectorSize == 0) {
6354 DEBUG(dbgs() << "LV: The target has no vector registers.\n");
6358 assert(MaxVectorSize <= 64 && "Did not expect to pack so many elements"
6359 " into one vector!");
6361 unsigned MaxVF = MaxVectorSize;
6362 if (MaximizeBandwidth && !OptForSize) {
6363 // Collect all viable vectorization factors.
6364 SmallVector<unsigned, 8> VFs;
6365 unsigned NewMaxVectorSize = WidestRegister / SmallestType;
6366 for (unsigned VS = MaxVectorSize; VS <= NewMaxVectorSize; VS *= 2)
6369 // For each VF calculate its register usage.
6370 auto RUs = calculateRegisterUsage(VFs);
6372 // Select the largest VF which doesn't require more registers than existing
6374 unsigned TargetNumRegisters = TTI.getNumberOfRegisters(true);
6375 for (int i = RUs.size() - 1; i >= 0; --i) {
6376 if (RUs[i].MaxLocalUsers <= TargetNumRegisters) {
6385 LoopVectorizationCostModel::VectorizationFactor
6386 LoopVectorizationCostModel::selectVectorizationFactor(unsigned MaxVF) {
6387 float Cost = expectedCost(1).first;
6389 const float ScalarCost = Cost;
6392 DEBUG(dbgs() << "LV: Scalar loop costs: " << (int)ScalarCost << ".\n");
6394 bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled;
6395 // Ignore scalar width, because the user explicitly wants vectorization.
6396 if (ForceVectorization && MaxVF > 1) {
6398 Cost = expectedCost(Width).first / (float)Width;
6401 for (unsigned i = 2; i <= MaxVF; i *= 2) {
6402 // Notice that the vector loop needs to be executed less times, so
6403 // we need to divide the cost of the vector loops by the width of
6404 // the vector elements.
6405 VectorizationCostTy C = expectedCost(i);
6406 float VectorCost = C.first / (float)i;
6407 DEBUG(dbgs() << "LV: Vector loop of width " << i
6408 << " costs: " << (int)VectorCost << ".\n");
6409 if (!C.second && !ForceVectorization) {
6411 dbgs() << "LV: Not considering vector loop of width " << i
6412 << " because it will not generate any vector instructions.\n");
6415 if (VectorCost < Cost) {
6421 DEBUG(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs()
6422 << "LV: Vectorization seems to be not beneficial, "
6423 << "but was forced by a user.\n");
6424 DEBUG(dbgs() << "LV: Selecting VF: " << Width << ".\n");
6425 VectorizationFactor Factor = {Width, (unsigned)(Width * Cost)};
6429 std::pair<unsigned, unsigned>
6430 LoopVectorizationCostModel::getSmallestAndWidestTypes() {
6431 unsigned MinWidth = -1U;
6432 unsigned MaxWidth = 8;
6433 const DataLayout &DL = TheFunction->getParent()->getDataLayout();
6436 for (BasicBlock *BB : TheLoop->blocks()) {
6437 // For each instruction in the loop.
6438 for (Instruction &I : *BB) {
6439 Type *T = I.getType();
6441 // Skip ignored values.
6442 if (ValuesToIgnore.count(&I))
6445 // Only examine Loads, Stores and PHINodes.
6446 if (!isa<LoadInst>(I) && !isa<StoreInst>(I) && !isa<PHINode>(I))
6449 // Examine PHI nodes that are reduction variables. Update the type to
6450 // account for the recurrence type.
6451 if (auto *PN = dyn_cast<PHINode>(&I)) {
6452 if (!Legal->isReductionVariable(PN))
6454 RecurrenceDescriptor RdxDesc = (*Legal->getReductionVars())[PN];
6455 T = RdxDesc.getRecurrenceType();
6458 // Examine the stored values.
6459 if (auto *ST = dyn_cast<StoreInst>(&I))
6460 T = ST->getValueOperand()->getType();
6462 // Ignore loaded pointer types and stored pointer types that are not
6465 // FIXME: The check here attempts to predict whether a load or store will
6466 // be vectorized. We only know this for certain after a VF has
6467 // been selected. Here, we assume that if an access can be
6468 // vectorized, it will be. We should also look at extending this
6469 // optimization to non-pointer types.
6471 if (T->isPointerTy() && !isConsecutiveLoadOrStore(&I) &&
6472 !Legal->isAccessInterleaved(&I) && !Legal->isLegalGatherOrScatter(&I))
6475 MinWidth = std::min(MinWidth,
6476 (unsigned)DL.getTypeSizeInBits(T->getScalarType()));
6477 MaxWidth = std::max(MaxWidth,
6478 (unsigned)DL.getTypeSizeInBits(T->getScalarType()));
6482 return {MinWidth, MaxWidth};
6485 unsigned LoopVectorizationCostModel::selectInterleaveCount(bool OptForSize,
6487 unsigned LoopCost) {
6489 // -- The interleave heuristics --
6490 // We interleave the loop in order to expose ILP and reduce the loop overhead.
6491 // There are many micro-architectural considerations that we can't predict
6492 // at this level. For example, frontend pressure (on decode or fetch) due to
6493 // code size, or the number and capabilities of the execution ports.
6495 // We use the following heuristics to select the interleave count:
6496 // 1. If the code has reductions, then we interleave to break the cross
6497 // iteration dependency.
6498 // 2. If the loop is really small, then we interleave to reduce the loop
6500 // 3. We don't interleave if we think that we will spill registers to memory
6501 // due to the increased register pressure.
6503 // When we optimize for size, we don't interleave.
6507 // We used the distance for the interleave count.
6508 if (Legal->getMaxSafeDepDistBytes() != -1U)
6511 // Do not interleave loops with a relatively small trip count.
6512 unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
6513 if (TC > 1 && TC < TinyTripCountInterleaveThreshold)
6516 unsigned TargetNumRegisters = TTI.getNumberOfRegisters(VF > 1);
6517 DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters
6521 if (ForceTargetNumScalarRegs.getNumOccurrences() > 0)
6522 TargetNumRegisters = ForceTargetNumScalarRegs;
6524 if (ForceTargetNumVectorRegs.getNumOccurrences() > 0)
6525 TargetNumRegisters = ForceTargetNumVectorRegs;
6528 RegisterUsage R = calculateRegisterUsage({VF})[0];
6529 // We divide by these constants so assume that we have at least one
6530 // instruction that uses at least one register.
6531 R.MaxLocalUsers = std::max(R.MaxLocalUsers, 1U);
6532 R.NumInstructions = std::max(R.NumInstructions, 1U);
6534 // We calculate the interleave count using the following formula.
6535 // Subtract the number of loop invariants from the number of available
6536 // registers. These registers are used by all of the interleaved instances.
6537 // Next, divide the remaining registers by the number of registers that is
6538 // required by the loop, in order to estimate how many parallel instances
6539 // fit without causing spills. All of this is rounded down if necessary to be
6540 // a power of two. We want power of two interleave count to simplify any
6541 // addressing operations or alignment considerations.
6542 unsigned IC = PowerOf2Floor((TargetNumRegisters - R.LoopInvariantRegs) /
6545 // Don't count the induction variable as interleaved.
6546 if (EnableIndVarRegisterHeur)
6547 IC = PowerOf2Floor((TargetNumRegisters - R.LoopInvariantRegs - 1) /
6548 std::max(1U, (R.MaxLocalUsers - 1)));
6550 // Clamp the interleave ranges to reasonable counts.
6551 unsigned MaxInterleaveCount = TTI.getMaxInterleaveFactor(VF);
6553 // Check if the user has overridden the max.
6555 if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
6556 MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor;
6558 if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
6559 MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor;
6562 // If we did not calculate the cost for VF (because the user selected the VF)
6563 // then we calculate the cost of VF here.
6565 LoopCost = expectedCost(VF).first;
6567 // Clamp the calculated IC to be between the 1 and the max interleave count
6568 // that the target allows.
6569 if (IC > MaxInterleaveCount)
6570 IC = MaxInterleaveCount;
6574 // Interleave if we vectorized this loop and there is a reduction that could
6575 // benefit from interleaving.
6576 if (VF > 1 && Legal->getReductionVars()->size()) {
6577 DEBUG(dbgs() << "LV: Interleaving because of reductions.\n");
6581 // Note that if we've already vectorized the loop we will have done the
6582 // runtime check and so interleaving won't require further checks.
6583 bool InterleavingRequiresRuntimePointerCheck =
6584 (VF == 1 && Legal->getRuntimePointerChecking()->Need);
6586 // We want to interleave small loops in order to reduce the loop overhead and
6587 // potentially expose ILP opportunities.
6588 DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n');
6589 if (!InterleavingRequiresRuntimePointerCheck && LoopCost < SmallLoopCost) {
6590 // We assume that the cost overhead is 1 and we use the cost model
6591 // to estimate the cost of the loop and interleave until the cost of the
6592 // loop overhead is about 5% of the cost of the loop.
6594 std::min(IC, (unsigned)PowerOf2Floor(SmallLoopCost / LoopCost));
6596 // Interleave until store/load ports (estimated by max interleave count) are
6598 unsigned NumStores = Legal->getNumStores();
6599 unsigned NumLoads = Legal->getNumLoads();
6600 unsigned StoresIC = IC / (NumStores ? NumStores : 1);
6601 unsigned LoadsIC = IC / (NumLoads ? NumLoads : 1);
6603 // If we have a scalar reduction (vector reductions are already dealt with
6604 // by this point), we can increase the critical path length if the loop
6605 // we're interleaving is inside another loop. Limit, by default to 2, so the
6606 // critical path only gets increased by one reduction operation.
6607 if (Legal->getReductionVars()->size() && TheLoop->getLoopDepth() > 1) {
6608 unsigned F = static_cast<unsigned>(MaxNestedScalarReductionIC);
6609 SmallIC = std::min(SmallIC, F);
6610 StoresIC = std::min(StoresIC, F);
6611 LoadsIC = std::min(LoadsIC, F);
6614 if (EnableLoadStoreRuntimeInterleave &&
6615 std::max(StoresIC, LoadsIC) > SmallIC) {
6616 DEBUG(dbgs() << "LV: Interleaving to saturate store or load ports.\n");
6617 return std::max(StoresIC, LoadsIC);
6620 DEBUG(dbgs() << "LV: Interleaving to reduce branch cost.\n");
6624 // Interleave if this is a large loop (small loops are already dealt with by
6625 // this point) that could benefit from interleaving.
6626 bool HasReductions = (Legal->getReductionVars()->size() > 0);
6627 if (TTI.enableAggressiveInterleaving(HasReductions)) {
6628 DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
6632 DEBUG(dbgs() << "LV: Not Interleaving.\n");
6636 SmallVector<LoopVectorizationCostModel::RegisterUsage, 8>
6637 LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<unsigned> VFs) {
6638 // This function calculates the register usage by measuring the highest number
6639 // of values that are alive at a single location. Obviously, this is a very
6640 // rough estimation. We scan the loop in a topological order in order and
6641 // assign a number to each instruction. We use RPO to ensure that defs are
6642 // met before their users. We assume that each instruction that has in-loop
6643 // users starts an interval. We record every time that an in-loop value is
6644 // used, so we have a list of the first and last occurrences of each
6645 // instruction. Next, we transpose this data structure into a multi map that
6646 // holds the list of intervals that *end* at a specific location. This multi
6647 // map allows us to perform a linear search. We scan the instructions linearly
6648 // and record each time that a new interval starts, by placing it in a set.
6649 // If we find this value in the multi-map then we remove it from the set.
6650 // The max register usage is the maximum size of the set.
6651 // We also search for instructions that are defined outside the loop, but are
6652 // used inside the loop. We need this number separately from the max-interval
6653 // usage number because when we unroll, loop-invariant values do not take
6655 LoopBlocksDFS DFS(TheLoop);
6659 RU.NumInstructions = 0;
6661 // Each 'key' in the map opens a new interval. The values
6662 // of the map are the index of the 'last seen' usage of the
6663 // instruction that is the key.
6664 typedef DenseMap<Instruction *, unsigned> IntervalMap;
6665 // Maps instruction to its index.
6666 DenseMap<unsigned, Instruction *> IdxToInstr;
6667 // Marks the end of each interval.
6668 IntervalMap EndPoint;
6669 // Saves the list of instruction indices that are used in the loop.
6670 SmallSet<Instruction *, 8> Ends;
6671 // Saves the list of values that are used in the loop but are
6672 // defined outside the loop, such as arguments and constants.
6673 SmallPtrSet<Value *, 8> LoopInvariants;
6676 for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
6677 RU.NumInstructions += BB->size();
6678 for (Instruction &I : *BB) {
6679 IdxToInstr[Index++] = &I;
6681 // Save the end location of each USE.
6682 for (Value *U : I.operands()) {
6683 auto *Instr = dyn_cast<Instruction>(U);
6685 // Ignore non-instruction values such as arguments, constants, etc.
6689 // If this instruction is outside the loop then record it and continue.
6690 if (!TheLoop->contains(Instr)) {
6691 LoopInvariants.insert(Instr);
6695 // Overwrite previous end points.
6696 EndPoint[Instr] = Index;
6702 // Saves the list of intervals that end with the index in 'key'.
6703 typedef SmallVector<Instruction *, 2> InstrList;
6704 DenseMap<unsigned, InstrList> TransposeEnds;
6706 // Transpose the EndPoints to a list of values that end at each index.
6707 for (auto &Interval : EndPoint)
6708 TransposeEnds[Interval.second].push_back(Interval.first);
6710 SmallSet<Instruction *, 8> OpenIntervals;
6712 // Get the size of the widest register.
6713 unsigned MaxSafeDepDist = -1U;
6714 if (Legal->getMaxSafeDepDistBytes() != -1U)
6715 MaxSafeDepDist = Legal->getMaxSafeDepDistBytes() * 8;
6716 unsigned WidestRegister =
6717 std::min(TTI.getRegisterBitWidth(true), MaxSafeDepDist);
6718 const DataLayout &DL = TheFunction->getParent()->getDataLayout();
6720 SmallVector<RegisterUsage, 8> RUs(VFs.size());
6721 SmallVector<unsigned, 8> MaxUsages(VFs.size(), 0);
6723 DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
6725 // A lambda that gets the register usage for the given type and VF.
6726 auto GetRegUsage = [&DL, WidestRegister](Type *Ty, unsigned VF) {
6727 if (Ty->isTokenTy())
6729 unsigned TypeSize = DL.getTypeSizeInBits(Ty->getScalarType());
6730 return std::max<unsigned>(1, VF * TypeSize / WidestRegister);
6733 for (unsigned int i = 0; i < Index; ++i) {
6734 Instruction *I = IdxToInstr[i];
6736 // Remove all of the instructions that end at this location.
6737 InstrList &List = TransposeEnds[i];
6738 for (Instruction *ToRemove : List)
6739 OpenIntervals.erase(ToRemove);
6741 // Ignore instructions that are never used within the loop.
6745 // Skip ignored values.
6746 if (ValuesToIgnore.count(I))
6749 // For each VF find the maximum usage of registers.
6750 for (unsigned j = 0, e = VFs.size(); j < e; ++j) {
6752 MaxUsages[j] = std::max(MaxUsages[j], OpenIntervals.size());
6755 collectUniformsAndScalars(VFs[j]);
6756 // Count the number of live intervals.
6757 unsigned RegUsage = 0;
6758 for (auto Inst : OpenIntervals) {
6759 // Skip ignored values for VF > 1.
6760 if (VecValuesToIgnore.count(Inst) ||
6761 isScalarAfterVectorization(Inst, VFs[j]))
6763 RegUsage += GetRegUsage(Inst->getType(), VFs[j]);
6765 MaxUsages[j] = std::max(MaxUsages[j], RegUsage);
6768 DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # "
6769 << OpenIntervals.size() << '\n');
6771 // Add the current instruction to the list of open intervals.
6772 OpenIntervals.insert(I);
6775 for (unsigned i = 0, e = VFs.size(); i < e; ++i) {
6776 unsigned Invariant = 0;
6778 Invariant = LoopInvariants.size();
6780 for (auto Inst : LoopInvariants)
6781 Invariant += GetRegUsage(Inst->getType(), VFs[i]);
6784 DEBUG(dbgs() << "LV(REG): VF = " << VFs[i] << '\n');
6785 DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsages[i] << '\n');
6786 DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << '\n');
6787 DEBUG(dbgs() << "LV(REG): LoopSize: " << RU.NumInstructions << '\n');
6789 RU.LoopInvariantRegs = Invariant;
6790 RU.MaxLocalUsers = MaxUsages[i];
6797 void LoopVectorizationCostModel::collectInstsToScalarize(unsigned VF) {
6799 // If we aren't vectorizing the loop, or if we've already collected the
6800 // instructions to scalarize, there's nothing to do. Collection may already
6801 // have occurred if we have a user-selected VF and are now computing the
6802 // expected cost for interleaving.
6803 if (VF < 2 || InstsToScalarize.count(VF))
6806 // Initialize a mapping for VF in InstsToScalalarize. If we find that it's
6807 // not profitable to scalarize any instructions, the presence of VF in the
6808 // map will indicate that we've analyzed it already.
6809 ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF];
6811 // Find all the instructions that are scalar with predication in the loop and
6812 // determine if it would be better to not if-convert the blocks they are in.
6813 // If so, we also record the instructions to scalarize.
6814 for (BasicBlock *BB : TheLoop->blocks()) {
6815 if (!Legal->blockNeedsPredication(BB))
6817 for (Instruction &I : *BB)
6818 if (Legal->isScalarWithPredication(&I)) {
6819 ScalarCostsTy ScalarCosts;
6820 if (computePredInstDiscount(&I, ScalarCosts, VF) >= 0)
6821 ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end());
6823 // Remember that BB will remain after vectorization.
6824 PredicatedBBsAfterVectorization.insert(BB);
6829 int LoopVectorizationCostModel::computePredInstDiscount(
6830 Instruction *PredInst, DenseMap<Instruction *, unsigned> &ScalarCosts,
6833 assert(!isUniformAfterVectorization(PredInst, VF) &&
6834 "Instruction marked uniform-after-vectorization will be predicated");
6836 // Initialize the discount to zero, meaning that the scalar version and the
6837 // vector version cost the same.
6840 // Holds instructions to analyze. The instructions we visit are mapped in
6841 // ScalarCosts. Those instructions are the ones that would be scalarized if
6842 // we find that the scalar version costs less.
6843 SmallVector<Instruction *, 8> Worklist;
6845 // Returns true if the given instruction can be scalarized.
6846 auto canBeScalarized = [&](Instruction *I) -> bool {
6848 // We only attempt to scalarize instructions forming a single-use chain
6849 // from the original predicated block that would otherwise be vectorized.
6850 // Although not strictly necessary, we give up on instructions we know will
6851 // already be scalar to avoid traversing chains that are unlikely to be
6853 if (!I->hasOneUse() || PredInst->getParent() != I->getParent() ||
6854 isScalarAfterVectorization(I, VF))
6857 // If the instruction is scalar with predication, it will be analyzed
6858 // separately. We ignore it within the context of PredInst.
6859 if (Legal->isScalarWithPredication(I))
6862 // If any of the instruction's operands are uniform after vectorization,
6863 // the instruction cannot be scalarized. This prevents, for example, a
6864 // masked load from being scalarized.
6866 // We assume we will only emit a value for lane zero of an instruction
6867 // marked uniform after vectorization, rather than VF identical values.
6868 // Thus, if we scalarize an instruction that uses a uniform, we would
6869 // create uses of values corresponding to the lanes we aren't emitting code
6870 // for. This behavior can be changed by allowing getScalarValue to clone
6871 // the lane zero values for uniforms rather than asserting.
6872 for (Use &U : I->operands())
6873 if (auto *J = dyn_cast<Instruction>(U.get()))
6874 if (isUniformAfterVectorization(J, VF))
6877 // Otherwise, we can scalarize the instruction.
6881 // Returns true if an operand that cannot be scalarized must be extracted
6882 // from a vector. We will account for this scalarization overhead below. Note
6883 // that the non-void predicated instructions are placed in their own blocks,
6884 // and their return values are inserted into vectors. Thus, an extract would
6885 // still be required.
6886 auto needsExtract = [&](Instruction *I) -> bool {
6887 return TheLoop->contains(I) && !isScalarAfterVectorization(I, VF);
6890 // Compute the expected cost discount from scalarizing the entire expression
6891 // feeding the predicated instruction. We currently only consider expressions
6892 // that are single-use instruction chains.
6893 Worklist.push_back(PredInst);
6894 while (!Worklist.empty()) {
6895 Instruction *I = Worklist.pop_back_val();
6897 // If we've already analyzed the instruction, there's nothing to do.
6898 if (ScalarCosts.count(I))
6901 // Compute the cost of the vector instruction. Note that this cost already
6902 // includes the scalarization overhead of the predicated instruction.
6903 unsigned VectorCost = getInstructionCost(I, VF).first;
6905 // Compute the cost of the scalarized instruction. This cost is the cost of
6906 // the instruction as if it wasn't if-converted and instead remained in the
6907 // predicated block. We will scale this cost by block probability after
6908 // computing the scalarization overhead.
6909 unsigned ScalarCost = VF * getInstructionCost(I, 1).first;
6911 // Compute the scalarization overhead of needed insertelement instructions
6913 if (Legal->isScalarWithPredication(I) && !I->getType()->isVoidTy()) {
6914 ScalarCost += TTI.getScalarizationOverhead(ToVectorTy(I->getType(), VF),
6916 ScalarCost += VF * TTI.getCFInstrCost(Instruction::PHI);
6919 // Compute the scalarization overhead of needed extractelement
6920 // instructions. For each of the instruction's operands, if the operand can
6921 // be scalarized, add it to the worklist; otherwise, account for the
6923 for (Use &U : I->operands())
6924 if (auto *J = dyn_cast<Instruction>(U.get())) {
6925 assert(VectorType::isValidElementType(J->getType()) &&
6926 "Instruction has non-scalar type");
6927 if (canBeScalarized(J))
6928 Worklist.push_back(J);
6929 else if (needsExtract(J))
6930 ScalarCost += TTI.getScalarizationOverhead(
6931 ToVectorTy(J->getType(),VF), false, true);
6934 // Scale the total scalar cost by block probability.
6935 ScalarCost /= getReciprocalPredBlockProb();
6937 // Compute the discount. A non-negative discount means the vector version
6938 // of the instruction costs more, and scalarizing would be beneficial.
6939 Discount += VectorCost - ScalarCost;
6940 ScalarCosts[I] = ScalarCost;
6946 LoopVectorizationCostModel::VectorizationCostTy
6947 LoopVectorizationCostModel::expectedCost(unsigned VF) {
6948 VectorizationCostTy Cost;
6950 // Collect Uniform and Scalar instructions after vectorization with VF.
6951 collectUniformsAndScalars(VF);
6953 // Collect the instructions (and their associated costs) that will be more
6954 // profitable to scalarize.
6955 collectInstsToScalarize(VF);
6958 for (BasicBlock *BB : TheLoop->blocks()) {
6959 VectorizationCostTy BlockCost;
6961 // For each instruction in the old loop.
6962 for (Instruction &I : *BB) {
6963 // Skip dbg intrinsics.
6964 if (isa<DbgInfoIntrinsic>(I))
6967 // Skip ignored values.
6968 if (ValuesToIgnore.count(&I))
6971 VectorizationCostTy C = getInstructionCost(&I, VF);
6973 // Check if we should override the cost.
6974 if (ForceTargetInstructionCost.getNumOccurrences() > 0)
6975 C.first = ForceTargetInstructionCost;
6977 BlockCost.first += C.first;
6978 BlockCost.second |= C.second;
6979 DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first << " for VF "
6980 << VF << " For instruction: " << I << '\n');
6983 // If we are vectorizing a predicated block, it will have been
6984 // if-converted. This means that the block's instructions (aside from
6985 // stores and instructions that may divide by zero) will now be
6986 // unconditionally executed. For the scalar case, we may not always execute
6987 // the predicated block. Thus, scale the block's cost by the probability of
6989 if (VF == 1 && Legal->blockNeedsPredication(BB))
6990 BlockCost.first /= getReciprocalPredBlockProb();
6992 Cost.first += BlockCost.first;
6993 Cost.second |= BlockCost.second;
6999 /// \brief Gets Address Access SCEV after verifying that the access pattern
7000 /// is loop invariant except the induction variable dependence.
7002 /// This SCEV can be sent to the Target in order to estimate the address
7003 /// calculation cost.
7004 static const SCEV *getAddressAccessSCEV(
7006 LoopVectorizationLegality *Legal,
7007 ScalarEvolution *SE,
7008 const Loop *TheLoop) {
7009 auto *Gep = dyn_cast<GetElementPtrInst>(Ptr);
7013 // We are looking for a gep with all loop invariant indices except for one
7014 // which should be an induction variable.
7015 unsigned NumOperands = Gep->getNumOperands();
7016 for (unsigned i = 1; i < NumOperands; ++i) {
7017 Value *Opd = Gep->getOperand(i);
7018 if (!SE->isLoopInvariant(SE->getSCEV(Opd), TheLoop) &&
7019 !Legal->isInductionVariable(Opd))
7023 // Now we know we have a GEP ptr, %inv, %ind, %inv. return the Ptr SCEV.
7024 return SE->getSCEV(Ptr);
7027 static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) {
7028 return Legal->hasStride(I->getOperand(0)) ||
7029 Legal->hasStride(I->getOperand(1));
7032 unsigned LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I,
7034 Type *ValTy = getMemInstValueType(I);
7035 auto SE = PSE.getSE();
7037 unsigned Alignment = getMemInstAlignment(I);
7038 unsigned AS = getMemInstAddressSpace(I);
7039 Value *Ptr = getPointerOperand(I);
7040 Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
7042 // Figure out whether the access is strided and get the stride value
7043 // if it's known in compile time
7044 const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, Legal, SE, TheLoop);
7046 // Get the cost of the scalar memory instruction and address computation.
7047 unsigned Cost = VF * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV);
7050 TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment,
7053 // Get the overhead of the extractelement and insertelement instructions
7054 // we might create due to scalarization.
7055 Cost += getScalarizationOverhead(I, VF, TTI);
7057 // If we have a predicated store, it may not be executed for each vector
7058 // lane. Scale the cost by the probability of executing the predicated
7060 if (Legal->isScalarWithPredication(I))
7061 Cost /= getReciprocalPredBlockProb();
7066 unsigned LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I,
7068 Type *ValTy = getMemInstValueType(I);
7069 Type *VectorTy = ToVectorTy(ValTy, VF);
7070 unsigned Alignment = getMemInstAlignment(I);
7071 Value *Ptr = getPointerOperand(I);
7072 unsigned AS = getMemInstAddressSpace(I);
7073 int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
7075 assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
7076 "Stride should be 1 or -1 for consecutive memory access");
7078 if (Legal->isMaskRequired(I))
7079 Cost += TTI.getMaskedMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
7081 Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS, I);
7083 bool Reverse = ConsecutiveStride < 0;
7085 Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
7089 unsigned LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I,
7091 LoadInst *LI = cast<LoadInst>(I);
7092 Type *ValTy = LI->getType();
7093 Type *VectorTy = ToVectorTy(ValTy, VF);
7094 unsigned Alignment = LI->getAlignment();
7095 unsigned AS = LI->getPointerAddressSpace();
7097 return TTI.getAddressComputationCost(ValTy) +
7098 TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS) +
7099 TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy);
7102 unsigned LoopVectorizationCostModel::getGatherScatterCost(Instruction *I,
7104 Type *ValTy = getMemInstValueType(I);
7105 Type *VectorTy = ToVectorTy(ValTy, VF);
7106 unsigned Alignment = getMemInstAlignment(I);
7107 Value *Ptr = getPointerOperand(I);
7109 return TTI.getAddressComputationCost(VectorTy) +
7110 TTI.getGatherScatterOpCost(I->getOpcode(), VectorTy, Ptr,
7111 Legal->isMaskRequired(I), Alignment);
7114 unsigned LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I,
7116 Type *ValTy = getMemInstValueType(I);
7117 Type *VectorTy = ToVectorTy(ValTy, VF);
7118 unsigned AS = getMemInstAddressSpace(I);
7120 auto Group = Legal->getInterleavedAccessGroup(I);
7121 assert(Group && "Fail to get an interleaved access group.");
7123 unsigned InterleaveFactor = Group->getFactor();
7124 Type *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor);
7126 // Holds the indices of existing members in an interleaved load group.
7127 // An interleaved store group doesn't need this as it doesn't allow gaps.
7128 SmallVector<unsigned, 4> Indices;
7129 if (isa<LoadInst>(I)) {
7130 for (unsigned i = 0; i < InterleaveFactor; i++)
7131 if (Group->getMember(i))
7132 Indices.push_back(i);
7135 // Calculate the cost of the whole interleaved group.
7136 unsigned Cost = TTI.getInterleavedMemoryOpCost(I->getOpcode(), WideVecTy,
7137 Group->getFactor(), Indices,
7138 Group->getAlignment(), AS);
7140 if (Group->isReverse())
7141 Cost += Group->getNumMembers() *
7142 TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
7146 unsigned LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I,
7149 // Calculate scalar cost only. Vectorization cost should be ready at this
7152 Type *ValTy = getMemInstValueType(I);
7153 unsigned Alignment = getMemInstAlignment(I);
7154 unsigned AS = getMemInstAddressSpace(I);
7156 return TTI.getAddressComputationCost(ValTy) +
7157 TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS, I);
7159 return getWideningCost(I, VF);
7162 LoopVectorizationCostModel::VectorizationCostTy
7163 LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
7164 // If we know that this instruction will remain uniform, check the cost of
7165 // the scalar version.
7166 if (isUniformAfterVectorization(I, VF))
7169 if (VF > 1 && isProfitableToScalarize(I, VF))
7170 return VectorizationCostTy(InstsToScalarize[VF][I], false);
7172 // Forced scalars do not have any scalarization overhead.
7173 if (VF > 1 && ForcedScalars.count(VF) &&
7174 ForcedScalars.find(VF)->second.count(I))
7175 return VectorizationCostTy((getInstructionCost(I, 1).first * VF), false);
7178 unsigned C = getInstructionCost(I, VF, VectorTy);
7180 bool TypeNotScalarized =
7181 VF > 1 && VectorTy->isVectorTy() && TTI.getNumberOfParts(VectorTy) < VF;
7182 return VectorizationCostTy(C, TypeNotScalarized);
7185 void LoopVectorizationCostModel::setCostBasedWideningDecision(unsigned VF) {
7188 for (BasicBlock *BB : TheLoop->blocks()) {
7189 // For each instruction in the old loop.
7190 for (Instruction &I : *BB) {
7191 Value *Ptr = getPointerOperand(&I);
7195 if (isa<LoadInst>(&I) && Legal->isUniform(Ptr)) {
7196 // Scalar load + broadcast
7197 unsigned Cost = getUniformMemOpCost(&I, VF);
7198 setWideningDecision(&I, VF, CM_Scalarize, Cost);
7202 // We assume that widening is the best solution when possible.
7203 if (Legal->memoryInstructionCanBeWidened(&I, VF)) {
7204 unsigned Cost = getConsecutiveMemOpCost(&I, VF);
7205 setWideningDecision(&I, VF, CM_Widen, Cost);
7209 // Choose between Interleaving, Gather/Scatter or Scalarization.
7210 unsigned InterleaveCost = UINT_MAX;
7211 unsigned NumAccesses = 1;
7212 if (Legal->isAccessInterleaved(&I)) {
7213 auto Group = Legal->getInterleavedAccessGroup(&I);
7214 assert(Group && "Fail to get an interleaved access group.");
7216 // Make one decision for the whole group.
7217 if (getWideningDecision(&I, VF) != CM_Unknown)
7220 NumAccesses = Group->getNumMembers();
7221 InterleaveCost = getInterleaveGroupCost(&I, VF);
7224 unsigned GatherScatterCost =
7225 Legal->isLegalGatherOrScatter(&I)
7226 ? getGatherScatterCost(&I, VF) * NumAccesses
7229 unsigned ScalarizationCost =
7230 getMemInstScalarizationCost(&I, VF) * NumAccesses;
7232 // Choose better solution for the current VF,
7233 // write down this decision and use it during vectorization.
7235 InstWidening Decision;
7236 if (InterleaveCost <= GatherScatterCost &&
7237 InterleaveCost < ScalarizationCost) {
7238 Decision = CM_Interleave;
7239 Cost = InterleaveCost;
7240 } else if (GatherScatterCost < ScalarizationCost) {
7241 Decision = CM_GatherScatter;
7242 Cost = GatherScatterCost;
7244 Decision = CM_Scalarize;
7245 Cost = ScalarizationCost;
7247 // If the instructions belongs to an interleave group, the whole group
7248 // receives the same decision. The whole group receives the cost, but
7249 // the cost will actually be assigned to one instruction.
7250 if (auto Group = Legal->getInterleavedAccessGroup(&I))
7251 setWideningDecision(Group, VF, Decision, Cost);
7253 setWideningDecision(&I, VF, Decision, Cost);
7257 // Make sure that any load of address and any other address computation
7258 // remains scalar unless there is gather/scatter support. This avoids
7259 // inevitable extracts into address registers, and also has the benefit of
7260 // activating LSR more, since that pass can't optimize vectorized
7262 if (TTI.prefersVectorizedAddressing())
7265 // Start with all scalar pointer uses.
7266 SmallPtrSet<Instruction *, 8> AddrDefs;
7267 for (BasicBlock *BB : TheLoop->blocks())
7268 for (Instruction &I : *BB) {
7269 Instruction *PtrDef =
7270 dyn_cast_or_null<Instruction>(getPointerOperand(&I));
7271 if (PtrDef && TheLoop->contains(PtrDef) &&
7272 getWideningDecision(&I, VF) != CM_GatherScatter)
7273 AddrDefs.insert(PtrDef);
7276 // Add all instructions used to generate the addresses.
7277 SmallVector<Instruction *, 4> Worklist;
7278 for (auto *I : AddrDefs)
7279 Worklist.push_back(I);
7280 while (!Worklist.empty()) {
7281 Instruction *I = Worklist.pop_back_val();
7282 for (auto &Op : I->operands())
7283 if (auto *InstOp = dyn_cast<Instruction>(Op))
7284 if ((InstOp->getParent() == I->getParent()) && !isa<PHINode>(InstOp) &&
7285 AddrDefs.insert(InstOp).second == true)
7286 Worklist.push_back(InstOp);
7289 for (auto *I : AddrDefs) {
7290 if (isa<LoadInst>(I)) {
7291 // Setting the desired widening decision should ideally be handled in
7292 // by cost functions, but since this involves the task of finding out
7293 // if the loaded register is involved in an address computation, it is
7294 // instead changed here when we know this is the case.
7295 if (getWideningDecision(I, VF) == CM_Widen)
7296 // Scalarize a widened load of address.
7297 setWideningDecision(I, VF, CM_Scalarize,
7298 (VF * getMemoryInstructionCost(I, 1)));
7299 else if (auto Group = Legal->getInterleavedAccessGroup(I)) {
7300 // Scalarize an interleave group of address loads.
7301 for (unsigned I = 0; I < Group->getFactor(); ++I) {
7302 if (Instruction *Member = Group->getMember(I))
7303 setWideningDecision(Member, VF, CM_Scalarize,
7304 (VF * getMemoryInstructionCost(Member, 1)));
7308 // Make sure I gets scalarized and a cost estimate without
7309 // scalarization overhead.
7310 ForcedScalars[VF].insert(I);
7314 unsigned LoopVectorizationCostModel::getInstructionCost(Instruction *I,
7317 Type *RetTy = I->getType();
7318 if (canTruncateToMinimalBitwidth(I, VF))
7319 RetTy = IntegerType::get(RetTy->getContext(), MinBWs[I]);
7320 VectorTy = isScalarAfterVectorization(I, VF) ? RetTy : ToVectorTy(RetTy, VF);
7321 auto SE = PSE.getSE();
7323 // TODO: We need to estimate the cost of intrinsic calls.
7324 switch (I->getOpcode()) {
7325 case Instruction::GetElementPtr:
7326 // We mark this instruction as zero-cost because the cost of GEPs in
7327 // vectorized code depends on whether the corresponding memory instruction
7328 // is scalarized or not. Therefore, we handle GEPs with the memory
7329 // instruction cost.
7331 case Instruction::Br: {
7332 // In cases of scalarized and predicated instructions, there will be VF
7333 // predicated blocks in the vectorized loop. Each branch around these
7334 // blocks requires also an extract of its vector compare i1 element.
7335 bool ScalarPredicatedBB = false;
7336 BranchInst *BI = cast<BranchInst>(I);
7337 if (VF > 1 && BI->isConditional() &&
7338 (PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) ||
7339 PredicatedBBsAfterVectorization.count(BI->getSuccessor(1))))
7340 ScalarPredicatedBB = true;
7342 if (ScalarPredicatedBB) {
7343 // Return cost for branches around scalarized and predicated blocks.
7345 VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
7346 return (TTI.getScalarizationOverhead(Vec_i1Ty, false, true) +
7347 (TTI.getCFInstrCost(Instruction::Br) * VF));
7348 } else if (I->getParent() == TheLoop->getLoopLatch() || VF == 1)
7349 // The back-edge branch will remain, as will all scalar branches.
7350 return TTI.getCFInstrCost(Instruction::Br);
7352 // This branch will be eliminated by if-conversion.
7354 // Note: We currently assume zero cost for an unconditional branch inside
7355 // a predicated block since it will become a fall-through, although we
7356 // may decide in the future to call TTI for all branches.
7358 case Instruction::PHI: {
7359 auto *Phi = cast<PHINode>(I);
7361 // First-order recurrences are replaced by vector shuffles inside the loop.
7362 if (VF > 1 && Legal->isFirstOrderRecurrence(Phi))
7363 return TTI.getShuffleCost(TargetTransformInfo::SK_ExtractSubvector,
7364 VectorTy, VF - 1, VectorTy);
7366 // Phi nodes in non-header blocks (not inductions, reductions, etc.) are
7367 // converted into select instructions. We require N - 1 selects per phi
7368 // node, where N is the number of incoming values.
7369 if (VF > 1 && Phi->getParent() != TheLoop->getHeader())
7370 return (Phi->getNumIncomingValues() - 1) *
7371 TTI.getCmpSelInstrCost(
7372 Instruction::Select, ToVectorTy(Phi->getType(), VF),
7373 ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF));
7375 return TTI.getCFInstrCost(Instruction::PHI);
7377 case Instruction::UDiv:
7378 case Instruction::SDiv:
7379 case Instruction::URem:
7380 case Instruction::SRem:
7381 // If we have a predicated instruction, it may not be executed for each
7382 // vector lane. Get the scalarization cost and scale this amount by the
7383 // probability of executing the predicated block. If the instruction is not
7384 // predicated, we fall through to the next case.
7385 if (VF > 1 && Legal->isScalarWithPredication(I)) {
7388 // These instructions have a non-void type, so account for the phi nodes
7389 // that we will create. This cost is likely to be zero. The phi node
7390 // cost, if any, should be scaled by the block probability because it
7391 // models a copy at the end of each predicated block.
7392 Cost += VF * TTI.getCFInstrCost(Instruction::PHI);
7394 // The cost of the non-predicated instruction.
7395 Cost += VF * TTI.getArithmeticInstrCost(I->getOpcode(), RetTy);
7397 // The cost of insertelement and extractelement instructions needed for
7399 Cost += getScalarizationOverhead(I, VF, TTI);
7401 // Scale the cost by the probability of executing the predicated blocks.
7402 // This assumes the predicated block for each vector lane is equally
7404 return Cost / getReciprocalPredBlockProb();
7407 case Instruction::Add:
7408 case Instruction::FAdd:
7409 case Instruction::Sub:
7410 case Instruction::FSub:
7411 case Instruction::Mul:
7412 case Instruction::FMul:
7413 case Instruction::FDiv:
7414 case Instruction::FRem:
7415 case Instruction::Shl:
7416 case Instruction::LShr:
7417 case Instruction::AShr:
7418 case Instruction::And:
7419 case Instruction::Or:
7420 case Instruction::Xor: {
7421 // Since we will replace the stride by 1 the multiplication should go away.
7422 if (I->getOpcode() == Instruction::Mul && isStrideMul(I, Legal))
7424 // Certain instructions can be cheaper to vectorize if they have a constant
7425 // second vector operand. One example of this are shifts on x86.
7426 TargetTransformInfo::OperandValueKind Op1VK =
7427 TargetTransformInfo::OK_AnyValue;
7428 TargetTransformInfo::OperandValueKind Op2VK =
7429 TargetTransformInfo::OK_AnyValue;
7430 TargetTransformInfo::OperandValueProperties Op1VP =
7431 TargetTransformInfo::OP_None;
7432 TargetTransformInfo::OperandValueProperties Op2VP =
7433 TargetTransformInfo::OP_None;
7434 Value *Op2 = I->getOperand(1);
7436 // Check for a splat or for a non uniform vector of constants.
7437 if (isa<ConstantInt>(Op2)) {
7438 ConstantInt *CInt = cast<ConstantInt>(Op2);
7439 if (CInt && CInt->getValue().isPowerOf2())
7440 Op2VP = TargetTransformInfo::OP_PowerOf2;
7441 Op2VK = TargetTransformInfo::OK_UniformConstantValue;
7442 } else if (isa<ConstantVector>(Op2) || isa<ConstantDataVector>(Op2)) {
7443 Op2VK = TargetTransformInfo::OK_NonUniformConstantValue;
7444 Constant *SplatValue = cast<Constant>(Op2)->getSplatValue();
7446 ConstantInt *CInt = dyn_cast<ConstantInt>(SplatValue);
7447 if (CInt && CInt->getValue().isPowerOf2())
7448 Op2VP = TargetTransformInfo::OP_PowerOf2;
7449 Op2VK = TargetTransformInfo::OK_UniformConstantValue;
7451 } else if (Legal->isUniform(Op2)) {
7452 Op2VK = TargetTransformInfo::OK_UniformValue;
7454 SmallVector<const Value *, 4> Operands(I->operand_values());
7455 unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
7456 return N * TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy, Op1VK,
7457 Op2VK, Op1VP, Op2VP, Operands);
7459 case Instruction::Select: {
7460 SelectInst *SI = cast<SelectInst>(I);
7461 const SCEV *CondSCEV = SE->getSCEV(SI->getCondition());
7462 bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop));
7463 Type *CondTy = SI->getCondition()->getType();
7465 CondTy = VectorType::get(CondTy, VF);
7467 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy, I);
7469 case Instruction::ICmp:
7470 case Instruction::FCmp: {
7471 Type *ValTy = I->getOperand(0)->getType();
7472 Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0));
7473 if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF))
7474 ValTy = IntegerType::get(ValTy->getContext(), MinBWs[Op0AsInstruction]);
7475 VectorTy = ToVectorTy(ValTy, VF);
7476 return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, nullptr, I);
7478 case Instruction::Store:
7479 case Instruction::Load: {
7480 unsigned Width = VF;
7482 InstWidening Decision = getWideningDecision(I, Width);
7483 assert(Decision != CM_Unknown &&
7484 "CM decision should be taken at this point");
7485 if (Decision == CM_Scalarize)
7488 VectorTy = ToVectorTy(getMemInstValueType(I), Width);
7489 return getMemoryInstructionCost(I, VF);
7491 case Instruction::ZExt:
7492 case Instruction::SExt:
7493 case Instruction::FPToUI:
7494 case Instruction::FPToSI:
7495 case Instruction::FPExt:
7496 case Instruction::PtrToInt:
7497 case Instruction::IntToPtr:
7498 case Instruction::SIToFP:
7499 case Instruction::UIToFP:
7500 case Instruction::Trunc:
7501 case Instruction::FPTrunc:
7502 case Instruction::BitCast: {
7503 // We optimize the truncation of induction variables having constant
7504 // integer steps. The cost of these truncations is the same as the scalar
7506 if (isOptimizableIVTruncate(I, VF)) {
7507 auto *Trunc = cast<TruncInst>(I);
7508 return TTI.getCastInstrCost(Instruction::Trunc, Trunc->getDestTy(),
7509 Trunc->getSrcTy(), Trunc);
7512 Type *SrcScalarTy = I->getOperand(0)->getType();
7514 VectorTy->isVectorTy() ? ToVectorTy(SrcScalarTy, VF) : SrcScalarTy;
7515 if (canTruncateToMinimalBitwidth(I, VF)) {
7516 // This cast is going to be shrunk. This may remove the cast or it might
7517 // turn it into slightly different cast. For example, if MinBW == 16,
7518 // "zext i8 %1 to i32" becomes "zext i8 %1 to i16".
7520 // Calculate the modified src and dest types.
7521 Type *MinVecTy = VectorTy;
7522 if (I->getOpcode() == Instruction::Trunc) {
7523 SrcVecTy = smallestIntegerVectorType(SrcVecTy, MinVecTy);
7525 largestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
7526 } else if (I->getOpcode() == Instruction::ZExt ||
7527 I->getOpcode() == Instruction::SExt) {
7528 SrcVecTy = largestIntegerVectorType(SrcVecTy, MinVecTy);
7530 smallestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
7534 unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
7535 return N * TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy, I);
7537 case Instruction::Call: {
7538 bool NeedToScalarize;
7539 CallInst *CI = cast<CallInst>(I);
7540 unsigned CallCost = getVectorCallCost(CI, VF, TTI, TLI, NeedToScalarize);
7541 if (getVectorIntrinsicIDForCall(CI, TLI))
7542 return std::min(CallCost, getVectorIntrinsicCost(CI, VF, TTI, TLI));
7546 // The cost of executing VF copies of the scalar instruction. This opcode
7547 // is unknown. Assume that it is the same as 'mul'.
7548 return VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy) +
7549 getScalarizationOverhead(I, VF, TTI);
7553 char LoopVectorize::ID = 0;
7554 static const char lv_name[] = "Loop Vectorization";
7555 INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false)
7556 INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass)
7557 INITIALIZE_PASS_DEPENDENCY(BasicAAWrapperPass)
7558 INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass)
7559 INITIALIZE_PASS_DEPENDENCY(GlobalsAAWrapperPass)
7560 INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker)
7561 INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass)
7562 INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
7563 INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass)
7564 INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
7565 INITIALIZE_PASS_DEPENDENCY(LoopAccessLegacyAnalysis)
7566 INITIALIZE_PASS_DEPENDENCY(DemandedBitsWrapperPass)
7567 INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass)
7568 INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
7571 Pass *createLoopVectorizePass(bool NoUnrolling, bool AlwaysVectorize) {
7572 return new LoopVectorize(NoUnrolling, AlwaysVectorize);
7576 bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) {
7578 // Check if the pointer operand of a load or store instruction is
7580 if (auto *Ptr = getPointerOperand(Inst))
7581 return Legal->isConsecutivePtr(Ptr);
7585 void LoopVectorizationCostModel::collectValuesToIgnore() {
7586 // Ignore ephemeral values.
7587 CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore);
7589 // Ignore type-promoting instructions we identified during reduction
7591 for (auto &Reduction : *Legal->getReductionVars()) {
7592 RecurrenceDescriptor &RedDes = Reduction.second;
7593 SmallPtrSetImpl<Instruction *> &Casts = RedDes.getCastInsts();
7594 VecValuesToIgnore.insert(Casts.begin(), Casts.end());
7598 LoopVectorizationCostModel::VectorizationFactor
7599 LoopVectorizationPlanner::plan(bool OptForSize, unsigned UserVF) {
7601 // Width 1 means no vectorize, cost 0 means uncomputed cost.
7602 const LoopVectorizationCostModel::VectorizationFactor NoVectorization = {1U,
7604 Optional<unsigned> MaybeMaxVF = CM.computeMaxVF(OptForSize);
7605 if (!MaybeMaxVF.hasValue()) // Cases considered too costly to vectorize.
7606 return NoVectorization;
7609 DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
7610 assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two");
7611 // Collect the instructions (and their associated costs) that will be more
7612 // profitable to scalarize.
7613 CM.selectUserVectorizationFactor(UserVF);
7617 unsigned MaxVF = MaybeMaxVF.getValue();
7618 assert(MaxVF != 0 && "MaxVF is zero.");
7620 return NoVectorization;
7622 // Select the optimal vectorization factor.
7623 return CM.selectVectorizationFactor(MaxVF);
7626 void LoopVectorizationPlanner::executePlan(InnerLoopVectorizer &ILV) {
7627 // Perform the actual loop transformation.
7629 // 1. Create a new empty loop. Unlink the old loop and connect the new one.
7630 ILV.createVectorizedLoopSkeleton();
7632 //===------------------------------------------------===//
7634 // Notice: any optimization or new instruction that go
7635 // into the code below should also be implemented in
7638 //===------------------------------------------------===//
7640 // 2. Copy and widen instructions from the old loop into the new loop.
7642 // Move instructions to handle first-order recurrences.
7643 DenseMap<Instruction *, Instruction *> SinkAfter = Legal->getSinkAfter();
7644 for (auto &Entry : SinkAfter) {
7645 Entry.first->removeFromParent();
7646 Entry.first->insertAfter(Entry.second);
7647 DEBUG(dbgs() << "Sinking" << *Entry.first << " after" << *Entry.second
7648 << " to vectorize a 1st order recurrence.\n");
7651 // Collect instructions from the original loop that will become trivially dead
7652 // in the vectorized loop. We don't need to vectorize these instructions. For
7653 // example, original induction update instructions can become dead because we
7654 // separately emit induction "steps" when generating code for the new loop.
7655 // Similarly, we create a new latch condition when setting up the structure
7656 // of the new loop, so the old one can become dead.
7657 SmallPtrSet<Instruction *, 4> DeadInstructions;
7658 collectTriviallyDeadInstructions(DeadInstructions);
7660 // Scan the loop in a topological order to ensure that defs are vectorized
7662 LoopBlocksDFS DFS(OrigLoop);
7665 // Vectorize all instructions in the original loop that will not become
7666 // trivially dead when vectorized.
7667 for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO()))
7668 for (Instruction &I : *BB)
7669 if (!DeadInstructions.count(&I))
7670 ILV.vectorizeInstruction(I);
7672 // 3. Fix the vectorized code: take care of header phi's, live-outs,
7673 // predication, updating analyses.
7674 ILV.fixVectorizedLoop();
7677 void LoopVectorizationPlanner::collectTriviallyDeadInstructions(
7678 SmallPtrSetImpl<Instruction *> &DeadInstructions) {
7679 BasicBlock *Latch = OrigLoop->getLoopLatch();
7681 // We create new control-flow for the vectorized loop, so the original
7682 // condition will be dead after vectorization if it's only used by the
7684 auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0));
7685 if (Cmp && Cmp->hasOneUse())
7686 DeadInstructions.insert(Cmp);
7688 // We create new "steps" for induction variable updates to which the original
7689 // induction variables map. An original update instruction will be dead if
7690 // all its users except the induction variable are dead.
7691 for (auto &Induction : *Legal->getInductionVars()) {
7692 PHINode *Ind = Induction.first;
7693 auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
7694 if (all_of(IndUpdate->users(), [&](User *U) -> bool {
7695 return U == Ind || DeadInstructions.count(cast<Instruction>(U));
7697 DeadInstructions.insert(IndUpdate);
7701 void InnerLoopUnroller::vectorizeMemoryInstruction(Instruction *Instr) {
7702 auto *SI = dyn_cast<StoreInst>(Instr);
7703 bool IfPredicateInstr = (SI && Legal->blockNeedsPredication(SI->getParent()));
7705 return scalarizeInstruction(Instr, IfPredicateInstr);
7708 Value *InnerLoopUnroller::reverseVector(Value *Vec) { return Vec; }
7710 Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) { return V; }
7712 Value *InnerLoopUnroller::getStepVector(Value *Val, int StartIdx, Value *Step,
7713 Instruction::BinaryOps BinOp) {
7714 // When unrolling and the VF is 1, we only need to add a simple scalar.
7715 Type *Ty = Val->getType();
7716 assert(!Ty->isVectorTy() && "Val must be a scalar");
7718 if (Ty->isFloatingPointTy()) {
7719 Constant *C = ConstantFP::get(Ty, (double)StartIdx);
7721 // Floating point operations had to be 'fast' to enable the unrolling.
7722 Value *MulOp = addFastMathFlag(Builder.CreateFMul(C, Step));
7723 return addFastMathFlag(Builder.CreateBinOp(BinOp, Val, MulOp));
7725 Constant *C = ConstantInt::get(Ty, StartIdx);
7726 return Builder.CreateAdd(Val, Builder.CreateMul(C, Step), "induction");
7729 static void AddRuntimeUnrollDisableMetaData(Loop *L) {
7730 SmallVector<Metadata *, 4> MDs;
7731 // Reserve first location for self reference to the LoopID metadata node.
7732 MDs.push_back(nullptr);
7733 bool IsUnrollMetadata = false;
7734 MDNode *LoopID = L->getLoopID();
7736 // First find existing loop unrolling disable metadata.
7737 for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
7738 auto *MD = dyn_cast<MDNode>(LoopID->getOperand(i));
7740 const auto *S = dyn_cast<MDString>(MD->getOperand(0));
7742 S && S->getString().startswith("llvm.loop.unroll.disable");
7744 MDs.push_back(LoopID->getOperand(i));
7748 if (!IsUnrollMetadata) {
7749 // Add runtime unroll disable metadata.
7750 LLVMContext &Context = L->getHeader()->getContext();
7751 SmallVector<Metadata *, 1> DisableOperands;
7752 DisableOperands.push_back(
7753 MDString::get(Context, "llvm.loop.unroll.runtime.disable"));
7754 MDNode *DisableNode = MDNode::get(Context, DisableOperands);
7755 MDs.push_back(DisableNode);
7756 MDNode *NewLoopID = MDNode::get(Context, MDs);
7757 // Set operand 0 to refer to the loop id itself.
7758 NewLoopID->replaceOperandWith(0, NewLoopID);
7759 L->setLoopID(NewLoopID);
7763 bool LoopVectorizePass::processLoop(Loop *L) {
7764 assert(L->empty() && "Only process inner loops.");
7767 const std::string DebugLocStr = getDebugLocString(L);
7770 DEBUG(dbgs() << "\nLV: Checking a loop in \""
7771 << L->getHeader()->getParent()->getName() << "\" from "
7772 << DebugLocStr << "\n");
7774 LoopVectorizeHints Hints(L, DisableUnrolling, *ORE);
7776 DEBUG(dbgs() << "LV: Loop hints:"
7778 << (Hints.getForce() == LoopVectorizeHints::FK_Disabled
7780 : (Hints.getForce() == LoopVectorizeHints::FK_Enabled
7783 << " width=" << Hints.getWidth()
7784 << " unroll=" << Hints.getInterleave() << "\n");
7786 // Function containing loop
7787 Function *F = L->getHeader()->getParent();
7789 // Looking at the diagnostic output is the only way to determine if a loop
7790 // was vectorized (other than looking at the IR or machine code), so it
7791 // is important to generate an optimization remark for each loop. Most of
7792 // these messages are generated as OptimizationRemarkAnalysis. Remarks
7793 // generated as OptimizationRemark and OptimizationRemarkMissed are
7794 // less verbose reporting vectorized loops and unvectorized loops that may
7795 // benefit from vectorization, respectively.
7797 if (!Hints.allowVectorization(F, L, AlwaysVectorize)) {
7798 DEBUG(dbgs() << "LV: Loop hints prevent vectorization.\n");
7802 PredicatedScalarEvolution PSE(*SE, *L);
7804 // Check if it is legal to vectorize the loop.
7805 LoopVectorizationRequirements Requirements(*ORE);
7806 LoopVectorizationLegality LVL(L, PSE, DT, TLI, AA, F, TTI, GetLAA, LI, ORE,
7807 &Requirements, &Hints);
7808 if (!LVL.canVectorize()) {
7809 DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n");
7810 emitMissedWarning(F, L, Hints, ORE);
7814 // Check the function attributes to find out if this function should be
7815 // optimized for size.
7817 Hints.getForce() != LoopVectorizeHints::FK_Enabled && F->optForSize();
7819 // Check the loop for a trip count threshold: vectorize loops with a tiny trip
7820 // count by optimizing for size, to minimize overheads.
7821 unsigned ExpectedTC = SE->getSmallConstantMaxTripCount(L);
7822 bool HasExpectedTC = (ExpectedTC > 0);
7824 if (!HasExpectedTC && LoopVectorizeWithBlockFrequency) {
7825 auto EstimatedTC = getLoopEstimatedTripCount(L);
7827 ExpectedTC = *EstimatedTC;
7828 HasExpectedTC = true;
7832 if (HasExpectedTC && ExpectedTC < TinyTripCountVectorThreshold) {
7833 DEBUG(dbgs() << "LV: Found a loop with a very small trip count. "
7834 << "This loop is worth vectorizing only if no scalar "
7835 << "iteration overheads are incurred.");
7836 if (Hints.getForce() == LoopVectorizeHints::FK_Enabled)
7837 DEBUG(dbgs() << " But vectorizing was explicitly forced.\n");
7839 DEBUG(dbgs() << "\n");
7840 // Loops with a very small trip count are considered for vectorization
7841 // under OptForSize, thereby making sure the cost of their loop body is
7842 // dominant, free of runtime guards and scalar iteration overheads.
7847 // Check the function attributes to see if implicit floats are allowed.
7848 // FIXME: This check doesn't seem possibly correct -- what if the loop is
7849 // an integer loop and the vector instructions selected are purely integer
7850 // vector instructions?
7851 if (F->hasFnAttribute(Attribute::NoImplicitFloat)) {
7852 DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat"
7853 "attribute is used.\n");
7854 ORE->emit(createMissedAnalysis(Hints.vectorizeAnalysisPassName(),
7855 "NoImplicitFloat", L)
7856 << "loop not vectorized due to NoImplicitFloat attribute");
7857 emitMissedWarning(F, L, Hints, ORE);
7861 // Check if the target supports potentially unsafe FP vectorization.
7862 // FIXME: Add a check for the type of safety issue (denormal, signaling)
7863 // for the target we're vectorizing for, to make sure none of the
7864 // additional fp-math flags can help.
7865 if (Hints.isPotentiallyUnsafe() &&
7866 TTI->isFPVectorizationPotentiallyUnsafe()) {
7867 DEBUG(dbgs() << "LV: Potentially unsafe FP op prevents vectorization.\n");
7869 createMissedAnalysis(Hints.vectorizeAnalysisPassName(), "UnsafeFP", L)
7870 << "loop not vectorized due to unsafe FP support.");
7871 emitMissedWarning(F, L, Hints, ORE);
7875 // Use the cost model.
7876 LoopVectorizationCostModel CM(L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE, F,
7878 CM.collectValuesToIgnore();
7880 // Use the planner for vectorization.
7881 LoopVectorizationPlanner LVP(L, LI, &LVL, CM);
7883 // Get user vectorization factor.
7884 unsigned UserVF = Hints.getWidth();
7886 // Plan how to best vectorize, return the best VF and its cost.
7887 LoopVectorizationCostModel::VectorizationFactor VF =
7888 LVP.plan(OptForSize, UserVF);
7890 // Select the interleave count.
7891 unsigned IC = CM.selectInterleaveCount(OptForSize, VF.Width, VF.Cost);
7893 // Get user interleave count.
7894 unsigned UserIC = Hints.getInterleave();
7896 // Identify the diagnostic messages that should be produced.
7897 std::pair<StringRef, std::string> VecDiagMsg, IntDiagMsg;
7898 bool VectorizeLoop = true, InterleaveLoop = true;
7899 if (Requirements.doesNotMeet(F, L, Hints)) {
7900 DEBUG(dbgs() << "LV: Not vectorizing: loop did not meet vectorization "
7902 emitMissedWarning(F, L, Hints, ORE);
7906 if (VF.Width == 1) {
7907 DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
7908 VecDiagMsg = std::make_pair(
7909 "VectorizationNotBeneficial",
7910 "the cost-model indicates that vectorization is not beneficial");
7911 VectorizeLoop = false;
7914 if (IC == 1 && UserIC <= 1) {
7915 // Tell the user interleaving is not beneficial.
7916 DEBUG(dbgs() << "LV: Interleaving is not beneficial.\n");
7917 IntDiagMsg = std::make_pair(
7918 "InterleavingNotBeneficial",
7919 "the cost-model indicates that interleaving is not beneficial");
7920 InterleaveLoop = false;
7922 IntDiagMsg.first = "InterleavingNotBeneficialAndDisabled";
7923 IntDiagMsg.second +=
7924 " and is explicitly disabled or interleave count is set to 1";
7926 } else if (IC > 1 && UserIC == 1) {
7927 // Tell the user interleaving is beneficial, but it explicitly disabled.
7929 << "LV: Interleaving is beneficial but is explicitly disabled.");
7930 IntDiagMsg = std::make_pair(
7931 "InterleavingBeneficialButDisabled",
7932 "the cost-model indicates that interleaving is beneficial "
7933 "but is explicitly disabled or interleave count is set to 1");
7934 InterleaveLoop = false;
7937 // Override IC if user provided an interleave count.
7938 IC = UserIC > 0 ? UserIC : IC;
7940 // Emit diagnostic messages, if any.
7941 const char *VAPassName = Hints.vectorizeAnalysisPassName();
7942 if (!VectorizeLoop && !InterleaveLoop) {
7943 // Do not vectorize or interleaving the loop.
7944 ORE->emit(OptimizationRemarkMissed(VAPassName, VecDiagMsg.first,
7945 L->getStartLoc(), L->getHeader())
7946 << VecDiagMsg.second);
7947 ORE->emit(OptimizationRemarkMissed(LV_NAME, IntDiagMsg.first,
7948 L->getStartLoc(), L->getHeader())
7949 << IntDiagMsg.second);
7951 } else if (!VectorizeLoop && InterleaveLoop) {
7952 DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
7953 ORE->emit(OptimizationRemarkAnalysis(VAPassName, VecDiagMsg.first,
7954 L->getStartLoc(), L->getHeader())
7955 << VecDiagMsg.second);
7956 } else if (VectorizeLoop && !InterleaveLoop) {
7957 DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width << ") in "
7958 << DebugLocStr << '\n');
7959 ORE->emit(OptimizationRemarkAnalysis(LV_NAME, IntDiagMsg.first,
7960 L->getStartLoc(), L->getHeader())
7961 << IntDiagMsg.second);
7962 } else if (VectorizeLoop && InterleaveLoop) {
7963 DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width << ") in "
7964 << DebugLocStr << '\n');
7965 DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
7968 using namespace ore;
7969 if (!VectorizeLoop) {
7970 assert(IC > 1 && "interleave count should not be 1 or 0");
7971 // If we decided that it is not legal to vectorize the loop, then
7973 InnerLoopUnroller Unroller(L, PSE, LI, DT, TLI, TTI, AC, ORE, IC, &LVL,
7975 LVP.executePlan(Unroller);
7977 ORE->emit(OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(),
7979 << "interleaved loop (interleaved count: "
7980 << NV("InterleaveCount", IC) << ")");
7982 // If we decided that it is *legal* to vectorize the loop, then do it.
7983 InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, IC,
7985 LVP.executePlan(LB);
7988 // Add metadata to disable runtime unrolling a scalar loop when there are
7989 // no runtime checks about strides and memory. A scalar loop that is
7990 // rarely used is not worth unrolling.
7991 if (!LB.areSafetyChecksAdded())
7992 AddRuntimeUnrollDisableMetaData(L);
7994 // Report the vectorization decision.
7995 ORE->emit(OptimizationRemark(LV_NAME, "Vectorized", L->getStartLoc(),
7997 << "vectorized loop (vectorization width: "
7998 << NV("VectorizationFactor", VF.Width)
7999 << ", interleaved count: " << NV("InterleaveCount", IC) << ")");
8002 // Mark the loop as already vectorized to avoid vectorizing again.
8003 Hints.setAlreadyVectorized();
8005 DEBUG(verifyFunction(*L->getHeader()->getParent()));
8009 bool LoopVectorizePass::runImpl(
8010 Function &F, ScalarEvolution &SE_, LoopInfo &LI_, TargetTransformInfo &TTI_,
8011 DominatorTree &DT_, BlockFrequencyInfo &BFI_, TargetLibraryInfo *TLI_,
8012 DemandedBits &DB_, AliasAnalysis &AA_, AssumptionCache &AC_,
8013 std::function<const LoopAccessInfo &(Loop &)> &GetLAA_,
8014 OptimizationRemarkEmitter &ORE_) {
8029 // 1. the target claims to have no vector registers, and
8030 // 2. interleaving won't help ILP.
8032 // The second condition is necessary because, even if the target has no
8033 // vector registers, loop vectorization may still enable scalar
8035 if (!TTI->getNumberOfRegisters(true) && TTI->getMaxInterleaveFactor(1) < 2)
8038 bool Changed = false;
8040 // The vectorizer requires loops to be in simplified form.
8041 // Since simplification may add new inner loops, it has to run before the
8042 // legality and profitability checks. This means running the loop vectorizer
8043 // will simplify all loops, regardless of whether anything end up being
8046 Changed |= simplifyLoop(L, DT, LI, SE, AC, false /* PreserveLCSSA */);
8048 // Build up a worklist of inner-loops to vectorize. This is necessary as
8049 // the act of vectorizing or partially unrolling a loop creates new loops
8050 // and can invalidate iterators across the loops.
8051 SmallVector<Loop *, 8> Worklist;
8054 addAcyclicInnerLoop(*L, Worklist);
8056 LoopsAnalyzed += Worklist.size();
8058 // Now walk the identified inner loops.
8059 while (!Worklist.empty()) {
8060 Loop *L = Worklist.pop_back_val();
8062 // For the inner loops we actually process, form LCSSA to simplify the
8064 Changed |= formLCSSARecursively(*L, *DT, LI, SE);
8066 Changed |= processLoop(L);
8069 // Process each loop nest in the function.
8075 PreservedAnalyses LoopVectorizePass::run(Function &F,
8076 FunctionAnalysisManager &AM) {
8077 auto &SE = AM.getResult<ScalarEvolutionAnalysis>(F);
8078 auto &LI = AM.getResult<LoopAnalysis>(F);
8079 auto &TTI = AM.getResult<TargetIRAnalysis>(F);
8080 auto &DT = AM.getResult<DominatorTreeAnalysis>(F);
8081 auto &BFI = AM.getResult<BlockFrequencyAnalysis>(F);
8082 auto &TLI = AM.getResult<TargetLibraryAnalysis>(F);
8083 auto &AA = AM.getResult<AAManager>(F);
8084 auto &AC = AM.getResult<AssumptionAnalysis>(F);
8085 auto &DB = AM.getResult<DemandedBitsAnalysis>(F);
8086 auto &ORE = AM.getResult<OptimizationRemarkEmitterAnalysis>(F);
8088 auto &LAM = AM.getResult<LoopAnalysisManagerFunctionProxy>(F).getManager();
8089 std::function<const LoopAccessInfo &(Loop &)> GetLAA =
8090 [&](Loop &L) -> const LoopAccessInfo & {
8091 LoopStandardAnalysisResults AR = {AA, AC, DT, LI, SE, TLI, TTI};
8092 return LAM.getResult<LoopAccessAnalysis>(L, AR);
8095 runImpl(F, SE, LI, TTI, DT, BFI, &TLI, DB, AA, AC, GetLAA, ORE);
8097 return PreservedAnalyses::all();
8098 PreservedAnalyses PA;
8099 PA.preserve<LoopAnalysis>();
8100 PA.preserve<DominatorTreeAnalysis>();
8101 PA.preserve<BasicAA>();
8102 PA.preserve<GlobalsAA>();