forked from OSchip/llvm-project
8734 lines
346 KiB
C++
8734 lines
346 KiB
C++
//===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===//
|
|
//
|
|
// The LLVM Compiler Infrastructure
|
|
//
|
|
// This file is distributed under the University of Illinois Open Source
|
|
// License. See LICENSE.TXT for details.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops
|
|
// and generates target-independent LLVM-IR.
|
|
// The vectorizer uses the TargetTransformInfo analysis to estimate the costs
|
|
// of instructions in order to estimate the profitability of vectorization.
|
|
//
|
|
// The loop vectorizer combines consecutive loop iterations into a single
|
|
// 'wide' iteration. After this transformation the index is incremented
|
|
// by the SIMD vector width, and not by one.
|
|
//
|
|
// This pass has three parts:
|
|
// 1. The main loop pass that drives the different parts.
|
|
// 2. LoopVectorizationLegality - A unit that checks for the legality
|
|
// of the vectorization.
|
|
// 3. InnerLoopVectorizer - A unit that performs the actual
|
|
// widening of instructions.
|
|
// 4. LoopVectorizationCostModel - A unit that checks for the profitability
|
|
// of vectorization. It decides on the optimal vector width, which
|
|
// can be one, if vectorization is not profitable.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
//
|
|
// The reduction-variable vectorization is based on the paper:
|
|
// D. Nuzman and R. Henderson. Multi-platform Auto-vectorization.
|
|
//
|
|
// Variable uniformity checks are inspired by:
|
|
// Karrenberg, R. and Hack, S. Whole Function Vectorization.
|
|
//
|
|
// The interleaved access vectorization is based on the paper:
|
|
// Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved
|
|
// Data for SIMD
|
|
//
|
|
// Other ideas/concepts are from:
|
|
// A. Zaks and D. Nuzman. Autovectorization in GCC-two years later.
|
|
//
|
|
// S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of
|
|
// Vectorizing Compilers.
|
|
//
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
#include "llvm/Transforms/Vectorize/LoopVectorize.h"
|
|
#include "VPlan.h"
|
|
#include "llvm/ADT/DenseMap.h"
|
|
#include "llvm/ADT/Hashing.h"
|
|
#include "llvm/ADT/MapVector.h"
|
|
#include "llvm/ADT/Optional.h"
|
|
#include "llvm/ADT/SCCIterator.h"
|
|
#include "llvm/ADT/SetVector.h"
|
|
#include "llvm/ADT/SmallPtrSet.h"
|
|
#include "llvm/ADT/SmallSet.h"
|
|
#include "llvm/ADT/SmallVector.h"
|
|
#include "llvm/ADT/Statistic.h"
|
|
#include "llvm/ADT/StringExtras.h"
|
|
#include "llvm/Analysis/CodeMetrics.h"
|
|
#include "llvm/Analysis/GlobalsModRef.h"
|
|
#include "llvm/Analysis/LoopInfo.h"
|
|
#include "llvm/Analysis/LoopIterator.h"
|
|
#include "llvm/Analysis/LoopPass.h"
|
|
#include "llvm/Analysis/ScalarEvolutionExpander.h"
|
|
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
|
|
#include "llvm/Analysis/ValueTracking.h"
|
|
#include "llvm/Analysis/VectorUtils.h"
|
|
#include "llvm/IR/Constants.h"
|
|
#include "llvm/IR/DataLayout.h"
|
|
#include "llvm/IR/DebugInfo.h"
|
|
#include "llvm/IR/DerivedTypes.h"
|
|
#include "llvm/IR/DiagnosticInfo.h"
|
|
#include "llvm/IR/Dominators.h"
|
|
#include "llvm/IR/Function.h"
|
|
#include "llvm/IR/IRBuilder.h"
|
|
#include "llvm/IR/Instructions.h"
|
|
#include "llvm/IR/IntrinsicInst.h"
|
|
#include "llvm/IR/LLVMContext.h"
|
|
#include "llvm/IR/Module.h"
|
|
#include "llvm/IR/PatternMatch.h"
|
|
#include "llvm/IR/Type.h"
|
|
#include "llvm/IR/User.h"
|
|
#include "llvm/IR/Value.h"
|
|
#include "llvm/IR/ValueHandle.h"
|
|
#include "llvm/IR/Verifier.h"
|
|
#include "llvm/Pass.h"
|
|
#include "llvm/Support/BranchProbability.h"
|
|
#include "llvm/Support/CommandLine.h"
|
|
#include "llvm/Support/Debug.h"
|
|
#include "llvm/Support/raw_ostream.h"
|
|
#include "llvm/Transforms/Scalar.h"
|
|
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
|
|
#include "llvm/Transforms/Utils/Local.h"
|
|
#include "llvm/Transforms/Utils/LoopSimplify.h"
|
|
#include "llvm/Transforms/Utils/LoopUtils.h"
|
|
#include "llvm/Transforms/Utils/LoopVersioning.h"
|
|
#include "llvm/Transforms/Vectorize.h"
|
|
#include <algorithm>
|
|
#include <functional>
|
|
#include <map>
|
|
#include <tuple>
|
|
|
|
using namespace llvm;
|
|
using namespace llvm::PatternMatch;
|
|
|
|
#define LV_NAME "loop-vectorize"
|
|
#define DEBUG_TYPE LV_NAME
|
|
|
|
STATISTIC(LoopsVectorized, "Number of loops vectorized");
|
|
STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization");
|
|
|
|
static cl::opt<bool>
|
|
EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden,
|
|
cl::desc("Enable if-conversion during vectorization."));
|
|
|
|
/// Loops with a known constant trip count below this number are vectorized only
|
|
/// if no scalar iteration overheads are incurred.
|
|
static cl::opt<unsigned> TinyTripCountVectorThreshold(
|
|
"vectorizer-min-trip-count", cl::init(16), cl::Hidden,
|
|
cl::desc("Loops with a constant trip count that is smaller than this "
|
|
"value are vectorized only if no scalar iteration overheads "
|
|
"are incurred."));
|
|
|
|
static cl::opt<bool> MaximizeBandwidth(
|
|
"vectorizer-maximize-bandwidth", cl::init(false), cl::Hidden,
|
|
cl::desc("Maximize bandwidth when selecting vectorization factor which "
|
|
"will be determined by the smallest type in loop."));
|
|
|
|
static cl::opt<bool> EnableInterleavedMemAccesses(
|
|
"enable-interleaved-mem-accesses", cl::init(false), cl::Hidden,
|
|
cl::desc("Enable vectorization on interleaved memory accesses in a loop"));
|
|
|
|
/// Maximum factor for an interleaved memory access.
|
|
static cl::opt<unsigned> MaxInterleaveGroupFactor(
|
|
"max-interleave-group-factor", cl::Hidden,
|
|
cl::desc("Maximum factor for an interleaved access group (default = 8)"),
|
|
cl::init(8));
|
|
|
|
/// We don't interleave loops with a known constant trip count below this
|
|
/// number.
|
|
static const unsigned TinyTripCountInterleaveThreshold = 128;
|
|
|
|
static cl::opt<unsigned> ForceTargetNumScalarRegs(
|
|
"force-target-num-scalar-regs", cl::init(0), cl::Hidden,
|
|
cl::desc("A flag that overrides the target's number of scalar registers."));
|
|
|
|
static cl::opt<unsigned> ForceTargetNumVectorRegs(
|
|
"force-target-num-vector-regs", cl::init(0), cl::Hidden,
|
|
cl::desc("A flag that overrides the target's number of vector registers."));
|
|
|
|
/// Maximum vectorization interleave count.
|
|
static const unsigned MaxInterleaveFactor = 16;
|
|
|
|
static cl::opt<unsigned> ForceTargetMaxScalarInterleaveFactor(
|
|
"force-target-max-scalar-interleave", cl::init(0), cl::Hidden,
|
|
cl::desc("A flag that overrides the target's max interleave factor for "
|
|
"scalar loops."));
|
|
|
|
static cl::opt<unsigned> ForceTargetMaxVectorInterleaveFactor(
|
|
"force-target-max-vector-interleave", cl::init(0), cl::Hidden,
|
|
cl::desc("A flag that overrides the target's max interleave factor for "
|
|
"vectorized loops."));
|
|
|
|
static cl::opt<unsigned> ForceTargetInstructionCost(
|
|
"force-target-instruction-cost", cl::init(0), cl::Hidden,
|
|
cl::desc("A flag that overrides the target's expected cost for "
|
|
"an instruction to a single constant value. Mostly "
|
|
"useful for getting consistent testing."));
|
|
|
|
static cl::opt<unsigned> SmallLoopCost(
|
|
"small-loop-cost", cl::init(20), cl::Hidden,
|
|
cl::desc(
|
|
"The cost of a loop that is considered 'small' by the interleaver."));
|
|
|
|
static cl::opt<bool> LoopVectorizeWithBlockFrequency(
|
|
"loop-vectorize-with-block-frequency", cl::init(false), cl::Hidden,
|
|
cl::desc("Enable the use of the block frequency analysis to access PGO "
|
|
"heuristics minimizing code growth in cold regions and being more "
|
|
"aggressive in hot regions."));
|
|
|
|
// Runtime interleave loops for load/store throughput.
|
|
static cl::opt<bool> EnableLoadStoreRuntimeInterleave(
|
|
"enable-loadstore-runtime-interleave", cl::init(true), cl::Hidden,
|
|
cl::desc(
|
|
"Enable runtime interleaving until load/store ports are saturated"));
|
|
|
|
/// The number of stores in a loop that are allowed to need predication.
|
|
static cl::opt<unsigned> NumberOfStoresToPredicate(
|
|
"vectorize-num-stores-pred", cl::init(1), cl::Hidden,
|
|
cl::desc("Max number of stores to be predicated behind an if."));
|
|
|
|
static cl::opt<bool> EnableIndVarRegisterHeur(
|
|
"enable-ind-var-reg-heur", cl::init(true), cl::Hidden,
|
|
cl::desc("Count the induction variable only once when interleaving"));
|
|
|
|
static cl::opt<bool> EnableCondStoresVectorization(
|
|
"enable-cond-stores-vec", cl::init(true), cl::Hidden,
|
|
cl::desc("Enable if predication of stores during vectorization."));
|
|
|
|
static cl::opt<unsigned> MaxNestedScalarReductionIC(
|
|
"max-nested-scalar-reduction-interleave", cl::init(2), cl::Hidden,
|
|
cl::desc("The maximum interleave count to use when interleaving a scalar "
|
|
"reduction in a nested loop."));
|
|
|
|
static cl::opt<unsigned> PragmaVectorizeMemoryCheckThreshold(
|
|
"pragma-vectorize-memory-check-threshold", cl::init(128), cl::Hidden,
|
|
cl::desc("The maximum allowed number of runtime memory checks with a "
|
|
"vectorize(enable) pragma."));
|
|
|
|
static cl::opt<unsigned> VectorizeSCEVCheckThreshold(
|
|
"vectorize-scev-check-threshold", cl::init(16), cl::Hidden,
|
|
cl::desc("The maximum number of SCEV checks allowed."));
|
|
|
|
static cl::opt<unsigned> PragmaVectorizeSCEVCheckThreshold(
|
|
"pragma-vectorize-scev-check-threshold", cl::init(128), cl::Hidden,
|
|
cl::desc("The maximum number of SCEV checks allowed with a "
|
|
"vectorize(enable) pragma"));
|
|
|
|
/// Create an analysis remark that explains why vectorization failed
|
|
///
|
|
/// \p PassName is the name of the pass (e.g. can be AlwaysPrint). \p
|
|
/// RemarkName is the identifier for the remark. If \p I is passed it is an
|
|
/// instruction that prevents vectorization. Otherwise \p TheLoop is used for
|
|
/// the location of the remark. \return the remark object that can be
|
|
/// streamed to.
|
|
static OptimizationRemarkAnalysis
|
|
createMissedAnalysis(const char *PassName, StringRef RemarkName, Loop *TheLoop,
|
|
Instruction *I = nullptr) {
|
|
Value *CodeRegion = TheLoop->getHeader();
|
|
DebugLoc DL = TheLoop->getStartLoc();
|
|
|
|
if (I) {
|
|
CodeRegion = I->getParent();
|
|
// If there is no debug location attached to the instruction, revert back to
|
|
// using the loop's.
|
|
if (I->getDebugLoc())
|
|
DL = I->getDebugLoc();
|
|
}
|
|
|
|
OptimizationRemarkAnalysis R(PassName, RemarkName, DL, CodeRegion);
|
|
R << "loop not vectorized: ";
|
|
return R;
|
|
}
|
|
|
|
namespace {
|
|
|
|
// Forward declarations.
|
|
class LoopVectorizeHints;
|
|
class LoopVectorizationLegality;
|
|
class LoopVectorizationCostModel;
|
|
class LoopVectorizationRequirements;
|
|
class VPInterleaveRecipe;
|
|
class VPReplicateRecipe;
|
|
class VPWidenIntOrFpInductionRecipe;
|
|
class VPWidenRecipe;
|
|
|
|
/// Returns true if the given loop body has a cycle, excluding the loop
|
|
/// itself.
|
|
static bool hasCyclesInLoopBody(const Loop &L) {
|
|
if (!L.empty())
|
|
return true;
|
|
|
|
for (const auto &SCC :
|
|
make_range(scc_iterator<Loop, LoopBodyTraits>::begin(L),
|
|
scc_iterator<Loop, LoopBodyTraits>::end(L))) {
|
|
if (SCC.size() > 1) {
|
|
DEBUG(dbgs() << "LVL: Detected a cycle in the loop body:\n");
|
|
DEBUG(L.dump());
|
|
return true;
|
|
}
|
|
}
|
|
return false;
|
|
}
|
|
|
|
/// A helper function for converting Scalar types to vector types.
|
|
/// If the incoming type is void, we return void. If the VF is 1, we return
|
|
/// the scalar type.
|
|
static Type *ToVectorTy(Type *Scalar, unsigned VF) {
|
|
if (Scalar->isVoidTy() || VF == 1)
|
|
return Scalar;
|
|
return VectorType::get(Scalar, VF);
|
|
}
|
|
|
|
// FIXME: The following helper functions have multiple implementations
|
|
// in the project. They can be effectively organized in a common Load/Store
|
|
// utilities unit.
|
|
|
|
/// A helper function that returns the pointer operand of a load or store
|
|
/// instruction.
|
|
static Value *getPointerOperand(Value *I) {
|
|
if (auto *LI = dyn_cast<LoadInst>(I))
|
|
return LI->getPointerOperand();
|
|
if (auto *SI = dyn_cast<StoreInst>(I))
|
|
return SI->getPointerOperand();
|
|
return nullptr;
|
|
}
|
|
|
|
/// A helper function that returns the type of loaded or stored value.
|
|
static Type *getMemInstValueType(Value *I) {
|
|
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
|
|
"Expected Load or Store instruction");
|
|
if (auto *LI = dyn_cast<LoadInst>(I))
|
|
return LI->getType();
|
|
return cast<StoreInst>(I)->getValueOperand()->getType();
|
|
}
|
|
|
|
/// A helper function that returns the alignment of load or store instruction.
|
|
static unsigned getMemInstAlignment(Value *I) {
|
|
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
|
|
"Expected Load or Store instruction");
|
|
if (auto *LI = dyn_cast<LoadInst>(I))
|
|
return LI->getAlignment();
|
|
return cast<StoreInst>(I)->getAlignment();
|
|
}
|
|
|
|
/// A helper function that returns the address space of the pointer operand of
|
|
/// load or store instruction.
|
|
static unsigned getMemInstAddressSpace(Value *I) {
|
|
assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
|
|
"Expected Load or Store instruction");
|
|
if (auto *LI = dyn_cast<LoadInst>(I))
|
|
return LI->getPointerAddressSpace();
|
|
return cast<StoreInst>(I)->getPointerAddressSpace();
|
|
}
|
|
|
|
/// A helper function that returns true if the given type is irregular. The
|
|
/// type is irregular if its allocated size doesn't equal the store size of an
|
|
/// element of the corresponding vector type at the given vectorization factor.
|
|
static bool hasIrregularType(Type *Ty, const DataLayout &DL, unsigned VF) {
|
|
|
|
// Determine if an array of VF elements of type Ty is "bitcast compatible"
|
|
// with a <VF x Ty> vector.
|
|
if (VF > 1) {
|
|
auto *VectorTy = VectorType::get(Ty, VF);
|
|
return VF * DL.getTypeAllocSize(Ty) != DL.getTypeStoreSize(VectorTy);
|
|
}
|
|
|
|
// If the vectorization factor is one, we just check if an array of type Ty
|
|
// requires padding between elements.
|
|
return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty);
|
|
}
|
|
|
|
/// A helper function that returns the reciprocal of the block probability of
|
|
/// predicated blocks. If we return X, we are assuming the predicated block
|
|
/// will execute once for for every X iterations of the loop header.
|
|
///
|
|
/// TODO: We should use actual block probability here, if available. Currently,
|
|
/// we always assume predicated blocks have a 50% chance of executing.
|
|
static unsigned getReciprocalPredBlockProb() { return 2; }
|
|
|
|
/// A helper function that adds a 'fast' flag to floating-point operations.
|
|
static Value *addFastMathFlag(Value *V) {
|
|
if (isa<FPMathOperator>(V)) {
|
|
FastMathFlags Flags;
|
|
Flags.setUnsafeAlgebra();
|
|
cast<Instruction>(V)->setFastMathFlags(Flags);
|
|
}
|
|
return V;
|
|
}
|
|
|
|
/// A helper function that returns an integer or floating-point constant with
|
|
/// value C.
|
|
static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) {
|
|
return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C)
|
|
: ConstantFP::get(Ty, C);
|
|
}
|
|
|
|
} // end anonymous namespace
|
|
|
|
namespace llvm {
|
|
/// InnerLoopVectorizer vectorizes loops which contain only one basic
|
|
/// block to a specified vectorization factor (VF).
|
|
/// This class performs the widening of scalars into vectors, or multiple
|
|
/// scalars. This class also implements the following features:
|
|
/// * It inserts an epilogue loop for handling loops that don't have iteration
|
|
/// counts that are known to be a multiple of the vectorization factor.
|
|
/// * It handles the code generation for reduction variables.
|
|
/// * Scalarization (implementation using scalars) of un-vectorizable
|
|
/// instructions.
|
|
/// InnerLoopVectorizer does not perform any vectorization-legality
|
|
/// checks, and relies on the caller to check for the different legality
|
|
/// aspects. The InnerLoopVectorizer relies on the
|
|
/// LoopVectorizationLegality class to provide information about the induction
|
|
/// and reduction variables that were found to a given vectorization factor.
|
|
class InnerLoopVectorizer {
|
|
public:
|
|
InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
|
|
LoopInfo *LI, DominatorTree *DT,
|
|
const TargetLibraryInfo *TLI,
|
|
const TargetTransformInfo *TTI, AssumptionCache *AC,
|
|
OptimizationRemarkEmitter *ORE, unsigned VecWidth,
|
|
unsigned UnrollFactor, LoopVectorizationLegality *LVL,
|
|
LoopVectorizationCostModel *CM)
|
|
: OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI),
|
|
AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor),
|
|
Builder(PSE.getSE()->getContext()), Induction(nullptr),
|
|
OldInduction(nullptr), VectorLoopValueMap(UnrollFactor, VecWidth),
|
|
TripCount(nullptr), VectorTripCount(nullptr), Legal(LVL), Cost(CM),
|
|
AddedSafetyChecks(false) {}
|
|
|
|
/// Create a new empty loop. Unlink the old loop and connect the new one.
|
|
/// Return the pre-header block of the new loop.
|
|
BasicBlock *createVectorizedLoopSkeleton();
|
|
|
|
/// Widen a single instruction within the innermost loop.
|
|
void widenInstruction(Instruction &I);
|
|
|
|
/// Fix the vectorized code, taking care of header phi's, live-outs, and more.
|
|
void fixVectorizedLoop();
|
|
|
|
// Return true if any runtime check is added.
|
|
bool areSafetyChecksAdded() { return AddedSafetyChecks; }
|
|
|
|
virtual ~InnerLoopVectorizer() {}
|
|
|
|
/// A type for vectorized values in the new loop. Each value from the
|
|
/// original loop, when vectorized, is represented by UF vector values in the
|
|
/// new unrolled loop, where UF is the unroll factor.
|
|
typedef SmallVector<Value *, 2> VectorParts;
|
|
|
|
/// A helper function that computes the predicate of the block BB, assuming
|
|
/// that the header block of the loop is set to True. It returns the *entry*
|
|
/// mask for the block BB.
|
|
VectorParts createBlockInMask(BasicBlock *BB);
|
|
|
|
/// Vectorize a single PHINode in a block. This method handles the induction
|
|
/// variable canonicalization. It supports both VF = 1 for unrolled loops and
|
|
/// arbitrary length vectors.
|
|
void widenPHIInstruction(Instruction *PN, unsigned UF, unsigned VF);
|
|
|
|
/// A helper function to scalarize a single Instruction in the innermost loop.
|
|
/// Generates a sequence of scalar instances for each lane between \p MinLane
|
|
/// and \p MaxLane, times each part between \p MinPart and \p MaxPart,
|
|
/// inclusive..
|
|
void scalarizeInstruction(Instruction *Instr, const VPIteration &Instance,
|
|
bool IfPredicateInstr);
|
|
|
|
/// Widen an integer or floating-point induction variable \p IV. If \p Trunc
|
|
/// is provided, the integer induction variable will first be truncated to
|
|
/// the corresponding type.
|
|
void widenIntOrFpInduction(PHINode *IV, TruncInst *Trunc = nullptr);
|
|
|
|
/// getOrCreateVectorValue and getOrCreateScalarValue coordinate to generate a
|
|
/// vector or scalar value on-demand if one is not yet available. When
|
|
/// vectorizing a loop, we visit the definition of an instruction before its
|
|
/// uses. When visiting the definition, we either vectorize or scalarize the
|
|
/// instruction, creating an entry for it in the corresponding map. (In some
|
|
/// cases, such as induction variables, we will create both vector and scalar
|
|
/// entries.) Then, as we encounter uses of the definition, we derive values
|
|
/// for each scalar or vector use unless such a value is already available.
|
|
/// For example, if we scalarize a definition and one of its uses is vector,
|
|
/// we build the required vector on-demand with an insertelement sequence
|
|
/// when visiting the use. Otherwise, if the use is scalar, we can use the
|
|
/// existing scalar definition.
|
|
///
|
|
/// Return a value in the new loop corresponding to \p V from the original
|
|
/// loop at unroll index \p Part. If the value has already been vectorized,
|
|
/// the corresponding vector entry in VectorLoopValueMap is returned. If,
|
|
/// however, the value has a scalar entry in VectorLoopValueMap, we construct
|
|
/// a new vector value on-demand by inserting the scalar values into a vector
|
|
/// with an insertelement sequence. If the value has been neither vectorized
|
|
/// nor scalarized, it must be loop invariant, so we simply broadcast the
|
|
/// value into a vector.
|
|
Value *getOrCreateVectorValue(Value *V, unsigned Part);
|
|
|
|
/// Return a value in the new loop corresponding to \p V from the original
|
|
/// loop at unroll and vector indices \p Instance. If the value has been
|
|
/// vectorized but not scalarized, the necessary extractelement instruction
|
|
/// will be generated.
|
|
Value *getOrCreateScalarValue(Value *V, const VPIteration &Instance);
|
|
|
|
/// Construct the vector value of a scalarized value \p V one lane at a time.
|
|
void packScalarIntoVectorValue(Value *V, const VPIteration &Instance);
|
|
|
|
/// Try to vectorize the interleaved access group that \p Instr belongs to.
|
|
void vectorizeInterleaveGroup(Instruction *Instr);
|
|
|
|
protected:
|
|
/// A small list of PHINodes.
|
|
typedef SmallVector<PHINode *, 4> PhiVector;
|
|
|
|
/// A type for scalarized values in the new loop. Each value from the
|
|
/// original loop, when scalarized, is represented by UF x VF scalar values
|
|
/// in the new unrolled loop, where UF is the unroll factor and VF is the
|
|
/// vectorization factor.
|
|
typedef SmallVector<SmallVector<Value *, 4>, 2> ScalarParts;
|
|
|
|
// When we if-convert we need to create edge masks. We have to cache values
|
|
// so that we don't end up with exponential recursion/IR.
|
|
typedef DenseMap<std::pair<BasicBlock *, BasicBlock *>, VectorParts>
|
|
EdgeMaskCacheTy;
|
|
typedef DenseMap<BasicBlock *, VectorParts> BlockMaskCacheTy;
|
|
|
|
/// Set up the values of the IVs correctly when exiting the vector loop.
|
|
void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II,
|
|
Value *CountRoundDown, Value *EndValue,
|
|
BasicBlock *MiddleBlock);
|
|
|
|
/// Create a new induction variable inside L.
|
|
PHINode *createInductionVariable(Loop *L, Value *Start, Value *End,
|
|
Value *Step, Instruction *DL);
|
|
|
|
/// Handle all cross-iteration phis in the header.
|
|
void fixCrossIterationPHIs();
|
|
|
|
/// Fix a first-order recurrence. This is the second phase of vectorizing
|
|
/// this phi node.
|
|
void fixFirstOrderRecurrence(PHINode *Phi);
|
|
|
|
/// Fix a reduction cross-iteration phi. This is the second phase of
|
|
/// vectorizing this phi node.
|
|
void fixReduction(PHINode *Phi);
|
|
|
|
/// \brief The Loop exit block may have single value PHI nodes with some
|
|
/// incoming value. While vectorizing we only handled real values
|
|
/// that were defined inside the loop and we should have one value for
|
|
/// each predecessor of its parent basic block. See PR14725.
|
|
void fixLCSSAPHIs();
|
|
|
|
/// Iteratively sink the scalarized operands of a predicated instruction into
|
|
/// the block that was created for it.
|
|
void sinkScalarOperands(Instruction *PredInst);
|
|
|
|
/// Shrinks vector element sizes to the smallest bitwidth they can be legally
|
|
/// represented as.
|
|
void truncateToMinimalBitwidths();
|
|
|
|
/// A helper function that computes the predicate of the edge between SRC
|
|
/// and DST.
|
|
VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst);
|
|
|
|
/// Insert the new loop to the loop hierarchy and pass manager
|
|
/// and update the analysis passes.
|
|
void updateAnalysis();
|
|
|
|
/// Vectorize Load and Store instructions,
|
|
virtual void vectorizeMemoryInstruction(Instruction *Instr);
|
|
|
|
/// Create a broadcast instruction. This method generates a broadcast
|
|
/// instruction (shuffle) for loop invariant values and for the induction
|
|
/// value. If this is the induction variable then we extend it to N, N+1, ...
|
|
/// this is needed because each iteration in the loop corresponds to a SIMD
|
|
/// element.
|
|
virtual Value *getBroadcastInstrs(Value *V);
|
|
|
|
/// This function adds (StartIdx, StartIdx + Step, StartIdx + 2*Step, ...)
|
|
/// to each vector element of Val. The sequence starts at StartIndex.
|
|
/// \p Opcode is relevant for FP induction variable.
|
|
virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step,
|
|
Instruction::BinaryOps Opcode =
|
|
Instruction::BinaryOpsEnd);
|
|
|
|
/// Compute scalar induction steps. \p ScalarIV is the scalar induction
|
|
/// variable on which to base the steps, \p Step is the size of the step, and
|
|
/// \p EntryVal is the value from the original loop that maps to the steps.
|
|
/// Note that \p EntryVal doesn't have to be an induction variable (e.g., it
|
|
/// can be a truncate instruction).
|
|
void buildScalarSteps(Value *ScalarIV, Value *Step, Value *EntryVal,
|
|
const InductionDescriptor &ID);
|
|
|
|
/// Create a vector induction phi node based on an existing scalar one. \p
|
|
/// EntryVal is the value from the original loop that maps to the vector phi
|
|
/// node, and \p Step is the loop-invariant step. If \p EntryVal is a
|
|
/// truncate instruction, instead of widening the original IV, we widen a
|
|
/// version of the IV truncated to \p EntryVal's type.
|
|
void createVectorIntOrFpInductionPHI(const InductionDescriptor &II,
|
|
Value *Step, Instruction *EntryVal);
|
|
|
|
/// Returns true if an instruction \p I should be scalarized instead of
|
|
/// vectorized for the chosen vectorization factor.
|
|
bool shouldScalarizeInstruction(Instruction *I) const;
|
|
|
|
/// Returns true if we should generate a scalar version of \p IV.
|
|
bool needsScalarInduction(Instruction *IV) const;
|
|
|
|
/// Generate a shuffle sequence that will reverse the vector Vec.
|
|
virtual Value *reverseVector(Value *Vec);
|
|
|
|
/// Returns (and creates if needed) the original loop trip count.
|
|
Value *getOrCreateTripCount(Loop *NewLoop);
|
|
|
|
/// Returns (and creates if needed) the trip count of the widened loop.
|
|
Value *getOrCreateVectorTripCount(Loop *NewLoop);
|
|
|
|
/// Returns a bitcasted value to the requested vector type.
|
|
/// Also handles bitcasts of vector<float> <-> vector<pointer> types.
|
|
Value *createBitOrPointerCast(Value *V, VectorType *DstVTy,
|
|
const DataLayout &DL);
|
|
|
|
/// Emit a bypass check to see if the vector trip count is zero, including if
|
|
/// it overflows.
|
|
void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass);
|
|
/// Emit a bypass check to see if all of the SCEV assumptions we've
|
|
/// had to make are correct.
|
|
void emitSCEVChecks(Loop *L, BasicBlock *Bypass);
|
|
/// Emit bypass checks to check any memory assumptions we may have made.
|
|
void emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass);
|
|
|
|
/// Add additional metadata to \p To that was not present on \p Orig.
|
|
///
|
|
/// Currently this is used to add the noalias annotations based on the
|
|
/// inserted memchecks. Use this for instructions that are *cloned* into the
|
|
/// vector loop.
|
|
void addNewMetadata(Instruction *To, const Instruction *Orig);
|
|
|
|
/// Add metadata from one instruction to another.
|
|
///
|
|
/// This includes both the original MDs from \p From and additional ones (\see
|
|
/// addNewMetadata). Use this for *newly created* instructions in the vector
|
|
/// loop.
|
|
void addMetadata(Instruction *To, Instruction *From);
|
|
|
|
/// \brief Similar to the previous function but it adds the metadata to a
|
|
/// vector of instructions.
|
|
void addMetadata(ArrayRef<Value *> To, Instruction *From);
|
|
|
|
/// \brief Set the debug location in the builder using the debug location in
|
|
/// the instruction.
|
|
void setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr);
|
|
|
|
/// The original loop.
|
|
Loop *OrigLoop;
|
|
/// A wrapper around ScalarEvolution used to add runtime SCEV checks. Applies
|
|
/// dynamic knowledge to simplify SCEV expressions and converts them to a
|
|
/// more usable form.
|
|
PredicatedScalarEvolution &PSE;
|
|
/// Loop Info.
|
|
LoopInfo *LI;
|
|
/// Dominator Tree.
|
|
DominatorTree *DT;
|
|
/// Alias Analysis.
|
|
AliasAnalysis *AA;
|
|
/// Target Library Info.
|
|
const TargetLibraryInfo *TLI;
|
|
/// Target Transform Info.
|
|
const TargetTransformInfo *TTI;
|
|
/// Assumption Cache.
|
|
AssumptionCache *AC;
|
|
/// Interface to emit optimization remarks.
|
|
OptimizationRemarkEmitter *ORE;
|
|
|
|
/// \brief LoopVersioning. It's only set up (non-null) if memchecks were
|
|
/// used.
|
|
///
|
|
/// This is currently only used to add no-alias metadata based on the
|
|
/// memchecks. The actually versioning is performed manually.
|
|
std::unique_ptr<LoopVersioning> LVer;
|
|
|
|
/// The vectorization SIMD factor to use. Each vector will have this many
|
|
/// vector elements.
|
|
unsigned VF;
|
|
|
|
/// The vectorization unroll factor to use. Each scalar is vectorized to this
|
|
/// many different vector instructions.
|
|
unsigned UF;
|
|
|
|
/// The builder that we use
|
|
IRBuilder<> Builder;
|
|
|
|
// --- Vectorization state ---
|
|
|
|
/// The vector-loop preheader.
|
|
BasicBlock *LoopVectorPreHeader;
|
|
/// The scalar-loop preheader.
|
|
BasicBlock *LoopScalarPreHeader;
|
|
/// Middle Block between the vector and the scalar.
|
|
BasicBlock *LoopMiddleBlock;
|
|
/// The ExitBlock of the scalar loop.
|
|
BasicBlock *LoopExitBlock;
|
|
/// The vector loop body.
|
|
BasicBlock *LoopVectorBody;
|
|
/// The scalar loop body.
|
|
BasicBlock *LoopScalarBody;
|
|
/// A list of all bypass blocks. The first block is the entry of the loop.
|
|
SmallVector<BasicBlock *, 4> LoopBypassBlocks;
|
|
|
|
/// The new Induction variable which was added to the new block.
|
|
PHINode *Induction;
|
|
/// The induction variable of the old basic block.
|
|
PHINode *OldInduction;
|
|
|
|
/// Maps values from the original loop to their corresponding values in the
|
|
/// vectorized loop. A key value can map to either vector values, scalar
|
|
/// values or both kinds of values, depending on whether the key was
|
|
/// vectorized and scalarized.
|
|
VectorizerValueMap VectorLoopValueMap;
|
|
|
|
/// Store instructions that were predicated.
|
|
SmallVector<Instruction *, 4> PredicatedInstructions;
|
|
EdgeMaskCacheTy EdgeMaskCache;
|
|
BlockMaskCacheTy BlockMaskCache;
|
|
/// Trip count of the original loop.
|
|
Value *TripCount;
|
|
/// Trip count of the widened loop (TripCount - TripCount % (VF*UF))
|
|
Value *VectorTripCount;
|
|
|
|
/// The legality analysis.
|
|
LoopVectorizationLegality *Legal;
|
|
|
|
/// The profitablity analysis.
|
|
LoopVectorizationCostModel *Cost;
|
|
|
|
// Record whether runtime checks are added.
|
|
bool AddedSafetyChecks;
|
|
|
|
// Holds the end values for each induction variable. We save the end values
|
|
// so we can later fix-up the external users of the induction variables.
|
|
DenseMap<PHINode *, Value *> IVEndValues;
|
|
|
|
friend class LoopVectorizationPlanner;
|
|
};
|
|
|
|
class InnerLoopUnroller : public InnerLoopVectorizer {
|
|
public:
|
|
InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE,
|
|
LoopInfo *LI, DominatorTree *DT,
|
|
const TargetLibraryInfo *TLI,
|
|
const TargetTransformInfo *TTI, AssumptionCache *AC,
|
|
OptimizationRemarkEmitter *ORE, unsigned UnrollFactor,
|
|
LoopVectorizationLegality *LVL,
|
|
LoopVectorizationCostModel *CM)
|
|
: InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, 1,
|
|
UnrollFactor, LVL, CM) {}
|
|
|
|
private:
|
|
Value *getBroadcastInstrs(Value *V) override;
|
|
Value *getStepVector(Value *Val, int StartIdx, Value *Step,
|
|
Instruction::BinaryOps Opcode =
|
|
Instruction::BinaryOpsEnd) override;
|
|
Value *reverseVector(Value *Vec) override;
|
|
};
|
|
|
|
/// \brief Look for a meaningful debug location on the instruction or it's
|
|
/// operands.
|
|
static Instruction *getDebugLocFromInstOrOperands(Instruction *I) {
|
|
if (!I)
|
|
return I;
|
|
|
|
DebugLoc Empty;
|
|
if (I->getDebugLoc() != Empty)
|
|
return I;
|
|
|
|
for (User::op_iterator OI = I->op_begin(), OE = I->op_end(); OI != OE; ++OI) {
|
|
if (Instruction *OpInst = dyn_cast<Instruction>(*OI))
|
|
if (OpInst->getDebugLoc() != Empty)
|
|
return OpInst;
|
|
}
|
|
|
|
return I;
|
|
}
|
|
|
|
void InnerLoopVectorizer::setDebugLocFromInst(IRBuilder<> &B, const Value *Ptr) {
|
|
if (const Instruction *Inst = dyn_cast_or_null<Instruction>(Ptr)) {
|
|
const DILocation *DIL = Inst->getDebugLoc();
|
|
if (DIL && Inst->getFunction()->isDebugInfoForProfiling())
|
|
B.SetCurrentDebugLocation(DIL->cloneWithDuplicationFactor(UF * VF));
|
|
else
|
|
B.SetCurrentDebugLocation(DIL);
|
|
} else
|
|
B.SetCurrentDebugLocation(DebugLoc());
|
|
}
|
|
|
|
#ifndef NDEBUG
|
|
/// \return string containing a file name and a line # for the given loop.
|
|
static std::string getDebugLocString(const Loop *L) {
|
|
std::string Result;
|
|
if (L) {
|
|
raw_string_ostream OS(Result);
|
|
if (const DebugLoc LoopDbgLoc = L->getStartLoc())
|
|
LoopDbgLoc.print(OS);
|
|
else
|
|
// Just print the module name.
|
|
OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier();
|
|
OS.flush();
|
|
}
|
|
return Result;
|
|
}
|
|
#endif
|
|
|
|
void InnerLoopVectorizer::addNewMetadata(Instruction *To,
|
|
const Instruction *Orig) {
|
|
// If the loop was versioned with memchecks, add the corresponding no-alias
|
|
// metadata.
|
|
if (LVer && (isa<LoadInst>(Orig) || isa<StoreInst>(Orig)))
|
|
LVer->annotateInstWithNoAlias(To, Orig);
|
|
}
|
|
|
|
void InnerLoopVectorizer::addMetadata(Instruction *To,
|
|
Instruction *From) {
|
|
propagateMetadata(To, From);
|
|
addNewMetadata(To, From);
|
|
}
|
|
|
|
void InnerLoopVectorizer::addMetadata(ArrayRef<Value *> To,
|
|
Instruction *From) {
|
|
for (Value *V : To) {
|
|
if (Instruction *I = dyn_cast<Instruction>(V))
|
|
addMetadata(I, From);
|
|
}
|
|
}
|
|
|
|
} // namespace llvm
|
|
|
|
namespace {
|
|
|
|
/// \brief The group of interleaved loads/stores sharing the same stride and
|
|
/// close to each other.
|
|
///
|
|
/// Each member in this group has an index starting from 0, and the largest
|
|
/// index should be less than interleaved factor, which is equal to the absolute
|
|
/// value of the access's stride.
|
|
///
|
|
/// E.g. An interleaved load group of factor 4:
|
|
/// for (unsigned i = 0; i < 1024; i+=4) {
|
|
/// a = A[i]; // Member of index 0
|
|
/// b = A[i+1]; // Member of index 1
|
|
/// d = A[i+3]; // Member of index 3
|
|
/// ...
|
|
/// }
|
|
///
|
|
/// An interleaved store group of factor 4:
|
|
/// for (unsigned i = 0; i < 1024; i+=4) {
|
|
/// ...
|
|
/// A[i] = a; // Member of index 0
|
|
/// A[i+1] = b; // Member of index 1
|
|
/// A[i+2] = c; // Member of index 2
|
|
/// A[i+3] = d; // Member of index 3
|
|
/// }
|
|
///
|
|
/// Note: the interleaved load group could have gaps (missing members), but
|
|
/// the interleaved store group doesn't allow gaps.
|
|
class InterleaveGroup {
|
|
public:
|
|
InterleaveGroup(Instruction *Instr, int Stride, unsigned Align)
|
|
: Align(Align), SmallestKey(0), LargestKey(0), InsertPos(Instr) {
|
|
assert(Align && "The alignment should be non-zero");
|
|
|
|
Factor = std::abs(Stride);
|
|
assert(Factor > 1 && "Invalid interleave factor");
|
|
|
|
Reverse = Stride < 0;
|
|
Members[0] = Instr;
|
|
}
|
|
|
|
bool isReverse() const { return Reverse; }
|
|
unsigned getFactor() const { return Factor; }
|
|
unsigned getAlignment() const { return Align; }
|
|
unsigned getNumMembers() const { return Members.size(); }
|
|
|
|
/// \brief Try to insert a new member \p Instr with index \p Index and
|
|
/// alignment \p NewAlign. The index is related to the leader and it could be
|
|
/// negative if it is the new leader.
|
|
///
|
|
/// \returns false if the instruction doesn't belong to the group.
|
|
bool insertMember(Instruction *Instr, int Index, unsigned NewAlign) {
|
|
assert(NewAlign && "The new member's alignment should be non-zero");
|
|
|
|
int Key = Index + SmallestKey;
|
|
|
|
// Skip if there is already a member with the same index.
|
|
if (Members.count(Key))
|
|
return false;
|
|
|
|
if (Key > LargestKey) {
|
|
// The largest index is always less than the interleave factor.
|
|
if (Index >= static_cast<int>(Factor))
|
|
return false;
|
|
|
|
LargestKey = Key;
|
|
} else if (Key < SmallestKey) {
|
|
// The largest index is always less than the interleave factor.
|
|
if (LargestKey - Key >= static_cast<int>(Factor))
|
|
return false;
|
|
|
|
SmallestKey = Key;
|
|
}
|
|
|
|
// It's always safe to select the minimum alignment.
|
|
Align = std::min(Align, NewAlign);
|
|
Members[Key] = Instr;
|
|
return true;
|
|
}
|
|
|
|
/// \brief Get the member with the given index \p Index
|
|
///
|
|
/// \returns nullptr if contains no such member.
|
|
Instruction *getMember(unsigned Index) const {
|
|
int Key = SmallestKey + Index;
|
|
if (!Members.count(Key))
|
|
return nullptr;
|
|
|
|
return Members.find(Key)->second;
|
|
}
|
|
|
|
/// \brief Get the index for the given member. Unlike the key in the member
|
|
/// map, the index starts from 0.
|
|
unsigned getIndex(Instruction *Instr) const {
|
|
for (auto I : Members)
|
|
if (I.second == Instr)
|
|
return I.first - SmallestKey;
|
|
|
|
llvm_unreachable("InterleaveGroup contains no such member");
|
|
}
|
|
|
|
Instruction *getInsertPos() const { return InsertPos; }
|
|
void setInsertPos(Instruction *Inst) { InsertPos = Inst; }
|
|
|
|
private:
|
|
unsigned Factor; // Interleave Factor.
|
|
bool Reverse;
|
|
unsigned Align;
|
|
DenseMap<int, Instruction *> Members;
|
|
int SmallestKey;
|
|
int LargestKey;
|
|
|
|
// To avoid breaking dependences, vectorized instructions of an interleave
|
|
// group should be inserted at either the first load or the last store in
|
|
// program order.
|
|
//
|
|
// E.g. %even = load i32 // Insert Position
|
|
// %add = add i32 %even // Use of %even
|
|
// %odd = load i32
|
|
//
|
|
// store i32 %even
|
|
// %odd = add i32 // Def of %odd
|
|
// store i32 %odd // Insert Position
|
|
Instruction *InsertPos;
|
|
};
|
|
|
|
/// \brief Drive the analysis of interleaved memory accesses in the loop.
|
|
///
|
|
/// Use this class to analyze interleaved accesses only when we can vectorize
|
|
/// a loop. Otherwise it's meaningless to do analysis as the vectorization
|
|
/// on interleaved accesses is unsafe.
|
|
///
|
|
/// The analysis collects interleave groups and records the relationships
|
|
/// between the member and the group in a map.
|
|
class InterleavedAccessInfo {
|
|
public:
|
|
InterleavedAccessInfo(PredicatedScalarEvolution &PSE, Loop *L,
|
|
DominatorTree *DT, LoopInfo *LI)
|
|
: PSE(PSE), TheLoop(L), DT(DT), LI(LI), LAI(nullptr),
|
|
RequiresScalarEpilogue(false) {}
|
|
|
|
~InterleavedAccessInfo() {
|
|
SmallSet<InterleaveGroup *, 4> DelSet;
|
|
// Avoid releasing a pointer twice.
|
|
for (auto &I : InterleaveGroupMap)
|
|
DelSet.insert(I.second);
|
|
for (auto *Ptr : DelSet)
|
|
delete Ptr;
|
|
}
|
|
|
|
/// \brief Analyze the interleaved accesses and collect them in interleave
|
|
/// groups. Substitute symbolic strides using \p Strides.
|
|
void analyzeInterleaving(const ValueToValueMap &Strides);
|
|
|
|
/// \brief Check if \p Instr belongs to any interleave group.
|
|
bool isInterleaved(Instruction *Instr) const {
|
|
return InterleaveGroupMap.count(Instr);
|
|
}
|
|
|
|
/// \brief Return the maximum interleave factor of all interleaved groups.
|
|
unsigned getMaxInterleaveFactor() const {
|
|
unsigned MaxFactor = 1;
|
|
for (auto &Entry : InterleaveGroupMap)
|
|
MaxFactor = std::max(MaxFactor, Entry.second->getFactor());
|
|
return MaxFactor;
|
|
}
|
|
|
|
/// \brief Get the interleave group that \p Instr belongs to.
|
|
///
|
|
/// \returns nullptr if doesn't have such group.
|
|
InterleaveGroup *getInterleaveGroup(Instruction *Instr) const {
|
|
if (InterleaveGroupMap.count(Instr))
|
|
return InterleaveGroupMap.find(Instr)->second;
|
|
return nullptr;
|
|
}
|
|
|
|
/// \brief Returns true if an interleaved group that may access memory
|
|
/// out-of-bounds requires a scalar epilogue iteration for correctness.
|
|
bool requiresScalarEpilogue() const { return RequiresScalarEpilogue; }
|
|
|
|
/// \brief Initialize the LoopAccessInfo used for dependence checking.
|
|
void setLAI(const LoopAccessInfo *Info) { LAI = Info; }
|
|
|
|
private:
|
|
/// A wrapper around ScalarEvolution, used to add runtime SCEV checks.
|
|
/// Simplifies SCEV expressions in the context of existing SCEV assumptions.
|
|
/// The interleaved access analysis can also add new predicates (for example
|
|
/// by versioning strides of pointers).
|
|
PredicatedScalarEvolution &PSE;
|
|
Loop *TheLoop;
|
|
DominatorTree *DT;
|
|
LoopInfo *LI;
|
|
const LoopAccessInfo *LAI;
|
|
|
|
/// True if the loop may contain non-reversed interleaved groups with
|
|
/// out-of-bounds accesses. We ensure we don't speculatively access memory
|
|
/// out-of-bounds by executing at least one scalar epilogue iteration.
|
|
bool RequiresScalarEpilogue;
|
|
|
|
/// Holds the relationships between the members and the interleave group.
|
|
DenseMap<Instruction *, InterleaveGroup *> InterleaveGroupMap;
|
|
|
|
/// Holds dependences among the memory accesses in the loop. It maps a source
|
|
/// access to a set of dependent sink accesses.
|
|
DenseMap<Instruction *, SmallPtrSet<Instruction *, 2>> Dependences;
|
|
|
|
/// \brief The descriptor for a strided memory access.
|
|
struct StrideDescriptor {
|
|
StrideDescriptor(int64_t Stride, const SCEV *Scev, uint64_t Size,
|
|
unsigned Align)
|
|
: Stride(Stride), Scev(Scev), Size(Size), Align(Align) {}
|
|
|
|
StrideDescriptor() = default;
|
|
|
|
// The access's stride. It is negative for a reverse access.
|
|
int64_t Stride = 0;
|
|
const SCEV *Scev = nullptr; // The scalar expression of this access
|
|
uint64_t Size = 0; // The size of the memory object.
|
|
unsigned Align = 0; // The alignment of this access.
|
|
};
|
|
|
|
/// \brief A type for holding instructions and their stride descriptors.
|
|
typedef std::pair<Instruction *, StrideDescriptor> StrideEntry;
|
|
|
|
/// \brief Create a new interleave group with the given instruction \p Instr,
|
|
/// stride \p Stride and alignment \p Align.
|
|
///
|
|
/// \returns the newly created interleave group.
|
|
InterleaveGroup *createInterleaveGroup(Instruction *Instr, int Stride,
|
|
unsigned Align) {
|
|
assert(!InterleaveGroupMap.count(Instr) &&
|
|
"Already in an interleaved access group");
|
|
InterleaveGroupMap[Instr] = new InterleaveGroup(Instr, Stride, Align);
|
|
return InterleaveGroupMap[Instr];
|
|
}
|
|
|
|
/// \brief Release the group and remove all the relationships.
|
|
void releaseGroup(InterleaveGroup *Group) {
|
|
for (unsigned i = 0; i < Group->getFactor(); i++)
|
|
if (Instruction *Member = Group->getMember(i))
|
|
InterleaveGroupMap.erase(Member);
|
|
|
|
delete Group;
|
|
}
|
|
|
|
/// \brief Collect all the accesses with a constant stride in program order.
|
|
void collectConstStrideAccesses(
|
|
MapVector<Instruction *, StrideDescriptor> &AccessStrideInfo,
|
|
const ValueToValueMap &Strides);
|
|
|
|
/// \brief Returns true if \p Stride is allowed in an interleaved group.
|
|
static bool isStrided(int Stride) {
|
|
unsigned Factor = std::abs(Stride);
|
|
return Factor >= 2 && Factor <= MaxInterleaveGroupFactor;
|
|
}
|
|
|
|
/// \brief Returns true if \p BB is a predicated block.
|
|
bool isPredicated(BasicBlock *BB) const {
|
|
return LoopAccessInfo::blockNeedsPredication(BB, TheLoop, DT);
|
|
}
|
|
|
|
/// \brief Returns true if LoopAccessInfo can be used for dependence queries.
|
|
bool areDependencesValid() const {
|
|
return LAI && LAI->getDepChecker().getDependences();
|
|
}
|
|
|
|
/// \brief Returns true if memory accesses \p A and \p B can be reordered, if
|
|
/// necessary, when constructing interleaved groups.
|
|
///
|
|
/// \p A must precede \p B in program order. We return false if reordering is
|
|
/// not necessary or is prevented because \p A and \p B may be dependent.
|
|
bool canReorderMemAccessesForInterleavedGroups(StrideEntry *A,
|
|
StrideEntry *B) const {
|
|
|
|
// Code motion for interleaved accesses can potentially hoist strided loads
|
|
// and sink strided stores. The code below checks the legality of the
|
|
// following two conditions:
|
|
//
|
|
// 1. Potentially moving a strided load (B) before any store (A) that
|
|
// precedes B, or
|
|
//
|
|
// 2. Potentially moving a strided store (A) after any load or store (B)
|
|
// that A precedes.
|
|
//
|
|
// It's legal to reorder A and B if we know there isn't a dependence from A
|
|
// to B. Note that this determination is conservative since some
|
|
// dependences could potentially be reordered safely.
|
|
|
|
// A is potentially the source of a dependence.
|
|
auto *Src = A->first;
|
|
auto SrcDes = A->second;
|
|
|
|
// B is potentially the sink of a dependence.
|
|
auto *Sink = B->first;
|
|
auto SinkDes = B->second;
|
|
|
|
// Code motion for interleaved accesses can't violate WAR dependences.
|
|
// Thus, reordering is legal if the source isn't a write.
|
|
if (!Src->mayWriteToMemory())
|
|
return true;
|
|
|
|
// At least one of the accesses must be strided.
|
|
if (!isStrided(SrcDes.Stride) && !isStrided(SinkDes.Stride))
|
|
return true;
|
|
|
|
// If dependence information is not available from LoopAccessInfo,
|
|
// conservatively assume the instructions can't be reordered.
|
|
if (!areDependencesValid())
|
|
return false;
|
|
|
|
// If we know there is a dependence from source to sink, assume the
|
|
// instructions can't be reordered. Otherwise, reordering is legal.
|
|
return !Dependences.count(Src) || !Dependences.lookup(Src).count(Sink);
|
|
}
|
|
|
|
/// \brief Collect the dependences from LoopAccessInfo.
|
|
///
|
|
/// We process the dependences once during the interleaved access analysis to
|
|
/// enable constant-time dependence queries.
|
|
void collectDependences() {
|
|
if (!areDependencesValid())
|
|
return;
|
|
auto *Deps = LAI->getDepChecker().getDependences();
|
|
for (auto Dep : *Deps)
|
|
Dependences[Dep.getSource(*LAI)].insert(Dep.getDestination(*LAI));
|
|
}
|
|
};
|
|
|
|
/// Utility class for getting and setting loop vectorizer hints in the form
|
|
/// of loop metadata.
|
|
/// This class keeps a number of loop annotations locally (as member variables)
|
|
/// and can, upon request, write them back as metadata on the loop. It will
|
|
/// initially scan the loop for existing metadata, and will update the local
|
|
/// values based on information in the loop.
|
|
/// We cannot write all values to metadata, as the mere presence of some info,
|
|
/// for example 'force', means a decision has been made. So, we need to be
|
|
/// careful NOT to add them if the user hasn't specifically asked so.
|
|
class LoopVectorizeHints {
|
|
enum HintKind { HK_WIDTH, HK_UNROLL, HK_FORCE, HK_ISVECTORIZED };
|
|
|
|
/// Hint - associates name and validation with the hint value.
|
|
struct Hint {
|
|
const char *Name;
|
|
unsigned Value; // This may have to change for non-numeric values.
|
|
HintKind Kind;
|
|
|
|
Hint(const char *Name, unsigned Value, HintKind Kind)
|
|
: Name(Name), Value(Value), Kind(Kind) {}
|
|
|
|
bool validate(unsigned Val) {
|
|
switch (Kind) {
|
|
case HK_WIDTH:
|
|
return isPowerOf2_32(Val) && Val <= VectorizerParams::MaxVectorWidth;
|
|
case HK_UNROLL:
|
|
return isPowerOf2_32(Val) && Val <= MaxInterleaveFactor;
|
|
case HK_FORCE:
|
|
return (Val <= 1);
|
|
case HK_ISVECTORIZED:
|
|
return (Val==0 || Val==1);
|
|
}
|
|
return false;
|
|
}
|
|
};
|
|
|
|
/// Vectorization width.
|
|
Hint Width;
|
|
/// Vectorization interleave factor.
|
|
Hint Interleave;
|
|
/// Vectorization forced
|
|
Hint Force;
|
|
|
|
/// Already Vectorized
|
|
Hint IsVectorized;
|
|
/// Return the loop metadata prefix.
|
|
static StringRef Prefix() { return "llvm.loop."; }
|
|
|
|
/// True if there is any unsafe math in the loop.
|
|
bool PotentiallyUnsafe;
|
|
|
|
public:
|
|
enum ForceKind {
|
|
FK_Undefined = -1, ///< Not selected.
|
|
FK_Disabled = 0, ///< Forcing disabled.
|
|
FK_Enabled = 1, ///< Forcing enabled.
|
|
};
|
|
|
|
LoopVectorizeHints(const Loop *L, bool DisableInterleaving,
|
|
OptimizationRemarkEmitter &ORE)
|
|
: Width("vectorize.width", VectorizerParams::VectorizationFactor,
|
|
HK_WIDTH),
|
|
Interleave("interleave.count", DisableInterleaving, HK_UNROLL),
|
|
Force("vectorize.enable", FK_Undefined, HK_FORCE),
|
|
IsVectorized("isvectorized", 0, HK_ISVECTORIZED),
|
|
PotentiallyUnsafe(false), TheLoop(L), ORE(ORE) {
|
|
// Populate values with existing loop metadata.
|
|
getHintsFromMetadata();
|
|
|
|
// force-vector-interleave overrides DisableInterleaving.
|
|
if (VectorizerParams::isInterleaveForced())
|
|
Interleave.Value = VectorizerParams::VectorizationInterleave;
|
|
|
|
if (IsVectorized.Value != 1)
|
|
// If the vectorization width and interleaving count are both 1 then
|
|
// consider the loop to have been already vectorized because there's
|
|
// nothing more that we can do.
|
|
IsVectorized.Value = Width.Value == 1 && Interleave.Value == 1;
|
|
DEBUG(if (DisableInterleaving && Interleave.Value == 1) dbgs()
|
|
<< "LV: Interleaving disabled by the pass manager\n");
|
|
}
|
|
|
|
/// Mark the loop L as already vectorized by setting the width to 1.
|
|
void setAlreadyVectorized() {
|
|
IsVectorized.Value = 1;
|
|
Hint Hints[] = {IsVectorized};
|
|
writeHintsToMetadata(Hints);
|
|
}
|
|
|
|
bool allowVectorization(Function *F, Loop *L, bool AlwaysVectorize) const {
|
|
if (getForce() == LoopVectorizeHints::FK_Disabled) {
|
|
DEBUG(dbgs() << "LV: Not vectorizing: #pragma vectorize disable.\n");
|
|
emitRemarkWithHints();
|
|
return false;
|
|
}
|
|
|
|
if (!AlwaysVectorize && getForce() != LoopVectorizeHints::FK_Enabled) {
|
|
DEBUG(dbgs() << "LV: Not vectorizing: No #pragma vectorize enable.\n");
|
|
emitRemarkWithHints();
|
|
return false;
|
|
}
|
|
|
|
if (getIsVectorized() == 1) {
|
|
DEBUG(dbgs() << "LV: Not vectorizing: Disabled/already vectorized.\n");
|
|
// FIXME: Add interleave.disable metadata. This will allow
|
|
// vectorize.disable to be used without disabling the pass and errors
|
|
// to differentiate between disabled vectorization and a width of 1.
|
|
ORE.emit(OptimizationRemarkAnalysis(vectorizeAnalysisPassName(),
|
|
"AllDisabled", L->getStartLoc(),
|
|
L->getHeader())
|
|
<< "loop not vectorized: vectorization and interleaving are "
|
|
"explicitly disabled, or the loop has already been "
|
|
"vectorized");
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
/// Dumps all the hint information.
|
|
void emitRemarkWithHints() const {
|
|
using namespace ore;
|
|
if (Force.Value == LoopVectorizeHints::FK_Disabled)
|
|
ORE.emit(OptimizationRemarkMissed(LV_NAME, "MissedExplicitlyDisabled",
|
|
TheLoop->getStartLoc(),
|
|
TheLoop->getHeader())
|
|
<< "loop not vectorized: vectorization is explicitly disabled");
|
|
else {
|
|
OptimizationRemarkMissed R(LV_NAME, "MissedDetails",
|
|
TheLoop->getStartLoc(), TheLoop->getHeader());
|
|
R << "loop not vectorized";
|
|
if (Force.Value == LoopVectorizeHints::FK_Enabled) {
|
|
R << " (Force=" << NV("Force", true);
|
|
if (Width.Value != 0)
|
|
R << ", Vector Width=" << NV("VectorWidth", Width.Value);
|
|
if (Interleave.Value != 0)
|
|
R << ", Interleave Count=" << NV("InterleaveCount", Interleave.Value);
|
|
R << ")";
|
|
}
|
|
ORE.emit(R);
|
|
}
|
|
}
|
|
|
|
unsigned getWidth() const { return Width.Value; }
|
|
unsigned getInterleave() const { return Interleave.Value; }
|
|
unsigned getIsVectorized() const { return IsVectorized.Value; }
|
|
enum ForceKind getForce() const { return (ForceKind)Force.Value; }
|
|
|
|
/// \brief If hints are provided that force vectorization, use the AlwaysPrint
|
|
/// pass name to force the frontend to print the diagnostic.
|
|
const char *vectorizeAnalysisPassName() const {
|
|
if (getWidth() == 1)
|
|
return LV_NAME;
|
|
if (getForce() == LoopVectorizeHints::FK_Disabled)
|
|
return LV_NAME;
|
|
if (getForce() == LoopVectorizeHints::FK_Undefined && getWidth() == 0)
|
|
return LV_NAME;
|
|
return OptimizationRemarkAnalysis::AlwaysPrint;
|
|
}
|
|
|
|
bool allowReordering() const {
|
|
// When enabling loop hints are provided we allow the vectorizer to change
|
|
// the order of operations that is given by the scalar loop. This is not
|
|
// enabled by default because can be unsafe or inefficient. For example,
|
|
// reordering floating-point operations will change the way round-off
|
|
// error accumulates in the loop.
|
|
return getForce() == LoopVectorizeHints::FK_Enabled || getWidth() > 1;
|
|
}
|
|
|
|
bool isPotentiallyUnsafe() const {
|
|
// Avoid FP vectorization if the target is unsure about proper support.
|
|
// This may be related to the SIMD unit in the target not handling
|
|
// IEEE 754 FP ops properly, or bad single-to-double promotions.
|
|
// Otherwise, a sequence of vectorized loops, even without reduction,
|
|
// could lead to different end results on the destination vectors.
|
|
return getForce() != LoopVectorizeHints::FK_Enabled && PotentiallyUnsafe;
|
|
}
|
|
|
|
void setPotentiallyUnsafe() { PotentiallyUnsafe = true; }
|
|
|
|
private:
|
|
/// Find hints specified in the loop metadata and update local values.
|
|
void getHintsFromMetadata() {
|
|
MDNode *LoopID = TheLoop->getLoopID();
|
|
if (!LoopID)
|
|
return;
|
|
|
|
// First operand should refer to the loop id itself.
|
|
assert(LoopID->getNumOperands() > 0 && "requires at least one operand");
|
|
assert(LoopID->getOperand(0) == LoopID && "invalid loop id");
|
|
|
|
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
|
|
const MDString *S = nullptr;
|
|
SmallVector<Metadata *, 4> Args;
|
|
|
|
// The expected hint is either a MDString or a MDNode with the first
|
|
// operand a MDString.
|
|
if (const MDNode *MD = dyn_cast<MDNode>(LoopID->getOperand(i))) {
|
|
if (!MD || MD->getNumOperands() == 0)
|
|
continue;
|
|
S = dyn_cast<MDString>(MD->getOperand(0));
|
|
for (unsigned i = 1, ie = MD->getNumOperands(); i < ie; ++i)
|
|
Args.push_back(MD->getOperand(i));
|
|
} else {
|
|
S = dyn_cast<MDString>(LoopID->getOperand(i));
|
|
assert(Args.size() == 0 && "too many arguments for MDString");
|
|
}
|
|
|
|
if (!S)
|
|
continue;
|
|
|
|
// Check if the hint starts with the loop metadata prefix.
|
|
StringRef Name = S->getString();
|
|
if (Args.size() == 1)
|
|
setHint(Name, Args[0]);
|
|
}
|
|
}
|
|
|
|
/// Checks string hint with one operand and set value if valid.
|
|
void setHint(StringRef Name, Metadata *Arg) {
|
|
if (!Name.startswith(Prefix()))
|
|
return;
|
|
Name = Name.substr(Prefix().size(), StringRef::npos);
|
|
|
|
const ConstantInt *C = mdconst::dyn_extract<ConstantInt>(Arg);
|
|
if (!C)
|
|
return;
|
|
unsigned Val = C->getZExtValue();
|
|
|
|
Hint *Hints[] = {&Width, &Interleave, &Force, &IsVectorized};
|
|
for (auto H : Hints) {
|
|
if (Name == H->Name) {
|
|
if (H->validate(Val))
|
|
H->Value = Val;
|
|
else
|
|
DEBUG(dbgs() << "LV: ignoring invalid hint '" << Name << "'\n");
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Create a new hint from name / value pair.
|
|
MDNode *createHintMetadata(StringRef Name, unsigned V) const {
|
|
LLVMContext &Context = TheLoop->getHeader()->getContext();
|
|
Metadata *MDs[] = {MDString::get(Context, Name),
|
|
ConstantAsMetadata::get(
|
|
ConstantInt::get(Type::getInt32Ty(Context), V))};
|
|
return MDNode::get(Context, MDs);
|
|
}
|
|
|
|
/// Matches metadata with hint name.
|
|
bool matchesHintMetadataName(MDNode *Node, ArrayRef<Hint> HintTypes) {
|
|
MDString *Name = dyn_cast<MDString>(Node->getOperand(0));
|
|
if (!Name)
|
|
return false;
|
|
|
|
for (auto H : HintTypes)
|
|
if (Name->getString().endswith(H.Name))
|
|
return true;
|
|
return false;
|
|
}
|
|
|
|
/// Sets current hints into loop metadata, keeping other values intact.
|
|
void writeHintsToMetadata(ArrayRef<Hint> HintTypes) {
|
|
if (HintTypes.size() == 0)
|
|
return;
|
|
|
|
// Reserve the first element to LoopID (see below).
|
|
SmallVector<Metadata *, 4> MDs(1);
|
|
// If the loop already has metadata, then ignore the existing operands.
|
|
MDNode *LoopID = TheLoop->getLoopID();
|
|
if (LoopID) {
|
|
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
|
|
MDNode *Node = cast<MDNode>(LoopID->getOperand(i));
|
|
// If node in update list, ignore old value.
|
|
if (!matchesHintMetadataName(Node, HintTypes))
|
|
MDs.push_back(Node);
|
|
}
|
|
}
|
|
|
|
// Now, add the missing hints.
|
|
for (auto H : HintTypes)
|
|
MDs.push_back(createHintMetadata(Twine(Prefix(), H.Name).str(), H.Value));
|
|
|
|
// Replace current metadata node with new one.
|
|
LLVMContext &Context = TheLoop->getHeader()->getContext();
|
|
MDNode *NewLoopID = MDNode::get(Context, MDs);
|
|
// Set operand 0 to refer to the loop id itself.
|
|
NewLoopID->replaceOperandWith(0, NewLoopID);
|
|
|
|
TheLoop->setLoopID(NewLoopID);
|
|
}
|
|
|
|
/// The loop these hints belong to.
|
|
const Loop *TheLoop;
|
|
|
|
/// Interface to emit optimization remarks.
|
|
OptimizationRemarkEmitter &ORE;
|
|
};
|
|
|
|
static void emitMissedWarning(Function *F, Loop *L,
|
|
const LoopVectorizeHints &LH,
|
|
OptimizationRemarkEmitter *ORE) {
|
|
LH.emitRemarkWithHints();
|
|
|
|
if (LH.getForce() == LoopVectorizeHints::FK_Enabled) {
|
|
if (LH.getWidth() != 1)
|
|
ORE->emit(DiagnosticInfoOptimizationFailure(
|
|
DEBUG_TYPE, "FailedRequestedVectorization",
|
|
L->getStartLoc(), L->getHeader())
|
|
<< "loop not vectorized: "
|
|
<< "failed explicitly specified loop vectorization");
|
|
else if (LH.getInterleave() != 1)
|
|
ORE->emit(DiagnosticInfoOptimizationFailure(
|
|
DEBUG_TYPE, "FailedRequestedInterleaving", L->getStartLoc(),
|
|
L->getHeader())
|
|
<< "loop not interleaved: "
|
|
<< "failed explicitly specified loop interleaving");
|
|
}
|
|
}
|
|
|
|
/// LoopVectorizationLegality checks if it is legal to vectorize a loop, and
|
|
/// to what vectorization factor.
|
|
/// This class does not look at the profitability of vectorization, only the
|
|
/// legality. This class has two main kinds of checks:
|
|
/// * Memory checks - The code in canVectorizeMemory checks if vectorization
|
|
/// will change the order of memory accesses in a way that will change the
|
|
/// correctness of the program.
|
|
/// * Scalars checks - The code in canVectorizeInstrs and canVectorizeMemory
|
|
/// checks for a number of different conditions, such as the availability of a
|
|
/// single induction variable, that all types are supported and vectorize-able,
|
|
/// etc. This code reflects the capabilities of InnerLoopVectorizer.
|
|
/// This class is also used by InnerLoopVectorizer for identifying
|
|
/// induction variable and the different reduction variables.
|
|
class LoopVectorizationLegality {
|
|
public:
|
|
LoopVectorizationLegality(
|
|
Loop *L, PredicatedScalarEvolution &PSE, DominatorTree *DT,
|
|
TargetLibraryInfo *TLI, AliasAnalysis *AA, Function *F,
|
|
const TargetTransformInfo *TTI,
|
|
std::function<const LoopAccessInfo &(Loop &)> *GetLAA, LoopInfo *LI,
|
|
OptimizationRemarkEmitter *ORE, LoopVectorizationRequirements *R,
|
|
LoopVectorizeHints *H)
|
|
: NumPredStores(0), TheLoop(L), PSE(PSE), TLI(TLI), TTI(TTI), DT(DT),
|
|
GetLAA(GetLAA), LAI(nullptr), ORE(ORE), InterleaveInfo(PSE, L, DT, LI),
|
|
PrimaryInduction(nullptr), WidestIndTy(nullptr), HasFunNoNaNAttr(false),
|
|
Requirements(R), Hints(H) {}
|
|
|
|
/// ReductionList contains the reduction descriptors for all
|
|
/// of the reductions that were found in the loop.
|
|
typedef DenseMap<PHINode *, RecurrenceDescriptor> ReductionList;
|
|
|
|
/// InductionList saves induction variables and maps them to the
|
|
/// induction descriptor.
|
|
typedef MapVector<PHINode *, InductionDescriptor> InductionList;
|
|
|
|
/// RecurrenceSet contains the phi nodes that are recurrences other than
|
|
/// inductions and reductions.
|
|
typedef SmallPtrSet<const PHINode *, 8> RecurrenceSet;
|
|
|
|
/// Returns true if it is legal to vectorize this loop.
|
|
/// This does not mean that it is profitable to vectorize this
|
|
/// loop, only that it is legal to do so.
|
|
bool canVectorize();
|
|
|
|
/// Returns the primary induction variable.
|
|
PHINode *getPrimaryInduction() { return PrimaryInduction; }
|
|
|
|
/// Returns the reduction variables found in the loop.
|
|
ReductionList *getReductionVars() { return &Reductions; }
|
|
|
|
/// Returns the induction variables found in the loop.
|
|
InductionList *getInductionVars() { return &Inductions; }
|
|
|
|
/// Return the first-order recurrences found in the loop.
|
|
RecurrenceSet *getFirstOrderRecurrences() { return &FirstOrderRecurrences; }
|
|
|
|
/// Return the set of instructions to sink to handle first-order recurrences.
|
|
DenseMap<Instruction *, Instruction *> &getSinkAfter() { return SinkAfter; }
|
|
|
|
/// Returns the widest induction type.
|
|
Type *getWidestInductionType() { return WidestIndTy; }
|
|
|
|
/// Returns True if V is an induction variable in this loop.
|
|
bool isInductionVariable(const Value *V);
|
|
|
|
/// Returns True if PN is a reduction variable in this loop.
|
|
bool isReductionVariable(PHINode *PN) { return Reductions.count(PN); }
|
|
|
|
/// Returns True if Phi is a first-order recurrence in this loop.
|
|
bool isFirstOrderRecurrence(const PHINode *Phi);
|
|
|
|
/// Return true if the block BB needs to be predicated in order for the loop
|
|
/// to be vectorized.
|
|
bool blockNeedsPredication(BasicBlock *BB);
|
|
|
|
/// Check if this pointer is consecutive when vectorizing. This happens
|
|
/// when the last index of the GEP is the induction variable, or that the
|
|
/// pointer itself is an induction variable.
|
|
/// This check allows us to vectorize A[idx] into a wide load/store.
|
|
/// Returns:
|
|
/// 0 - Stride is unknown or non-consecutive.
|
|
/// 1 - Address is consecutive.
|
|
/// -1 - Address is consecutive, and decreasing.
|
|
int isConsecutivePtr(Value *Ptr);
|
|
|
|
/// Returns true if the value V is uniform within the loop.
|
|
bool isUniform(Value *V);
|
|
|
|
/// Returns the information that we collected about runtime memory check.
|
|
const RuntimePointerChecking *getRuntimePointerChecking() const {
|
|
return LAI->getRuntimePointerChecking();
|
|
}
|
|
|
|
const LoopAccessInfo *getLAI() const { return LAI; }
|
|
|
|
/// \brief Check if \p Instr belongs to any interleaved access group.
|
|
bool isAccessInterleaved(Instruction *Instr) {
|
|
return InterleaveInfo.isInterleaved(Instr);
|
|
}
|
|
|
|
/// \brief Return the maximum interleave factor of all interleaved groups.
|
|
unsigned getMaxInterleaveFactor() const {
|
|
return InterleaveInfo.getMaxInterleaveFactor();
|
|
}
|
|
|
|
/// \brief Get the interleaved access group that \p Instr belongs to.
|
|
const InterleaveGroup *getInterleavedAccessGroup(Instruction *Instr) {
|
|
return InterleaveInfo.getInterleaveGroup(Instr);
|
|
}
|
|
|
|
/// \brief Returns true if an interleaved group requires a scalar iteration
|
|
/// to handle accesses with gaps.
|
|
bool requiresScalarEpilogue() const {
|
|
return InterleaveInfo.requiresScalarEpilogue();
|
|
}
|
|
|
|
unsigned getMaxSafeDepDistBytes() { return LAI->getMaxSafeDepDistBytes(); }
|
|
|
|
bool hasStride(Value *V) { return LAI->hasStride(V); }
|
|
|
|
/// Returns true if the target machine supports masked store operation
|
|
/// for the given \p DataType and kind of access to \p Ptr.
|
|
bool isLegalMaskedStore(Type *DataType, Value *Ptr) {
|
|
return isConsecutivePtr(Ptr) && TTI->isLegalMaskedStore(DataType);
|
|
}
|
|
/// Returns true if the target machine supports masked load operation
|
|
/// for the given \p DataType and kind of access to \p Ptr.
|
|
bool isLegalMaskedLoad(Type *DataType, Value *Ptr) {
|
|
return isConsecutivePtr(Ptr) && TTI->isLegalMaskedLoad(DataType);
|
|
}
|
|
/// Returns true if the target machine supports masked scatter operation
|
|
/// for the given \p DataType.
|
|
bool isLegalMaskedScatter(Type *DataType) {
|
|
return TTI->isLegalMaskedScatter(DataType);
|
|
}
|
|
/// Returns true if the target machine supports masked gather operation
|
|
/// for the given \p DataType.
|
|
bool isLegalMaskedGather(Type *DataType) {
|
|
return TTI->isLegalMaskedGather(DataType);
|
|
}
|
|
/// Returns true if the target machine can represent \p V as a masked gather
|
|
/// or scatter operation.
|
|
bool isLegalGatherOrScatter(Value *V) {
|
|
auto *LI = dyn_cast<LoadInst>(V);
|
|
auto *SI = dyn_cast<StoreInst>(V);
|
|
if (!LI && !SI)
|
|
return false;
|
|
auto *Ptr = getPointerOperand(V);
|
|
auto *Ty = cast<PointerType>(Ptr->getType())->getElementType();
|
|
return (LI && isLegalMaskedGather(Ty)) || (SI && isLegalMaskedScatter(Ty));
|
|
}
|
|
|
|
/// Returns true if vector representation of the instruction \p I
|
|
/// requires mask.
|
|
bool isMaskRequired(const Instruction *I) { return (MaskedOp.count(I) != 0); }
|
|
unsigned getNumStores() const { return LAI->getNumStores(); }
|
|
unsigned getNumLoads() const { return LAI->getNumLoads(); }
|
|
unsigned getNumPredStores() const { return NumPredStores; }
|
|
|
|
/// Returns true if \p I is an instruction that will be scalarized with
|
|
/// predication. Such instructions include conditional stores and
|
|
/// instructions that may divide by zero.
|
|
bool isScalarWithPredication(Instruction *I);
|
|
|
|
/// Returns true if \p I is a memory instruction with consecutive memory
|
|
/// access that can be widened.
|
|
bool memoryInstructionCanBeWidened(Instruction *I, unsigned VF = 1);
|
|
|
|
// Returns true if the NoNaN attribute is set on the function.
|
|
bool hasFunNoNaNAttr() const { return HasFunNoNaNAttr; }
|
|
|
|
private:
|
|
/// Check if a single basic block loop is vectorizable.
|
|
/// At this point we know that this is a loop with a constant trip count
|
|
/// and we only need to check individual instructions.
|
|
bool canVectorizeInstrs();
|
|
|
|
/// When we vectorize loops we may change the order in which
|
|
/// we read and write from memory. This method checks if it is
|
|
/// legal to vectorize the code, considering only memory constrains.
|
|
/// Returns true if the loop is vectorizable
|
|
bool canVectorizeMemory();
|
|
|
|
/// Return true if we can vectorize this loop using the IF-conversion
|
|
/// transformation.
|
|
bool canVectorizeWithIfConvert();
|
|
|
|
/// Return true if all of the instructions in the block can be speculatively
|
|
/// executed. \p SafePtrs is a list of addresses that are known to be legal
|
|
/// and we know that we can read from them without segfault.
|
|
bool blockCanBePredicated(BasicBlock *BB, SmallPtrSetImpl<Value *> &SafePtrs);
|
|
|
|
/// Updates the vectorization state by adding \p Phi to the inductions list.
|
|
/// This can set \p Phi as the main induction of the loop if \p Phi is a
|
|
/// better choice for the main induction than the existing one.
|
|
void addInductionPhi(PHINode *Phi, const InductionDescriptor &ID,
|
|
SmallPtrSetImpl<Value *> &AllowedExit);
|
|
|
|
/// Create an analysis remark that explains why vectorization failed
|
|
///
|
|
/// \p RemarkName is the identifier for the remark. If \p I is passed it is
|
|
/// an instruction that prevents vectorization. Otherwise the loop is used
|
|
/// for the location of the remark. \return the remark object that can be
|
|
/// streamed to.
|
|
OptimizationRemarkAnalysis
|
|
createMissedAnalysis(StringRef RemarkName, Instruction *I = nullptr) const {
|
|
return ::createMissedAnalysis(Hints->vectorizeAnalysisPassName(),
|
|
RemarkName, TheLoop, I);
|
|
}
|
|
|
|
/// \brief If an access has a symbolic strides, this maps the pointer value to
|
|
/// the stride symbol.
|
|
const ValueToValueMap *getSymbolicStrides() {
|
|
// FIXME: Currently, the set of symbolic strides is sometimes queried before
|
|
// it's collected. This happens from canVectorizeWithIfConvert, when the
|
|
// pointer is checked to reference consecutive elements suitable for a
|
|
// masked access.
|
|
return LAI ? &LAI->getSymbolicStrides() : nullptr;
|
|
}
|
|
|
|
unsigned NumPredStores;
|
|
|
|
/// The loop that we evaluate.
|
|
Loop *TheLoop;
|
|
/// A wrapper around ScalarEvolution used to add runtime SCEV checks.
|
|
/// Applies dynamic knowledge to simplify SCEV expressions in the context
|
|
/// of existing SCEV assumptions. The analysis will also add a minimal set
|
|
/// of new predicates if this is required to enable vectorization and
|
|
/// unrolling.
|
|
PredicatedScalarEvolution &PSE;
|
|
/// Target Library Info.
|
|
TargetLibraryInfo *TLI;
|
|
/// Target Transform Info
|
|
const TargetTransformInfo *TTI;
|
|
/// Dominator Tree.
|
|
DominatorTree *DT;
|
|
// LoopAccess analysis.
|
|
std::function<const LoopAccessInfo &(Loop &)> *GetLAA;
|
|
// And the loop-accesses info corresponding to this loop. This pointer is
|
|
// null until canVectorizeMemory sets it up.
|
|
const LoopAccessInfo *LAI;
|
|
/// Interface to emit optimization remarks.
|
|
OptimizationRemarkEmitter *ORE;
|
|
|
|
/// The interleave access information contains groups of interleaved accesses
|
|
/// with the same stride and close to each other.
|
|
InterleavedAccessInfo InterleaveInfo;
|
|
|
|
// --- vectorization state --- //
|
|
|
|
/// Holds the primary induction variable. This is the counter of the
|
|
/// loop.
|
|
PHINode *PrimaryInduction;
|
|
/// Holds the reduction variables.
|
|
ReductionList Reductions;
|
|
/// Holds all of the induction variables that we found in the loop.
|
|
/// Notice that inductions don't need to start at zero and that induction
|
|
/// variables can be pointers.
|
|
InductionList Inductions;
|
|
/// Holds the phi nodes that are first-order recurrences.
|
|
RecurrenceSet FirstOrderRecurrences;
|
|
/// Holds instructions that need to sink past other instructions to handle
|
|
/// first-order recurrences.
|
|
DenseMap<Instruction *, Instruction *> SinkAfter;
|
|
/// Holds the widest induction type encountered.
|
|
Type *WidestIndTy;
|
|
|
|
/// Allowed outside users. This holds the induction and reduction
|
|
/// vars which can be accessed from outside the loop.
|
|
SmallPtrSet<Value *, 4> AllowedExit;
|
|
|
|
/// Can we assume the absence of NaNs.
|
|
bool HasFunNoNaNAttr;
|
|
|
|
/// Vectorization requirements that will go through late-evaluation.
|
|
LoopVectorizationRequirements *Requirements;
|
|
|
|
/// Used to emit an analysis of any legality issues.
|
|
LoopVectorizeHints *Hints;
|
|
|
|
/// While vectorizing these instructions we have to generate a
|
|
/// call to the appropriate masked intrinsic
|
|
SmallPtrSet<const Instruction *, 8> MaskedOp;
|
|
};
|
|
|
|
/// LoopVectorizationCostModel - estimates the expected speedups due to
|
|
/// vectorization.
|
|
/// In many cases vectorization is not profitable. This can happen because of
|
|
/// a number of reasons. In this class we mainly attempt to predict the
|
|
/// expected speedup/slowdowns due to the supported instruction set. We use the
|
|
/// TargetTransformInfo to query the different backends for the cost of
|
|
/// different operations.
|
|
class LoopVectorizationCostModel {
|
|
public:
|
|
LoopVectorizationCostModel(Loop *L, PredicatedScalarEvolution &PSE,
|
|
LoopInfo *LI, LoopVectorizationLegality *Legal,
|
|
const TargetTransformInfo &TTI,
|
|
const TargetLibraryInfo *TLI, DemandedBits *DB,
|
|
AssumptionCache *AC,
|
|
OptimizationRemarkEmitter *ORE, const Function *F,
|
|
const LoopVectorizeHints *Hints)
|
|
: TheLoop(L), PSE(PSE), LI(LI), Legal(Legal), TTI(TTI), TLI(TLI), DB(DB),
|
|
AC(AC), ORE(ORE), TheFunction(F), Hints(Hints) {}
|
|
|
|
/// \return An upper bound for the vectorization factor, or None if
|
|
/// vectorization should be avoided up front.
|
|
Optional<unsigned> computeMaxVF(bool OptForSize);
|
|
|
|
/// Information about vectorization costs
|
|
struct VectorizationFactor {
|
|
unsigned Width; // Vector width with best cost
|
|
unsigned Cost; // Cost of the loop with that width
|
|
};
|
|
/// \return The most profitable vectorization factor and the cost of that VF.
|
|
/// This method checks every power of two up to MaxVF. If UserVF is not ZERO
|
|
/// then this vectorization factor will be selected if vectorization is
|
|
/// possible.
|
|
VectorizationFactor selectVectorizationFactor(unsigned MaxVF);
|
|
|
|
/// Setup cost-based decisions for user vectorization factor.
|
|
void selectUserVectorizationFactor(unsigned UserVF) {
|
|
collectUniformsAndScalars(UserVF);
|
|
collectInstsToScalarize(UserVF);
|
|
}
|
|
|
|
/// \return The size (in bits) of the smallest and widest types in the code
|
|
/// that needs to be vectorized. We ignore values that remain scalar such as
|
|
/// 64 bit loop indices.
|
|
std::pair<unsigned, unsigned> getSmallestAndWidestTypes();
|
|
|
|
/// \return The desired interleave count.
|
|
/// If interleave count has been specified by metadata it will be returned.
|
|
/// Otherwise, the interleave count is computed and returned. VF and LoopCost
|
|
/// are the selected vectorization factor and the cost of the selected VF.
|
|
unsigned selectInterleaveCount(bool OptForSize, unsigned VF,
|
|
unsigned LoopCost);
|
|
|
|
/// Memory access instruction may be vectorized in more than one way.
|
|
/// Form of instruction after vectorization depends on cost.
|
|
/// This function takes cost-based decisions for Load/Store instructions
|
|
/// and collects them in a map. This decisions map is used for building
|
|
/// the lists of loop-uniform and loop-scalar instructions.
|
|
/// The calculated cost is saved with widening decision in order to
|
|
/// avoid redundant calculations.
|
|
void setCostBasedWideningDecision(unsigned VF);
|
|
|
|
/// \brief A struct that represents some properties of the register usage
|
|
/// of a loop.
|
|
struct RegisterUsage {
|
|
/// Holds the number of loop invariant values that are used in the loop.
|
|
unsigned LoopInvariantRegs;
|
|
/// Holds the maximum number of concurrent live intervals in the loop.
|
|
unsigned MaxLocalUsers;
|
|
/// Holds the number of instructions in the loop.
|
|
unsigned NumInstructions;
|
|
};
|
|
|
|
/// \return Returns information about the register usages of the loop for the
|
|
/// given vectorization factors.
|
|
SmallVector<RegisterUsage, 8> calculateRegisterUsage(ArrayRef<unsigned> VFs);
|
|
|
|
/// Collect values we want to ignore in the cost model.
|
|
void collectValuesToIgnore();
|
|
|
|
/// \returns The smallest bitwidth each instruction can be represented with.
|
|
/// The vector equivalents of these instructions should be truncated to this
|
|
/// type.
|
|
const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const {
|
|
return MinBWs;
|
|
}
|
|
|
|
/// \returns True if it is more profitable to scalarize instruction \p I for
|
|
/// vectorization factor \p VF.
|
|
bool isProfitableToScalarize(Instruction *I, unsigned VF) const {
|
|
assert(VF > 1 && "Profitable to scalarize relevant only for VF > 1.");
|
|
auto Scalars = InstsToScalarize.find(VF);
|
|
assert(Scalars != InstsToScalarize.end() &&
|
|
"VF not yet analyzed for scalarization profitability");
|
|
return Scalars->second.count(I);
|
|
}
|
|
|
|
/// Returns true if \p I is known to be uniform after vectorization.
|
|
bool isUniformAfterVectorization(Instruction *I, unsigned VF) const {
|
|
if (VF == 1)
|
|
return true;
|
|
assert(Uniforms.count(VF) && "VF not yet analyzed for uniformity");
|
|
auto UniformsPerVF = Uniforms.find(VF);
|
|
return UniformsPerVF->second.count(I);
|
|
}
|
|
|
|
/// Returns true if \p I is known to be scalar after vectorization.
|
|
bool isScalarAfterVectorization(Instruction *I, unsigned VF) const {
|
|
if (VF == 1)
|
|
return true;
|
|
assert(Scalars.count(VF) && "Scalar values are not calculated for VF");
|
|
auto ScalarsPerVF = Scalars.find(VF);
|
|
return ScalarsPerVF->second.count(I);
|
|
}
|
|
|
|
/// \returns True if instruction \p I can be truncated to a smaller bitwidth
|
|
/// for vectorization factor \p VF.
|
|
bool canTruncateToMinimalBitwidth(Instruction *I, unsigned VF) const {
|
|
return VF > 1 && MinBWs.count(I) && !isProfitableToScalarize(I, VF) &&
|
|
!isScalarAfterVectorization(I, VF);
|
|
}
|
|
|
|
/// Decision that was taken during cost calculation for memory instruction.
|
|
enum InstWidening {
|
|
CM_Unknown,
|
|
CM_Widen,
|
|
CM_Interleave,
|
|
CM_GatherScatter,
|
|
CM_Scalarize
|
|
};
|
|
|
|
/// Save vectorization decision \p W and \p Cost taken by the cost model for
|
|
/// instruction \p I and vector width \p VF.
|
|
void setWideningDecision(Instruction *I, unsigned VF, InstWidening W,
|
|
unsigned Cost) {
|
|
assert(VF >= 2 && "Expected VF >=2");
|
|
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
|
|
}
|
|
|
|
/// Save vectorization decision \p W and \p Cost taken by the cost model for
|
|
/// interleaving group \p Grp and vector width \p VF.
|
|
void setWideningDecision(const InterleaveGroup *Grp, unsigned VF,
|
|
InstWidening W, unsigned Cost) {
|
|
assert(VF >= 2 && "Expected VF >=2");
|
|
/// Broadcast this decicion to all instructions inside the group.
|
|
/// But the cost will be assigned to one instruction only.
|
|
for (unsigned i = 0; i < Grp->getFactor(); ++i) {
|
|
if (auto *I = Grp->getMember(i)) {
|
|
if (Grp->getInsertPos() == I)
|
|
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost);
|
|
else
|
|
WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0);
|
|
}
|
|
}
|
|
}
|
|
|
|
/// Return the cost model decision for the given instruction \p I and vector
|
|
/// width \p VF. Return CM_Unknown if this instruction did not pass
|
|
/// through the cost modeling.
|
|
InstWidening getWideningDecision(Instruction *I, unsigned VF) {
|
|
assert(VF >= 2 && "Expected VF >=2");
|
|
std::pair<Instruction *, unsigned> InstOnVF = std::make_pair(I, VF);
|
|
auto Itr = WideningDecisions.find(InstOnVF);
|
|
if (Itr == WideningDecisions.end())
|
|
return CM_Unknown;
|
|
return Itr->second.first;
|
|
}
|
|
|
|
/// Return the vectorization cost for the given instruction \p I and vector
|
|
/// width \p VF.
|
|
unsigned getWideningCost(Instruction *I, unsigned VF) {
|
|
assert(VF >= 2 && "Expected VF >=2");
|
|
std::pair<Instruction *, unsigned> InstOnVF = std::make_pair(I, VF);
|
|
assert(WideningDecisions.count(InstOnVF) && "The cost is not calculated");
|
|
return WideningDecisions[InstOnVF].second;
|
|
}
|
|
|
|
/// Return True if instruction \p I is an optimizable truncate whose operand
|
|
/// is an induction variable. Such a truncate will be removed by adding a new
|
|
/// induction variable with the destination type.
|
|
bool isOptimizableIVTruncate(Instruction *I, unsigned VF) {
|
|
|
|
// If the instruction is not a truncate, return false.
|
|
auto *Trunc = dyn_cast<TruncInst>(I);
|
|
if (!Trunc)
|
|
return false;
|
|
|
|
// Get the source and destination types of the truncate.
|
|
Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF);
|
|
Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF);
|
|
|
|
// If the truncate is free for the given types, return false. Replacing a
|
|
// free truncate with an induction variable would add an induction variable
|
|
// update instruction to each iteration of the loop. We exclude from this
|
|
// check the primary induction variable since it will need an update
|
|
// instruction regardless.
|
|
Value *Op = Trunc->getOperand(0);
|
|
if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy))
|
|
return false;
|
|
|
|
// If the truncated value is not an induction variable, return false.
|
|
return Legal->isInductionVariable(Op);
|
|
}
|
|
|
|
/// Collects the instructions to scalarize for each predicated instruction in
|
|
/// the loop.
|
|
void collectInstsToScalarize(unsigned VF);
|
|
|
|
/// Collect Uniform and Scalar values for the given \p VF.
|
|
/// The sets depend on CM decision for Load/Store instructions
|
|
/// that may be vectorized as interleave, gather-scatter or scalarized.
|
|
void collectUniformsAndScalars(unsigned VF) {
|
|
// Do the analysis once.
|
|
if (VF == 1 || Uniforms.count(VF))
|
|
return;
|
|
setCostBasedWideningDecision(VF);
|
|
collectLoopUniforms(VF);
|
|
collectLoopScalars(VF);
|
|
}
|
|
|
|
private:
|
|
/// \return An upper bound for the vectorization factor, larger than zero.
|
|
/// One is returned if vectorization should best be avoided due to cost.
|
|
unsigned computeFeasibleMaxVF(bool OptForSize, unsigned ConstTripCount = 0);
|
|
|
|
/// The vectorization cost is a combination of the cost itself and a boolean
|
|
/// indicating whether any of the contributing operations will actually
|
|
/// operate on
|
|
/// vector values after type legalization in the backend. If this latter value
|
|
/// is
|
|
/// false, then all operations will be scalarized (i.e. no vectorization has
|
|
/// actually taken place).
|
|
typedef std::pair<unsigned, bool> VectorizationCostTy;
|
|
|
|
/// Returns the expected execution cost. The unit of the cost does
|
|
/// not matter because we use the 'cost' units to compare different
|
|
/// vector widths. The cost that is returned is *not* normalized by
|
|
/// the factor width.
|
|
VectorizationCostTy expectedCost(unsigned VF);
|
|
|
|
/// Returns the execution time cost of an instruction for a given vector
|
|
/// width. Vector width of one means scalar.
|
|
VectorizationCostTy getInstructionCost(Instruction *I, unsigned VF);
|
|
|
|
/// The cost-computation logic from getInstructionCost which provides
|
|
/// the vector type as an output parameter.
|
|
unsigned getInstructionCost(Instruction *I, unsigned VF, Type *&VectorTy);
|
|
|
|
/// Calculate vectorization cost of memory instruction \p I.
|
|
unsigned getMemoryInstructionCost(Instruction *I, unsigned VF);
|
|
|
|
/// The cost computation for scalarized memory instruction.
|
|
unsigned getMemInstScalarizationCost(Instruction *I, unsigned VF);
|
|
|
|
/// The cost computation for interleaving group of memory instructions.
|
|
unsigned getInterleaveGroupCost(Instruction *I, unsigned VF);
|
|
|
|
/// The cost computation for Gather/Scatter instruction.
|
|
unsigned getGatherScatterCost(Instruction *I, unsigned VF);
|
|
|
|
/// The cost computation for widening instruction \p I with consecutive
|
|
/// memory access.
|
|
unsigned getConsecutiveMemOpCost(Instruction *I, unsigned VF);
|
|
|
|
/// The cost calculation for Load instruction \p I with uniform pointer -
|
|
/// scalar load + broadcast.
|
|
unsigned getUniformMemOpCost(Instruction *I, unsigned VF);
|
|
|
|
/// Returns whether the instruction is a load or store and will be a emitted
|
|
/// as a vector operation.
|
|
bool isConsecutiveLoadOrStore(Instruction *I);
|
|
|
|
/// Create an analysis remark that explains why vectorization failed
|
|
///
|
|
/// \p RemarkName is the identifier for the remark. \return the remark object
|
|
/// that can be streamed to.
|
|
OptimizationRemarkAnalysis createMissedAnalysis(StringRef RemarkName) {
|
|
return ::createMissedAnalysis(Hints->vectorizeAnalysisPassName(),
|
|
RemarkName, TheLoop);
|
|
}
|
|
|
|
/// Map of scalar integer values to the smallest bitwidth they can be legally
|
|
/// represented as. The vector equivalents of these values should be truncated
|
|
/// to this type.
|
|
MapVector<Instruction *, uint64_t> MinBWs;
|
|
|
|
/// A type representing the costs for instructions if they were to be
|
|
/// scalarized rather than vectorized. The entries are Instruction-Cost
|
|
/// pairs.
|
|
typedef DenseMap<Instruction *, unsigned> ScalarCostsTy;
|
|
|
|
/// A set containing all BasicBlocks that are known to present after
|
|
/// vectorization as a predicated block.
|
|
SmallPtrSet<BasicBlock *, 4> PredicatedBBsAfterVectorization;
|
|
|
|
/// A map holding scalar costs for different vectorization factors. The
|
|
/// presence of a cost for an instruction in the mapping indicates that the
|
|
/// instruction will be scalarized when vectorizing with the associated
|
|
/// vectorization factor. The entries are VF-ScalarCostTy pairs.
|
|
DenseMap<unsigned, ScalarCostsTy> InstsToScalarize;
|
|
|
|
/// Holds the instructions known to be uniform after vectorization.
|
|
/// The data is collected per VF.
|
|
DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> Uniforms;
|
|
|
|
/// Holds the instructions known to be scalar after vectorization.
|
|
/// The data is collected per VF.
|
|
DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> Scalars;
|
|
|
|
/// Holds the instructions (address computations) that are forced to be
|
|
/// scalarized.
|
|
DenseMap<unsigned, SmallPtrSet<Instruction *, 4>> ForcedScalars;
|
|
|
|
/// Returns the expected difference in cost from scalarizing the expression
|
|
/// feeding a predicated instruction \p PredInst. The instructions to
|
|
/// scalarize and their scalar costs are collected in \p ScalarCosts. A
|
|
/// non-negative return value implies the expression will be scalarized.
|
|
/// Currently, only single-use chains are considered for scalarization.
|
|
int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts,
|
|
unsigned VF);
|
|
|
|
/// Collect the instructions that are uniform after vectorization. An
|
|
/// instruction is uniform if we represent it with a single scalar value in
|
|
/// the vectorized loop corresponding to each vector iteration. Examples of
|
|
/// uniform instructions include pointer operands of consecutive or
|
|
/// interleaved memory accesses. Note that although uniformity implies an
|
|
/// instruction will be scalar, the reverse is not true. In general, a
|
|
/// scalarized instruction will be represented by VF scalar values in the
|
|
/// vectorized loop, each corresponding to an iteration of the original
|
|
/// scalar loop.
|
|
void collectLoopUniforms(unsigned VF);
|
|
|
|
/// Collect the instructions that are scalar after vectorization. An
|
|
/// instruction is scalar if it is known to be uniform or will be scalarized
|
|
/// during vectorization. Non-uniform scalarized instructions will be
|
|
/// represented by VF values in the vectorized loop, each corresponding to an
|
|
/// iteration of the original scalar loop.
|
|
void collectLoopScalars(unsigned VF);
|
|
|
|
/// Keeps cost model vectorization decision and cost for instructions.
|
|
/// Right now it is used for memory instructions only.
|
|
typedef DenseMap<std::pair<Instruction *, unsigned>,
|
|
std::pair<InstWidening, unsigned>>
|
|
DecisionList;
|
|
|
|
DecisionList WideningDecisions;
|
|
|
|
public:
|
|
/// The loop that we evaluate.
|
|
Loop *TheLoop;
|
|
/// Predicated scalar evolution analysis.
|
|
PredicatedScalarEvolution &PSE;
|
|
/// Loop Info analysis.
|
|
LoopInfo *LI;
|
|
/// Vectorization legality.
|
|
LoopVectorizationLegality *Legal;
|
|
/// Vector target information.
|
|
const TargetTransformInfo &TTI;
|
|
/// Target Library Info.
|
|
const TargetLibraryInfo *TLI;
|
|
/// Demanded bits analysis.
|
|
DemandedBits *DB;
|
|
/// Assumption cache.
|
|
AssumptionCache *AC;
|
|
/// Interface to emit optimization remarks.
|
|
OptimizationRemarkEmitter *ORE;
|
|
|
|
const Function *TheFunction;
|
|
/// Loop Vectorize Hint.
|
|
const LoopVectorizeHints *Hints;
|
|
/// Values to ignore in the cost model.
|
|
SmallPtrSet<const Value *, 16> ValuesToIgnore;
|
|
/// Values to ignore in the cost model when VF > 1.
|
|
SmallPtrSet<const Value *, 16> VecValuesToIgnore;
|
|
};
|
|
|
|
} // end anonymous namespace
|
|
|
|
namespace llvm {
|
|
/// InnerLoopVectorizer vectorizes loops which contain only one basic
|
|
/// LoopVectorizationPlanner - drives the vectorization process after having
|
|
/// passed Legality checks.
|
|
/// The planner builds and optimizes the Vectorization Plans which record the
|
|
/// decisions how to vectorize the given loop. In particular, represent the
|
|
/// control-flow of the vectorized version, the replication of instructions that
|
|
/// are to be scalarized, and interleave access groups.
|
|
class LoopVectorizationPlanner {
|
|
/// The loop that we evaluate.
|
|
Loop *OrigLoop;
|
|
|
|
/// Loop Info analysis.
|
|
LoopInfo *LI;
|
|
|
|
/// Target Library Info.
|
|
const TargetLibraryInfo *TLI;
|
|
|
|
/// Target Transform Info.
|
|
const TargetTransformInfo *TTI;
|
|
|
|
/// The legality analysis.
|
|
LoopVectorizationLegality *Legal;
|
|
|
|
/// The profitablity analysis.
|
|
LoopVectorizationCostModel &CM;
|
|
|
|
SmallVector<VPlan *, 4> VPlans;
|
|
|
|
unsigned BestVF;
|
|
unsigned BestUF;
|
|
|
|
public:
|
|
LoopVectorizationPlanner(Loop *L, LoopInfo *LI, const TargetLibraryInfo *TLI,
|
|
const TargetTransformInfo *TTI,
|
|
LoopVectorizationLegality *Legal,
|
|
LoopVectorizationCostModel &CM)
|
|
: OrigLoop(L), LI(LI), TLI(TLI), TTI(TTI), Legal(Legal), CM(CM),
|
|
BestVF(0), BestUF(0) {}
|
|
|
|
~LoopVectorizationPlanner() {
|
|
while (!VPlans.empty()) {
|
|
VPlan *Plan = VPlans.back();
|
|
VPlans.pop_back();
|
|
delete Plan;
|
|
}
|
|
}
|
|
|
|
/// Plan how to best vectorize, return the best VF and its cost.
|
|
LoopVectorizationCostModel::VectorizationFactor plan(bool OptForSize,
|
|
unsigned UserVF);
|
|
|
|
/// Finalize the best decision and dispose of all other VPlans.
|
|
void setBestPlan(unsigned VF, unsigned UF);
|
|
|
|
/// Generate the IR code for the body of the vectorized loop according to the
|
|
/// best selected VPlan.
|
|
void executePlan(InnerLoopVectorizer &LB, DominatorTree *DT);
|
|
|
|
void printPlans(raw_ostream &O) {
|
|
for (VPlan *Plan : VPlans)
|
|
O << *Plan;
|
|
}
|
|
|
|
protected:
|
|
/// Collect the instructions from the original loop that would be trivially
|
|
/// dead in the vectorized loop if generated.
|
|
void collectTriviallyDeadInstructions(
|
|
SmallPtrSetImpl<Instruction *> &DeadInstructions);
|
|
|
|
/// A range of powers-of-2 vectorization factors with fixed start and
|
|
/// adjustable end. The range includes start and excludes end, e.g.,:
|
|
/// [1, 9) = {1, 2, 4, 8}
|
|
struct VFRange {
|
|
const unsigned Start; // A power of 2.
|
|
unsigned End; // Need not be a power of 2. If End <= Start range is empty.
|
|
};
|
|
|
|
/// Test a \p Predicate on a \p Range of VF's. Return the value of applying
|
|
/// \p Predicate on Range.Start, possibly decreasing Range.End such that the
|
|
/// returned value holds for the entire \p Range.
|
|
bool getDecisionAndClampRange(const std::function<bool(unsigned)> &Predicate,
|
|
VFRange &Range);
|
|
|
|
/// Build VPlans for power-of-2 VF's between \p MinVF and \p MaxVF inclusive,
|
|
/// according to the information gathered by Legal when it checked if it is
|
|
/// legal to vectorize the loop.
|
|
void buildVPlans(unsigned MinVF, unsigned MaxVF);
|
|
|
|
private:
|
|
/// Check if \I belongs to an Interleave Group within the given VF \p Range,
|
|
/// \return true in the first returned value if so and false otherwise.
|
|
/// Build a new VPInterleaveGroup Recipe if \I is the primary member of an IG
|
|
/// for \p Range.Start, and provide it as the second returned value.
|
|
/// Note that if \I is an adjunct member of an IG for \p Range.Start, the
|
|
/// \return value is <true, nullptr>, as it is handled by another recipe.
|
|
/// \p Range.End may be decreased to ensure same decision from \p Range.Start
|
|
/// to \p Range.End.
|
|
VPInterleaveRecipe *tryToInterleaveMemory(Instruction *I, VFRange &Range);
|
|
|
|
/// Check if an induction recipe should be constructed for \I within the given
|
|
/// VF \p Range. If so build and return it. If not, return null. \p Range.End
|
|
/// may be decreased to ensure same decision from \p Range.Start to
|
|
/// \p Range.End.
|
|
VPWidenIntOrFpInductionRecipe *tryToOptimizeInduction(Instruction *I,
|
|
VFRange &Range);
|
|
|
|
/// Check if \I can be widened within the given VF \p Range. If \I can be
|
|
/// widened for Range.Start, extend \p LastWidenRecipe to include \p I if
|
|
/// possible or else build a new VPWidenRecipe for it, and return the
|
|
/// VPWidenRecipe that includes \p I. If \p I cannot be widened for
|
|
/// Range.Start \return null. Range.End may be decreased to ensure same
|
|
/// decision from \p Range.Start to \p Range.End.
|
|
VPWidenRecipe *tryToWiden(Instruction *I, VPWidenRecipe *LastWidenRecipe,
|
|
VFRange &Range);
|
|
|
|
/// Build a VPReplicationRecipe for \p I and enclose it within a Region if it
|
|
/// is predicated. \return \p VPBB augmented with this new recipe if \p I is
|
|
/// not predicated, otherwise \return a new VPBasicBlock that succeeds the new
|
|
/// Region. Update the packing decision of predicated instructions if they
|
|
/// feed \p I. Range.End may be decreased to ensure same recipe behavior from
|
|
/// \p Range.Start to \p Range.End.
|
|
VPBasicBlock *handleReplication(
|
|
Instruction *I, VFRange &Range, VPBasicBlock *VPBB,
|
|
DenseMap<Instruction *, VPReplicateRecipe *> &PredInst2Recipe);
|
|
|
|
/// Create a replicating region for instruction \p I that requires
|
|
/// predication. \p PredRecipe is a VPReplicateRecipe holding \p I.
|
|
VPRegionBlock *createReplicateRegion(Instruction *I,
|
|
VPRecipeBase *PredRecipe);
|
|
|
|
/// Build a VPlan according to the information gathered by Legal. \return a
|
|
/// VPlan for vectorization factors \p Range.Start and up to \p Range.End
|
|
/// exclusive, possibly decreasing \p Range.End.
|
|
VPlan *buildVPlan(VFRange &Range);
|
|
};
|
|
|
|
} // namespace llvm
|
|
|
|
namespace {
|
|
|
|
/// \brief This holds vectorization requirements that must be verified late in
|
|
/// the process. The requirements are set by legalize and costmodel. Once
|
|
/// vectorization has been determined to be possible and profitable the
|
|
/// requirements can be verified by looking for metadata or compiler options.
|
|
/// For example, some loops require FP commutativity which is only allowed if
|
|
/// vectorization is explicitly specified or if the fast-math compiler option
|
|
/// has been provided.
|
|
/// Late evaluation of these requirements allows helpful diagnostics to be
|
|
/// composed that tells the user what need to be done to vectorize the loop. For
|
|
/// example, by specifying #pragma clang loop vectorize or -ffast-math. Late
|
|
/// evaluation should be used only when diagnostics can generated that can be
|
|
/// followed by a non-expert user.
|
|
class LoopVectorizationRequirements {
|
|
public:
|
|
LoopVectorizationRequirements(OptimizationRemarkEmitter &ORE)
|
|
: NumRuntimePointerChecks(0), UnsafeAlgebraInst(nullptr), ORE(ORE) {}
|
|
|
|
void addUnsafeAlgebraInst(Instruction *I) {
|
|
// First unsafe algebra instruction.
|
|
if (!UnsafeAlgebraInst)
|
|
UnsafeAlgebraInst = I;
|
|
}
|
|
|
|
void addRuntimePointerChecks(unsigned Num) { NumRuntimePointerChecks = Num; }
|
|
|
|
bool doesNotMeet(Function *F, Loop *L, const LoopVectorizeHints &Hints) {
|
|
const char *PassName = Hints.vectorizeAnalysisPassName();
|
|
bool Failed = false;
|
|
if (UnsafeAlgebraInst && !Hints.allowReordering()) {
|
|
ORE.emit(
|
|
OptimizationRemarkAnalysisFPCommute(PassName, "CantReorderFPOps",
|
|
UnsafeAlgebraInst->getDebugLoc(),
|
|
UnsafeAlgebraInst->getParent())
|
|
<< "loop not vectorized: cannot prove it is safe to reorder "
|
|
"floating-point operations");
|
|
Failed = true;
|
|
}
|
|
|
|
// Test if runtime memcheck thresholds are exceeded.
|
|
bool PragmaThresholdReached =
|
|
NumRuntimePointerChecks > PragmaVectorizeMemoryCheckThreshold;
|
|
bool ThresholdReached =
|
|
NumRuntimePointerChecks > VectorizerParams::RuntimeMemoryCheckThreshold;
|
|
if ((ThresholdReached && !Hints.allowReordering()) ||
|
|
PragmaThresholdReached) {
|
|
ORE.emit(OptimizationRemarkAnalysisAliasing(PassName, "CantReorderMemOps",
|
|
L->getStartLoc(),
|
|
L->getHeader())
|
|
<< "loop not vectorized: cannot prove it is safe to reorder "
|
|
"memory operations");
|
|
DEBUG(dbgs() << "LV: Too many memory checks needed.\n");
|
|
Failed = true;
|
|
}
|
|
|
|
return Failed;
|
|
}
|
|
|
|
private:
|
|
unsigned NumRuntimePointerChecks;
|
|
Instruction *UnsafeAlgebraInst;
|
|
|
|
/// Interface to emit optimization remarks.
|
|
OptimizationRemarkEmitter &ORE;
|
|
};
|
|
|
|
static void addAcyclicInnerLoop(Loop &L, SmallVectorImpl<Loop *> &V) {
|
|
if (L.empty()) {
|
|
if (!hasCyclesInLoopBody(L))
|
|
V.push_back(&L);
|
|
return;
|
|
}
|
|
for (Loop *InnerL : L)
|
|
addAcyclicInnerLoop(*InnerL, V);
|
|
}
|
|
|
|
/// The LoopVectorize Pass.
|
|
struct LoopVectorize : public FunctionPass {
|
|
/// Pass identification, replacement for typeid
|
|
static char ID;
|
|
|
|
explicit LoopVectorize(bool NoUnrolling = false, bool AlwaysVectorize = true)
|
|
: FunctionPass(ID) {
|
|
Impl.DisableUnrolling = NoUnrolling;
|
|
Impl.AlwaysVectorize = AlwaysVectorize;
|
|
initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
|
|
}
|
|
|
|
LoopVectorizePass Impl;
|
|
|
|
bool runOnFunction(Function &F) override {
|
|
if (skipFunction(F))
|
|
return false;
|
|
|
|
auto *SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE();
|
|
auto *LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo();
|
|
auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F);
|
|
auto *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree();
|
|
auto *BFI = &getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI();
|
|
auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>();
|
|
auto *TLI = TLIP ? &TLIP->getTLI() : nullptr;
|
|
auto *AA = &getAnalysis<AAResultsWrapperPass>().getAAResults();
|
|
auto *AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F);
|
|
auto *LAA = &getAnalysis<LoopAccessLegacyAnalysis>();
|
|
auto *DB = &getAnalysis<DemandedBitsWrapperPass>().getDemandedBits();
|
|
auto *ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE();
|
|
|
|
std::function<const LoopAccessInfo &(Loop &)> GetLAA =
|
|
[&](Loop &L) -> const LoopAccessInfo & { return LAA->getInfo(&L); };
|
|
|
|
return Impl.runImpl(F, *SE, *LI, *TTI, *DT, *BFI, TLI, *DB, *AA, *AC,
|
|
GetLAA, *ORE);
|
|
}
|
|
|
|
void getAnalysisUsage(AnalysisUsage &AU) const override {
|
|
AU.addRequired<AssumptionCacheTracker>();
|
|
AU.addRequired<BlockFrequencyInfoWrapperPass>();
|
|
AU.addRequired<DominatorTreeWrapperPass>();
|
|
AU.addRequired<LoopInfoWrapperPass>();
|
|
AU.addRequired<ScalarEvolutionWrapperPass>();
|
|
AU.addRequired<TargetTransformInfoWrapperPass>();
|
|
AU.addRequired<AAResultsWrapperPass>();
|
|
AU.addRequired<LoopAccessLegacyAnalysis>();
|
|
AU.addRequired<DemandedBitsWrapperPass>();
|
|
AU.addRequired<OptimizationRemarkEmitterWrapperPass>();
|
|
AU.addPreserved<LoopInfoWrapperPass>();
|
|
AU.addPreserved<DominatorTreeWrapperPass>();
|
|
AU.addPreserved<BasicAAWrapperPass>();
|
|
AU.addPreserved<GlobalsAAWrapperPass>();
|
|
}
|
|
};
|
|
|
|
} // end anonymous namespace
|
|
|
|
//===----------------------------------------------------------------------===//
|
|
// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
|
|
// LoopVectorizationCostModel and LoopVectorizationPlanner.
|
|
//===----------------------------------------------------------------------===//
|
|
|
|
Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) {
|
|
// We need to place the broadcast of invariant variables outside the loop.
|
|
Instruction *Instr = dyn_cast<Instruction>(V);
|
|
bool NewInstr = (Instr && Instr->getParent() == LoopVectorBody);
|
|
bool Invariant = OrigLoop->isLoopInvariant(V) && !NewInstr;
|
|
|
|
// Place the code for broadcasting invariant variables in the new preheader.
|
|
IRBuilder<>::InsertPointGuard Guard(Builder);
|
|
if (Invariant)
|
|
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
|
|
|
|
// Broadcast the scalar into all locations in the vector.
|
|
Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast");
|
|
|
|
return Shuf;
|
|
}
|
|
|
|
void InnerLoopVectorizer::createVectorIntOrFpInductionPHI(
|
|
const InductionDescriptor &II, Value *Step, Instruction *EntryVal) {
|
|
Value *Start = II.getStartValue();
|
|
|
|
// Construct the initial value of the vector IV in the vector loop preheader
|
|
auto CurrIP = Builder.saveIP();
|
|
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
|
|
if (isa<TruncInst>(EntryVal)) {
|
|
assert(Start->getType()->isIntegerTy() &&
|
|
"Truncation requires an integer type");
|
|
auto *TruncType = cast<IntegerType>(EntryVal->getType());
|
|
Step = Builder.CreateTrunc(Step, TruncType);
|
|
Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType);
|
|
}
|
|
Value *SplatStart = Builder.CreateVectorSplat(VF, Start);
|
|
Value *SteppedStart =
|
|
getStepVector(SplatStart, 0, Step, II.getInductionOpcode());
|
|
|
|
// We create vector phi nodes for both integer and floating-point induction
|
|
// variables. Here, we determine the kind of arithmetic we will perform.
|
|
Instruction::BinaryOps AddOp;
|
|
Instruction::BinaryOps MulOp;
|
|
if (Step->getType()->isIntegerTy()) {
|
|
AddOp = Instruction::Add;
|
|
MulOp = Instruction::Mul;
|
|
} else {
|
|
AddOp = II.getInductionOpcode();
|
|
MulOp = Instruction::FMul;
|
|
}
|
|
|
|
// Multiply the vectorization factor by the step using integer or
|
|
// floating-point arithmetic as appropriate.
|
|
Value *ConstVF = getSignedIntOrFpConstant(Step->getType(), VF);
|
|
Value *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, Step, ConstVF));
|
|
|
|
// Create a vector splat to use in the induction update.
|
|
//
|
|
// FIXME: If the step is non-constant, we create the vector splat with
|
|
// IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't
|
|
// handle a constant vector splat.
|
|
Value *SplatVF = isa<Constant>(Mul)
|
|
? ConstantVector::getSplat(VF, cast<Constant>(Mul))
|
|
: Builder.CreateVectorSplat(VF, Mul);
|
|
Builder.restoreIP(CurrIP);
|
|
|
|
// We may need to add the step a number of times, depending on the unroll
|
|
// factor. The last of those goes into the PHI.
|
|
PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind",
|
|
&*LoopVectorBody->getFirstInsertionPt());
|
|
Instruction *LastInduction = VecInd;
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
VectorLoopValueMap.setVectorValue(EntryVal, Part, LastInduction);
|
|
if (isa<TruncInst>(EntryVal))
|
|
addMetadata(LastInduction, EntryVal);
|
|
LastInduction = cast<Instruction>(addFastMathFlag(
|
|
Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add")));
|
|
}
|
|
|
|
// Move the last step to the end of the latch block. This ensures consistent
|
|
// placement of all induction updates.
|
|
auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch();
|
|
auto *Br = cast<BranchInst>(LoopVectorLatch->getTerminator());
|
|
auto *ICmp = cast<Instruction>(Br->getCondition());
|
|
LastInduction->moveBefore(ICmp);
|
|
LastInduction->setName("vec.ind.next");
|
|
|
|
VecInd->addIncoming(SteppedStart, LoopVectorPreHeader);
|
|
VecInd->addIncoming(LastInduction, LoopVectorLatch);
|
|
}
|
|
|
|
bool InnerLoopVectorizer::shouldScalarizeInstruction(Instruction *I) const {
|
|
return Cost->isScalarAfterVectorization(I, VF) ||
|
|
Cost->isProfitableToScalarize(I, VF);
|
|
}
|
|
|
|
bool InnerLoopVectorizer::needsScalarInduction(Instruction *IV) const {
|
|
if (shouldScalarizeInstruction(IV))
|
|
return true;
|
|
auto isScalarInst = [&](User *U) -> bool {
|
|
auto *I = cast<Instruction>(U);
|
|
return (OrigLoop->contains(I) && shouldScalarizeInstruction(I));
|
|
};
|
|
return any_of(IV->users(), isScalarInst);
|
|
}
|
|
|
|
void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, TruncInst *Trunc) {
|
|
|
|
assert((IV->getType()->isIntegerTy() || IV != OldInduction) &&
|
|
"Primary induction variable must have an integer type");
|
|
|
|
auto II = Legal->getInductionVars()->find(IV);
|
|
assert(II != Legal->getInductionVars()->end() && "IV is not an induction");
|
|
|
|
auto ID = II->second;
|
|
assert(IV->getType() == ID.getStartValue()->getType() && "Types must match");
|
|
|
|
// The scalar value to broadcast. This will be derived from the canonical
|
|
// induction variable.
|
|
Value *ScalarIV = nullptr;
|
|
|
|
// The value from the original loop to which we are mapping the new induction
|
|
// variable.
|
|
Instruction *EntryVal = Trunc ? cast<Instruction>(Trunc) : IV;
|
|
|
|
// True if we have vectorized the induction variable.
|
|
auto VectorizedIV = false;
|
|
|
|
// Determine if we want a scalar version of the induction variable. This is
|
|
// true if the induction variable itself is not widened, or if it has at
|
|
// least one user in the loop that is not widened.
|
|
auto NeedsScalarIV = VF > 1 && needsScalarInduction(EntryVal);
|
|
|
|
// Generate code for the induction step. Note that induction steps are
|
|
// required to be loop-invariant
|
|
assert(PSE.getSE()->isLoopInvariant(ID.getStep(), OrigLoop) &&
|
|
"Induction step should be loop invariant");
|
|
auto &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
|
|
Value *Step = nullptr;
|
|
if (PSE.getSE()->isSCEVable(IV->getType())) {
|
|
SCEVExpander Exp(*PSE.getSE(), DL, "induction");
|
|
Step = Exp.expandCodeFor(ID.getStep(), ID.getStep()->getType(),
|
|
LoopVectorPreHeader->getTerminator());
|
|
} else {
|
|
Step = cast<SCEVUnknown>(ID.getStep())->getValue();
|
|
}
|
|
|
|
// Try to create a new independent vector induction variable. If we can't
|
|
// create the phi node, we will splat the scalar induction variable in each
|
|
// loop iteration.
|
|
if (VF > 1 && !shouldScalarizeInstruction(EntryVal)) {
|
|
createVectorIntOrFpInductionPHI(ID, Step, EntryVal);
|
|
VectorizedIV = true;
|
|
}
|
|
|
|
// If we haven't yet vectorized the induction variable, or if we will create
|
|
// a scalar one, we need to define the scalar induction variable and step
|
|
// values. If we were given a truncation type, truncate the canonical
|
|
// induction variable and step. Otherwise, derive these values from the
|
|
// induction descriptor.
|
|
if (!VectorizedIV || NeedsScalarIV) {
|
|
ScalarIV = Induction;
|
|
if (IV != OldInduction) {
|
|
ScalarIV = IV->getType()->isIntegerTy()
|
|
? Builder.CreateSExtOrTrunc(Induction, IV->getType())
|
|
: Builder.CreateCast(Instruction::SIToFP, Induction,
|
|
IV->getType());
|
|
ScalarIV = ID.transform(Builder, ScalarIV, PSE.getSE(), DL);
|
|
ScalarIV->setName("offset.idx");
|
|
}
|
|
if (Trunc) {
|
|
auto *TruncType = cast<IntegerType>(Trunc->getType());
|
|
assert(Step->getType()->isIntegerTy() &&
|
|
"Truncation requires an integer step");
|
|
ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType);
|
|
Step = Builder.CreateTrunc(Step, TruncType);
|
|
}
|
|
}
|
|
|
|
// If we haven't yet vectorized the induction variable, splat the scalar
|
|
// induction variable, and build the necessary step vectors.
|
|
if (!VectorizedIV) {
|
|
Value *Broadcasted = getBroadcastInstrs(ScalarIV);
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *EntryPart =
|
|
getStepVector(Broadcasted, VF * Part, Step, ID.getInductionOpcode());
|
|
VectorLoopValueMap.setVectorValue(EntryVal, Part, EntryPart);
|
|
if (Trunc)
|
|
addMetadata(EntryPart, Trunc);
|
|
}
|
|
}
|
|
|
|
// If an induction variable is only used for counting loop iterations or
|
|
// calculating addresses, it doesn't need to be widened. Create scalar steps
|
|
// that can be used by instructions we will later scalarize. Note that the
|
|
// addition of the scalar steps will not increase the number of instructions
|
|
// in the loop in the common case prior to InstCombine. We will be trading
|
|
// one vector extract for each scalar step.
|
|
if (NeedsScalarIV)
|
|
buildScalarSteps(ScalarIV, Step, EntryVal, ID);
|
|
}
|
|
|
|
Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx, Value *Step,
|
|
Instruction::BinaryOps BinOp) {
|
|
// Create and check the types.
|
|
assert(Val->getType()->isVectorTy() && "Must be a vector");
|
|
int VLen = Val->getType()->getVectorNumElements();
|
|
|
|
Type *STy = Val->getType()->getScalarType();
|
|
assert((STy->isIntegerTy() || STy->isFloatingPointTy()) &&
|
|
"Induction Step must be an integer or FP");
|
|
assert(Step->getType() == STy && "Step has wrong type");
|
|
|
|
SmallVector<Constant *, 8> Indices;
|
|
|
|
if (STy->isIntegerTy()) {
|
|
// Create a vector of consecutive numbers from zero to VF.
|
|
for (int i = 0; i < VLen; ++i)
|
|
Indices.push_back(ConstantInt::get(STy, StartIdx + i));
|
|
|
|
// Add the consecutive indices to the vector value.
|
|
Constant *Cv = ConstantVector::get(Indices);
|
|
assert(Cv->getType() == Val->getType() && "Invalid consecutive vec");
|
|
Step = Builder.CreateVectorSplat(VLen, Step);
|
|
assert(Step->getType() == Val->getType() && "Invalid step vec");
|
|
// FIXME: The newly created binary instructions should contain nsw/nuw flags,
|
|
// which can be found from the original scalar operations.
|
|
Step = Builder.CreateMul(Cv, Step);
|
|
return Builder.CreateAdd(Val, Step, "induction");
|
|
}
|
|
|
|
// Floating point induction.
|
|
assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) &&
|
|
"Binary Opcode should be specified for FP induction");
|
|
// Create a vector of consecutive numbers from zero to VF.
|
|
for (int i = 0; i < VLen; ++i)
|
|
Indices.push_back(ConstantFP::get(STy, (double)(StartIdx + i)));
|
|
|
|
// Add the consecutive indices to the vector value.
|
|
Constant *Cv = ConstantVector::get(Indices);
|
|
|
|
Step = Builder.CreateVectorSplat(VLen, Step);
|
|
|
|
// Floating point operations had to be 'fast' to enable the induction.
|
|
FastMathFlags Flags;
|
|
Flags.setUnsafeAlgebra();
|
|
|
|
Value *MulOp = Builder.CreateFMul(Cv, Step);
|
|
if (isa<Instruction>(MulOp))
|
|
// Have to check, MulOp may be a constant
|
|
cast<Instruction>(MulOp)->setFastMathFlags(Flags);
|
|
|
|
Value *BOp = Builder.CreateBinOp(BinOp, Val, MulOp, "induction");
|
|
if (isa<Instruction>(BOp))
|
|
cast<Instruction>(BOp)->setFastMathFlags(Flags);
|
|
return BOp;
|
|
}
|
|
|
|
void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step,
|
|
Value *EntryVal,
|
|
const InductionDescriptor &ID) {
|
|
|
|
// We shouldn't have to build scalar steps if we aren't vectorizing.
|
|
assert(VF > 1 && "VF should be greater than one");
|
|
|
|
// Get the value type and ensure it and the step have the same integer type.
|
|
Type *ScalarIVTy = ScalarIV->getType()->getScalarType();
|
|
assert(ScalarIVTy == Step->getType() &&
|
|
"Val and Step should have the same type");
|
|
|
|
// We build scalar steps for both integer and floating-point induction
|
|
// variables. Here, we determine the kind of arithmetic we will perform.
|
|
Instruction::BinaryOps AddOp;
|
|
Instruction::BinaryOps MulOp;
|
|
if (ScalarIVTy->isIntegerTy()) {
|
|
AddOp = Instruction::Add;
|
|
MulOp = Instruction::Mul;
|
|
} else {
|
|
AddOp = ID.getInductionOpcode();
|
|
MulOp = Instruction::FMul;
|
|
}
|
|
|
|
// Determine the number of scalars we need to generate for each unroll
|
|
// iteration. If EntryVal is uniform, we only need to generate the first
|
|
// lane. Otherwise, we generate all VF values.
|
|
unsigned Lanes =
|
|
Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF) ? 1
|
|
: VF;
|
|
// Compute the scalar steps and save the results in VectorLoopValueMap.
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
|
|
auto *StartIdx = getSignedIntOrFpConstant(ScalarIVTy, VF * Part + Lane);
|
|
auto *Mul = addFastMathFlag(Builder.CreateBinOp(MulOp, StartIdx, Step));
|
|
auto *Add = addFastMathFlag(Builder.CreateBinOp(AddOp, ScalarIV, Mul));
|
|
VectorLoopValueMap.setScalarValue(EntryVal, {Part, Lane}, Add);
|
|
}
|
|
}
|
|
}
|
|
|
|
int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) {
|
|
|
|
const ValueToValueMap &Strides = getSymbolicStrides() ? *getSymbolicStrides() :
|
|
ValueToValueMap();
|
|
|
|
int Stride = getPtrStride(PSE, Ptr, TheLoop, Strides, true, false);
|
|
if (Stride == 1 || Stride == -1)
|
|
return Stride;
|
|
return 0;
|
|
}
|
|
|
|
bool LoopVectorizationLegality::isUniform(Value *V) {
|
|
return LAI->isUniform(V);
|
|
}
|
|
|
|
Value *InnerLoopVectorizer::getOrCreateVectorValue(Value *V, unsigned Part) {
|
|
assert(V != Induction && "The new induction variable should not be used.");
|
|
assert(!V->getType()->isVectorTy() && "Can't widen a vector");
|
|
assert(!V->getType()->isVoidTy() && "Type does not produce a value");
|
|
|
|
// If we have a stride that is replaced by one, do it here.
|
|
if (Legal->hasStride(V))
|
|
V = ConstantInt::get(V->getType(), 1);
|
|
|
|
// If we have a vector mapped to this value, return it.
|
|
if (VectorLoopValueMap.hasVectorValue(V, Part))
|
|
return VectorLoopValueMap.getVectorValue(V, Part);
|
|
|
|
// If the value has not been vectorized, check if it has been scalarized
|
|
// instead. If it has been scalarized, and we actually need the value in
|
|
// vector form, we will construct the vector values on demand.
|
|
if (VectorLoopValueMap.hasAnyScalarValue(V)) {
|
|
|
|
Value *ScalarValue = VectorLoopValueMap.getScalarValue(V, {Part, 0});
|
|
|
|
// If we've scalarized a value, that value should be an instruction.
|
|
auto *I = cast<Instruction>(V);
|
|
|
|
// If we aren't vectorizing, we can just copy the scalar map values over to
|
|
// the vector map.
|
|
if (VF == 1) {
|
|
VectorLoopValueMap.setVectorValue(V, Part, ScalarValue);
|
|
return ScalarValue;
|
|
}
|
|
|
|
// Get the last scalar instruction we generated for V and Part. If the value
|
|
// is known to be uniform after vectorization, this corresponds to lane zero
|
|
// of the Part unroll iteration. Otherwise, the last instruction is the one
|
|
// we created for the last vector lane of the Part unroll iteration.
|
|
unsigned LastLane = Cost->isUniformAfterVectorization(I, VF) ? 0 : VF - 1;
|
|
auto *LastInst = cast<Instruction>(
|
|
VectorLoopValueMap.getScalarValue(V, {Part, LastLane}));
|
|
|
|
// Set the insert point after the last scalarized instruction. This ensures
|
|
// the insertelement sequence will directly follow the scalar definitions.
|
|
auto OldIP = Builder.saveIP();
|
|
auto NewIP = std::next(BasicBlock::iterator(LastInst));
|
|
Builder.SetInsertPoint(&*NewIP);
|
|
|
|
// However, if we are vectorizing, we need to construct the vector values.
|
|
// If the value is known to be uniform after vectorization, we can just
|
|
// broadcast the scalar value corresponding to lane zero for each unroll
|
|
// iteration. Otherwise, we construct the vector values using insertelement
|
|
// instructions. Since the resulting vectors are stored in
|
|
// VectorLoopValueMap, we will only generate the insertelements once.
|
|
Value *VectorValue = nullptr;
|
|
if (Cost->isUniformAfterVectorization(I, VF)) {
|
|
VectorValue = getBroadcastInstrs(ScalarValue);
|
|
VectorLoopValueMap.setVectorValue(V, Part, VectorValue);
|
|
} else {
|
|
// Initialize packing with insertelements to start from undef.
|
|
Value *Undef = UndefValue::get(VectorType::get(V->getType(), VF));
|
|
VectorLoopValueMap.setVectorValue(V, Part, Undef);
|
|
for (unsigned Lane = 0; Lane < VF; ++Lane)
|
|
packScalarIntoVectorValue(V, {Part, Lane});
|
|
VectorValue = VectorLoopValueMap.getVectorValue(V, Part);
|
|
}
|
|
Builder.restoreIP(OldIP);
|
|
return VectorValue;
|
|
}
|
|
|
|
// If this scalar is unknown, assume that it is a constant or that it is
|
|
// loop invariant. Broadcast V and save the value for future uses.
|
|
Value *B = getBroadcastInstrs(V);
|
|
VectorLoopValueMap.setVectorValue(V, Part, B);
|
|
return B;
|
|
}
|
|
|
|
Value *
|
|
InnerLoopVectorizer::getOrCreateScalarValue(Value *V,
|
|
const VPIteration &Instance) {
|
|
// If the value is not an instruction contained in the loop, it should
|
|
// already be scalar.
|
|
if (OrigLoop->isLoopInvariant(V))
|
|
return V;
|
|
|
|
assert(Instance.Lane > 0
|
|
? !Cost->isUniformAfterVectorization(cast<Instruction>(V), VF)
|
|
: true && "Uniform values only have lane zero");
|
|
|
|
// If the value from the original loop has not been vectorized, it is
|
|
// represented by UF x VF scalar values in the new loop. Return the requested
|
|
// scalar value.
|
|
if (VectorLoopValueMap.hasScalarValue(V, Instance))
|
|
return VectorLoopValueMap.getScalarValue(V, Instance);
|
|
|
|
// If the value has not been scalarized, get its entry in VectorLoopValueMap
|
|
// for the given unroll part. If this entry is not a vector type (i.e., the
|
|
// vectorization factor is one), there is no need to generate an
|
|
// extractelement instruction.
|
|
auto *U = getOrCreateVectorValue(V, Instance.Part);
|
|
if (!U->getType()->isVectorTy()) {
|
|
assert(VF == 1 && "Value not scalarized has non-vector type");
|
|
return U;
|
|
}
|
|
|
|
// Otherwise, the value from the original loop has been vectorized and is
|
|
// represented by UF vector values. Extract and return the requested scalar
|
|
// value from the appropriate vector lane.
|
|
return Builder.CreateExtractElement(U, Builder.getInt32(Instance.Lane));
|
|
}
|
|
|
|
void InnerLoopVectorizer::packScalarIntoVectorValue(
|
|
Value *V, const VPIteration &Instance) {
|
|
assert(V != Induction && "The new induction variable should not be used.");
|
|
assert(!V->getType()->isVectorTy() && "Can't pack a vector");
|
|
assert(!V->getType()->isVoidTy() && "Type does not produce a value");
|
|
|
|
Value *ScalarInst = VectorLoopValueMap.getScalarValue(V, Instance);
|
|
Value *VectorValue = VectorLoopValueMap.getVectorValue(V, Instance.Part);
|
|
VectorValue = Builder.CreateInsertElement(VectorValue, ScalarInst,
|
|
Builder.getInt32(Instance.Lane));
|
|
VectorLoopValueMap.resetVectorValue(V, Instance.Part, VectorValue);
|
|
}
|
|
|
|
Value *InnerLoopVectorizer::reverseVector(Value *Vec) {
|
|
assert(Vec->getType()->isVectorTy() && "Invalid type");
|
|
SmallVector<Constant *, 8> ShuffleMask;
|
|
for (unsigned i = 0; i < VF; ++i)
|
|
ShuffleMask.push_back(Builder.getInt32(VF - i - 1));
|
|
|
|
return Builder.CreateShuffleVector(Vec, UndefValue::get(Vec->getType()),
|
|
ConstantVector::get(ShuffleMask),
|
|
"reverse");
|
|
}
|
|
|
|
// Try to vectorize the interleave group that \p Instr belongs to.
|
|
//
|
|
// E.g. Translate following interleaved load group (factor = 3):
|
|
// for (i = 0; i < N; i+=3) {
|
|
// R = Pic[i]; // Member of index 0
|
|
// G = Pic[i+1]; // Member of index 1
|
|
// B = Pic[i+2]; // Member of index 2
|
|
// ... // do something to R, G, B
|
|
// }
|
|
// To:
|
|
// %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B
|
|
// %R.vec = shuffle %wide.vec, undef, <0, 3, 6, 9> ; R elements
|
|
// %G.vec = shuffle %wide.vec, undef, <1, 4, 7, 10> ; G elements
|
|
// %B.vec = shuffle %wide.vec, undef, <2, 5, 8, 11> ; B elements
|
|
//
|
|
// Or translate following interleaved store group (factor = 3):
|
|
// for (i = 0; i < N; i+=3) {
|
|
// ... do something to R, G, B
|
|
// Pic[i] = R; // Member of index 0
|
|
// Pic[i+1] = G; // Member of index 1
|
|
// Pic[i+2] = B; // Member of index 2
|
|
// }
|
|
// To:
|
|
// %R_G.vec = shuffle %R.vec, %G.vec, <0, 1, 2, ..., 7>
|
|
// %B_U.vec = shuffle %B.vec, undef, <0, 1, 2, 3, u, u, u, u>
|
|
// %interleaved.vec = shuffle %R_G.vec, %B_U.vec,
|
|
// <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> ; Interleave R,G,B elements
|
|
// store <12 x i32> %interleaved.vec ; Write 4 tuples of R,G,B
|
|
void InnerLoopVectorizer::vectorizeInterleaveGroup(Instruction *Instr) {
|
|
const InterleaveGroup *Group = Legal->getInterleavedAccessGroup(Instr);
|
|
assert(Group && "Fail to get an interleaved access group.");
|
|
|
|
// Skip if current instruction is not the insert position.
|
|
if (Instr != Group->getInsertPos())
|
|
return;
|
|
|
|
const DataLayout &DL = Instr->getModule()->getDataLayout();
|
|
Value *Ptr = getPointerOperand(Instr);
|
|
|
|
// Prepare for the vector type of the interleaved load/store.
|
|
Type *ScalarTy = getMemInstValueType(Instr);
|
|
unsigned InterleaveFactor = Group->getFactor();
|
|
Type *VecTy = VectorType::get(ScalarTy, InterleaveFactor * VF);
|
|
Type *PtrTy = VecTy->getPointerTo(getMemInstAddressSpace(Instr));
|
|
|
|
// Prepare for the new pointers.
|
|
setDebugLocFromInst(Builder, Ptr);
|
|
SmallVector<Value *, 2> NewPtrs;
|
|
unsigned Index = Group->getIndex(Instr);
|
|
|
|
// If the group is reverse, adjust the index to refer to the last vector lane
|
|
// instead of the first. We adjust the index from the first vector lane,
|
|
// rather than directly getting the pointer for lane VF - 1, because the
|
|
// pointer operand of the interleaved access is supposed to be uniform. For
|
|
// uniform instructions, we're only required to generate a value for the
|
|
// first vector lane in each unroll iteration.
|
|
if (Group->isReverse())
|
|
Index += (VF - 1) * Group->getFactor();
|
|
|
|
for (unsigned Part = 0; Part < UF; Part++) {
|
|
Value *NewPtr = getOrCreateScalarValue(Ptr, {Part, 0});
|
|
|
|
// Notice current instruction could be any index. Need to adjust the address
|
|
// to the member of index 0.
|
|
//
|
|
// E.g. a = A[i+1]; // Member of index 1 (Current instruction)
|
|
// b = A[i]; // Member of index 0
|
|
// Current pointer is pointed to A[i+1], adjust it to A[i].
|
|
//
|
|
// E.g. A[i+1] = a; // Member of index 1
|
|
// A[i] = b; // Member of index 0
|
|
// A[i+2] = c; // Member of index 2 (Current instruction)
|
|
// Current pointer is pointed to A[i+2], adjust it to A[i].
|
|
NewPtr = Builder.CreateGEP(NewPtr, Builder.getInt32(-Index));
|
|
|
|
// Cast to the vector pointer type.
|
|
NewPtrs.push_back(Builder.CreateBitCast(NewPtr, PtrTy));
|
|
}
|
|
|
|
setDebugLocFromInst(Builder, Instr);
|
|
Value *UndefVec = UndefValue::get(VecTy);
|
|
|
|
// Vectorize the interleaved load group.
|
|
if (isa<LoadInst>(Instr)) {
|
|
|
|
// For each unroll part, create a wide load for the group.
|
|
SmallVector<Value *, 2> NewLoads;
|
|
for (unsigned Part = 0; Part < UF; Part++) {
|
|
auto *NewLoad = Builder.CreateAlignedLoad(
|
|
NewPtrs[Part], Group->getAlignment(), "wide.vec");
|
|
addMetadata(NewLoad, Instr);
|
|
NewLoads.push_back(NewLoad);
|
|
}
|
|
|
|
// For each member in the group, shuffle out the appropriate data from the
|
|
// wide loads.
|
|
for (unsigned I = 0; I < InterleaveFactor; ++I) {
|
|
Instruction *Member = Group->getMember(I);
|
|
|
|
// Skip the gaps in the group.
|
|
if (!Member)
|
|
continue;
|
|
|
|
Constant *StrideMask = createStrideMask(Builder, I, InterleaveFactor, VF);
|
|
for (unsigned Part = 0; Part < UF; Part++) {
|
|
Value *StridedVec = Builder.CreateShuffleVector(
|
|
NewLoads[Part], UndefVec, StrideMask, "strided.vec");
|
|
|
|
// If this member has different type, cast the result type.
|
|
if (Member->getType() != ScalarTy) {
|
|
VectorType *OtherVTy = VectorType::get(Member->getType(), VF);
|
|
StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL);
|
|
}
|
|
|
|
if (Group->isReverse())
|
|
StridedVec = reverseVector(StridedVec);
|
|
|
|
VectorLoopValueMap.setVectorValue(Member, Part, StridedVec);
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
|
|
// The sub vector type for current instruction.
|
|
VectorType *SubVT = VectorType::get(ScalarTy, VF);
|
|
|
|
// Vectorize the interleaved store group.
|
|
for (unsigned Part = 0; Part < UF; Part++) {
|
|
// Collect the stored vector from each member.
|
|
SmallVector<Value *, 4> StoredVecs;
|
|
for (unsigned i = 0; i < InterleaveFactor; i++) {
|
|
// Interleaved store group doesn't allow a gap, so each index has a member
|
|
Instruction *Member = Group->getMember(i);
|
|
assert(Member && "Fail to get a member from an interleaved store group");
|
|
|
|
Value *StoredVec = getOrCreateVectorValue(
|
|
cast<StoreInst>(Member)->getValueOperand(), Part);
|
|
if (Group->isReverse())
|
|
StoredVec = reverseVector(StoredVec);
|
|
|
|
// If this member has different type, cast it to a unified type.
|
|
|
|
if (StoredVec->getType() != SubVT)
|
|
StoredVec = createBitOrPointerCast(StoredVec, SubVT, DL);
|
|
|
|
StoredVecs.push_back(StoredVec);
|
|
}
|
|
|
|
// Concatenate all vectors into a wide vector.
|
|
Value *WideVec = concatenateVectors(Builder, StoredVecs);
|
|
|
|
// Interleave the elements in the wide vector.
|
|
Constant *IMask = createInterleaveMask(Builder, VF, InterleaveFactor);
|
|
Value *IVec = Builder.CreateShuffleVector(WideVec, UndefVec, IMask,
|
|
"interleaved.vec");
|
|
|
|
Instruction *NewStoreInstr =
|
|
Builder.CreateAlignedStore(IVec, NewPtrs[Part], Group->getAlignment());
|
|
addMetadata(NewStoreInstr, Instr);
|
|
}
|
|
}
|
|
|
|
void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr) {
|
|
// Attempt to issue a wide load.
|
|
LoadInst *LI = dyn_cast<LoadInst>(Instr);
|
|
StoreInst *SI = dyn_cast<StoreInst>(Instr);
|
|
|
|
assert((LI || SI) && "Invalid Load/Store instruction");
|
|
|
|
LoopVectorizationCostModel::InstWidening Decision =
|
|
Cost->getWideningDecision(Instr, VF);
|
|
assert(Decision != LoopVectorizationCostModel::CM_Unknown &&
|
|
"CM decision should be taken at this point");
|
|
if (Decision == LoopVectorizationCostModel::CM_Interleave)
|
|
return vectorizeInterleaveGroup(Instr);
|
|
|
|
Type *ScalarDataTy = getMemInstValueType(Instr);
|
|
Type *DataTy = VectorType::get(ScalarDataTy, VF);
|
|
Value *Ptr = getPointerOperand(Instr);
|
|
unsigned Alignment = getMemInstAlignment(Instr);
|
|
// An alignment of 0 means target abi alignment. We need to use the scalar's
|
|
// target abi alignment in such a case.
|
|
const DataLayout &DL = Instr->getModule()->getDataLayout();
|
|
if (!Alignment)
|
|
Alignment = DL.getABITypeAlignment(ScalarDataTy);
|
|
unsigned AddressSpace = getMemInstAddressSpace(Instr);
|
|
|
|
// Determine if the pointer operand of the access is either consecutive or
|
|
// reverse consecutive.
|
|
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
|
|
bool Reverse = ConsecutiveStride < 0;
|
|
bool CreateGatherScatter =
|
|
(Decision == LoopVectorizationCostModel::CM_GatherScatter);
|
|
|
|
// Either Ptr feeds a vector load/store, or a vector GEP should feed a vector
|
|
// gather/scatter. Otherwise Decision should have been to Scalarize.
|
|
assert((ConsecutiveStride || CreateGatherScatter) &&
|
|
"The instruction should be scalarized");
|
|
|
|
// Handle consecutive loads/stores.
|
|
if (ConsecutiveStride)
|
|
Ptr = getOrCreateScalarValue(Ptr, {0, 0});
|
|
|
|
VectorParts Mask = createBlockInMask(Instr->getParent());
|
|
// Handle Stores:
|
|
if (SI) {
|
|
assert(!Legal->isUniform(SI->getPointerOperand()) &&
|
|
"We do not allow storing to uniform addresses");
|
|
setDebugLocFromInst(Builder, SI);
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Instruction *NewSI = nullptr;
|
|
Value *StoredVal = getOrCreateVectorValue(SI->getValueOperand(), Part);
|
|
if (CreateGatherScatter) {
|
|
Value *MaskPart = Legal->isMaskRequired(SI) ? Mask[Part] : nullptr;
|
|
Value *VectorGep = getOrCreateVectorValue(Ptr, Part);
|
|
NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment,
|
|
MaskPart);
|
|
} else {
|
|
// Calculate the pointer for the specific unroll-part.
|
|
Value *PartPtr =
|
|
Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(Part * VF));
|
|
|
|
if (Reverse) {
|
|
// If we store to reverse consecutive memory locations, then we need
|
|
// to reverse the order of elements in the stored value.
|
|
StoredVal = reverseVector(StoredVal);
|
|
// We don't want to update the value in the map as it might be used in
|
|
// another expression. So don't call resetVectorValue(StoredVal).
|
|
|
|
// If the address is consecutive but reversed, then the
|
|
// wide store needs to start at the last vector element.
|
|
PartPtr =
|
|
Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(-Part * VF));
|
|
PartPtr =
|
|
Builder.CreateGEP(nullptr, PartPtr, Builder.getInt32(1 - VF));
|
|
if (Mask[Part]) // The reverse of a null all-one mask is a null mask.
|
|
Mask[Part] = reverseVector(Mask[Part]);
|
|
}
|
|
|
|
Value *VecPtr =
|
|
Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
|
|
|
|
if (Legal->isMaskRequired(SI) && Mask[Part])
|
|
NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment,
|
|
Mask[Part]);
|
|
else
|
|
NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment);
|
|
}
|
|
addMetadata(NewSI, SI);
|
|
}
|
|
return;
|
|
}
|
|
|
|
// Handle loads.
|
|
assert(LI && "Must have a load instruction");
|
|
setDebugLocFromInst(Builder, LI);
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *NewLI;
|
|
if (CreateGatherScatter) {
|
|
Value *MaskPart = Legal->isMaskRequired(LI) ? Mask[Part] : nullptr;
|
|
Value *VectorGep = getOrCreateVectorValue(Ptr, Part);
|
|
NewLI = Builder.CreateMaskedGather(VectorGep, Alignment, MaskPart,
|
|
nullptr, "wide.masked.gather");
|
|
addMetadata(NewLI, LI);
|
|
} else {
|
|
// Calculate the pointer for the specific unroll-part.
|
|
Value *PartPtr =
|
|
Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(Part * VF));
|
|
|
|
if (Reverse) {
|
|
// If the address is consecutive but reversed, then the
|
|
// wide load needs to start at the last vector element.
|
|
PartPtr = Builder.CreateGEP(nullptr, Ptr, Builder.getInt32(-Part * VF));
|
|
PartPtr = Builder.CreateGEP(nullptr, PartPtr, Builder.getInt32(1 - VF));
|
|
if (Mask[Part]) // The reverse of a null all-one mask is a null mask.
|
|
Mask[Part] = reverseVector(Mask[Part]);
|
|
}
|
|
|
|
Value *VecPtr =
|
|
Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
|
|
if (Legal->isMaskRequired(LI) && Mask[Part])
|
|
NewLI = Builder.CreateMaskedLoad(VecPtr, Alignment, Mask[Part],
|
|
UndefValue::get(DataTy),
|
|
"wide.masked.load");
|
|
else
|
|
NewLI = Builder.CreateAlignedLoad(VecPtr, Alignment, "wide.load");
|
|
|
|
// Add metadata to the load, but setVectorValue to the reverse shuffle.
|
|
addMetadata(NewLI, LI);
|
|
if (Reverse)
|
|
NewLI = reverseVector(NewLI);
|
|
}
|
|
VectorLoopValueMap.setVectorValue(Instr, Part, NewLI);
|
|
}
|
|
}
|
|
|
|
void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr,
|
|
const VPIteration &Instance,
|
|
bool IfPredicateInstr) {
|
|
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
|
|
|
|
setDebugLocFromInst(Builder, Instr);
|
|
|
|
// Does this instruction return a value ?
|
|
bool IsVoidRetTy = Instr->getType()->isVoidTy();
|
|
|
|
Instruction *Cloned = Instr->clone();
|
|
if (!IsVoidRetTy)
|
|
Cloned->setName(Instr->getName() + ".cloned");
|
|
|
|
// Replace the operands of the cloned instructions with their scalar
|
|
// equivalents in the new loop.
|
|
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
|
|
auto *NewOp = getOrCreateScalarValue(Instr->getOperand(op), Instance);
|
|
Cloned->setOperand(op, NewOp);
|
|
}
|
|
addNewMetadata(Cloned, Instr);
|
|
|
|
// Place the cloned scalar in the new loop.
|
|
Builder.Insert(Cloned);
|
|
|
|
// Add the cloned scalar to the scalar map entry.
|
|
VectorLoopValueMap.setScalarValue(Instr, Instance, Cloned);
|
|
|
|
// If we just cloned a new assumption, add it the assumption cache.
|
|
if (auto *II = dyn_cast<IntrinsicInst>(Cloned))
|
|
if (II->getIntrinsicID() == Intrinsic::assume)
|
|
AC->registerAssumption(II);
|
|
|
|
// End if-block.
|
|
if (IfPredicateInstr)
|
|
PredicatedInstructions.push_back(Cloned);
|
|
}
|
|
|
|
PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start,
|
|
Value *End, Value *Step,
|
|
Instruction *DL) {
|
|
BasicBlock *Header = L->getHeader();
|
|
BasicBlock *Latch = L->getLoopLatch();
|
|
// As we're just creating this loop, it's possible no latch exists
|
|
// yet. If so, use the header as this will be a single block loop.
|
|
if (!Latch)
|
|
Latch = Header;
|
|
|
|
IRBuilder<> Builder(&*Header->getFirstInsertionPt());
|
|
Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction);
|
|
setDebugLocFromInst(Builder, OldInst);
|
|
auto *Induction = Builder.CreatePHI(Start->getType(), 2, "index");
|
|
|
|
Builder.SetInsertPoint(Latch->getTerminator());
|
|
setDebugLocFromInst(Builder, OldInst);
|
|
|
|
// Create i+1 and fill the PHINode.
|
|
Value *Next = Builder.CreateAdd(Induction, Step, "index.next");
|
|
Induction->addIncoming(Start, L->getLoopPreheader());
|
|
Induction->addIncoming(Next, Latch);
|
|
// Create the compare.
|
|
Value *ICmp = Builder.CreateICmpEQ(Next, End);
|
|
Builder.CreateCondBr(ICmp, L->getExitBlock(), Header);
|
|
|
|
// Now we have two terminators. Remove the old one from the block.
|
|
Latch->getTerminator()->eraseFromParent();
|
|
|
|
return Induction;
|
|
}
|
|
|
|
Value *InnerLoopVectorizer::getOrCreateTripCount(Loop *L) {
|
|
if (TripCount)
|
|
return TripCount;
|
|
|
|
IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
|
|
// Find the loop boundaries.
|
|
ScalarEvolution *SE = PSE.getSE();
|
|
const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount();
|
|
assert(BackedgeTakenCount != SE->getCouldNotCompute() &&
|
|
"Invalid loop count");
|
|
|
|
Type *IdxTy = Legal->getWidestInductionType();
|
|
|
|
// The exit count might have the type of i64 while the phi is i32. This can
|
|
// happen if we have an induction variable that is sign extended before the
|
|
// compare. The only way that we get a backedge taken count is that the
|
|
// induction variable was signed and as such will not overflow. In such a case
|
|
// truncation is legal.
|
|
if (BackedgeTakenCount->getType()->getPrimitiveSizeInBits() >
|
|
IdxTy->getPrimitiveSizeInBits())
|
|
BackedgeTakenCount = SE->getTruncateOrNoop(BackedgeTakenCount, IdxTy);
|
|
BackedgeTakenCount = SE->getNoopOrZeroExtend(BackedgeTakenCount, IdxTy);
|
|
|
|
// Get the total trip count from the count by adding 1.
|
|
const SCEV *ExitCount = SE->getAddExpr(
|
|
BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType()));
|
|
|
|
const DataLayout &DL = L->getHeader()->getModule()->getDataLayout();
|
|
|
|
// Expand the trip count and place the new instructions in the preheader.
|
|
// Notice that the pre-header does not change, only the loop body.
|
|
SCEVExpander Exp(*SE, DL, "induction");
|
|
|
|
// Count holds the overall loop count (N).
|
|
TripCount = Exp.expandCodeFor(ExitCount, ExitCount->getType(),
|
|
L->getLoopPreheader()->getTerminator());
|
|
|
|
if (TripCount->getType()->isPointerTy())
|
|
TripCount =
|
|
CastInst::CreatePointerCast(TripCount, IdxTy, "exitcount.ptrcnt.to.int",
|
|
L->getLoopPreheader()->getTerminator());
|
|
|
|
return TripCount;
|
|
}
|
|
|
|
Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) {
|
|
if (VectorTripCount)
|
|
return VectorTripCount;
|
|
|
|
Value *TC = getOrCreateTripCount(L);
|
|
IRBuilder<> Builder(L->getLoopPreheader()->getTerminator());
|
|
|
|
// Now we need to generate the expression for the part of the loop that the
|
|
// vectorized body will execute. This is equal to N - (N % Step) if scalar
|
|
// iterations are not required for correctness, or N - Step, otherwise. Step
|
|
// is equal to the vectorization factor (number of SIMD elements) times the
|
|
// unroll factor (number of SIMD instructions).
|
|
Constant *Step = ConstantInt::get(TC->getType(), VF * UF);
|
|
Value *R = Builder.CreateURem(TC, Step, "n.mod.vf");
|
|
|
|
// If there is a non-reversed interleaved group that may speculatively access
|
|
// memory out-of-bounds, we need to ensure that there will be at least one
|
|
// iteration of the scalar epilogue loop. Thus, if the step evenly divides
|
|
// the trip count, we set the remainder to be equal to the step. If the step
|
|
// does not evenly divide the trip count, no adjustment is necessary since
|
|
// there will already be scalar iterations. Note that the minimum iterations
|
|
// check ensures that N >= Step.
|
|
if (VF > 1 && Legal->requiresScalarEpilogue()) {
|
|
auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0));
|
|
R = Builder.CreateSelect(IsZero, Step, R);
|
|
}
|
|
|
|
VectorTripCount = Builder.CreateSub(TC, R, "n.vec");
|
|
|
|
return VectorTripCount;
|
|
}
|
|
|
|
Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy,
|
|
const DataLayout &DL) {
|
|
// Verify that V is a vector type with same number of elements as DstVTy.
|
|
unsigned VF = DstVTy->getNumElements();
|
|
VectorType *SrcVecTy = cast<VectorType>(V->getType());
|
|
assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match");
|
|
Type *SrcElemTy = SrcVecTy->getElementType();
|
|
Type *DstElemTy = DstVTy->getElementType();
|
|
assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) &&
|
|
"Vector elements must have same size");
|
|
|
|
// Do a direct cast if element types are castable.
|
|
if (CastInst::isBitOrNoopPointerCastable(SrcElemTy, DstElemTy, DL)) {
|
|
return Builder.CreateBitOrPointerCast(V, DstVTy);
|
|
}
|
|
// V cannot be directly casted to desired vector type.
|
|
// May happen when V is a floating point vector but DstVTy is a vector of
|
|
// pointers or vice-versa. Handle this using a two-step bitcast using an
|
|
// intermediate Integer type for the bitcast i.e. Ptr <-> Int <-> Float.
|
|
assert((DstElemTy->isPointerTy() != SrcElemTy->isPointerTy()) &&
|
|
"Only one type should be a pointer type");
|
|
assert((DstElemTy->isFloatingPointTy() != SrcElemTy->isFloatingPointTy()) &&
|
|
"Only one type should be a floating point type");
|
|
Type *IntTy =
|
|
IntegerType::getIntNTy(V->getContext(), DL.getTypeSizeInBits(SrcElemTy));
|
|
VectorType *VecIntTy = VectorType::get(IntTy, VF);
|
|
Value *CastVal = Builder.CreateBitOrPointerCast(V, VecIntTy);
|
|
return Builder.CreateBitOrPointerCast(CastVal, DstVTy);
|
|
}
|
|
|
|
void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L,
|
|
BasicBlock *Bypass) {
|
|
Value *Count = getOrCreateTripCount(L);
|
|
BasicBlock *BB = L->getLoopPreheader();
|
|
IRBuilder<> Builder(BB->getTerminator());
|
|
|
|
// Generate code to check if the loop's trip count is less than VF * UF, or
|
|
// equal to it in case a scalar epilogue is required; this implies that the
|
|
// vector trip count is zero. This check also covers the case where adding one
|
|
// to the backedge-taken count overflowed leading to an incorrect trip count
|
|
// of zero. In this case we will also jump to the scalar loop.
|
|
auto P = Legal->requiresScalarEpilogue() ? ICmpInst::ICMP_ULE
|
|
: ICmpInst::ICMP_ULT;
|
|
Value *CheckMinIters = Builder.CreateICmp(
|
|
P, Count, ConstantInt::get(Count->getType(), VF * UF), "min.iters.check");
|
|
|
|
BasicBlock *NewBB = BB->splitBasicBlock(BB->getTerminator(), "vector.ph");
|
|
// Update dominator tree immediately if the generated block is a
|
|
// LoopBypassBlock because SCEV expansions to generate loop bypass
|
|
// checks may query it before the current function is finished.
|
|
DT->addNewBlock(NewBB, BB);
|
|
if (L->getParentLoop())
|
|
L->getParentLoop()->addBasicBlockToLoop(NewBB, *LI);
|
|
ReplaceInstWithInst(BB->getTerminator(),
|
|
BranchInst::Create(Bypass, NewBB, CheckMinIters));
|
|
LoopBypassBlocks.push_back(BB);
|
|
}
|
|
|
|
void InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) {
|
|
BasicBlock *BB = L->getLoopPreheader();
|
|
|
|
// Generate the code to check that the SCEV assumptions that we made.
|
|
// We want the new basic block to start at the first instruction in a
|
|
// sequence of instructions that form a check.
|
|
SCEVExpander Exp(*PSE.getSE(), Bypass->getModule()->getDataLayout(),
|
|
"scev.check");
|
|
Value *SCEVCheck =
|
|
Exp.expandCodeForPredicate(&PSE.getUnionPredicate(), BB->getTerminator());
|
|
|
|
if (auto *C = dyn_cast<ConstantInt>(SCEVCheck))
|
|
if (C->isZero())
|
|
return;
|
|
|
|
// Create a new block containing the stride check.
|
|
BB->setName("vector.scevcheck");
|
|
auto *NewBB = BB->splitBasicBlock(BB->getTerminator(), "vector.ph");
|
|
// Update dominator tree immediately if the generated block is a
|
|
// LoopBypassBlock because SCEV expansions to generate loop bypass
|
|
// checks may query it before the current function is finished.
|
|
DT->addNewBlock(NewBB, BB);
|
|
if (L->getParentLoop())
|
|
L->getParentLoop()->addBasicBlockToLoop(NewBB, *LI);
|
|
ReplaceInstWithInst(BB->getTerminator(),
|
|
BranchInst::Create(Bypass, NewBB, SCEVCheck));
|
|
LoopBypassBlocks.push_back(BB);
|
|
AddedSafetyChecks = true;
|
|
}
|
|
|
|
void InnerLoopVectorizer::emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass) {
|
|
BasicBlock *BB = L->getLoopPreheader();
|
|
|
|
// Generate the code that checks in runtime if arrays overlap. We put the
|
|
// checks into a separate block to make the more common case of few elements
|
|
// faster.
|
|
Instruction *FirstCheckInst;
|
|
Instruction *MemRuntimeCheck;
|
|
std::tie(FirstCheckInst, MemRuntimeCheck) =
|
|
Legal->getLAI()->addRuntimeChecks(BB->getTerminator());
|
|
if (!MemRuntimeCheck)
|
|
return;
|
|
|
|
// Create a new block containing the memory check.
|
|
BB->setName("vector.memcheck");
|
|
auto *NewBB = BB->splitBasicBlock(BB->getTerminator(), "vector.ph");
|
|
// Update dominator tree immediately if the generated block is a
|
|
// LoopBypassBlock because SCEV expansions to generate loop bypass
|
|
// checks may query it before the current function is finished.
|
|
DT->addNewBlock(NewBB, BB);
|
|
if (L->getParentLoop())
|
|
L->getParentLoop()->addBasicBlockToLoop(NewBB, *LI);
|
|
ReplaceInstWithInst(BB->getTerminator(),
|
|
BranchInst::Create(Bypass, NewBB, MemRuntimeCheck));
|
|
LoopBypassBlocks.push_back(BB);
|
|
AddedSafetyChecks = true;
|
|
|
|
// We currently don't use LoopVersioning for the actual loop cloning but we
|
|
// still use it to add the noalias metadata.
|
|
LVer = llvm::make_unique<LoopVersioning>(*Legal->getLAI(), OrigLoop, LI, DT,
|
|
PSE.getSE());
|
|
LVer->prepareNoAliasMetadata();
|
|
}
|
|
|
|
BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() {
|
|
/*
|
|
In this function we generate a new loop. The new loop will contain
|
|
the vectorized instructions while the old loop will continue to run the
|
|
scalar remainder.
|
|
|
|
[ ] <-- loop iteration number check.
|
|
/ |
|
|
/ v
|
|
| [ ] <-- vector loop bypass (may consist of multiple blocks).
|
|
| / |
|
|
| / v
|
|
|| [ ] <-- vector pre header.
|
|
|/ |
|
|
| v
|
|
| [ ] \
|
|
| [ ]_| <-- vector loop.
|
|
| |
|
|
| v
|
|
| -[ ] <--- middle-block.
|
|
| / |
|
|
| / v
|
|
-|- >[ ] <--- new preheader.
|
|
| |
|
|
| v
|
|
| [ ] \
|
|
| [ ]_| <-- old scalar loop to handle remainder.
|
|
\ |
|
|
\ v
|
|
>[ ] <-- exit block.
|
|
...
|
|
*/
|
|
|
|
BasicBlock *OldBasicBlock = OrigLoop->getHeader();
|
|
BasicBlock *VectorPH = OrigLoop->getLoopPreheader();
|
|
BasicBlock *ExitBlock = OrigLoop->getExitBlock();
|
|
assert(VectorPH && "Invalid loop structure");
|
|
assert(ExitBlock && "Must have an exit block");
|
|
|
|
// Some loops have a single integer induction variable, while other loops
|
|
// don't. One example is c++ iterators that often have multiple pointer
|
|
// induction variables. In the code below we also support a case where we
|
|
// don't have a single induction variable.
|
|
//
|
|
// We try to obtain an induction variable from the original loop as hard
|
|
// as possible. However if we don't find one that:
|
|
// - is an integer
|
|
// - counts from zero, stepping by one
|
|
// - is the size of the widest induction variable type
|
|
// then we create a new one.
|
|
OldInduction = Legal->getPrimaryInduction();
|
|
Type *IdxTy = Legal->getWidestInductionType();
|
|
|
|
// Split the single block loop into the two loop structure described above.
|
|
BasicBlock *VecBody =
|
|
VectorPH->splitBasicBlock(VectorPH->getTerminator(), "vector.body");
|
|
BasicBlock *MiddleBlock =
|
|
VecBody->splitBasicBlock(VecBody->getTerminator(), "middle.block");
|
|
BasicBlock *ScalarPH =
|
|
MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(), "scalar.ph");
|
|
|
|
// Create and register the new vector loop.
|
|
Loop *Lp = new Loop();
|
|
Loop *ParentLoop = OrigLoop->getParentLoop();
|
|
|
|
// Insert the new loop into the loop nest and register the new basic blocks
|
|
// before calling any utilities such as SCEV that require valid LoopInfo.
|
|
if (ParentLoop) {
|
|
ParentLoop->addChildLoop(Lp);
|
|
ParentLoop->addBasicBlockToLoop(ScalarPH, *LI);
|
|
ParentLoop->addBasicBlockToLoop(MiddleBlock, *LI);
|
|
} else {
|
|
LI->addTopLevelLoop(Lp);
|
|
}
|
|
Lp->addBasicBlockToLoop(VecBody, *LI);
|
|
|
|
// Find the loop boundaries.
|
|
Value *Count = getOrCreateTripCount(Lp);
|
|
|
|
Value *StartIdx = ConstantInt::get(IdxTy, 0);
|
|
|
|
// Now, compare the new count to zero. If it is zero skip the vector loop and
|
|
// jump to the scalar loop. This check also covers the case where the
|
|
// backedge-taken count is uint##_max: adding one to it will overflow leading
|
|
// to an incorrect trip count of zero. In this (rare) case we will also jump
|
|
// to the scalar loop.
|
|
emitMinimumIterationCountCheck(Lp, ScalarPH);
|
|
|
|
// Generate the code to check any assumptions that we've made for SCEV
|
|
// expressions.
|
|
emitSCEVChecks(Lp, ScalarPH);
|
|
|
|
// Generate the code that checks in runtime if arrays overlap. We put the
|
|
// checks into a separate block to make the more common case of few elements
|
|
// faster.
|
|
emitMemRuntimeChecks(Lp, ScalarPH);
|
|
|
|
// Generate the induction variable.
|
|
// The loop step is equal to the vectorization factor (num of SIMD elements)
|
|
// times the unroll factor (num of SIMD instructions).
|
|
Value *CountRoundDown = getOrCreateVectorTripCount(Lp);
|
|
Constant *Step = ConstantInt::get(IdxTy, VF * UF);
|
|
Induction =
|
|
createInductionVariable(Lp, StartIdx, CountRoundDown, Step,
|
|
getDebugLocFromInstOrOperands(OldInduction));
|
|
|
|
// We are going to resume the execution of the scalar loop.
|
|
// Go over all of the induction variables that we found and fix the
|
|
// PHIs that are left in the scalar version of the loop.
|
|
// The starting values of PHI nodes depend on the counter of the last
|
|
// iteration in the vectorized loop.
|
|
// If we come from a bypass edge then we need to start from the original
|
|
// start value.
|
|
|
|
// This variable saves the new starting index for the scalar loop. It is used
|
|
// to test if there are any tail iterations left once the vector loop has
|
|
// completed.
|
|
LoopVectorizationLegality::InductionList *List = Legal->getInductionVars();
|
|
for (auto &InductionEntry : *List) {
|
|
PHINode *OrigPhi = InductionEntry.first;
|
|
InductionDescriptor II = InductionEntry.second;
|
|
|
|
// Create phi nodes to merge from the backedge-taken check block.
|
|
PHINode *BCResumeVal = PHINode::Create(
|
|
OrigPhi->getType(), 3, "bc.resume.val", ScalarPH->getTerminator());
|
|
Value *&EndValue = IVEndValues[OrigPhi];
|
|
if (OrigPhi == OldInduction) {
|
|
// We know what the end value is.
|
|
EndValue = CountRoundDown;
|
|
} else {
|
|
IRBuilder<> B(Lp->getLoopPreheader()->getTerminator());
|
|
Type *StepType = II.getStep()->getType();
|
|
Instruction::CastOps CastOp =
|
|
CastInst::getCastOpcode(CountRoundDown, true, StepType, true);
|
|
Value *CRD = B.CreateCast(CastOp, CountRoundDown, StepType, "cast.crd");
|
|
const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
|
|
EndValue = II.transform(B, CRD, PSE.getSE(), DL);
|
|
EndValue->setName("ind.end");
|
|
}
|
|
|
|
// The new PHI merges the original incoming value, in case of a bypass,
|
|
// or the value at the end of the vectorized loop.
|
|
BCResumeVal->addIncoming(EndValue, MiddleBlock);
|
|
|
|
// Fix the scalar body counter (PHI node).
|
|
unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH);
|
|
|
|
// The old induction's phi node in the scalar body needs the truncated
|
|
// value.
|
|
for (BasicBlock *BB : LoopBypassBlocks)
|
|
BCResumeVal->addIncoming(II.getStartValue(), BB);
|
|
OrigPhi->setIncomingValue(BlockIdx, BCResumeVal);
|
|
}
|
|
|
|
// Add a check in the middle block to see if we have completed
|
|
// all of the iterations in the first vector loop.
|
|
// If (N - N%VF) == N, then we *don't* need to run the remainder.
|
|
Value *CmpN =
|
|
CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, Count,
|
|
CountRoundDown, "cmp.n", MiddleBlock->getTerminator());
|
|
ReplaceInstWithInst(MiddleBlock->getTerminator(),
|
|
BranchInst::Create(ExitBlock, ScalarPH, CmpN));
|
|
|
|
// Get ready to start creating new instructions into the vectorized body.
|
|
Builder.SetInsertPoint(&*VecBody->getFirstInsertionPt());
|
|
|
|
// Save the state.
|
|
LoopVectorPreHeader = Lp->getLoopPreheader();
|
|
LoopScalarPreHeader = ScalarPH;
|
|
LoopMiddleBlock = MiddleBlock;
|
|
LoopExitBlock = ExitBlock;
|
|
LoopVectorBody = VecBody;
|
|
LoopScalarBody = OldBasicBlock;
|
|
|
|
// Keep all loop hints from the original loop on the vector loop (we'll
|
|
// replace the vectorizer-specific hints below).
|
|
if (MDNode *LID = OrigLoop->getLoopID())
|
|
Lp->setLoopID(LID);
|
|
|
|
LoopVectorizeHints Hints(Lp, true, *ORE);
|
|
Hints.setAlreadyVectorized();
|
|
|
|
return LoopVectorPreHeader;
|
|
}
|
|
|
|
// Fix up external users of the induction variable. At this point, we are
|
|
// in LCSSA form, with all external PHIs that use the IV having one input value,
|
|
// coming from the remainder loop. We need those PHIs to also have a correct
|
|
// value for the IV when arriving directly from the middle block.
|
|
void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi,
|
|
const InductionDescriptor &II,
|
|
Value *CountRoundDown, Value *EndValue,
|
|
BasicBlock *MiddleBlock) {
|
|
// There are two kinds of external IV usages - those that use the value
|
|
// computed in the last iteration (the PHI) and those that use the penultimate
|
|
// value (the value that feeds into the phi from the loop latch).
|
|
// We allow both, but they, obviously, have different values.
|
|
|
|
assert(OrigLoop->getExitBlock() && "Expected a single exit block");
|
|
|
|
DenseMap<Value *, Value *> MissingVals;
|
|
|
|
// An external user of the last iteration's value should see the value that
|
|
// the remainder loop uses to initialize its own IV.
|
|
Value *PostInc = OrigPhi->getIncomingValueForBlock(OrigLoop->getLoopLatch());
|
|
for (User *U : PostInc->users()) {
|
|
Instruction *UI = cast<Instruction>(U);
|
|
if (!OrigLoop->contains(UI)) {
|
|
assert(isa<PHINode>(UI) && "Expected LCSSA form");
|
|
MissingVals[UI] = EndValue;
|
|
}
|
|
}
|
|
|
|
// An external user of the penultimate value need to see EndValue - Step.
|
|
// The simplest way to get this is to recompute it from the constituent SCEVs,
|
|
// that is Start + (Step * (CRD - 1)).
|
|
for (User *U : OrigPhi->users()) {
|
|
auto *UI = cast<Instruction>(U);
|
|
if (!OrigLoop->contains(UI)) {
|
|
const DataLayout &DL =
|
|
OrigLoop->getHeader()->getModule()->getDataLayout();
|
|
assert(isa<PHINode>(UI) && "Expected LCSSA form");
|
|
|
|
IRBuilder<> B(MiddleBlock->getTerminator());
|
|
Value *CountMinusOne = B.CreateSub(
|
|
CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1));
|
|
Value *CMO =
|
|
!II.getStep()->getType()->isIntegerTy()
|
|
? B.CreateCast(Instruction::SIToFP, CountMinusOne,
|
|
II.getStep()->getType())
|
|
: B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType());
|
|
CMO->setName("cast.cmo");
|
|
Value *Escape = II.transform(B, CMO, PSE.getSE(), DL);
|
|
Escape->setName("ind.escape");
|
|
MissingVals[UI] = Escape;
|
|
}
|
|
}
|
|
|
|
for (auto &I : MissingVals) {
|
|
PHINode *PHI = cast<PHINode>(I.first);
|
|
// One corner case we have to handle is two IVs "chasing" each-other,
|
|
// that is %IV2 = phi [...], [ %IV1, %latch ]
|
|
// In this case, if IV1 has an external use, we need to avoid adding both
|
|
// "last value of IV1" and "penultimate value of IV2". So, verify that we
|
|
// don't already have an incoming value for the middle block.
|
|
if (PHI->getBasicBlockIndex(MiddleBlock) == -1)
|
|
PHI->addIncoming(I.second, MiddleBlock);
|
|
}
|
|
}
|
|
|
|
namespace {
|
|
struct CSEDenseMapInfo {
|
|
static bool canHandle(const Instruction *I) {
|
|
return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) ||
|
|
isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I);
|
|
}
|
|
static inline Instruction *getEmptyKey() {
|
|
return DenseMapInfo<Instruction *>::getEmptyKey();
|
|
}
|
|
static inline Instruction *getTombstoneKey() {
|
|
return DenseMapInfo<Instruction *>::getTombstoneKey();
|
|
}
|
|
static unsigned getHashValue(const Instruction *I) {
|
|
assert(canHandle(I) && "Unknown instruction!");
|
|
return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(),
|
|
I->value_op_end()));
|
|
}
|
|
static bool isEqual(const Instruction *LHS, const Instruction *RHS) {
|
|
if (LHS == getEmptyKey() || RHS == getEmptyKey() ||
|
|
LHS == getTombstoneKey() || RHS == getTombstoneKey())
|
|
return LHS == RHS;
|
|
return LHS->isIdenticalTo(RHS);
|
|
}
|
|
};
|
|
}
|
|
|
|
///\brief Perform cse of induction variable instructions.
|
|
static void cse(BasicBlock *BB) {
|
|
// Perform simple cse.
|
|
SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap;
|
|
for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E;) {
|
|
Instruction *In = &*I++;
|
|
|
|
if (!CSEDenseMapInfo::canHandle(In))
|
|
continue;
|
|
|
|
// Check if we can replace this instruction with any of the
|
|
// visited instructions.
|
|
if (Instruction *V = CSEMap.lookup(In)) {
|
|
In->replaceAllUsesWith(V);
|
|
In->eraseFromParent();
|
|
continue;
|
|
}
|
|
|
|
CSEMap[In] = In;
|
|
}
|
|
}
|
|
|
|
/// \brief Estimate the overhead of scalarizing an instruction. This is a
|
|
/// convenience wrapper for the type-based getScalarizationOverhead API.
|
|
static unsigned getScalarizationOverhead(Instruction *I, unsigned VF,
|
|
const TargetTransformInfo &TTI) {
|
|
if (VF == 1)
|
|
return 0;
|
|
|
|
unsigned Cost = 0;
|
|
Type *RetTy = ToVectorTy(I->getType(), VF);
|
|
if (!RetTy->isVoidTy() &&
|
|
(!isa<LoadInst>(I) ||
|
|
!TTI.supportsEfficientVectorElementLoadStore()))
|
|
Cost += TTI.getScalarizationOverhead(RetTy, true, false);
|
|
|
|
if (CallInst *CI = dyn_cast<CallInst>(I)) {
|
|
SmallVector<const Value *, 4> Operands(CI->arg_operands());
|
|
Cost += TTI.getOperandsScalarizationOverhead(Operands, VF);
|
|
}
|
|
else if (!isa<StoreInst>(I) ||
|
|
!TTI.supportsEfficientVectorElementLoadStore()) {
|
|
SmallVector<const Value *, 4> Operands(I->operand_values());
|
|
Cost += TTI.getOperandsScalarizationOverhead(Operands, VF);
|
|
}
|
|
|
|
return Cost;
|
|
}
|
|
|
|
// Estimate cost of a call instruction CI if it were vectorized with factor VF.
|
|
// Return the cost of the instruction, including scalarization overhead if it's
|
|
// needed. The flag NeedToScalarize shows if the call needs to be scalarized -
|
|
// i.e. either vector version isn't available, or is too expensive.
|
|
static unsigned getVectorCallCost(CallInst *CI, unsigned VF,
|
|
const TargetTransformInfo &TTI,
|
|
const TargetLibraryInfo *TLI,
|
|
bool &NeedToScalarize) {
|
|
Function *F = CI->getCalledFunction();
|
|
StringRef FnName = CI->getCalledFunction()->getName();
|
|
Type *ScalarRetTy = CI->getType();
|
|
SmallVector<Type *, 4> Tys, ScalarTys;
|
|
for (auto &ArgOp : CI->arg_operands())
|
|
ScalarTys.push_back(ArgOp->getType());
|
|
|
|
// Estimate cost of scalarized vector call. The source operands are assumed
|
|
// to be vectors, so we need to extract individual elements from there,
|
|
// execute VF scalar calls, and then gather the result into the vector return
|
|
// value.
|
|
unsigned ScalarCallCost = TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys);
|
|
if (VF == 1)
|
|
return ScalarCallCost;
|
|
|
|
// Compute corresponding vector type for return value and arguments.
|
|
Type *RetTy = ToVectorTy(ScalarRetTy, VF);
|
|
for (Type *ScalarTy : ScalarTys)
|
|
Tys.push_back(ToVectorTy(ScalarTy, VF));
|
|
|
|
// Compute costs of unpacking argument values for the scalar calls and
|
|
// packing the return values to a vector.
|
|
unsigned ScalarizationCost = getScalarizationOverhead(CI, VF, TTI);
|
|
|
|
unsigned Cost = ScalarCallCost * VF + ScalarizationCost;
|
|
|
|
// If we can't emit a vector call for this function, then the currently found
|
|
// cost is the cost we need to return.
|
|
NeedToScalarize = true;
|
|
if (!TLI || !TLI->isFunctionVectorizable(FnName, VF) || CI->isNoBuiltin())
|
|
return Cost;
|
|
|
|
// If the corresponding vector cost is cheaper, return its cost.
|
|
unsigned VectorCallCost = TTI.getCallInstrCost(nullptr, RetTy, Tys);
|
|
if (VectorCallCost < Cost) {
|
|
NeedToScalarize = false;
|
|
return VectorCallCost;
|
|
}
|
|
return Cost;
|
|
}
|
|
|
|
// Estimate cost of an intrinsic call instruction CI if it were vectorized with
|
|
// factor VF. Return the cost of the instruction, including scalarization
|
|
// overhead if it's needed.
|
|
static unsigned getVectorIntrinsicCost(CallInst *CI, unsigned VF,
|
|
const TargetTransformInfo &TTI,
|
|
const TargetLibraryInfo *TLI) {
|
|
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
|
|
assert(ID && "Expected intrinsic call!");
|
|
|
|
FastMathFlags FMF;
|
|
if (auto *FPMO = dyn_cast<FPMathOperator>(CI))
|
|
FMF = FPMO->getFastMathFlags();
|
|
|
|
SmallVector<Value *, 4> Operands(CI->arg_operands());
|
|
return TTI.getIntrinsicInstrCost(ID, CI->getType(), Operands, FMF, VF);
|
|
}
|
|
|
|
static Type *smallestIntegerVectorType(Type *T1, Type *T2) {
|
|
auto *I1 = cast<IntegerType>(T1->getVectorElementType());
|
|
auto *I2 = cast<IntegerType>(T2->getVectorElementType());
|
|
return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2;
|
|
}
|
|
static Type *largestIntegerVectorType(Type *T1, Type *T2) {
|
|
auto *I1 = cast<IntegerType>(T1->getVectorElementType());
|
|
auto *I2 = cast<IntegerType>(T2->getVectorElementType());
|
|
return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2;
|
|
}
|
|
|
|
void InnerLoopVectorizer::truncateToMinimalBitwidths() {
|
|
// For every instruction `I` in MinBWs, truncate the operands, create a
|
|
// truncated version of `I` and reextend its result. InstCombine runs
|
|
// later and will remove any ext/trunc pairs.
|
|
//
|
|
SmallPtrSet<Value *, 4> Erased;
|
|
for (const auto &KV : Cost->getMinimalBitwidths()) {
|
|
// If the value wasn't vectorized, we must maintain the original scalar
|
|
// type. The absence of the value from VectorLoopValueMap indicates that it
|
|
// wasn't vectorized.
|
|
if (!VectorLoopValueMap.hasAnyVectorValue(KV.first))
|
|
continue;
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *I = getOrCreateVectorValue(KV.first, Part);
|
|
if (Erased.count(I) || I->use_empty() || !isa<Instruction>(I))
|
|
continue;
|
|
Type *OriginalTy = I->getType();
|
|
Type *ScalarTruncatedTy =
|
|
IntegerType::get(OriginalTy->getContext(), KV.second);
|
|
Type *TruncatedTy = VectorType::get(ScalarTruncatedTy,
|
|
OriginalTy->getVectorNumElements());
|
|
if (TruncatedTy == OriginalTy)
|
|
continue;
|
|
|
|
IRBuilder<> B(cast<Instruction>(I));
|
|
auto ShrinkOperand = [&](Value *V) -> Value * {
|
|
if (auto *ZI = dyn_cast<ZExtInst>(V))
|
|
if (ZI->getSrcTy() == TruncatedTy)
|
|
return ZI->getOperand(0);
|
|
return B.CreateZExtOrTrunc(V, TruncatedTy);
|
|
};
|
|
|
|
// The actual instruction modification depends on the instruction type,
|
|
// unfortunately.
|
|
Value *NewI = nullptr;
|
|
if (auto *BO = dyn_cast<BinaryOperator>(I)) {
|
|
NewI = B.CreateBinOp(BO->getOpcode(), ShrinkOperand(BO->getOperand(0)),
|
|
ShrinkOperand(BO->getOperand(1)));
|
|
|
|
// Any wrapping introduced by shrinking this operation shouldn't be
|
|
// considered undefined behavior. So, we can't unconditionally copy
|
|
// arithmetic wrapping flags to NewI.
|
|
cast<BinaryOperator>(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false);
|
|
} else if (auto *CI = dyn_cast<ICmpInst>(I)) {
|
|
NewI =
|
|
B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)),
|
|
ShrinkOperand(CI->getOperand(1)));
|
|
} else if (auto *SI = dyn_cast<SelectInst>(I)) {
|
|
NewI = B.CreateSelect(SI->getCondition(),
|
|
ShrinkOperand(SI->getTrueValue()),
|
|
ShrinkOperand(SI->getFalseValue()));
|
|
} else if (auto *CI = dyn_cast<CastInst>(I)) {
|
|
switch (CI->getOpcode()) {
|
|
default:
|
|
llvm_unreachable("Unhandled cast!");
|
|
case Instruction::Trunc:
|
|
NewI = ShrinkOperand(CI->getOperand(0));
|
|
break;
|
|
case Instruction::SExt:
|
|
NewI = B.CreateSExtOrTrunc(
|
|
CI->getOperand(0),
|
|
smallestIntegerVectorType(OriginalTy, TruncatedTy));
|
|
break;
|
|
case Instruction::ZExt:
|
|
NewI = B.CreateZExtOrTrunc(
|
|
CI->getOperand(0),
|
|
smallestIntegerVectorType(OriginalTy, TruncatedTy));
|
|
break;
|
|
}
|
|
} else if (auto *SI = dyn_cast<ShuffleVectorInst>(I)) {
|
|
auto Elements0 = SI->getOperand(0)->getType()->getVectorNumElements();
|
|
auto *O0 = B.CreateZExtOrTrunc(
|
|
SI->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements0));
|
|
auto Elements1 = SI->getOperand(1)->getType()->getVectorNumElements();
|
|
auto *O1 = B.CreateZExtOrTrunc(
|
|
SI->getOperand(1), VectorType::get(ScalarTruncatedTy, Elements1));
|
|
|
|
NewI = B.CreateShuffleVector(O0, O1, SI->getMask());
|
|
} else if (isa<LoadInst>(I)) {
|
|
// Don't do anything with the operands, just extend the result.
|
|
continue;
|
|
} else if (auto *IE = dyn_cast<InsertElementInst>(I)) {
|
|
auto Elements = IE->getOperand(0)->getType()->getVectorNumElements();
|
|
auto *O0 = B.CreateZExtOrTrunc(
|
|
IE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements));
|
|
auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy);
|
|
NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2));
|
|
} else if (auto *EE = dyn_cast<ExtractElementInst>(I)) {
|
|
auto Elements = EE->getOperand(0)->getType()->getVectorNumElements();
|
|
auto *O0 = B.CreateZExtOrTrunc(
|
|
EE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements));
|
|
NewI = B.CreateExtractElement(O0, EE->getOperand(2));
|
|
} else {
|
|
llvm_unreachable("Unhandled instruction type!");
|
|
}
|
|
|
|
// Lastly, extend the result.
|
|
NewI->takeName(cast<Instruction>(I));
|
|
Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy);
|
|
I->replaceAllUsesWith(Res);
|
|
cast<Instruction>(I)->eraseFromParent();
|
|
Erased.insert(I);
|
|
VectorLoopValueMap.resetVectorValue(KV.first, Part, Res);
|
|
}
|
|
}
|
|
|
|
// We'll have created a bunch of ZExts that are now parentless. Clean up.
|
|
for (const auto &KV : Cost->getMinimalBitwidths()) {
|
|
// If the value wasn't vectorized, we must maintain the original scalar
|
|
// type. The absence of the value from VectorLoopValueMap indicates that it
|
|
// wasn't vectorized.
|
|
if (!VectorLoopValueMap.hasAnyVectorValue(KV.first))
|
|
continue;
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *I = getOrCreateVectorValue(KV.first, Part);
|
|
ZExtInst *Inst = dyn_cast<ZExtInst>(I);
|
|
if (Inst && Inst->use_empty()) {
|
|
Value *NewI = Inst->getOperand(0);
|
|
Inst->eraseFromParent();
|
|
VectorLoopValueMap.resetVectorValue(KV.first, Part, NewI);
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
void InnerLoopVectorizer::fixVectorizedLoop() {
|
|
// Insert truncates and extends for any truncated instructions as hints to
|
|
// InstCombine.
|
|
if (VF > 1)
|
|
truncateToMinimalBitwidths();
|
|
|
|
// At this point every instruction in the original loop is widened to a
|
|
// vector form. Now we need to fix the recurrences in the loop. These PHI
|
|
// nodes are currently empty because we did not want to introduce cycles.
|
|
// This is the second stage of vectorizing recurrences.
|
|
fixCrossIterationPHIs();
|
|
|
|
// Update the dominator tree.
|
|
//
|
|
// FIXME: After creating the structure of the new loop, the dominator tree is
|
|
// no longer up-to-date, and it remains that way until we update it
|
|
// here. An out-of-date dominator tree is problematic for SCEV,
|
|
// because SCEVExpander uses it to guide code generation. The
|
|
// vectorizer use SCEVExpanders in several places. Instead, we should
|
|
// keep the dominator tree up-to-date as we go.
|
|
updateAnalysis();
|
|
|
|
// Fix-up external users of the induction variables.
|
|
for (auto &Entry : *Legal->getInductionVars())
|
|
fixupIVUsers(Entry.first, Entry.second,
|
|
getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)),
|
|
IVEndValues[Entry.first], LoopMiddleBlock);
|
|
|
|
fixLCSSAPHIs();
|
|
for (Instruction *PI : PredicatedInstructions)
|
|
sinkScalarOperands(&*PI);
|
|
|
|
// Remove redundant induction instructions.
|
|
cse(LoopVectorBody);
|
|
}
|
|
|
|
void InnerLoopVectorizer::fixCrossIterationPHIs() {
|
|
// In order to support recurrences we need to be able to vectorize Phi nodes.
|
|
// Phi nodes have cycles, so we need to vectorize them in two stages. This is
|
|
// stage #2: We now need to fix the recurrences by adding incoming edges to
|
|
// the currently empty PHI nodes. At this point every instruction in the
|
|
// original loop is widened to a vector form so we can use them to construct
|
|
// the incoming edges.
|
|
for (Instruction &I : *OrigLoop->getHeader()) {
|
|
PHINode *Phi = dyn_cast<PHINode>(&I);
|
|
if (!Phi)
|
|
break;
|
|
// Handle first-order recurrences and reductions that need to be fixed.
|
|
if (Legal->isFirstOrderRecurrence(Phi))
|
|
fixFirstOrderRecurrence(Phi);
|
|
else if (Legal->isReductionVariable(Phi))
|
|
fixReduction(Phi);
|
|
}
|
|
}
|
|
|
|
void InnerLoopVectorizer::fixFirstOrderRecurrence(PHINode *Phi) {
|
|
|
|
// This is the second phase of vectorizing first-order recurrences. An
|
|
// overview of the transformation is described below. Suppose we have the
|
|
// following loop.
|
|
//
|
|
// for (int i = 0; i < n; ++i)
|
|
// b[i] = a[i] - a[i - 1];
|
|
//
|
|
// There is a first-order recurrence on "a". For this loop, the shorthand
|
|
// scalar IR looks like:
|
|
//
|
|
// scalar.ph:
|
|
// s_init = a[-1]
|
|
// br scalar.body
|
|
//
|
|
// scalar.body:
|
|
// i = phi [0, scalar.ph], [i+1, scalar.body]
|
|
// s1 = phi [s_init, scalar.ph], [s2, scalar.body]
|
|
// s2 = a[i]
|
|
// b[i] = s2 - s1
|
|
// br cond, scalar.body, ...
|
|
//
|
|
// In this example, s1 is a recurrence because it's value depends on the
|
|
// previous iteration. In the first phase of vectorization, we created a
|
|
// temporary value for s1. We now complete the vectorization and produce the
|
|
// shorthand vector IR shown below (for VF = 4, UF = 1).
|
|
//
|
|
// vector.ph:
|
|
// v_init = vector(..., ..., ..., a[-1])
|
|
// br vector.body
|
|
//
|
|
// vector.body
|
|
// i = phi [0, vector.ph], [i+4, vector.body]
|
|
// v1 = phi [v_init, vector.ph], [v2, vector.body]
|
|
// v2 = a[i, i+1, i+2, i+3];
|
|
// v3 = vector(v1(3), v2(0, 1, 2))
|
|
// b[i, i+1, i+2, i+3] = v2 - v3
|
|
// br cond, vector.body, middle.block
|
|
//
|
|
// middle.block:
|
|
// x = v2(3)
|
|
// br scalar.ph
|
|
//
|
|
// scalar.ph:
|
|
// s_init = phi [x, middle.block], [a[-1], otherwise]
|
|
// br scalar.body
|
|
//
|
|
// After execution completes the vector loop, we extract the next value of
|
|
// the recurrence (x) to use as the initial value in the scalar loop.
|
|
|
|
// Get the original loop preheader and single loop latch.
|
|
auto *Preheader = OrigLoop->getLoopPreheader();
|
|
auto *Latch = OrigLoop->getLoopLatch();
|
|
|
|
// Get the initial and previous values of the scalar recurrence.
|
|
auto *ScalarInit = Phi->getIncomingValueForBlock(Preheader);
|
|
auto *Previous = Phi->getIncomingValueForBlock(Latch);
|
|
|
|
// Create a vector from the initial value.
|
|
auto *VectorInit = ScalarInit;
|
|
if (VF > 1) {
|
|
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
|
|
VectorInit = Builder.CreateInsertElement(
|
|
UndefValue::get(VectorType::get(VectorInit->getType(), VF)), VectorInit,
|
|
Builder.getInt32(VF - 1), "vector.recur.init");
|
|
}
|
|
|
|
// We constructed a temporary phi node in the first phase of vectorization.
|
|
// This phi node will eventually be deleted.
|
|
Builder.SetInsertPoint(
|
|
cast<Instruction>(VectorLoopValueMap.getVectorValue(Phi, 0)));
|
|
|
|
// Create a phi node for the new recurrence. The current value will either be
|
|
// the initial value inserted into a vector or loop-varying vector value.
|
|
auto *VecPhi = Builder.CreatePHI(VectorInit->getType(), 2, "vector.recur");
|
|
VecPhi->addIncoming(VectorInit, LoopVectorPreHeader);
|
|
|
|
// Get the vectorized previous value of the last part UF - 1. It appears last
|
|
// among all unrolled iterations, due to the order of their construction.
|
|
Value *PreviousLastPart = getOrCreateVectorValue(Previous, UF - 1);
|
|
|
|
// Set the insertion point after the previous value if it is an instruction.
|
|
// Note that the previous value may have been constant-folded so it is not
|
|
// guaranteed to be an instruction in the vector loop. Also, if the previous
|
|
// value is a phi node, we should insert after all the phi nodes to avoid
|
|
// breaking basic block verification.
|
|
if (LI->getLoopFor(LoopVectorBody)->isLoopInvariant(PreviousLastPart) ||
|
|
isa<PHINode>(PreviousLastPart))
|
|
Builder.SetInsertPoint(&*LoopVectorBody->getFirstInsertionPt());
|
|
else
|
|
Builder.SetInsertPoint(
|
|
&*++BasicBlock::iterator(cast<Instruction>(PreviousLastPart)));
|
|
|
|
// We will construct a vector for the recurrence by combining the values for
|
|
// the current and previous iterations. This is the required shuffle mask.
|
|
SmallVector<Constant *, 8> ShuffleMask(VF);
|
|
ShuffleMask[0] = Builder.getInt32(VF - 1);
|
|
for (unsigned I = 1; I < VF; ++I)
|
|
ShuffleMask[I] = Builder.getInt32(I + VF - 1);
|
|
|
|
// The vector from which to take the initial value for the current iteration
|
|
// (actual or unrolled). Initially, this is the vector phi node.
|
|
Value *Incoming = VecPhi;
|
|
|
|
// Shuffle the current and previous vector and update the vector parts.
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *PreviousPart = getOrCreateVectorValue(Previous, Part);
|
|
Value *PhiPart = VectorLoopValueMap.getVectorValue(Phi, Part);
|
|
auto *Shuffle =
|
|
VF > 1 ? Builder.CreateShuffleVector(Incoming, PreviousPart,
|
|
ConstantVector::get(ShuffleMask))
|
|
: Incoming;
|
|
PhiPart->replaceAllUsesWith(Shuffle);
|
|
cast<Instruction>(PhiPart)->eraseFromParent();
|
|
VectorLoopValueMap.resetVectorValue(Phi, Part, Shuffle);
|
|
Incoming = PreviousPart;
|
|
}
|
|
|
|
// Fix the latch value of the new recurrence in the vector loop.
|
|
VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
|
|
|
|
// Extract the last vector element in the middle block. This will be the
|
|
// initial value for the recurrence when jumping to the scalar loop.
|
|
auto *ExtractForScalar = Incoming;
|
|
if (VF > 1) {
|
|
Builder.SetInsertPoint(LoopMiddleBlock->getTerminator());
|
|
ExtractForScalar = Builder.CreateExtractElement(
|
|
ExtractForScalar, Builder.getInt32(VF - 1), "vector.recur.extract");
|
|
}
|
|
// Extract the second last element in the middle block if the
|
|
// Phi is used outside the loop. We need to extract the phi itself
|
|
// and not the last element (the phi update in the current iteration). This
|
|
// will be the value when jumping to the exit block from the LoopMiddleBlock,
|
|
// when the scalar loop is not run at all.
|
|
Value *ExtractForPhiUsedOutsideLoop = nullptr;
|
|
if (VF > 1)
|
|
ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement(
|
|
Incoming, Builder.getInt32(VF - 2), "vector.recur.extract.for.phi");
|
|
// When loop is unrolled without vectorizing, initialize
|
|
// ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value of
|
|
// `Incoming`. This is analogous to the vectorized case above: extracting the
|
|
// second last element when VF > 1.
|
|
else if (UF > 1)
|
|
ExtractForPhiUsedOutsideLoop = getOrCreateVectorValue(Previous, UF - 2);
|
|
|
|
// Fix the initial value of the original recurrence in the scalar loop.
|
|
Builder.SetInsertPoint(&*LoopScalarPreHeader->begin());
|
|
auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init");
|
|
for (auto *BB : predecessors(LoopScalarPreHeader)) {
|
|
auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit;
|
|
Start->addIncoming(Incoming, BB);
|
|
}
|
|
|
|
Phi->setIncomingValue(Phi->getBasicBlockIndex(LoopScalarPreHeader), Start);
|
|
Phi->setName("scalar.recur");
|
|
|
|
// Finally, fix users of the recurrence outside the loop. The users will need
|
|
// either the last value of the scalar recurrence or the last value of the
|
|
// vector recurrence we extracted in the middle block. Since the loop is in
|
|
// LCSSA form, we just need to find the phi node for the original scalar
|
|
// recurrence in the exit block, and then add an edge for the middle block.
|
|
for (auto &I : *LoopExitBlock) {
|
|
auto *LCSSAPhi = dyn_cast<PHINode>(&I);
|
|
if (!LCSSAPhi)
|
|
break;
|
|
if (LCSSAPhi->getIncomingValue(0) == Phi) {
|
|
LCSSAPhi->addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock);
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
|
|
void InnerLoopVectorizer::fixReduction(PHINode *Phi) {
|
|
Constant *Zero = Builder.getInt32(0);
|
|
|
|
// Get it's reduction variable descriptor.
|
|
assert(Legal->isReductionVariable(Phi) &&
|
|
"Unable to find the reduction variable");
|
|
RecurrenceDescriptor RdxDesc = (*Legal->getReductionVars())[Phi];
|
|
|
|
RecurrenceDescriptor::RecurrenceKind RK = RdxDesc.getRecurrenceKind();
|
|
TrackingVH<Value> ReductionStartValue = RdxDesc.getRecurrenceStartValue();
|
|
Instruction *LoopExitInst = RdxDesc.getLoopExitInstr();
|
|
RecurrenceDescriptor::MinMaxRecurrenceKind MinMaxKind =
|
|
RdxDesc.getMinMaxRecurrenceKind();
|
|
setDebugLocFromInst(Builder, ReductionStartValue);
|
|
|
|
// We need to generate a reduction vector from the incoming scalar.
|
|
// To do so, we need to generate the 'identity' vector and override
|
|
// one of the elements with the incoming scalar reduction. We need
|
|
// to do it in the vector-loop preheader.
|
|
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
|
|
|
|
// This is the vector-clone of the value that leaves the loop.
|
|
Type *VecTy = getOrCreateVectorValue(LoopExitInst, 0)->getType();
|
|
|
|
// Find the reduction identity variable. Zero for addition, or, xor,
|
|
// one for multiplication, -1 for And.
|
|
Value *Identity;
|
|
Value *VectorStart;
|
|
if (RK == RecurrenceDescriptor::RK_IntegerMinMax ||
|
|
RK == RecurrenceDescriptor::RK_FloatMinMax) {
|
|
// MinMax reduction have the start value as their identify.
|
|
if (VF == 1) {
|
|
VectorStart = Identity = ReductionStartValue;
|
|
} else {
|
|
VectorStart = Identity =
|
|
Builder.CreateVectorSplat(VF, ReductionStartValue, "minmax.ident");
|
|
}
|
|
} else {
|
|
// Handle other reduction kinds:
|
|
Constant *Iden = RecurrenceDescriptor::getRecurrenceIdentity(
|
|
RK, VecTy->getScalarType());
|
|
if (VF == 1) {
|
|
Identity = Iden;
|
|
// This vector is the Identity vector where the first element is the
|
|
// incoming scalar reduction.
|
|
VectorStart = ReductionStartValue;
|
|
} else {
|
|
Identity = ConstantVector::getSplat(VF, Iden);
|
|
|
|
// This vector is the Identity vector where the first element is the
|
|
// incoming scalar reduction.
|
|
VectorStart =
|
|
Builder.CreateInsertElement(Identity, ReductionStartValue, Zero);
|
|
}
|
|
}
|
|
|
|
// Fix the vector-loop phi.
|
|
|
|
// Reductions do not have to start at zero. They can start with
|
|
// any loop invariant values.
|
|
BasicBlock *Latch = OrigLoop->getLoopLatch();
|
|
Value *LoopVal = Phi->getIncomingValueForBlock(Latch);
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *VecRdxPhi = getOrCreateVectorValue(Phi, Part);
|
|
Value *Val = getOrCreateVectorValue(LoopVal, Part);
|
|
// Make sure to add the reduction stat value only to the
|
|
// first unroll part.
|
|
Value *StartVal = (Part == 0) ? VectorStart : Identity;
|
|
cast<PHINode>(VecRdxPhi)->addIncoming(StartVal, LoopVectorPreHeader);
|
|
cast<PHINode>(VecRdxPhi)
|
|
->addIncoming(Val, LI->getLoopFor(LoopVectorBody)->getLoopLatch());
|
|
}
|
|
|
|
// Before each round, move the insertion point right between
|
|
// the PHIs and the values we are going to write.
|
|
// This allows us to write both PHINodes and the extractelement
|
|
// instructions.
|
|
Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt());
|
|
|
|
setDebugLocFromInst(Builder, LoopExitInst);
|
|
|
|
// If the vector reduction can be performed in a smaller type, we truncate
|
|
// then extend the loop exit value to enable InstCombine to evaluate the
|
|
// entire expression in the smaller type.
|
|
if (VF > 1 && Phi->getType() != RdxDesc.getRecurrenceType()) {
|
|
Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF);
|
|
Builder.SetInsertPoint(LoopVectorBody->getTerminator());
|
|
VectorParts RdxParts(UF);
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
RdxParts[Part] = VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
|
|
Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
|
|
Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy)
|
|
: Builder.CreateZExt(Trunc, VecTy);
|
|
for (Value::user_iterator UI = RdxParts[Part]->user_begin();
|
|
UI != RdxParts[Part]->user_end();)
|
|
if (*UI != Trunc) {
|
|
(*UI++)->replaceUsesOfWith(RdxParts[Part], Extnd);
|
|
RdxParts[Part] = Extnd;
|
|
} else {
|
|
++UI;
|
|
}
|
|
}
|
|
Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt());
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy);
|
|
VectorLoopValueMap.resetVectorValue(LoopExitInst, Part, RdxParts[Part]);
|
|
}
|
|
}
|
|
|
|
// Reduce all of the unrolled parts into a single vector.
|
|
Value *ReducedPartRdx = VectorLoopValueMap.getVectorValue(LoopExitInst, 0);
|
|
unsigned Op = RecurrenceDescriptor::getRecurrenceBinOp(RK);
|
|
setDebugLocFromInst(Builder, ReducedPartRdx);
|
|
for (unsigned Part = 1; Part < UF; ++Part) {
|
|
Value *RdxPart = VectorLoopValueMap.getVectorValue(LoopExitInst, Part);
|
|
if (Op != Instruction::ICmp && Op != Instruction::FCmp)
|
|
// Floating point operations had to be 'fast' to enable the reduction.
|
|
ReducedPartRdx = addFastMathFlag(
|
|
Builder.CreateBinOp((Instruction::BinaryOps)Op, RdxPart,
|
|
ReducedPartRdx, "bin.rdx"));
|
|
else
|
|
ReducedPartRdx = RecurrenceDescriptor::createMinMaxOp(
|
|
Builder, MinMaxKind, ReducedPartRdx, RdxPart);
|
|
}
|
|
|
|
if (VF > 1) {
|
|
bool NoNaN = Legal->hasFunNoNaNAttr();
|
|
ReducedPartRdx =
|
|
createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx, NoNaN);
|
|
// If the reduction can be performed in a smaller type, we need to extend
|
|
// the reduction to the wider type before we branch to the original loop.
|
|
if (Phi->getType() != RdxDesc.getRecurrenceType())
|
|
ReducedPartRdx =
|
|
RdxDesc.isSigned()
|
|
? Builder.CreateSExt(ReducedPartRdx, Phi->getType())
|
|
: Builder.CreateZExt(ReducedPartRdx, Phi->getType());
|
|
}
|
|
|
|
// Create a phi node that merges control-flow from the backedge-taken check
|
|
// block and the middle block.
|
|
PHINode *BCBlockPhi = PHINode::Create(Phi->getType(), 2, "bc.merge.rdx",
|
|
LoopScalarPreHeader->getTerminator());
|
|
for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
|
|
BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]);
|
|
BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock);
|
|
|
|
// Now, we need to fix the users of the reduction variable
|
|
// inside and outside of the scalar remainder loop.
|
|
// We know that the loop is in LCSSA form. We need to update the
|
|
// PHI nodes in the exit blocks.
|
|
for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
|
|
LEE = LoopExitBlock->end();
|
|
LEI != LEE; ++LEI) {
|
|
PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
|
|
if (!LCSSAPhi)
|
|
break;
|
|
|
|
// All PHINodes need to have a single entry edge, or two if
|
|
// we already fixed them.
|
|
assert(LCSSAPhi->getNumIncomingValues() < 3 && "Invalid LCSSA PHI");
|
|
|
|
// We found a reduction value exit-PHI. Update it with the
|
|
// incoming bypass edge.
|
|
if (LCSSAPhi->getIncomingValue(0) == LoopExitInst)
|
|
LCSSAPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock);
|
|
} // end of the LCSSA phi scan.
|
|
|
|
// Fix the scalar loop reduction variable with the incoming reduction sum
|
|
// from the vector body and from the backedge value.
|
|
int IncomingEdgeBlockIdx =
|
|
Phi->getBasicBlockIndex(OrigLoop->getLoopLatch());
|
|
assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
|
|
// Pick the other block.
|
|
int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
|
|
Phi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi);
|
|
Phi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst);
|
|
}
|
|
|
|
void InnerLoopVectorizer::fixLCSSAPHIs() {
|
|
for (Instruction &LEI : *LoopExitBlock) {
|
|
auto *LCSSAPhi = dyn_cast<PHINode>(&LEI);
|
|
if (!LCSSAPhi)
|
|
break;
|
|
if (LCSSAPhi->getNumIncomingValues() == 1) {
|
|
assert(OrigLoop->isLoopInvariant(LCSSAPhi->getIncomingValue(0)) &&
|
|
"Incoming value isn't loop invariant");
|
|
LCSSAPhi->addIncoming(LCSSAPhi->getIncomingValue(0), LoopMiddleBlock);
|
|
}
|
|
}
|
|
}
|
|
|
|
void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) {
|
|
|
|
// The basic block and loop containing the predicated instruction.
|
|
auto *PredBB = PredInst->getParent();
|
|
auto *VectorLoop = LI->getLoopFor(PredBB);
|
|
|
|
// Initialize a worklist with the operands of the predicated instruction.
|
|
SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end());
|
|
|
|
// Holds instructions that we need to analyze again. An instruction may be
|
|
// reanalyzed if we don't yet know if we can sink it or not.
|
|
SmallVector<Instruction *, 8> InstsToReanalyze;
|
|
|
|
// Returns true if a given use occurs in the predicated block. Phi nodes use
|
|
// their operands in their corresponding predecessor blocks.
|
|
auto isBlockOfUsePredicated = [&](Use &U) -> bool {
|
|
auto *I = cast<Instruction>(U.getUser());
|
|
BasicBlock *BB = I->getParent();
|
|
if (auto *Phi = dyn_cast<PHINode>(I))
|
|
BB = Phi->getIncomingBlock(
|
|
PHINode::getIncomingValueNumForOperand(U.getOperandNo()));
|
|
return BB == PredBB;
|
|
};
|
|
|
|
// Iteratively sink the scalarized operands of the predicated instruction
|
|
// into the block we created for it. When an instruction is sunk, it's
|
|
// operands are then added to the worklist. The algorithm ends after one pass
|
|
// through the worklist doesn't sink a single instruction.
|
|
bool Changed;
|
|
do {
|
|
|
|
// Add the instructions that need to be reanalyzed to the worklist, and
|
|
// reset the changed indicator.
|
|
Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end());
|
|
InstsToReanalyze.clear();
|
|
Changed = false;
|
|
|
|
while (!Worklist.empty()) {
|
|
auto *I = dyn_cast<Instruction>(Worklist.pop_back_val());
|
|
|
|
// We can't sink an instruction if it is a phi node, is already in the
|
|
// predicated block, is not in the loop, or may have side effects.
|
|
if (!I || isa<PHINode>(I) || I->getParent() == PredBB ||
|
|
!VectorLoop->contains(I) || I->mayHaveSideEffects())
|
|
continue;
|
|
|
|
// It's legal to sink the instruction if all its uses occur in the
|
|
// predicated block. Otherwise, there's nothing to do yet, and we may
|
|
// need to reanalyze the instruction.
|
|
if (!all_of(I->uses(), isBlockOfUsePredicated)) {
|
|
InstsToReanalyze.push_back(I);
|
|
continue;
|
|
}
|
|
|
|
// Move the instruction to the beginning of the predicated block, and add
|
|
// it's operands to the worklist.
|
|
I->moveBefore(&*PredBB->getFirstInsertionPt());
|
|
Worklist.insert(I->op_begin(), I->op_end());
|
|
|
|
// The sinking may have enabled other instructions to be sunk, so we will
|
|
// need to iterate.
|
|
Changed = true;
|
|
}
|
|
} while (Changed);
|
|
}
|
|
|
|
InnerLoopVectorizer::VectorParts
|
|
InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) {
|
|
assert(is_contained(predecessors(Dst), Src) && "Invalid edge");
|
|
|
|
// Look for cached value.
|
|
std::pair<BasicBlock *, BasicBlock *> Edge(Src, Dst);
|
|
EdgeMaskCacheTy::iterator ECEntryIt = EdgeMaskCache.find(Edge);
|
|
if (ECEntryIt != EdgeMaskCache.end())
|
|
return ECEntryIt->second;
|
|
|
|
VectorParts SrcMask = createBlockInMask(Src);
|
|
|
|
// The terminator has to be a branch inst!
|
|
BranchInst *BI = dyn_cast<BranchInst>(Src->getTerminator());
|
|
assert(BI && "Unexpected terminator found");
|
|
|
|
if (!BI->isConditional())
|
|
return EdgeMaskCache[Edge] = SrcMask;
|
|
|
|
VectorParts EdgeMask(UF);
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
auto *EdgeMaskPart = getOrCreateVectorValue(BI->getCondition(), Part);
|
|
if (BI->getSuccessor(0) != Dst)
|
|
EdgeMaskPart = Builder.CreateNot(EdgeMaskPart);
|
|
|
|
if (SrcMask[Part]) // Otherwise block in-mask is all-one, no need to AND.
|
|
EdgeMaskPart = Builder.CreateAnd(EdgeMaskPart, SrcMask[Part]);
|
|
|
|
EdgeMask[Part] = EdgeMaskPart;
|
|
}
|
|
|
|
return EdgeMaskCache[Edge] = EdgeMask;
|
|
}
|
|
|
|
InnerLoopVectorizer::VectorParts
|
|
InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) {
|
|
assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
|
|
|
|
// Look for cached value.
|
|
BlockMaskCacheTy::iterator BCEntryIt = BlockMaskCache.find(BB);
|
|
if (BCEntryIt != BlockMaskCache.end())
|
|
return BCEntryIt->second;
|
|
|
|
// All-one mask is modelled as no-mask following the convention for masked
|
|
// load/store/gather/scatter. Initialize BlockMask to no-mask.
|
|
VectorParts BlockMask(UF);
|
|
for (unsigned Part = 0; Part < UF; ++Part)
|
|
BlockMask[Part] = nullptr;
|
|
|
|
// Loop incoming mask is all-one.
|
|
if (OrigLoop->getHeader() == BB)
|
|
return BlockMaskCache[BB] = BlockMask;
|
|
|
|
// This is the block mask. We OR all incoming edges.
|
|
for (auto *Predecessor : predecessors(BB)) {
|
|
VectorParts EdgeMask = createEdgeMask(Predecessor, BB);
|
|
if (!EdgeMask[0]) // Mask of predecessor is all-one so mask of block is too.
|
|
return BlockMaskCache[BB] = EdgeMask;
|
|
|
|
if (!BlockMask[0]) { // BlockMask has its initialized nullptr value.
|
|
BlockMask = EdgeMask;
|
|
continue;
|
|
}
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part)
|
|
BlockMask[Part] = Builder.CreateOr(BlockMask[Part], EdgeMask[Part]);
|
|
}
|
|
|
|
return BlockMaskCache[BB] = BlockMask;
|
|
}
|
|
|
|
void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, unsigned UF,
|
|
unsigned VF) {
|
|
PHINode *P = cast<PHINode>(PN);
|
|
// In order to support recurrences we need to be able to vectorize Phi nodes.
|
|
// Phi nodes have cycles, so we need to vectorize them in two stages. This is
|
|
// stage #1: We create a new vector PHI node with no incoming edges. We'll use
|
|
// this value when we vectorize all of the instructions that use the PHI.
|
|
if (Legal->isReductionVariable(P) || Legal->isFirstOrderRecurrence(P)) {
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
// This is phase one of vectorizing PHIs.
|
|
Type *VecTy =
|
|
(VF == 1) ? PN->getType() : VectorType::get(PN->getType(), VF);
|
|
Value *EntryPart = PHINode::Create(
|
|
VecTy, 2, "vec.phi", &*LoopVectorBody->getFirstInsertionPt());
|
|
VectorLoopValueMap.setVectorValue(P, Part, EntryPart);
|
|
}
|
|
return;
|
|
}
|
|
|
|
setDebugLocFromInst(Builder, P);
|
|
// Check for PHI nodes that are lowered to vector selects.
|
|
if (P->getParent() != OrigLoop->getHeader()) {
|
|
// We know that all PHIs in non-header blocks are converted into
|
|
// selects, so we don't have to worry about the insertion order and we
|
|
// can just use the builder.
|
|
// At this point we generate the predication tree. There may be
|
|
// duplications since this is a simple recursive scan, but future
|
|
// optimizations will clean it up.
|
|
|
|
unsigned NumIncoming = P->getNumIncomingValues();
|
|
|
|
// Generate a sequence of selects of the form:
|
|
// SELECT(Mask3, In3,
|
|
// SELECT(Mask2, In2,
|
|
// ( ...)))
|
|
VectorParts Entry(UF);
|
|
for (unsigned In = 0; In < NumIncoming; In++) {
|
|
VectorParts Cond =
|
|
createEdgeMask(P->getIncomingBlock(In), P->getParent());
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *In0 = getOrCreateVectorValue(P->getIncomingValue(In), Part);
|
|
// We might have single edge PHIs (blocks) - use an identity
|
|
// 'select' for the first PHI operand.
|
|
if (In == 0)
|
|
Entry[Part] = Builder.CreateSelect(Cond[Part], In0, In0);
|
|
else
|
|
// Select between the current value and the previous incoming edge
|
|
// based on the incoming mask.
|
|
Entry[Part] = Builder.CreateSelect(Cond[Part], In0, Entry[Part],
|
|
"predphi");
|
|
}
|
|
}
|
|
for (unsigned Part = 0; Part < UF; ++Part)
|
|
VectorLoopValueMap.setVectorValue(P, Part, Entry[Part]);
|
|
return;
|
|
}
|
|
|
|
// This PHINode must be an induction variable.
|
|
// Make sure that we know about it.
|
|
assert(Legal->getInductionVars()->count(P) && "Not an induction variable");
|
|
|
|
InductionDescriptor II = Legal->getInductionVars()->lookup(P);
|
|
const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout();
|
|
|
|
// FIXME: The newly created binary instructions should contain nsw/nuw flags,
|
|
// which can be found from the original scalar operations.
|
|
switch (II.getKind()) {
|
|
case InductionDescriptor::IK_NoInduction:
|
|
llvm_unreachable("Unknown induction");
|
|
case InductionDescriptor::IK_IntInduction:
|
|
case InductionDescriptor::IK_FpInduction:
|
|
llvm_unreachable("Integer/fp induction is handled elsewhere.");
|
|
case InductionDescriptor::IK_PtrInduction: {
|
|
// Handle the pointer induction variable case.
|
|
assert(P->getType()->isPointerTy() && "Unexpected type.");
|
|
// This is the normalized GEP that starts counting at zero.
|
|
Value *PtrInd = Induction;
|
|
PtrInd = Builder.CreateSExtOrTrunc(PtrInd, II.getStep()->getType());
|
|
// Determine the number of scalars we need to generate for each unroll
|
|
// iteration. If the instruction is uniform, we only need to generate the
|
|
// first lane. Otherwise, we generate all VF values.
|
|
unsigned Lanes = Cost->isUniformAfterVectorization(P, VF) ? 1 : VF;
|
|
// These are the scalar results. Notice that we don't generate vector GEPs
|
|
// because scalar GEPs result in better code.
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
for (unsigned Lane = 0; Lane < Lanes; ++Lane) {
|
|
Constant *Idx = ConstantInt::get(PtrInd->getType(), Lane + Part * VF);
|
|
Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx);
|
|
Value *SclrGep = II.transform(Builder, GlobalIdx, PSE.getSE(), DL);
|
|
SclrGep->setName("next.gep");
|
|
VectorLoopValueMap.setScalarValue(P, {Part, Lane}, SclrGep);
|
|
}
|
|
}
|
|
return;
|
|
}
|
|
}
|
|
}
|
|
|
|
/// A helper function for checking whether an integer division-related
|
|
/// instruction may divide by zero (in which case it must be predicated if
|
|
/// executed conditionally in the scalar code).
|
|
/// TODO: It may be worthwhile to generalize and check isKnownNonZero().
|
|
/// Non-zero divisors that are non compile-time constants will not be
|
|
/// converted into multiplication, so we will still end up scalarizing
|
|
/// the division, but can do so w/o predication.
|
|
static bool mayDivideByZero(Instruction &I) {
|
|
assert((I.getOpcode() == Instruction::UDiv ||
|
|
I.getOpcode() == Instruction::SDiv ||
|
|
I.getOpcode() == Instruction::URem ||
|
|
I.getOpcode() == Instruction::SRem) &&
|
|
"Unexpected instruction");
|
|
Value *Divisor = I.getOperand(1);
|
|
auto *CInt = dyn_cast<ConstantInt>(Divisor);
|
|
return !CInt || CInt->isZero();
|
|
}
|
|
|
|
void InnerLoopVectorizer::widenInstruction(Instruction &I) {
|
|
switch (I.getOpcode()) {
|
|
case Instruction::Br:
|
|
case Instruction::PHI:
|
|
llvm_unreachable("This instruction is handled by a different recipe.");
|
|
case Instruction::GetElementPtr: {
|
|
// Construct a vector GEP by widening the operands of the scalar GEP as
|
|
// necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP
|
|
// results in a vector of pointers when at least one operand of the GEP
|
|
// is vector-typed. Thus, to keep the representation compact, we only use
|
|
// vector-typed operands for loop-varying values.
|
|
auto *GEP = cast<GetElementPtrInst>(&I);
|
|
|
|
if (VF > 1 && OrigLoop->hasLoopInvariantOperands(GEP)) {
|
|
// If we are vectorizing, but the GEP has only loop-invariant operands,
|
|
// the GEP we build (by only using vector-typed operands for
|
|
// loop-varying values) would be a scalar pointer. Thus, to ensure we
|
|
// produce a vector of pointers, we need to either arbitrarily pick an
|
|
// operand to broadcast, or broadcast a clone of the original GEP.
|
|
// Here, we broadcast a clone of the original.
|
|
//
|
|
// TODO: If at some point we decide to scalarize instructions having
|
|
// loop-invariant operands, this special case will no longer be
|
|
// required. We would add the scalarization decision to
|
|
// collectLoopScalars() and teach getVectorValue() to broadcast
|
|
// the lane-zero scalar value.
|
|
auto *Clone = Builder.Insert(GEP->clone());
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *EntryPart = Builder.CreateVectorSplat(VF, Clone);
|
|
VectorLoopValueMap.setVectorValue(&I, Part, EntryPart);
|
|
addMetadata(EntryPart, GEP);
|
|
}
|
|
} else {
|
|
// If the GEP has at least one loop-varying operand, we are sure to
|
|
// produce a vector of pointers. But if we are only unrolling, we want
|
|
// to produce a scalar GEP for each unroll part. Thus, the GEP we
|
|
// produce with the code below will be scalar (if VF == 1) or vector
|
|
// (otherwise). Note that for the unroll-only case, we still maintain
|
|
// values in the vector mapping with initVector, as we do for other
|
|
// instructions.
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
|
|
// The pointer operand of the new GEP. If it's loop-invariant, we
|
|
// won't broadcast it.
|
|
auto *Ptr =
|
|
OrigLoop->isLoopInvariant(GEP->getPointerOperand())
|
|
? GEP->getPointerOperand()
|
|
: getOrCreateVectorValue(GEP->getPointerOperand(), Part);
|
|
|
|
// Collect all the indices for the new GEP. If any index is
|
|
// loop-invariant, we won't broadcast it.
|
|
SmallVector<Value *, 4> Indices;
|
|
for (auto &U : make_range(GEP->idx_begin(), GEP->idx_end())) {
|
|
if (OrigLoop->isLoopInvariant(U.get()))
|
|
Indices.push_back(U.get());
|
|
else
|
|
Indices.push_back(getOrCreateVectorValue(U.get(), Part));
|
|
}
|
|
|
|
// Create the new GEP. Note that this GEP may be a scalar if VF == 1,
|
|
// but it should be a vector, otherwise.
|
|
auto *NewGEP = GEP->isInBounds()
|
|
? Builder.CreateInBoundsGEP(Ptr, Indices)
|
|
: Builder.CreateGEP(Ptr, Indices);
|
|
assert((VF == 1 || NewGEP->getType()->isVectorTy()) &&
|
|
"NewGEP is not a pointer vector");
|
|
VectorLoopValueMap.setVectorValue(&I, Part, NewGEP);
|
|
addMetadata(NewGEP, GEP);
|
|
}
|
|
}
|
|
|
|
break;
|
|
}
|
|
case Instruction::UDiv:
|
|
case Instruction::SDiv:
|
|
case Instruction::SRem:
|
|
case Instruction::URem:
|
|
case Instruction::Add:
|
|
case Instruction::FAdd:
|
|
case Instruction::Sub:
|
|
case Instruction::FSub:
|
|
case Instruction::Mul:
|
|
case Instruction::FMul:
|
|
case Instruction::FDiv:
|
|
case Instruction::FRem:
|
|
case Instruction::Shl:
|
|
case Instruction::LShr:
|
|
case Instruction::AShr:
|
|
case Instruction::And:
|
|
case Instruction::Or:
|
|
case Instruction::Xor: {
|
|
// Just widen binops.
|
|
auto *BinOp = cast<BinaryOperator>(&I);
|
|
setDebugLocFromInst(Builder, BinOp);
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *A = getOrCreateVectorValue(BinOp->getOperand(0), Part);
|
|
Value *B = getOrCreateVectorValue(BinOp->getOperand(1), Part);
|
|
Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A, B);
|
|
|
|
if (BinaryOperator *VecOp = dyn_cast<BinaryOperator>(V))
|
|
VecOp->copyIRFlags(BinOp);
|
|
|
|
// Use this vector value for all users of the original instruction.
|
|
VectorLoopValueMap.setVectorValue(&I, Part, V);
|
|
addMetadata(V, BinOp);
|
|
}
|
|
|
|
break;
|
|
}
|
|
case Instruction::Select: {
|
|
// Widen selects.
|
|
// If the selector is loop invariant we can create a select
|
|
// instruction with a scalar condition. Otherwise, use vector-select.
|
|
auto *SE = PSE.getSE();
|
|
bool InvariantCond =
|
|
SE->isLoopInvariant(PSE.getSCEV(I.getOperand(0)), OrigLoop);
|
|
setDebugLocFromInst(Builder, &I);
|
|
|
|
// The condition can be loop invariant but still defined inside the
|
|
// loop. This means that we can't just use the original 'cond' value.
|
|
// We have to take the 'vectorized' value and pick the first lane.
|
|
// Instcombine will make this a no-op.
|
|
|
|
auto *ScalarCond = getOrCreateScalarValue(I.getOperand(0), {0, 0});
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *Cond = getOrCreateVectorValue(I.getOperand(0), Part);
|
|
Value *Op0 = getOrCreateVectorValue(I.getOperand(1), Part);
|
|
Value *Op1 = getOrCreateVectorValue(I.getOperand(2), Part);
|
|
Value *Sel =
|
|
Builder.CreateSelect(InvariantCond ? ScalarCond : Cond, Op0, Op1);
|
|
VectorLoopValueMap.setVectorValue(&I, Part, Sel);
|
|
addMetadata(Sel, &I);
|
|
}
|
|
|
|
break;
|
|
}
|
|
|
|
case Instruction::ICmp:
|
|
case Instruction::FCmp: {
|
|
// Widen compares. Generate vector compares.
|
|
bool FCmp = (I.getOpcode() == Instruction::FCmp);
|
|
auto *Cmp = dyn_cast<CmpInst>(&I);
|
|
setDebugLocFromInst(Builder, Cmp);
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *A = getOrCreateVectorValue(Cmp->getOperand(0), Part);
|
|
Value *B = getOrCreateVectorValue(Cmp->getOperand(1), Part);
|
|
Value *C = nullptr;
|
|
if (FCmp) {
|
|
// Propagate fast math flags.
|
|
IRBuilder<>::FastMathFlagGuard FMFG(Builder);
|
|
Builder.setFastMathFlags(Cmp->getFastMathFlags());
|
|
C = Builder.CreateFCmp(Cmp->getPredicate(), A, B);
|
|
} else {
|
|
C = Builder.CreateICmp(Cmp->getPredicate(), A, B);
|
|
}
|
|
VectorLoopValueMap.setVectorValue(&I, Part, C);
|
|
addMetadata(C, &I);
|
|
}
|
|
|
|
break;
|
|
}
|
|
|
|
case Instruction::Store:
|
|
case Instruction::Load:
|
|
vectorizeMemoryInstruction(&I);
|
|
break;
|
|
case Instruction::ZExt:
|
|
case Instruction::SExt:
|
|
case Instruction::FPToUI:
|
|
case Instruction::FPToSI:
|
|
case Instruction::FPExt:
|
|
case Instruction::PtrToInt:
|
|
case Instruction::IntToPtr:
|
|
case Instruction::SIToFP:
|
|
case Instruction::UIToFP:
|
|
case Instruction::Trunc:
|
|
case Instruction::FPTrunc:
|
|
case Instruction::BitCast: {
|
|
auto *CI = dyn_cast<CastInst>(&I);
|
|
setDebugLocFromInst(Builder, CI);
|
|
|
|
/// Vectorize casts.
|
|
Type *DestTy =
|
|
(VF == 1) ? CI->getType() : VectorType::get(CI->getType(), VF);
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
Value *A = getOrCreateVectorValue(CI->getOperand(0), Part);
|
|
Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy);
|
|
VectorLoopValueMap.setVectorValue(&I, Part, Cast);
|
|
addMetadata(Cast, &I);
|
|
}
|
|
break;
|
|
}
|
|
|
|
case Instruction::Call: {
|
|
// Ignore dbg intrinsics.
|
|
if (isa<DbgInfoIntrinsic>(I))
|
|
break;
|
|
setDebugLocFromInst(Builder, &I);
|
|
|
|
Module *M = I.getParent()->getParent()->getParent();
|
|
auto *CI = cast<CallInst>(&I);
|
|
|
|
StringRef FnName = CI->getCalledFunction()->getName();
|
|
Function *F = CI->getCalledFunction();
|
|
Type *RetTy = ToVectorTy(CI->getType(), VF);
|
|
SmallVector<Type *, 4> Tys;
|
|
for (Value *ArgOperand : CI->arg_operands())
|
|
Tys.push_back(ToVectorTy(ArgOperand->getType(), VF));
|
|
|
|
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
|
|
|
|
// The flag shows whether we use Intrinsic or a usual Call for vectorized
|
|
// version of the instruction.
|
|
// Is it beneficial to perform intrinsic call compared to lib call?
|
|
bool NeedToScalarize;
|
|
unsigned CallCost = getVectorCallCost(CI, VF, *TTI, TLI, NeedToScalarize);
|
|
bool UseVectorIntrinsic =
|
|
ID && getVectorIntrinsicCost(CI, VF, *TTI, TLI) <= CallCost;
|
|
assert((UseVectorIntrinsic || !NeedToScalarize) &&
|
|
"Instruction should be scalarized elsewhere.");
|
|
|
|
for (unsigned Part = 0; Part < UF; ++Part) {
|
|
SmallVector<Value *, 4> Args;
|
|
for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) {
|
|
Value *Arg = CI->getArgOperand(i);
|
|
// Some intrinsics have a scalar argument - don't replace it with a
|
|
// vector.
|
|
if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, i))
|
|
Arg = getOrCreateVectorValue(CI->getArgOperand(i), Part);
|
|
Args.push_back(Arg);
|
|
}
|
|
|
|
Function *VectorF;
|
|
if (UseVectorIntrinsic) {
|
|
// Use vector version of the intrinsic.
|
|
Type *TysForDecl[] = {CI->getType()};
|
|
if (VF > 1)
|
|
TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF);
|
|
VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl);
|
|
} else {
|
|
// Use vector version of the library call.
|
|
StringRef VFnName = TLI->getVectorizedFunction(FnName, VF);
|
|
assert(!VFnName.empty() && "Vector function name is empty.");
|
|
VectorF = M->getFunction(VFnName);
|
|
if (!VectorF) {
|
|
// Generate a declaration
|
|
FunctionType *FTy = FunctionType::get(RetTy, Tys, false);
|
|
VectorF =
|
|
Function::Create(FTy, Function::ExternalLinkage, VFnName, M);
|
|
VectorF->copyAttributesFrom(F);
|
|
}
|
|
}
|
|
assert(VectorF && "Can't create vector function.");
|
|
|
|
SmallVector<OperandBundleDef, 1> OpBundles;
|
|
CI->getOperandBundlesAsDefs(OpBundles);
|
|
CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles);
|
|
|
|
if (isa<FPMathOperator>(V))
|
|
V->copyFastMathFlags(CI);
|
|
|
|
VectorLoopValueMap.setVectorValue(&I, Part, V);
|
|
addMetadata(V, &I);
|
|
}
|
|
|
|
break;
|
|
}
|
|
|
|
default:
|
|
// All other instructions are scalarized.
|
|
DEBUG(dbgs() << "LV: Found an unhandled instruction: " << I);
|
|
llvm_unreachable("Unhandled instruction!");
|
|
} // end of switch.
|
|
}
|
|
|
|
void InnerLoopVectorizer::updateAnalysis() {
|
|
// Forget the original basic block.
|
|
PSE.getSE()->forgetLoop(OrigLoop);
|
|
|
|
// Update the dominator tree information.
|
|
assert(DT->properlyDominates(LoopBypassBlocks.front(), LoopExitBlock) &&
|
|
"Entry does not dominate exit.");
|
|
|
|
DT->addNewBlock(LoopMiddleBlock,
|
|
LI->getLoopFor(LoopVectorBody)->getLoopLatch());
|
|
DT->addNewBlock(LoopScalarPreHeader, LoopBypassBlocks[0]);
|
|
DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader);
|
|
DT->changeImmediateDominator(LoopExitBlock, LoopBypassBlocks[0]);
|
|
DEBUG(DT->verifyDomTree());
|
|
}
|
|
|
|
/// \brief Check whether it is safe to if-convert this phi node.
|
|
///
|
|
/// Phi nodes with constant expressions that can trap are not safe to if
|
|
/// convert.
|
|
static bool canIfConvertPHINodes(BasicBlock *BB) {
|
|
for (Instruction &I : *BB) {
|
|
auto *Phi = dyn_cast<PHINode>(&I);
|
|
if (!Phi)
|
|
return true;
|
|
for (Value *V : Phi->incoming_values())
|
|
if (auto *C = dyn_cast<Constant>(V))
|
|
if (C->canTrap())
|
|
return false;
|
|
}
|
|
return true;
|
|
}
|
|
|
|
bool LoopVectorizationLegality::canVectorizeWithIfConvert() {
|
|
if (!EnableIfConversion) {
|
|
ORE->emit(createMissedAnalysis("IfConversionDisabled")
|
|
<< "if-conversion is disabled");
|
|
return false;
|
|
}
|
|
|
|
assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable");
|
|
|
|
// A list of pointers that we can safely read and write to.
|
|
SmallPtrSet<Value *, 8> SafePointes;
|
|
|
|
// Collect safe addresses.
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
if (blockNeedsPredication(BB))
|
|
continue;
|
|
|
|
for (Instruction &I : *BB)
|
|
if (auto *Ptr = getPointerOperand(&I))
|
|
SafePointes.insert(Ptr);
|
|
}
|
|
|
|
// Collect the blocks that need predication.
|
|
BasicBlock *Header = TheLoop->getHeader();
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
// We don't support switch statements inside loops.
|
|
if (!isa<BranchInst>(BB->getTerminator())) {
|
|
ORE->emit(createMissedAnalysis("LoopContainsSwitch", BB->getTerminator())
|
|
<< "loop contains a switch statement");
|
|
return false;
|
|
}
|
|
|
|
// We must be able to predicate all blocks that need to be predicated.
|
|
if (blockNeedsPredication(BB)) {
|
|
if (!blockCanBePredicated(BB, SafePointes)) {
|
|
ORE->emit(createMissedAnalysis("NoCFGForSelect", BB->getTerminator())
|
|
<< "control flow cannot be substituted for a select");
|
|
return false;
|
|
}
|
|
} else if (BB != Header && !canIfConvertPHINodes(BB)) {
|
|
ORE->emit(createMissedAnalysis("NoCFGForSelect", BB->getTerminator())
|
|
<< "control flow cannot be substituted for a select");
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// We can if-convert this loop.
|
|
return true;
|
|
}
|
|
|
|
bool LoopVectorizationLegality::canVectorize() {
|
|
// Store the result and return it at the end instead of exiting early, in case
|
|
// allowExtraAnalysis is used to report multiple reasons for not vectorizing.
|
|
bool Result = true;
|
|
// We must have a loop in canonical form. Loops with indirectbr in them cannot
|
|
// be canonicalized.
|
|
if (!TheLoop->getLoopPreheader()) {
|
|
ORE->emit(createMissedAnalysis("CFGNotUnderstood")
|
|
<< "loop control flow is not understood by vectorizer");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// FIXME: The code is currently dead, since the loop gets sent to
|
|
// LoopVectorizationLegality is already an innermost loop.
|
|
//
|
|
// We can only vectorize innermost loops.
|
|
if (!TheLoop->empty()) {
|
|
ORE->emit(createMissedAnalysis("NotInnermostLoop")
|
|
<< "loop is not the innermost loop");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// We must have a single backedge.
|
|
if (TheLoop->getNumBackEdges() != 1) {
|
|
ORE->emit(createMissedAnalysis("CFGNotUnderstood")
|
|
<< "loop control flow is not understood by vectorizer");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// We must have a single exiting block.
|
|
if (!TheLoop->getExitingBlock()) {
|
|
ORE->emit(createMissedAnalysis("CFGNotUnderstood")
|
|
<< "loop control flow is not understood by vectorizer");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// We only handle bottom-tested loops, i.e. loop in which the condition is
|
|
// checked at the end of each iteration. With that we can assume that all
|
|
// instructions in the loop are executed the same number of times.
|
|
if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) {
|
|
ORE->emit(createMissedAnalysis("CFGNotUnderstood")
|
|
<< "loop control flow is not understood by vectorizer");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// We need to have a loop header.
|
|
DEBUG(dbgs() << "LV: Found a loop: " << TheLoop->getHeader()->getName()
|
|
<< '\n');
|
|
|
|
// Check if we can if-convert non-single-bb loops.
|
|
unsigned NumBlocks = TheLoop->getNumBlocks();
|
|
if (NumBlocks != 1 && !canVectorizeWithIfConvert()) {
|
|
DEBUG(dbgs() << "LV: Can't if-convert the loop.\n");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// Check if we can vectorize the instructions and CFG in this loop.
|
|
if (!canVectorizeInstrs()) {
|
|
DEBUG(dbgs() << "LV: Can't vectorize the instructions or CFG\n");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// Go over each instruction and look at memory deps.
|
|
if (!canVectorizeMemory()) {
|
|
DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
DEBUG(dbgs() << "LV: We can vectorize this loop"
|
|
<< (LAI->getRuntimePointerChecking()->Need
|
|
? " (with a runtime bound check)"
|
|
: "")
|
|
<< "!\n");
|
|
|
|
bool UseInterleaved = TTI->enableInterleavedAccessVectorization();
|
|
|
|
// If an override option has been passed in for interleaved accesses, use it.
|
|
if (EnableInterleavedMemAccesses.getNumOccurrences() > 0)
|
|
UseInterleaved = EnableInterleavedMemAccesses;
|
|
|
|
// Analyze interleaved memory accesses.
|
|
if (UseInterleaved)
|
|
InterleaveInfo.analyzeInterleaving(*getSymbolicStrides());
|
|
|
|
unsigned SCEVThreshold = VectorizeSCEVCheckThreshold;
|
|
if (Hints->getForce() == LoopVectorizeHints::FK_Enabled)
|
|
SCEVThreshold = PragmaVectorizeSCEVCheckThreshold;
|
|
|
|
if (PSE.getUnionPredicate().getComplexity() > SCEVThreshold) {
|
|
ORE->emit(createMissedAnalysis("TooManySCEVRunTimeChecks")
|
|
<< "Too many SCEV assumptions need to be made and checked "
|
|
<< "at runtime");
|
|
DEBUG(dbgs() << "LV: Too many SCEV checks needed.\n");
|
|
if (ORE->allowExtraAnalysis())
|
|
Result = false;
|
|
else
|
|
return false;
|
|
}
|
|
|
|
// Okay! We've done all the tests. If any have failed, return false. Otherwise
|
|
// we can vectorize, and at this point we don't have any other mem analysis
|
|
// which may limit our maximum vectorization factor, so just return true with
|
|
// no restrictions.
|
|
return Result;
|
|
}
|
|
|
|
static Type *convertPointerToIntegerType(const DataLayout &DL, Type *Ty) {
|
|
if (Ty->isPointerTy())
|
|
return DL.getIntPtrType(Ty);
|
|
|
|
// It is possible that char's or short's overflow when we ask for the loop's
|
|
// trip count, work around this by changing the type size.
|
|
if (Ty->getScalarSizeInBits() < 32)
|
|
return Type::getInt32Ty(Ty->getContext());
|
|
|
|
return Ty;
|
|
}
|
|
|
|
static Type *getWiderType(const DataLayout &DL, Type *Ty0, Type *Ty1) {
|
|
Ty0 = convertPointerToIntegerType(DL, Ty0);
|
|
Ty1 = convertPointerToIntegerType(DL, Ty1);
|
|
if (Ty0->getScalarSizeInBits() > Ty1->getScalarSizeInBits())
|
|
return Ty0;
|
|
return Ty1;
|
|
}
|
|
|
|
/// \brief Check that the instruction has outside loop users and is not an
|
|
/// identified reduction variable.
|
|
static bool hasOutsideLoopUser(const Loop *TheLoop, Instruction *Inst,
|
|
SmallPtrSetImpl<Value *> &AllowedExit) {
|
|
// Reduction and Induction instructions are allowed to have exit users. All
|
|
// other instructions must not have external users.
|
|
if (!AllowedExit.count(Inst))
|
|
// Check that all of the users of the loop are inside the BB.
|
|
for (User *U : Inst->users()) {
|
|
Instruction *UI = cast<Instruction>(U);
|
|
// This user may be a reduction exit value.
|
|
if (!TheLoop->contains(UI)) {
|
|
DEBUG(dbgs() << "LV: Found an outside user for : " << *UI << '\n');
|
|
return true;
|
|
}
|
|
}
|
|
return false;
|
|
}
|
|
|
|
void LoopVectorizationLegality::addInductionPhi(
|
|
PHINode *Phi, const InductionDescriptor &ID,
|
|
SmallPtrSetImpl<Value *> &AllowedExit) {
|
|
Inductions[Phi] = ID;
|
|
Type *PhiTy = Phi->getType();
|
|
const DataLayout &DL = Phi->getModule()->getDataLayout();
|
|
|
|
// Get the widest type.
|
|
if (!PhiTy->isFloatingPointTy()) {
|
|
if (!WidestIndTy)
|
|
WidestIndTy = convertPointerToIntegerType(DL, PhiTy);
|
|
else
|
|
WidestIndTy = getWiderType(DL, PhiTy, WidestIndTy);
|
|
}
|
|
|
|
// Int inductions are special because we only allow one IV.
|
|
if (ID.getKind() == InductionDescriptor::IK_IntInduction &&
|
|
ID.getConstIntStepValue() &&
|
|
ID.getConstIntStepValue()->isOne() &&
|
|
isa<Constant>(ID.getStartValue()) &&
|
|
cast<Constant>(ID.getStartValue())->isNullValue()) {
|
|
|
|
// Use the phi node with the widest type as induction. Use the last
|
|
// one if there are multiple (no good reason for doing this other
|
|
// than it is expedient). We've checked that it begins at zero and
|
|
// steps by one, so this is a canonical induction variable.
|
|
if (!PrimaryInduction || PhiTy == WidestIndTy)
|
|
PrimaryInduction = Phi;
|
|
}
|
|
|
|
// Both the PHI node itself, and the "post-increment" value feeding
|
|
// back into the PHI node may have external users.
|
|
// We can allow those uses, except if the SCEVs we have for them rely
|
|
// on predicates that only hold within the loop, since allowing the exit
|
|
// currently means re-using this SCEV outside the loop.
|
|
if (PSE.getUnionPredicate().isAlwaysTrue()) {
|
|
AllowedExit.insert(Phi);
|
|
AllowedExit.insert(Phi->getIncomingValueForBlock(TheLoop->getLoopLatch()));
|
|
}
|
|
|
|
DEBUG(dbgs() << "LV: Found an induction variable.\n");
|
|
return;
|
|
}
|
|
|
|
bool LoopVectorizationLegality::canVectorizeInstrs() {
|
|
BasicBlock *Header = TheLoop->getHeader();
|
|
|
|
// Look for the attribute signaling the absence of NaNs.
|
|
Function &F = *Header->getParent();
|
|
HasFunNoNaNAttr =
|
|
F.getFnAttribute("no-nans-fp-math").getValueAsString() == "true";
|
|
|
|
// For each block in the loop.
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
// Scan the instructions in the block and look for hazards.
|
|
for (Instruction &I : *BB) {
|
|
if (auto *Phi = dyn_cast<PHINode>(&I)) {
|
|
Type *PhiTy = Phi->getType();
|
|
// Check that this PHI type is allowed.
|
|
if (!PhiTy->isIntegerTy() && !PhiTy->isFloatingPointTy() &&
|
|
!PhiTy->isPointerTy()) {
|
|
ORE->emit(createMissedAnalysis("CFGNotUnderstood", Phi)
|
|
<< "loop control flow is not understood by vectorizer");
|
|
DEBUG(dbgs() << "LV: Found an non-int non-pointer PHI.\n");
|
|
return false;
|
|
}
|
|
|
|
// If this PHINode is not in the header block, then we know that we
|
|
// can convert it to select during if-conversion. No need to check if
|
|
// the PHIs in this block are induction or reduction variables.
|
|
if (BB != Header) {
|
|
// Check that this instruction has no outside users or is an
|
|
// identified reduction value with an outside user.
|
|
if (!hasOutsideLoopUser(TheLoop, Phi, AllowedExit))
|
|
continue;
|
|
ORE->emit(createMissedAnalysis("NeitherInductionNorReduction", Phi)
|
|
<< "value could not be identified as "
|
|
"an induction or reduction variable");
|
|
return false;
|
|
}
|
|
|
|
// We only allow if-converted PHIs with exactly two incoming values.
|
|
if (Phi->getNumIncomingValues() != 2) {
|
|
ORE->emit(createMissedAnalysis("CFGNotUnderstood", Phi)
|
|
<< "control flow not understood by vectorizer");
|
|
DEBUG(dbgs() << "LV: Found an invalid PHI.\n");
|
|
return false;
|
|
}
|
|
|
|
RecurrenceDescriptor RedDes;
|
|
if (RecurrenceDescriptor::isReductionPHI(Phi, TheLoop, RedDes)) {
|
|
if (RedDes.hasUnsafeAlgebra())
|
|
Requirements->addUnsafeAlgebraInst(RedDes.getUnsafeAlgebraInst());
|
|
AllowedExit.insert(RedDes.getLoopExitInstr());
|
|
Reductions[Phi] = RedDes;
|
|
continue;
|
|
}
|
|
|
|
InductionDescriptor ID;
|
|
if (InductionDescriptor::isInductionPHI(Phi, TheLoop, PSE, ID)) {
|
|
addInductionPhi(Phi, ID, AllowedExit);
|
|
if (ID.hasUnsafeAlgebra() && !HasFunNoNaNAttr)
|
|
Requirements->addUnsafeAlgebraInst(ID.getUnsafeAlgebraInst());
|
|
continue;
|
|
}
|
|
|
|
if (RecurrenceDescriptor::isFirstOrderRecurrence(Phi, TheLoop,
|
|
SinkAfter, DT)) {
|
|
FirstOrderRecurrences.insert(Phi);
|
|
continue;
|
|
}
|
|
|
|
// As a last resort, coerce the PHI to a AddRec expression
|
|
// and re-try classifying it a an induction PHI.
|
|
if (InductionDescriptor::isInductionPHI(Phi, TheLoop, PSE, ID, true)) {
|
|
addInductionPhi(Phi, ID, AllowedExit);
|
|
continue;
|
|
}
|
|
|
|
ORE->emit(createMissedAnalysis("NonReductionValueUsedOutsideLoop", Phi)
|
|
<< "value that could not be identified as "
|
|
"reduction is used outside the loop");
|
|
DEBUG(dbgs() << "LV: Found an unidentified PHI." << *Phi << "\n");
|
|
return false;
|
|
} // end of PHI handling
|
|
|
|
// We handle calls that:
|
|
// * Are debug info intrinsics.
|
|
// * Have a mapping to an IR intrinsic.
|
|
// * Have a vector version available.
|
|
auto *CI = dyn_cast<CallInst>(&I);
|
|
if (CI && !getVectorIntrinsicIDForCall(CI, TLI) &&
|
|
!isa<DbgInfoIntrinsic>(CI) &&
|
|
!(CI->getCalledFunction() && TLI &&
|
|
TLI->isFunctionVectorizable(CI->getCalledFunction()->getName()))) {
|
|
ORE->emit(createMissedAnalysis("CantVectorizeCall", CI)
|
|
<< "call instruction cannot be vectorized");
|
|
DEBUG(dbgs() << "LV: Found a non-intrinsic, non-libfunc callsite.\n");
|
|
return false;
|
|
}
|
|
|
|
// Intrinsics such as powi,cttz and ctlz are legal to vectorize if the
|
|
// second argument is the same (i.e. loop invariant)
|
|
if (CI && hasVectorInstrinsicScalarOpd(
|
|
getVectorIntrinsicIDForCall(CI, TLI), 1)) {
|
|
auto *SE = PSE.getSE();
|
|
if (!SE->isLoopInvariant(PSE.getSCEV(CI->getOperand(1)), TheLoop)) {
|
|
ORE->emit(createMissedAnalysis("CantVectorizeIntrinsic", CI)
|
|
<< "intrinsic instruction cannot be vectorized");
|
|
DEBUG(dbgs() << "LV: Found unvectorizable intrinsic " << *CI << "\n");
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Check that the instruction return type is vectorizable.
|
|
// Also, we can't vectorize extractelement instructions.
|
|
if ((!VectorType::isValidElementType(I.getType()) &&
|
|
!I.getType()->isVoidTy()) ||
|
|
isa<ExtractElementInst>(I)) {
|
|
ORE->emit(createMissedAnalysis("CantVectorizeInstructionReturnType", &I)
|
|
<< "instruction return type cannot be vectorized");
|
|
DEBUG(dbgs() << "LV: Found unvectorizable type.\n");
|
|
return false;
|
|
}
|
|
|
|
// Check that the stored type is vectorizable.
|
|
if (auto *ST = dyn_cast<StoreInst>(&I)) {
|
|
Type *T = ST->getValueOperand()->getType();
|
|
if (!VectorType::isValidElementType(T)) {
|
|
ORE->emit(createMissedAnalysis("CantVectorizeStore", ST)
|
|
<< "store instruction cannot be vectorized");
|
|
return false;
|
|
}
|
|
|
|
// FP instructions can allow unsafe algebra, thus vectorizable by
|
|
// non-IEEE-754 compliant SIMD units.
|
|
// This applies to floating-point math operations and calls, not memory
|
|
// operations, shuffles, or casts, as they don't change precision or
|
|
// semantics.
|
|
} else if (I.getType()->isFloatingPointTy() && (CI || I.isBinaryOp()) &&
|
|
!I.hasUnsafeAlgebra()) {
|
|
DEBUG(dbgs() << "LV: Found FP op with unsafe algebra.\n");
|
|
Hints->setPotentiallyUnsafe();
|
|
}
|
|
|
|
// Reduction instructions are allowed to have exit users.
|
|
// All other instructions must not have external users.
|
|
if (hasOutsideLoopUser(TheLoop, &I, AllowedExit)) {
|
|
ORE->emit(createMissedAnalysis("ValueUsedOutsideLoop", &I)
|
|
<< "value cannot be used outside the loop");
|
|
return false;
|
|
}
|
|
|
|
} // next instr.
|
|
}
|
|
|
|
if (!PrimaryInduction) {
|
|
DEBUG(dbgs() << "LV: Did not find one integer induction var.\n");
|
|
if (Inductions.empty()) {
|
|
ORE->emit(createMissedAnalysis("NoInductionVariable")
|
|
<< "loop induction variable could not be identified");
|
|
return false;
|
|
}
|
|
}
|
|
|
|
// Now we know the widest induction type, check if our found induction
|
|
// is the same size. If it's not, unset it here and InnerLoopVectorizer
|
|
// will create another.
|
|
if (PrimaryInduction && WidestIndTy != PrimaryInduction->getType())
|
|
PrimaryInduction = nullptr;
|
|
|
|
return true;
|
|
}
|
|
|
|
void LoopVectorizationCostModel::collectLoopScalars(unsigned VF) {
|
|
|
|
// We should not collect Scalars more than once per VF. Right now, this
|
|
// function is called from collectUniformsAndScalars(), which already does
|
|
// this check. Collecting Scalars for VF=1 does not make any sense.
|
|
assert(VF >= 2 && !Scalars.count(VF) &&
|
|
"This function should not be visited twice for the same VF");
|
|
|
|
SmallSetVector<Instruction *, 8> Worklist;
|
|
|
|
// These sets are used to seed the analysis with pointers used by memory
|
|
// accesses that will remain scalar.
|
|
SmallSetVector<Instruction *, 8> ScalarPtrs;
|
|
SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs;
|
|
|
|
// A helper that returns true if the use of Ptr by MemAccess will be scalar.
|
|
// The pointer operands of loads and stores will be scalar as long as the
|
|
// memory access is not a gather or scatter operation. The value operand of a
|
|
// store will remain scalar if the store is scalarized.
|
|
auto isScalarUse = [&](Instruction *MemAccess, Value *Ptr) {
|
|
InstWidening WideningDecision = getWideningDecision(MemAccess, VF);
|
|
assert(WideningDecision != CM_Unknown &&
|
|
"Widening decision should be ready at this moment");
|
|
if (auto *Store = dyn_cast<StoreInst>(MemAccess))
|
|
if (Ptr == Store->getValueOperand())
|
|
return WideningDecision == CM_Scalarize;
|
|
assert(Ptr == getPointerOperand(MemAccess) &&
|
|
"Ptr is neither a value or pointer operand");
|
|
return WideningDecision != CM_GatherScatter;
|
|
};
|
|
|
|
// A helper that returns true if the given value is a bitcast or
|
|
// getelementptr instruction contained in the loop.
|
|
auto isLoopVaryingBitCastOrGEP = [&](Value *V) {
|
|
return ((isa<BitCastInst>(V) && V->getType()->isPointerTy()) ||
|
|
isa<GetElementPtrInst>(V)) &&
|
|
!TheLoop->isLoopInvariant(V);
|
|
};
|
|
|
|
// A helper that evaluates a memory access's use of a pointer. If the use
|
|
// will be a scalar use, and the pointer is only used by memory accesses, we
|
|
// place the pointer in ScalarPtrs. Otherwise, the pointer is placed in
|
|
// PossibleNonScalarPtrs.
|
|
auto evaluatePtrUse = [&](Instruction *MemAccess, Value *Ptr) {
|
|
|
|
// We only care about bitcast and getelementptr instructions contained in
|
|
// the loop.
|
|
if (!isLoopVaryingBitCastOrGEP(Ptr))
|
|
return;
|
|
|
|
// If the pointer has already been identified as scalar (e.g., if it was
|
|
// also identified as uniform), there's nothing to do.
|
|
auto *I = cast<Instruction>(Ptr);
|
|
if (Worklist.count(I))
|
|
return;
|
|
|
|
// If the use of the pointer will be a scalar use, and all users of the
|
|
// pointer are memory accesses, place the pointer in ScalarPtrs. Otherwise,
|
|
// place the pointer in PossibleNonScalarPtrs.
|
|
if (isScalarUse(MemAccess, Ptr) && all_of(I->users(), [&](User *U) {
|
|
return isa<LoadInst>(U) || isa<StoreInst>(U);
|
|
}))
|
|
ScalarPtrs.insert(I);
|
|
else
|
|
PossibleNonScalarPtrs.insert(I);
|
|
};
|
|
|
|
// We seed the scalars analysis with three classes of instructions: (1)
|
|
// instructions marked uniform-after-vectorization, (2) bitcast and
|
|
// getelementptr instructions used by memory accesses requiring a scalar use,
|
|
// and (3) pointer induction variables and their update instructions (we
|
|
// currently only scalarize these).
|
|
//
|
|
// (1) Add to the worklist all instructions that have been identified as
|
|
// uniform-after-vectorization.
|
|
Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end());
|
|
|
|
// (2) Add to the worklist all bitcast and getelementptr instructions used by
|
|
// memory accesses requiring a scalar use. The pointer operands of loads and
|
|
// stores will be scalar as long as the memory accesses is not a gather or
|
|
// scatter operation. The value operand of a store will remain scalar if the
|
|
// store is scalarized.
|
|
for (auto *BB : TheLoop->blocks())
|
|
for (auto &I : *BB) {
|
|
if (auto *Load = dyn_cast<LoadInst>(&I)) {
|
|
evaluatePtrUse(Load, Load->getPointerOperand());
|
|
} else if (auto *Store = dyn_cast<StoreInst>(&I)) {
|
|
evaluatePtrUse(Store, Store->getPointerOperand());
|
|
evaluatePtrUse(Store, Store->getValueOperand());
|
|
}
|
|
}
|
|
for (auto *I : ScalarPtrs)
|
|
if (!PossibleNonScalarPtrs.count(I)) {
|
|
DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n");
|
|
Worklist.insert(I);
|
|
}
|
|
|
|
// (3) Add to the worklist all pointer induction variables and their update
|
|
// instructions.
|
|
//
|
|
// TODO: Once we are able to vectorize pointer induction variables we should
|
|
// no longer insert them into the worklist here.
|
|
auto *Latch = TheLoop->getLoopLatch();
|
|
for (auto &Induction : *Legal->getInductionVars()) {
|
|
auto *Ind = Induction.first;
|
|
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
|
|
if (Induction.second.getKind() != InductionDescriptor::IK_PtrInduction)
|
|
continue;
|
|
Worklist.insert(Ind);
|
|
Worklist.insert(IndUpdate);
|
|
DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n");
|
|
DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate << "\n");
|
|
}
|
|
|
|
// Insert the forced scalars.
|
|
// FIXME: Currently widenPHIInstruction() often creates a dead vector
|
|
// induction variable when the PHI user is scalarized.
|
|
if (ForcedScalars.count(VF))
|
|
for (auto *I : ForcedScalars.find(VF)->second)
|
|
Worklist.insert(I);
|
|
|
|
// Expand the worklist by looking through any bitcasts and getelementptr
|
|
// instructions we've already identified as scalar. This is similar to the
|
|
// expansion step in collectLoopUniforms(); however, here we're only
|
|
// expanding to include additional bitcasts and getelementptr instructions.
|
|
unsigned Idx = 0;
|
|
while (Idx != Worklist.size()) {
|
|
Instruction *Dst = Worklist[Idx++];
|
|
if (!isLoopVaryingBitCastOrGEP(Dst->getOperand(0)))
|
|
continue;
|
|
auto *Src = cast<Instruction>(Dst->getOperand(0));
|
|
if (all_of(Src->users(), [&](User *U) -> bool {
|
|
auto *J = cast<Instruction>(U);
|
|
return !TheLoop->contains(J) || Worklist.count(J) ||
|
|
((isa<LoadInst>(J) || isa<StoreInst>(J)) &&
|
|
isScalarUse(J, Src));
|
|
})) {
|
|
Worklist.insert(Src);
|
|
DEBUG(dbgs() << "LV: Found scalar instruction: " << *Src << "\n");
|
|
}
|
|
}
|
|
|
|
// An induction variable will remain scalar if all users of the induction
|
|
// variable and induction variable update remain scalar.
|
|
for (auto &Induction : *Legal->getInductionVars()) {
|
|
auto *Ind = Induction.first;
|
|
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
|
|
|
|
// We already considered pointer induction variables, so there's no reason
|
|
// to look at their users again.
|
|
//
|
|
// TODO: Once we are able to vectorize pointer induction variables we
|
|
// should no longer skip over them here.
|
|
if (Induction.second.getKind() == InductionDescriptor::IK_PtrInduction)
|
|
continue;
|
|
|
|
// Determine if all users of the induction variable are scalar after
|
|
// vectorization.
|
|
auto ScalarInd = all_of(Ind->users(), [&](User *U) -> bool {
|
|
auto *I = cast<Instruction>(U);
|
|
return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I);
|
|
});
|
|
if (!ScalarInd)
|
|
continue;
|
|
|
|
// Determine if all users of the induction variable update instruction are
|
|
// scalar after vectorization.
|
|
auto ScalarIndUpdate = all_of(IndUpdate->users(), [&](User *U) -> bool {
|
|
auto *I = cast<Instruction>(U);
|
|
return I == Ind || !TheLoop->contains(I) || Worklist.count(I);
|
|
});
|
|
if (!ScalarIndUpdate)
|
|
continue;
|
|
|
|
// The induction variable and its update instruction will remain scalar.
|
|
Worklist.insert(Ind);
|
|
Worklist.insert(IndUpdate);
|
|
DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n");
|
|
DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate << "\n");
|
|
}
|
|
|
|
Scalars[VF].insert(Worklist.begin(), Worklist.end());
|
|
}
|
|
|
|
bool LoopVectorizationLegality::isScalarWithPredication(Instruction *I) {
|
|
if (!blockNeedsPredication(I->getParent()))
|
|
return false;
|
|
switch(I->getOpcode()) {
|
|
default:
|
|
break;
|
|
case Instruction::Store:
|
|
return !isMaskRequired(I);
|
|
case Instruction::UDiv:
|
|
case Instruction::SDiv:
|
|
case Instruction::SRem:
|
|
case Instruction::URem:
|
|
return mayDivideByZero(*I);
|
|
}
|
|
return false;
|
|
}
|
|
|
|
bool LoopVectorizationLegality::memoryInstructionCanBeWidened(Instruction *I,
|
|
unsigned VF) {
|
|
// Get and ensure we have a valid memory instruction.
|
|
LoadInst *LI = dyn_cast<LoadInst>(I);
|
|
StoreInst *SI = dyn_cast<StoreInst>(I);
|
|
assert((LI || SI) && "Invalid memory instruction");
|
|
|
|
auto *Ptr = getPointerOperand(I);
|
|
|
|
// In order to be widened, the pointer should be consecutive, first of all.
|
|
if (!isConsecutivePtr(Ptr))
|
|
return false;
|
|
|
|
// If the instruction is a store located in a predicated block, it will be
|
|
// scalarized.
|
|
if (isScalarWithPredication(I))
|
|
return false;
|
|
|
|
// If the instruction's allocated size doesn't equal it's type size, it
|
|
// requires padding and will be scalarized.
|
|
auto &DL = I->getModule()->getDataLayout();
|
|
auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType();
|
|
if (hasIrregularType(ScalarTy, DL, VF))
|
|
return false;
|
|
|
|
return true;
|
|
}
|
|
|
|
void LoopVectorizationCostModel::collectLoopUniforms(unsigned VF) {
|
|
|
|
// We should not collect Uniforms more than once per VF. Right now,
|
|
// this function is called from collectUniformsAndScalars(), which
|
|
// already does this check. Collecting Uniforms for VF=1 does not make any
|
|
// sense.
|
|
|
|
assert(VF >= 2 && !Uniforms.count(VF) &&
|
|
"This function should not be visited twice for the same VF");
|
|
|
|
// Visit the list of Uniforms. If we'll not find any uniform value, we'll
|
|
// not analyze again. Uniforms.count(VF) will return 1.
|
|
Uniforms[VF].clear();
|
|
|
|
// We now know that the loop is vectorizable!
|
|
// Collect instructions inside the loop that will remain uniform after
|
|
// vectorization.
|
|
|
|
// Global values, params and instructions outside of current loop are out of
|
|
// scope.
|
|
auto isOutOfScope = [&](Value *V) -> bool {
|
|
Instruction *I = dyn_cast<Instruction>(V);
|
|
return (!I || !TheLoop->contains(I));
|
|
};
|
|
|
|
SetVector<Instruction *> Worklist;
|
|
BasicBlock *Latch = TheLoop->getLoopLatch();
|
|
|
|
// Start with the conditional branch. If the branch condition is an
|
|
// instruction contained in the loop that is only used by the branch, it is
|
|
// uniform.
|
|
auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0));
|
|
if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse()) {
|
|
Worklist.insert(Cmp);
|
|
DEBUG(dbgs() << "LV: Found uniform instruction: " << *Cmp << "\n");
|
|
}
|
|
|
|
// Holds consecutive and consecutive-like pointers. Consecutive-like pointers
|
|
// are pointers that are treated like consecutive pointers during
|
|
// vectorization. The pointer operands of interleaved accesses are an
|
|
// example.
|
|
SmallSetVector<Instruction *, 8> ConsecutiveLikePtrs;
|
|
|
|
// Holds pointer operands of instructions that are possibly non-uniform.
|
|
SmallPtrSet<Instruction *, 8> PossibleNonUniformPtrs;
|
|
|
|
auto isUniformDecision = [&](Instruction *I, unsigned VF) {
|
|
InstWidening WideningDecision = getWideningDecision(I, VF);
|
|
assert(WideningDecision != CM_Unknown &&
|
|
"Widening decision should be ready at this moment");
|
|
|
|
return (WideningDecision == CM_Widen ||
|
|
WideningDecision == CM_Interleave);
|
|
};
|
|
// Iterate over the instructions in the loop, and collect all
|
|
// consecutive-like pointer operands in ConsecutiveLikePtrs. If it's possible
|
|
// that a consecutive-like pointer operand will be scalarized, we collect it
|
|
// in PossibleNonUniformPtrs instead. We use two sets here because a single
|
|
// getelementptr instruction can be used by both vectorized and scalarized
|
|
// memory instructions. For example, if a loop loads and stores from the same
|
|
// location, but the store is conditional, the store will be scalarized, and
|
|
// the getelementptr won't remain uniform.
|
|
for (auto *BB : TheLoop->blocks())
|
|
for (auto &I : *BB) {
|
|
|
|
// If there's no pointer operand, there's nothing to do.
|
|
auto *Ptr = dyn_cast_or_null<Instruction>(getPointerOperand(&I));
|
|
if (!Ptr)
|
|
continue;
|
|
|
|
// True if all users of Ptr are memory accesses that have Ptr as their
|
|
// pointer operand.
|
|
auto UsersAreMemAccesses = all_of(Ptr->users(), [&](User *U) -> bool {
|
|
return getPointerOperand(U) == Ptr;
|
|
});
|
|
|
|
// Ensure the memory instruction will not be scalarized or used by
|
|
// gather/scatter, making its pointer operand non-uniform. If the pointer
|
|
// operand is used by any instruction other than a memory access, we
|
|
// conservatively assume the pointer operand may be non-uniform.
|
|
if (!UsersAreMemAccesses || !isUniformDecision(&I, VF))
|
|
PossibleNonUniformPtrs.insert(Ptr);
|
|
|
|
// If the memory instruction will be vectorized and its pointer operand
|
|
// is consecutive-like, or interleaving - the pointer operand should
|
|
// remain uniform.
|
|
else
|
|
ConsecutiveLikePtrs.insert(Ptr);
|
|
}
|
|
|
|
// Add to the Worklist all consecutive and consecutive-like pointers that
|
|
// aren't also identified as possibly non-uniform.
|
|
for (auto *V : ConsecutiveLikePtrs)
|
|
if (!PossibleNonUniformPtrs.count(V)) {
|
|
DEBUG(dbgs() << "LV: Found uniform instruction: " << *V << "\n");
|
|
Worklist.insert(V);
|
|
}
|
|
|
|
// Expand Worklist in topological order: whenever a new instruction
|
|
// is added , its users should be either already inside Worklist, or
|
|
// out of scope. It ensures a uniform instruction will only be used
|
|
// by uniform instructions or out of scope instructions.
|
|
unsigned idx = 0;
|
|
while (idx != Worklist.size()) {
|
|
Instruction *I = Worklist[idx++];
|
|
|
|
for (auto OV : I->operand_values()) {
|
|
if (isOutOfScope(OV))
|
|
continue;
|
|
auto *OI = cast<Instruction>(OV);
|
|
if (all_of(OI->users(), [&](User *U) -> bool {
|
|
auto *J = cast<Instruction>(U);
|
|
return !TheLoop->contains(J) || Worklist.count(J) ||
|
|
(OI == getPointerOperand(J) && isUniformDecision(J, VF));
|
|
})) {
|
|
Worklist.insert(OI);
|
|
DEBUG(dbgs() << "LV: Found uniform instruction: " << *OI << "\n");
|
|
}
|
|
}
|
|
}
|
|
|
|
// Returns true if Ptr is the pointer operand of a memory access instruction
|
|
// I, and I is known to not require scalarization.
|
|
auto isVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool {
|
|
return getPointerOperand(I) == Ptr && isUniformDecision(I, VF);
|
|
};
|
|
|
|
// For an instruction to be added into Worklist above, all its users inside
|
|
// the loop should also be in Worklist. However, this condition cannot be
|
|
// true for phi nodes that form a cyclic dependence. We must process phi
|
|
// nodes separately. An induction variable will remain uniform if all users
|
|
// of the induction variable and induction variable update remain uniform.
|
|
// The code below handles both pointer and non-pointer induction variables.
|
|
for (auto &Induction : *Legal->getInductionVars()) {
|
|
auto *Ind = Induction.first;
|
|
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
|
|
|
|
// Determine if all users of the induction variable are uniform after
|
|
// vectorization.
|
|
auto UniformInd = all_of(Ind->users(), [&](User *U) -> bool {
|
|
auto *I = cast<Instruction>(U);
|
|
return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) ||
|
|
isVectorizedMemAccessUse(I, Ind);
|
|
});
|
|
if (!UniformInd)
|
|
continue;
|
|
|
|
// Determine if all users of the induction variable update instruction are
|
|
// uniform after vectorization.
|
|
auto UniformIndUpdate = all_of(IndUpdate->users(), [&](User *U) -> bool {
|
|
auto *I = cast<Instruction>(U);
|
|
return I == Ind || !TheLoop->contains(I) || Worklist.count(I) ||
|
|
isVectorizedMemAccessUse(I, IndUpdate);
|
|
});
|
|
if (!UniformIndUpdate)
|
|
continue;
|
|
|
|
// The induction variable and its update instruction will remain uniform.
|
|
Worklist.insert(Ind);
|
|
Worklist.insert(IndUpdate);
|
|
DEBUG(dbgs() << "LV: Found uniform instruction: " << *Ind << "\n");
|
|
DEBUG(dbgs() << "LV: Found uniform instruction: " << *IndUpdate << "\n");
|
|
}
|
|
|
|
Uniforms[VF].insert(Worklist.begin(), Worklist.end());
|
|
}
|
|
|
|
bool LoopVectorizationLegality::canVectorizeMemory() {
|
|
LAI = &(*GetLAA)(*TheLoop);
|
|
InterleaveInfo.setLAI(LAI);
|
|
const OptimizationRemarkAnalysis *LAR = LAI->getReport();
|
|
if (LAR) {
|
|
OptimizationRemarkAnalysis VR(Hints->vectorizeAnalysisPassName(),
|
|
"loop not vectorized: ", *LAR);
|
|
ORE->emit(VR);
|
|
}
|
|
if (!LAI->canVectorizeMemory())
|
|
return false;
|
|
|
|
if (LAI->hasStoreToLoopInvariantAddress()) {
|
|
ORE->emit(createMissedAnalysis("CantVectorizeStoreToLoopInvariantAddress")
|
|
<< "write to a loop invariant address could not be vectorized");
|
|
DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n");
|
|
return false;
|
|
}
|
|
|
|
Requirements->addRuntimePointerChecks(LAI->getNumRuntimePointerChecks());
|
|
PSE.addPredicate(LAI->getPSE().getUnionPredicate());
|
|
|
|
return true;
|
|
}
|
|
|
|
bool LoopVectorizationLegality::isInductionVariable(const Value *V) {
|
|
Value *In0 = const_cast<Value *>(V);
|
|
PHINode *PN = dyn_cast_or_null<PHINode>(In0);
|
|
if (!PN)
|
|
return false;
|
|
|
|
return Inductions.count(PN);
|
|
}
|
|
|
|
bool LoopVectorizationLegality::isFirstOrderRecurrence(const PHINode *Phi) {
|
|
return FirstOrderRecurrences.count(Phi);
|
|
}
|
|
|
|
bool LoopVectorizationLegality::blockNeedsPredication(BasicBlock *BB) {
|
|
return LoopAccessInfo::blockNeedsPredication(BB, TheLoop, DT);
|
|
}
|
|
|
|
bool LoopVectorizationLegality::blockCanBePredicated(
|
|
BasicBlock *BB, SmallPtrSetImpl<Value *> &SafePtrs) {
|
|
const bool IsAnnotatedParallel = TheLoop->isAnnotatedParallel();
|
|
|
|
for (Instruction &I : *BB) {
|
|
// Check that we don't have a constant expression that can trap as operand.
|
|
for (Value *Operand : I.operands()) {
|
|
if (auto *C = dyn_cast<Constant>(Operand))
|
|
if (C->canTrap())
|
|
return false;
|
|
}
|
|
// We might be able to hoist the load.
|
|
if (I.mayReadFromMemory()) {
|
|
auto *LI = dyn_cast<LoadInst>(&I);
|
|
if (!LI)
|
|
return false;
|
|
if (!SafePtrs.count(LI->getPointerOperand())) {
|
|
if (isLegalMaskedLoad(LI->getType(), LI->getPointerOperand()) ||
|
|
isLegalMaskedGather(LI->getType())) {
|
|
MaskedOp.insert(LI);
|
|
continue;
|
|
}
|
|
// !llvm.mem.parallel_loop_access implies if-conversion safety.
|
|
if (IsAnnotatedParallel)
|
|
continue;
|
|
return false;
|
|
}
|
|
}
|
|
|
|
if (I.mayWriteToMemory()) {
|
|
auto *SI = dyn_cast<StoreInst>(&I);
|
|
// We only support predication of stores in basic blocks with one
|
|
// predecessor.
|
|
if (!SI)
|
|
return false;
|
|
|
|
// Build a masked store if it is legal for the target.
|
|
if (isLegalMaskedStore(SI->getValueOperand()->getType(),
|
|
SI->getPointerOperand()) ||
|
|
isLegalMaskedScatter(SI->getValueOperand()->getType())) {
|
|
MaskedOp.insert(SI);
|
|
continue;
|
|
}
|
|
|
|
bool isSafePtr = (SafePtrs.count(SI->getPointerOperand()) != 0);
|
|
bool isSinglePredecessor = SI->getParent()->getSinglePredecessor();
|
|
|
|
if (++NumPredStores > NumberOfStoresToPredicate || !isSafePtr ||
|
|
!isSinglePredecessor)
|
|
return false;
|
|
}
|
|
if (I.mayThrow())
|
|
return false;
|
|
}
|
|
|
|
return true;
|
|
}
|
|
|
|
void InterleavedAccessInfo::collectConstStrideAccesses(
|
|
MapVector<Instruction *, StrideDescriptor> &AccessStrideInfo,
|
|
const ValueToValueMap &Strides) {
|
|
|
|
auto &DL = TheLoop->getHeader()->getModule()->getDataLayout();
|
|
|
|
// Since it's desired that the load/store instructions be maintained in
|
|
// "program order" for the interleaved access analysis, we have to visit the
|
|
// blocks in the loop in reverse postorder (i.e., in a topological order).
|
|
// Such an ordering will ensure that any load/store that may be executed
|
|
// before a second load/store will precede the second load/store in
|
|
// AccessStrideInfo.
|
|
LoopBlocksDFS DFS(TheLoop);
|
|
DFS.perform(LI);
|
|
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO()))
|
|
for (auto &I : *BB) {
|
|
auto *LI = dyn_cast<LoadInst>(&I);
|
|
auto *SI = dyn_cast<StoreInst>(&I);
|
|
if (!LI && !SI)
|
|
continue;
|
|
|
|
Value *Ptr = getPointerOperand(&I);
|
|
// We don't check wrapping here because we don't know yet if Ptr will be
|
|
// part of a full group or a group with gaps. Checking wrapping for all
|
|
// pointers (even those that end up in groups with no gaps) will be overly
|
|
// conservative. For full groups, wrapping should be ok since if we would
|
|
// wrap around the address space we would do a memory access at nullptr
|
|
// even without the transformation. The wrapping checks are therefore
|
|
// deferred until after we've formed the interleaved groups.
|
|
int64_t Stride = getPtrStride(PSE, Ptr, TheLoop, Strides,
|
|
/*Assume=*/true, /*ShouldCheckWrap=*/false);
|
|
|
|
const SCEV *Scev = replaceSymbolicStrideSCEV(PSE, Strides, Ptr);
|
|
PointerType *PtrTy = dyn_cast<PointerType>(Ptr->getType());
|
|
uint64_t Size = DL.getTypeAllocSize(PtrTy->getElementType());
|
|
|
|
// An alignment of 0 means target ABI alignment.
|
|
unsigned Align = getMemInstAlignment(&I);
|
|
if (!Align)
|
|
Align = DL.getABITypeAlignment(PtrTy->getElementType());
|
|
|
|
AccessStrideInfo[&I] = StrideDescriptor(Stride, Scev, Size, Align);
|
|
}
|
|
}
|
|
|
|
// Analyze interleaved accesses and collect them into interleaved load and
|
|
// store groups.
|
|
//
|
|
// When generating code for an interleaved load group, we effectively hoist all
|
|
// loads in the group to the location of the first load in program order. When
|
|
// generating code for an interleaved store group, we sink all stores to the
|
|
// location of the last store. This code motion can change the order of load
|
|
// and store instructions and may break dependences.
|
|
//
|
|
// The code generation strategy mentioned above ensures that we won't violate
|
|
// any write-after-read (WAR) dependences.
|
|
//
|
|
// E.g., for the WAR dependence: a = A[i]; // (1)
|
|
// A[i] = b; // (2)
|
|
//
|
|
// The store group of (2) is always inserted at or below (2), and the load
|
|
// group of (1) is always inserted at or above (1). Thus, the instructions will
|
|
// never be reordered. All other dependences are checked to ensure the
|
|
// correctness of the instruction reordering.
|
|
//
|
|
// The algorithm visits all memory accesses in the loop in bottom-up program
|
|
// order. Program order is established by traversing the blocks in the loop in
|
|
// reverse postorder when collecting the accesses.
|
|
//
|
|
// We visit the memory accesses in bottom-up order because it can simplify the
|
|
// construction of store groups in the presence of write-after-write (WAW)
|
|
// dependences.
|
|
//
|
|
// E.g., for the WAW dependence: A[i] = a; // (1)
|
|
// A[i] = b; // (2)
|
|
// A[i + 1] = c; // (3)
|
|
//
|
|
// We will first create a store group with (3) and (2). (1) can't be added to
|
|
// this group because it and (2) are dependent. However, (1) can be grouped
|
|
// with other accesses that may precede it in program order. Note that a
|
|
// bottom-up order does not imply that WAW dependences should not be checked.
|
|
void InterleavedAccessInfo::analyzeInterleaving(
|
|
const ValueToValueMap &Strides) {
|
|
DEBUG(dbgs() << "LV: Analyzing interleaved accesses...\n");
|
|
|
|
// Holds all accesses with a constant stride.
|
|
MapVector<Instruction *, StrideDescriptor> AccessStrideInfo;
|
|
collectConstStrideAccesses(AccessStrideInfo, Strides);
|
|
|
|
if (AccessStrideInfo.empty())
|
|
return;
|
|
|
|
// Collect the dependences in the loop.
|
|
collectDependences();
|
|
|
|
// Holds all interleaved store groups temporarily.
|
|
SmallSetVector<InterleaveGroup *, 4> StoreGroups;
|
|
// Holds all interleaved load groups temporarily.
|
|
SmallSetVector<InterleaveGroup *, 4> LoadGroups;
|
|
|
|
// Search in bottom-up program order for pairs of accesses (A and B) that can
|
|
// form interleaved load or store groups. In the algorithm below, access A
|
|
// precedes access B in program order. We initialize a group for B in the
|
|
// outer loop of the algorithm, and then in the inner loop, we attempt to
|
|
// insert each A into B's group if:
|
|
//
|
|
// 1. A and B have the same stride,
|
|
// 2. A and B have the same memory object size, and
|
|
// 3. A belongs in B's group according to its distance from B.
|
|
//
|
|
// Special care is taken to ensure group formation will not break any
|
|
// dependences.
|
|
for (auto BI = AccessStrideInfo.rbegin(), E = AccessStrideInfo.rend();
|
|
BI != E; ++BI) {
|
|
Instruction *B = BI->first;
|
|
StrideDescriptor DesB = BI->second;
|
|
|
|
// Initialize a group for B if it has an allowable stride. Even if we don't
|
|
// create a group for B, we continue with the bottom-up algorithm to ensure
|
|
// we don't break any of B's dependences.
|
|
InterleaveGroup *Group = nullptr;
|
|
if (isStrided(DesB.Stride)) {
|
|
Group = getInterleaveGroup(B);
|
|
if (!Group) {
|
|
DEBUG(dbgs() << "LV: Creating an interleave group with:" << *B << '\n');
|
|
Group = createInterleaveGroup(B, DesB.Stride, DesB.Align);
|
|
}
|
|
if (B->mayWriteToMemory())
|
|
StoreGroups.insert(Group);
|
|
else
|
|
LoadGroups.insert(Group);
|
|
}
|
|
|
|
for (auto AI = std::next(BI); AI != E; ++AI) {
|
|
Instruction *A = AI->first;
|
|
StrideDescriptor DesA = AI->second;
|
|
|
|
// Our code motion strategy implies that we can't have dependences
|
|
// between accesses in an interleaved group and other accesses located
|
|
// between the first and last member of the group. Note that this also
|
|
// means that a group can't have more than one member at a given offset.
|
|
// The accesses in a group can have dependences with other accesses, but
|
|
// we must ensure we don't extend the boundaries of the group such that
|
|
// we encompass those dependent accesses.
|
|
//
|
|
// For example, assume we have the sequence of accesses shown below in a
|
|
// stride-2 loop:
|
|
//
|
|
// (1, 2) is a group | A[i] = a; // (1)
|
|
// | A[i-1] = b; // (2) |
|
|
// A[i-3] = c; // (3)
|
|
// A[i] = d; // (4) | (2, 4) is not a group
|
|
//
|
|
// Because accesses (2) and (3) are dependent, we can group (2) with (1)
|
|
// but not with (4). If we did, the dependent access (3) would be within
|
|
// the boundaries of the (2, 4) group.
|
|
if (!canReorderMemAccessesForInterleavedGroups(&*AI, &*BI)) {
|
|
|
|
// If a dependence exists and A is already in a group, we know that A
|
|
// must be a store since A precedes B and WAR dependences are allowed.
|
|
// Thus, A would be sunk below B. We release A's group to prevent this
|
|
// illegal code motion. A will then be free to form another group with
|
|
// instructions that precede it.
|
|
if (isInterleaved(A)) {
|
|
InterleaveGroup *StoreGroup = getInterleaveGroup(A);
|
|
StoreGroups.remove(StoreGroup);
|
|
releaseGroup(StoreGroup);
|
|
}
|
|
|
|
// If a dependence exists and A is not already in a group (or it was
|
|
// and we just released it), B might be hoisted above A (if B is a
|
|
// load) or another store might be sunk below A (if B is a store). In
|
|
// either case, we can't add additional instructions to B's group. B
|
|
// will only form a group with instructions that it precedes.
|
|
break;
|
|
}
|
|
|
|
// At this point, we've checked for illegal code motion. If either A or B
|
|
// isn't strided, there's nothing left to do.
|
|
if (!isStrided(DesA.Stride) || !isStrided(DesB.Stride))
|
|
continue;
|
|
|
|
// Ignore A if it's already in a group or isn't the same kind of memory
|
|
// operation as B.
|
|
if (isInterleaved(A) || A->mayReadFromMemory() != B->mayReadFromMemory())
|
|
continue;
|
|
|
|
// Check rules 1 and 2. Ignore A if its stride or size is different from
|
|
// that of B.
|
|
if (DesA.Stride != DesB.Stride || DesA.Size != DesB.Size)
|
|
continue;
|
|
|
|
// Ignore A if the memory object of A and B don't belong to the same
|
|
// address space
|
|
if (getMemInstAddressSpace(A) != getMemInstAddressSpace(B))
|
|
continue;
|
|
|
|
// Calculate the distance from A to B.
|
|
const SCEVConstant *DistToB = dyn_cast<SCEVConstant>(
|
|
PSE.getSE()->getMinusSCEV(DesA.Scev, DesB.Scev));
|
|
if (!DistToB)
|
|
continue;
|
|
int64_t DistanceToB = DistToB->getAPInt().getSExtValue();
|
|
|
|
// Check rule 3. Ignore A if its distance to B is not a multiple of the
|
|
// size.
|
|
if (DistanceToB % static_cast<int64_t>(DesB.Size))
|
|
continue;
|
|
|
|
// Ignore A if either A or B is in a predicated block. Although we
|
|
// currently prevent group formation for predicated accesses, we may be
|
|
// able to relax this limitation in the future once we handle more
|
|
// complicated blocks.
|
|
if (isPredicated(A->getParent()) || isPredicated(B->getParent()))
|
|
continue;
|
|
|
|
// The index of A is the index of B plus A's distance to B in multiples
|
|
// of the size.
|
|
int IndexA =
|
|
Group->getIndex(B) + DistanceToB / static_cast<int64_t>(DesB.Size);
|
|
|
|
// Try to insert A into B's group.
|
|
if (Group->insertMember(A, IndexA, DesA.Align)) {
|
|
DEBUG(dbgs() << "LV: Inserted:" << *A << '\n'
|
|
<< " into the interleave group with" << *B << '\n');
|
|
InterleaveGroupMap[A] = Group;
|
|
|
|
// Set the first load in program order as the insert position.
|
|
if (A->mayReadFromMemory())
|
|
Group->setInsertPos(A);
|
|
}
|
|
} // Iteration over A accesses.
|
|
} // Iteration over B accesses.
|
|
|
|
// Remove interleaved store groups with gaps.
|
|
for (InterleaveGroup *Group : StoreGroups)
|
|
if (Group->getNumMembers() != Group->getFactor())
|
|
releaseGroup(Group);
|
|
|
|
// Remove interleaved groups with gaps (currently only loads) whose memory
|
|
// accesses may wrap around. We have to revisit the getPtrStride analysis,
|
|
// this time with ShouldCheckWrap=true, since collectConstStrideAccesses does
|
|
// not check wrapping (see documentation there).
|
|
// FORNOW we use Assume=false;
|
|
// TODO: Change to Assume=true but making sure we don't exceed the threshold
|
|
// of runtime SCEV assumptions checks (thereby potentially failing to
|
|
// vectorize altogether).
|
|
// Additional optional optimizations:
|
|
// TODO: If we are peeling the loop and we know that the first pointer doesn't
|
|
// wrap then we can deduce that all pointers in the group don't wrap.
|
|
// This means that we can forcefully peel the loop in order to only have to
|
|
// check the first pointer for no-wrap. When we'll change to use Assume=true
|
|
// we'll only need at most one runtime check per interleaved group.
|
|
//
|
|
for (InterleaveGroup *Group : LoadGroups) {
|
|
|
|
// Case 1: A full group. Can Skip the checks; For full groups, if the wide
|
|
// load would wrap around the address space we would do a memory access at
|
|
// nullptr even without the transformation.
|
|
if (Group->getNumMembers() == Group->getFactor())
|
|
continue;
|
|
|
|
// Case 2: If first and last members of the group don't wrap this implies
|
|
// that all the pointers in the group don't wrap.
|
|
// So we check only group member 0 (which is always guaranteed to exist),
|
|
// and group member Factor - 1; If the latter doesn't exist we rely on
|
|
// peeling (if it is a non-reveresed accsess -- see Case 3).
|
|
Value *FirstMemberPtr = getPointerOperand(Group->getMember(0));
|
|
if (!getPtrStride(PSE, FirstMemberPtr, TheLoop, Strides, /*Assume=*/false,
|
|
/*ShouldCheckWrap=*/true)) {
|
|
DEBUG(dbgs() << "LV: Invalidate candidate interleaved group due to "
|
|
"first group member potentially pointer-wrapping.\n");
|
|
releaseGroup(Group);
|
|
continue;
|
|
}
|
|
Instruction *LastMember = Group->getMember(Group->getFactor() - 1);
|
|
if (LastMember) {
|
|
Value *LastMemberPtr = getPointerOperand(LastMember);
|
|
if (!getPtrStride(PSE, LastMemberPtr, TheLoop, Strides, /*Assume=*/false,
|
|
/*ShouldCheckWrap=*/true)) {
|
|
DEBUG(dbgs() << "LV: Invalidate candidate interleaved group due to "
|
|
"last group member potentially pointer-wrapping.\n");
|
|
releaseGroup(Group);
|
|
}
|
|
} else {
|
|
// Case 3: A non-reversed interleaved load group with gaps: We need
|
|
// to execute at least one scalar epilogue iteration. This will ensure
|
|
// we don't speculatively access memory out-of-bounds. We only need
|
|
// to look for a member at index factor - 1, since every group must have
|
|
// a member at index zero.
|
|
if (Group->isReverse()) {
|
|
releaseGroup(Group);
|
|
continue;
|
|
}
|
|
DEBUG(dbgs() << "LV: Interleaved group requires epilogue iteration.\n");
|
|
RequiresScalarEpilogue = true;
|
|
}
|
|
}
|
|
}
|
|
|
|
Optional<unsigned> LoopVectorizationCostModel::computeMaxVF(bool OptForSize) {
|
|
if (!EnableCondStoresVectorization && Legal->getNumPredStores()) {
|
|
ORE->emit(createMissedAnalysis("ConditionalStore")
|
|
<< "store that is conditionally executed prevents vectorization");
|
|
DEBUG(dbgs() << "LV: No vectorization. There are conditional stores.\n");
|
|
return None;
|
|
}
|
|
|
|
if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) {
|
|
// TODO: It may by useful to do since it's still likely to be dynamically
|
|
// uniform if the target can skip.
|
|
DEBUG(dbgs() << "LV: Not inserting runtime ptr check for divergent target");
|
|
|
|
ORE->emit(
|
|
createMissedAnalysis("CantVersionLoopWithDivergentTarget")
|
|
<< "runtime pointer checks needed. Not enabled for divergent target");
|
|
|
|
return None;
|
|
}
|
|
|
|
if (!OptForSize) // Remaining checks deal with scalar loop when OptForSize.
|
|
return computeFeasibleMaxVF(OptForSize);
|
|
|
|
if (Legal->getRuntimePointerChecking()->Need) {
|
|
ORE->emit(createMissedAnalysis("CantVersionLoopWithOptForSize")
|
|
<< "runtime pointer checks needed. Enable vectorization of this "
|
|
"loop with '#pragma clang loop vectorize(enable)' when "
|
|
"compiling with -Os/-Oz");
|
|
DEBUG(dbgs()
|
|
<< "LV: Aborting. Runtime ptr check is required with -Os/-Oz.\n");
|
|
return None;
|
|
}
|
|
|
|
// If we optimize the program for size, avoid creating the tail loop.
|
|
unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
|
|
DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n');
|
|
|
|
// If we don't know the precise trip count, don't try to vectorize.
|
|
if (TC < 2) {
|
|
ORE->emit(
|
|
createMissedAnalysis("UnknownLoopCountComplexCFG")
|
|
<< "unable to calculate the loop count due to complex control flow");
|
|
DEBUG(dbgs() << "LV: Aborting. A tail loop is required with -Os/-Oz.\n");
|
|
return None;
|
|
}
|
|
|
|
unsigned MaxVF = computeFeasibleMaxVF(OptForSize, TC);
|
|
|
|
if (TC % MaxVF != 0) {
|
|
// If the trip count that we found modulo the vectorization factor is not
|
|
// zero then we require a tail.
|
|
// FIXME: look for a smaller MaxVF that does divide TC rather than give up.
|
|
// FIXME: return None if loop requiresScalarEpilog(<MaxVF>), or look for a
|
|
// smaller MaxVF that does not require a scalar epilog.
|
|
|
|
ORE->emit(createMissedAnalysis("NoTailLoopWithOptForSize")
|
|
<< "cannot optimize for size and vectorize at the "
|
|
"same time. Enable vectorization of this loop "
|
|
"with '#pragma clang loop vectorize(enable)' "
|
|
"when compiling with -Os/-Oz");
|
|
DEBUG(dbgs() << "LV: Aborting. A tail loop is required with -Os/-Oz.\n");
|
|
return None;
|
|
}
|
|
|
|
return MaxVF;
|
|
}
|
|
|
|
unsigned
|
|
LoopVectorizationCostModel::computeFeasibleMaxVF(bool OptForSize,
|
|
unsigned ConstTripCount) {
|
|
MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI);
|
|
unsigned SmallestType, WidestType;
|
|
std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes();
|
|
unsigned WidestRegister = TTI.getRegisterBitWidth(true);
|
|
unsigned MaxSafeDepDist = -1U;
|
|
|
|
// Get the maximum safe dependence distance in bits computed by LAA. If the
|
|
// loop contains any interleaved accesses, we divide the dependence distance
|
|
// by the maximum interleave factor of all interleaved groups. Note that
|
|
// although the division ensures correctness, this is a fairly conservative
|
|
// computation because the maximum distance computed by LAA may not involve
|
|
// any of the interleaved accesses.
|
|
if (Legal->getMaxSafeDepDistBytes() != -1U)
|
|
MaxSafeDepDist =
|
|
Legal->getMaxSafeDepDistBytes() * 8 / Legal->getMaxInterleaveFactor();
|
|
|
|
WidestRegister =
|
|
((WidestRegister < MaxSafeDepDist) ? WidestRegister : MaxSafeDepDist);
|
|
unsigned MaxVectorSize = WidestRegister / WidestType;
|
|
|
|
DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestType << " / "
|
|
<< WidestType << " bits.\n");
|
|
DEBUG(dbgs() << "LV: The Widest register is: " << WidestRegister
|
|
<< " bits.\n");
|
|
|
|
if (MaxVectorSize == 0) {
|
|
DEBUG(dbgs() << "LV: The target has no vector registers.\n");
|
|
MaxVectorSize = 1;
|
|
} else if (ConstTripCount && ConstTripCount < MaxVectorSize &&
|
|
isPowerOf2_32(ConstTripCount))
|
|
MaxVectorSize = ConstTripCount;
|
|
|
|
assert(MaxVectorSize <= 64 && "Did not expect to pack so many elements"
|
|
" into one vector!");
|
|
|
|
unsigned MaxVF = MaxVectorSize;
|
|
if (MaximizeBandwidth && !OptForSize) {
|
|
// Collect all viable vectorization factors.
|
|
SmallVector<unsigned, 8> VFs;
|
|
unsigned NewMaxVectorSize = WidestRegister / SmallestType;
|
|
for (unsigned VS = MaxVectorSize; VS <= NewMaxVectorSize; VS *= 2)
|
|
VFs.push_back(VS);
|
|
|
|
// For each VF calculate its register usage.
|
|
auto RUs = calculateRegisterUsage(VFs);
|
|
|
|
// Select the largest VF which doesn't require more registers than existing
|
|
// ones.
|
|
unsigned TargetNumRegisters = TTI.getNumberOfRegisters(true);
|
|
for (int i = RUs.size() - 1; i >= 0; --i) {
|
|
if (RUs[i].MaxLocalUsers <= TargetNumRegisters) {
|
|
MaxVF = VFs[i];
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
return MaxVF;
|
|
}
|
|
|
|
LoopVectorizationCostModel::VectorizationFactor
|
|
LoopVectorizationCostModel::selectVectorizationFactor(unsigned MaxVF) {
|
|
float Cost = expectedCost(1).first;
|
|
#ifndef NDEBUG
|
|
const float ScalarCost = Cost;
|
|
#endif /* NDEBUG */
|
|
unsigned Width = 1;
|
|
DEBUG(dbgs() << "LV: Scalar loop costs: " << (int)ScalarCost << ".\n");
|
|
|
|
bool ForceVectorization = Hints->getForce() == LoopVectorizeHints::FK_Enabled;
|
|
// Ignore scalar width, because the user explicitly wants vectorization.
|
|
if (ForceVectorization && MaxVF > 1) {
|
|
Width = 2;
|
|
Cost = expectedCost(Width).first / (float)Width;
|
|
}
|
|
|
|
for (unsigned i = 2; i <= MaxVF; i *= 2) {
|
|
// Notice that the vector loop needs to be executed less times, so
|
|
// we need to divide the cost of the vector loops by the width of
|
|
// the vector elements.
|
|
VectorizationCostTy C = expectedCost(i);
|
|
float VectorCost = C.first / (float)i;
|
|
DEBUG(dbgs() << "LV: Vector loop of width " << i
|
|
<< " costs: " << (int)VectorCost << ".\n");
|
|
if (!C.second && !ForceVectorization) {
|
|
DEBUG(
|
|
dbgs() << "LV: Not considering vector loop of width " << i
|
|
<< " because it will not generate any vector instructions.\n");
|
|
continue;
|
|
}
|
|
if (VectorCost < Cost) {
|
|
Cost = VectorCost;
|
|
Width = i;
|
|
}
|
|
}
|
|
|
|
DEBUG(if (ForceVectorization && Width > 1 && Cost >= ScalarCost) dbgs()
|
|
<< "LV: Vectorization seems to be not beneficial, "
|
|
<< "but was forced by a user.\n");
|
|
DEBUG(dbgs() << "LV: Selecting VF: " << Width << ".\n");
|
|
VectorizationFactor Factor = {Width, (unsigned)(Width * Cost)};
|
|
return Factor;
|
|
}
|
|
|
|
std::pair<unsigned, unsigned>
|
|
LoopVectorizationCostModel::getSmallestAndWidestTypes() {
|
|
unsigned MinWidth = -1U;
|
|
unsigned MaxWidth = 8;
|
|
const DataLayout &DL = TheFunction->getParent()->getDataLayout();
|
|
|
|
// For each block.
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
// For each instruction in the loop.
|
|
for (Instruction &I : *BB) {
|
|
Type *T = I.getType();
|
|
|
|
// Skip ignored values.
|
|
if (ValuesToIgnore.count(&I))
|
|
continue;
|
|
|
|
// Only examine Loads, Stores and PHINodes.
|
|
if (!isa<LoadInst>(I) && !isa<StoreInst>(I) && !isa<PHINode>(I))
|
|
continue;
|
|
|
|
// Examine PHI nodes that are reduction variables. Update the type to
|
|
// account for the recurrence type.
|
|
if (auto *PN = dyn_cast<PHINode>(&I)) {
|
|
if (!Legal->isReductionVariable(PN))
|
|
continue;
|
|
RecurrenceDescriptor RdxDesc = (*Legal->getReductionVars())[PN];
|
|
T = RdxDesc.getRecurrenceType();
|
|
}
|
|
|
|
// Examine the stored values.
|
|
if (auto *ST = dyn_cast<StoreInst>(&I))
|
|
T = ST->getValueOperand()->getType();
|
|
|
|
// Ignore loaded pointer types and stored pointer types that are not
|
|
// vectorizable.
|
|
//
|
|
// FIXME: The check here attempts to predict whether a load or store will
|
|
// be vectorized. We only know this for certain after a VF has
|
|
// been selected. Here, we assume that if an access can be
|
|
// vectorized, it will be. We should also look at extending this
|
|
// optimization to non-pointer types.
|
|
//
|
|
if (T->isPointerTy() && !isConsecutiveLoadOrStore(&I) &&
|
|
!Legal->isAccessInterleaved(&I) && !Legal->isLegalGatherOrScatter(&I))
|
|
continue;
|
|
|
|
MinWidth = std::min(MinWidth,
|
|
(unsigned)DL.getTypeSizeInBits(T->getScalarType()));
|
|
MaxWidth = std::max(MaxWidth,
|
|
(unsigned)DL.getTypeSizeInBits(T->getScalarType()));
|
|
}
|
|
}
|
|
|
|
return {MinWidth, MaxWidth};
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::selectInterleaveCount(bool OptForSize,
|
|
unsigned VF,
|
|
unsigned LoopCost) {
|
|
|
|
// -- The interleave heuristics --
|
|
// We interleave the loop in order to expose ILP and reduce the loop overhead.
|
|
// There are many micro-architectural considerations that we can't predict
|
|
// at this level. For example, frontend pressure (on decode or fetch) due to
|
|
// code size, or the number and capabilities of the execution ports.
|
|
//
|
|
// We use the following heuristics to select the interleave count:
|
|
// 1. If the code has reductions, then we interleave to break the cross
|
|
// iteration dependency.
|
|
// 2. If the loop is really small, then we interleave to reduce the loop
|
|
// overhead.
|
|
// 3. We don't interleave if we think that we will spill registers to memory
|
|
// due to the increased register pressure.
|
|
|
|
// When we optimize for size, we don't interleave.
|
|
if (OptForSize)
|
|
return 1;
|
|
|
|
// We used the distance for the interleave count.
|
|
if (Legal->getMaxSafeDepDistBytes() != -1U)
|
|
return 1;
|
|
|
|
// Do not interleave loops with a relatively small trip count.
|
|
unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop);
|
|
if (TC > 1 && TC < TinyTripCountInterleaveThreshold)
|
|
return 1;
|
|
|
|
unsigned TargetNumRegisters = TTI.getNumberOfRegisters(VF > 1);
|
|
DEBUG(dbgs() << "LV: The target has " << TargetNumRegisters
|
|
<< " registers\n");
|
|
|
|
if (VF == 1) {
|
|
if (ForceTargetNumScalarRegs.getNumOccurrences() > 0)
|
|
TargetNumRegisters = ForceTargetNumScalarRegs;
|
|
} else {
|
|
if (ForceTargetNumVectorRegs.getNumOccurrences() > 0)
|
|
TargetNumRegisters = ForceTargetNumVectorRegs;
|
|
}
|
|
|
|
RegisterUsage R = calculateRegisterUsage({VF})[0];
|
|
// We divide by these constants so assume that we have at least one
|
|
// instruction that uses at least one register.
|
|
R.MaxLocalUsers = std::max(R.MaxLocalUsers, 1U);
|
|
R.NumInstructions = std::max(R.NumInstructions, 1U);
|
|
|
|
// We calculate the interleave count using the following formula.
|
|
// Subtract the number of loop invariants from the number of available
|
|
// registers. These registers are used by all of the interleaved instances.
|
|
// Next, divide the remaining registers by the number of registers that is
|
|
// required by the loop, in order to estimate how many parallel instances
|
|
// fit without causing spills. All of this is rounded down if necessary to be
|
|
// a power of two. We want power of two interleave count to simplify any
|
|
// addressing operations or alignment considerations.
|
|
unsigned IC = PowerOf2Floor((TargetNumRegisters - R.LoopInvariantRegs) /
|
|
R.MaxLocalUsers);
|
|
|
|
// Don't count the induction variable as interleaved.
|
|
if (EnableIndVarRegisterHeur)
|
|
IC = PowerOf2Floor((TargetNumRegisters - R.LoopInvariantRegs - 1) /
|
|
std::max(1U, (R.MaxLocalUsers - 1)));
|
|
|
|
// Clamp the interleave ranges to reasonable counts.
|
|
unsigned MaxInterleaveCount = TTI.getMaxInterleaveFactor(VF);
|
|
|
|
// Check if the user has overridden the max.
|
|
if (VF == 1) {
|
|
if (ForceTargetMaxScalarInterleaveFactor.getNumOccurrences() > 0)
|
|
MaxInterleaveCount = ForceTargetMaxScalarInterleaveFactor;
|
|
} else {
|
|
if (ForceTargetMaxVectorInterleaveFactor.getNumOccurrences() > 0)
|
|
MaxInterleaveCount = ForceTargetMaxVectorInterleaveFactor;
|
|
}
|
|
|
|
// If we did not calculate the cost for VF (because the user selected the VF)
|
|
// then we calculate the cost of VF here.
|
|
if (LoopCost == 0)
|
|
LoopCost = expectedCost(VF).first;
|
|
|
|
// Clamp the calculated IC to be between the 1 and the max interleave count
|
|
// that the target allows.
|
|
if (IC > MaxInterleaveCount)
|
|
IC = MaxInterleaveCount;
|
|
else if (IC < 1)
|
|
IC = 1;
|
|
|
|
// Interleave if we vectorized this loop and there is a reduction that could
|
|
// benefit from interleaving.
|
|
if (VF > 1 && Legal->getReductionVars()->size()) {
|
|
DEBUG(dbgs() << "LV: Interleaving because of reductions.\n");
|
|
return IC;
|
|
}
|
|
|
|
// Note that if we've already vectorized the loop we will have done the
|
|
// runtime check and so interleaving won't require further checks.
|
|
bool InterleavingRequiresRuntimePointerCheck =
|
|
(VF == 1 && Legal->getRuntimePointerChecking()->Need);
|
|
|
|
// We want to interleave small loops in order to reduce the loop overhead and
|
|
// potentially expose ILP opportunities.
|
|
DEBUG(dbgs() << "LV: Loop cost is " << LoopCost << '\n');
|
|
if (!InterleavingRequiresRuntimePointerCheck && LoopCost < SmallLoopCost) {
|
|
// We assume that the cost overhead is 1 and we use the cost model
|
|
// to estimate the cost of the loop and interleave until the cost of the
|
|
// loop overhead is about 5% of the cost of the loop.
|
|
unsigned SmallIC =
|
|
std::min(IC, (unsigned)PowerOf2Floor(SmallLoopCost / LoopCost));
|
|
|
|
// Interleave until store/load ports (estimated by max interleave count) are
|
|
// saturated.
|
|
unsigned NumStores = Legal->getNumStores();
|
|
unsigned NumLoads = Legal->getNumLoads();
|
|
unsigned StoresIC = IC / (NumStores ? NumStores : 1);
|
|
unsigned LoadsIC = IC / (NumLoads ? NumLoads : 1);
|
|
|
|
// If we have a scalar reduction (vector reductions are already dealt with
|
|
// by this point), we can increase the critical path length if the loop
|
|
// we're interleaving is inside another loop. Limit, by default to 2, so the
|
|
// critical path only gets increased by one reduction operation.
|
|
if (Legal->getReductionVars()->size() && TheLoop->getLoopDepth() > 1) {
|
|
unsigned F = static_cast<unsigned>(MaxNestedScalarReductionIC);
|
|
SmallIC = std::min(SmallIC, F);
|
|
StoresIC = std::min(StoresIC, F);
|
|
LoadsIC = std::min(LoadsIC, F);
|
|
}
|
|
|
|
if (EnableLoadStoreRuntimeInterleave &&
|
|
std::max(StoresIC, LoadsIC) > SmallIC) {
|
|
DEBUG(dbgs() << "LV: Interleaving to saturate store or load ports.\n");
|
|
return std::max(StoresIC, LoadsIC);
|
|
}
|
|
|
|
DEBUG(dbgs() << "LV: Interleaving to reduce branch cost.\n");
|
|
return SmallIC;
|
|
}
|
|
|
|
// Interleave if this is a large loop (small loops are already dealt with by
|
|
// this point) that could benefit from interleaving.
|
|
bool HasReductions = (Legal->getReductionVars()->size() > 0);
|
|
if (TTI.enableAggressiveInterleaving(HasReductions)) {
|
|
DEBUG(dbgs() << "LV: Interleaving to expose ILP.\n");
|
|
return IC;
|
|
}
|
|
|
|
DEBUG(dbgs() << "LV: Not Interleaving.\n");
|
|
return 1;
|
|
}
|
|
|
|
SmallVector<LoopVectorizationCostModel::RegisterUsage, 8>
|
|
LoopVectorizationCostModel::calculateRegisterUsage(ArrayRef<unsigned> VFs) {
|
|
// This function calculates the register usage by measuring the highest number
|
|
// of values that are alive at a single location. Obviously, this is a very
|
|
// rough estimation. We scan the loop in a topological order in order and
|
|
// assign a number to each instruction. We use RPO to ensure that defs are
|
|
// met before their users. We assume that each instruction that has in-loop
|
|
// users starts an interval. We record every time that an in-loop value is
|
|
// used, so we have a list of the first and last occurrences of each
|
|
// instruction. Next, we transpose this data structure into a multi map that
|
|
// holds the list of intervals that *end* at a specific location. This multi
|
|
// map allows us to perform a linear search. We scan the instructions linearly
|
|
// and record each time that a new interval starts, by placing it in a set.
|
|
// If we find this value in the multi-map then we remove it from the set.
|
|
// The max register usage is the maximum size of the set.
|
|
// We also search for instructions that are defined outside the loop, but are
|
|
// used inside the loop. We need this number separately from the max-interval
|
|
// usage number because when we unroll, loop-invariant values do not take
|
|
// more register.
|
|
LoopBlocksDFS DFS(TheLoop);
|
|
DFS.perform(LI);
|
|
|
|
RegisterUsage RU;
|
|
RU.NumInstructions = 0;
|
|
|
|
// Each 'key' in the map opens a new interval. The values
|
|
// of the map are the index of the 'last seen' usage of the
|
|
// instruction that is the key.
|
|
typedef DenseMap<Instruction *, unsigned> IntervalMap;
|
|
// Maps instruction to its index.
|
|
DenseMap<unsigned, Instruction *> IdxToInstr;
|
|
// Marks the end of each interval.
|
|
IntervalMap EndPoint;
|
|
// Saves the list of instruction indices that are used in the loop.
|
|
SmallSet<Instruction *, 8> Ends;
|
|
// Saves the list of values that are used in the loop but are
|
|
// defined outside the loop, such as arguments and constants.
|
|
SmallPtrSet<Value *, 8> LoopInvariants;
|
|
|
|
unsigned Index = 0;
|
|
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
|
|
RU.NumInstructions += BB->size();
|
|
for (Instruction &I : *BB) {
|
|
IdxToInstr[Index++] = &I;
|
|
|
|
// Save the end location of each USE.
|
|
for (Value *U : I.operands()) {
|
|
auto *Instr = dyn_cast<Instruction>(U);
|
|
|
|
// Ignore non-instruction values such as arguments, constants, etc.
|
|
if (!Instr)
|
|
continue;
|
|
|
|
// If this instruction is outside the loop then record it and continue.
|
|
if (!TheLoop->contains(Instr)) {
|
|
LoopInvariants.insert(Instr);
|
|
continue;
|
|
}
|
|
|
|
// Overwrite previous end points.
|
|
EndPoint[Instr] = Index;
|
|
Ends.insert(Instr);
|
|
}
|
|
}
|
|
}
|
|
|
|
// Saves the list of intervals that end with the index in 'key'.
|
|
typedef SmallVector<Instruction *, 2> InstrList;
|
|
DenseMap<unsigned, InstrList> TransposeEnds;
|
|
|
|
// Transpose the EndPoints to a list of values that end at each index.
|
|
for (auto &Interval : EndPoint)
|
|
TransposeEnds[Interval.second].push_back(Interval.first);
|
|
|
|
SmallSet<Instruction *, 8> OpenIntervals;
|
|
|
|
// Get the size of the widest register.
|
|
unsigned MaxSafeDepDist = -1U;
|
|
if (Legal->getMaxSafeDepDistBytes() != -1U)
|
|
MaxSafeDepDist = Legal->getMaxSafeDepDistBytes() * 8;
|
|
unsigned WidestRegister =
|
|
std::min(TTI.getRegisterBitWidth(true), MaxSafeDepDist);
|
|
const DataLayout &DL = TheFunction->getParent()->getDataLayout();
|
|
|
|
SmallVector<RegisterUsage, 8> RUs(VFs.size());
|
|
SmallVector<unsigned, 8> MaxUsages(VFs.size(), 0);
|
|
|
|
DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
|
|
|
|
// A lambda that gets the register usage for the given type and VF.
|
|
auto GetRegUsage = [&DL, WidestRegister](Type *Ty, unsigned VF) {
|
|
if (Ty->isTokenTy())
|
|
return 0U;
|
|
unsigned TypeSize = DL.getTypeSizeInBits(Ty->getScalarType());
|
|
return std::max<unsigned>(1, VF * TypeSize / WidestRegister);
|
|
};
|
|
|
|
for (unsigned int i = 0; i < Index; ++i) {
|
|
Instruction *I = IdxToInstr[i];
|
|
|
|
// Remove all of the instructions that end at this location.
|
|
InstrList &List = TransposeEnds[i];
|
|
for (Instruction *ToRemove : List)
|
|
OpenIntervals.erase(ToRemove);
|
|
|
|
// Ignore instructions that are never used within the loop.
|
|
if (!Ends.count(I))
|
|
continue;
|
|
|
|
// Skip ignored values.
|
|
if (ValuesToIgnore.count(I))
|
|
continue;
|
|
|
|
// For each VF find the maximum usage of registers.
|
|
for (unsigned j = 0, e = VFs.size(); j < e; ++j) {
|
|
if (VFs[j] == 1) {
|
|
MaxUsages[j] = std::max(MaxUsages[j], OpenIntervals.size());
|
|
continue;
|
|
}
|
|
collectUniformsAndScalars(VFs[j]);
|
|
// Count the number of live intervals.
|
|
unsigned RegUsage = 0;
|
|
for (auto Inst : OpenIntervals) {
|
|
// Skip ignored values for VF > 1.
|
|
if (VecValuesToIgnore.count(Inst) ||
|
|
isScalarAfterVectorization(Inst, VFs[j]))
|
|
continue;
|
|
RegUsage += GetRegUsage(Inst->getType(), VFs[j]);
|
|
}
|
|
MaxUsages[j] = std::max(MaxUsages[j], RegUsage);
|
|
}
|
|
|
|
DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # "
|
|
<< OpenIntervals.size() << '\n');
|
|
|
|
// Add the current instruction to the list of open intervals.
|
|
OpenIntervals.insert(I);
|
|
}
|
|
|
|
for (unsigned i = 0, e = VFs.size(); i < e; ++i) {
|
|
unsigned Invariant = 0;
|
|
if (VFs[i] == 1)
|
|
Invariant = LoopInvariants.size();
|
|
else {
|
|
for (auto Inst : LoopInvariants)
|
|
Invariant += GetRegUsage(Inst->getType(), VFs[i]);
|
|
}
|
|
|
|
DEBUG(dbgs() << "LV(REG): VF = " << VFs[i] << '\n');
|
|
DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsages[i] << '\n');
|
|
DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << '\n');
|
|
DEBUG(dbgs() << "LV(REG): LoopSize: " << RU.NumInstructions << '\n');
|
|
|
|
RU.LoopInvariantRegs = Invariant;
|
|
RU.MaxLocalUsers = MaxUsages[i];
|
|
RUs[i] = RU;
|
|
}
|
|
|
|
return RUs;
|
|
}
|
|
|
|
void LoopVectorizationCostModel::collectInstsToScalarize(unsigned VF) {
|
|
|
|
// If we aren't vectorizing the loop, or if we've already collected the
|
|
// instructions to scalarize, there's nothing to do. Collection may already
|
|
// have occurred if we have a user-selected VF and are now computing the
|
|
// expected cost for interleaving.
|
|
if (VF < 2 || InstsToScalarize.count(VF))
|
|
return;
|
|
|
|
// Initialize a mapping for VF in InstsToScalalarize. If we find that it's
|
|
// not profitable to scalarize any instructions, the presence of VF in the
|
|
// map will indicate that we've analyzed it already.
|
|
ScalarCostsTy &ScalarCostsVF = InstsToScalarize[VF];
|
|
|
|
// Find all the instructions that are scalar with predication in the loop and
|
|
// determine if it would be better to not if-convert the blocks they are in.
|
|
// If so, we also record the instructions to scalarize.
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
if (!Legal->blockNeedsPredication(BB))
|
|
continue;
|
|
for (Instruction &I : *BB)
|
|
if (Legal->isScalarWithPredication(&I)) {
|
|
ScalarCostsTy ScalarCosts;
|
|
if (computePredInstDiscount(&I, ScalarCosts, VF) >= 0)
|
|
ScalarCostsVF.insert(ScalarCosts.begin(), ScalarCosts.end());
|
|
|
|
// Remember that BB will remain after vectorization.
|
|
PredicatedBBsAfterVectorization.insert(BB);
|
|
}
|
|
}
|
|
}
|
|
|
|
int LoopVectorizationCostModel::computePredInstDiscount(
|
|
Instruction *PredInst, DenseMap<Instruction *, unsigned> &ScalarCosts,
|
|
unsigned VF) {
|
|
|
|
assert(!isUniformAfterVectorization(PredInst, VF) &&
|
|
"Instruction marked uniform-after-vectorization will be predicated");
|
|
|
|
// Initialize the discount to zero, meaning that the scalar version and the
|
|
// vector version cost the same.
|
|
int Discount = 0;
|
|
|
|
// Holds instructions to analyze. The instructions we visit are mapped in
|
|
// ScalarCosts. Those instructions are the ones that would be scalarized if
|
|
// we find that the scalar version costs less.
|
|
SmallVector<Instruction *, 8> Worklist;
|
|
|
|
// Returns true if the given instruction can be scalarized.
|
|
auto canBeScalarized = [&](Instruction *I) -> bool {
|
|
|
|
// We only attempt to scalarize instructions forming a single-use chain
|
|
// from the original predicated block that would otherwise be vectorized.
|
|
// Although not strictly necessary, we give up on instructions we know will
|
|
// already be scalar to avoid traversing chains that are unlikely to be
|
|
// beneficial.
|
|
if (!I->hasOneUse() || PredInst->getParent() != I->getParent() ||
|
|
isScalarAfterVectorization(I, VF))
|
|
return false;
|
|
|
|
// If the instruction is scalar with predication, it will be analyzed
|
|
// separately. We ignore it within the context of PredInst.
|
|
if (Legal->isScalarWithPredication(I))
|
|
return false;
|
|
|
|
// If any of the instruction's operands are uniform after vectorization,
|
|
// the instruction cannot be scalarized. This prevents, for example, a
|
|
// masked load from being scalarized.
|
|
//
|
|
// We assume we will only emit a value for lane zero of an instruction
|
|
// marked uniform after vectorization, rather than VF identical values.
|
|
// Thus, if we scalarize an instruction that uses a uniform, we would
|
|
// create uses of values corresponding to the lanes we aren't emitting code
|
|
// for. This behavior can be changed by allowing getScalarValue to clone
|
|
// the lane zero values for uniforms rather than asserting.
|
|
for (Use &U : I->operands())
|
|
if (auto *J = dyn_cast<Instruction>(U.get()))
|
|
if (isUniformAfterVectorization(J, VF))
|
|
return false;
|
|
|
|
// Otherwise, we can scalarize the instruction.
|
|
return true;
|
|
};
|
|
|
|
// Returns true if an operand that cannot be scalarized must be extracted
|
|
// from a vector. We will account for this scalarization overhead below. Note
|
|
// that the non-void predicated instructions are placed in their own blocks,
|
|
// and their return values are inserted into vectors. Thus, an extract would
|
|
// still be required.
|
|
auto needsExtract = [&](Instruction *I) -> bool {
|
|
return TheLoop->contains(I) && !isScalarAfterVectorization(I, VF);
|
|
};
|
|
|
|
// Compute the expected cost discount from scalarizing the entire expression
|
|
// feeding the predicated instruction. We currently only consider expressions
|
|
// that are single-use instruction chains.
|
|
Worklist.push_back(PredInst);
|
|
while (!Worklist.empty()) {
|
|
Instruction *I = Worklist.pop_back_val();
|
|
|
|
// If we've already analyzed the instruction, there's nothing to do.
|
|
if (ScalarCosts.count(I))
|
|
continue;
|
|
|
|
// Compute the cost of the vector instruction. Note that this cost already
|
|
// includes the scalarization overhead of the predicated instruction.
|
|
unsigned VectorCost = getInstructionCost(I, VF).first;
|
|
|
|
// Compute the cost of the scalarized instruction. This cost is the cost of
|
|
// the instruction as if it wasn't if-converted and instead remained in the
|
|
// predicated block. We will scale this cost by block probability after
|
|
// computing the scalarization overhead.
|
|
unsigned ScalarCost = VF * getInstructionCost(I, 1).first;
|
|
|
|
// Compute the scalarization overhead of needed insertelement instructions
|
|
// and phi nodes.
|
|
if (Legal->isScalarWithPredication(I) && !I->getType()->isVoidTy()) {
|
|
ScalarCost += TTI.getScalarizationOverhead(ToVectorTy(I->getType(), VF),
|
|
true, false);
|
|
ScalarCost += VF * TTI.getCFInstrCost(Instruction::PHI);
|
|
}
|
|
|
|
// Compute the scalarization overhead of needed extractelement
|
|
// instructions. For each of the instruction's operands, if the operand can
|
|
// be scalarized, add it to the worklist; otherwise, account for the
|
|
// overhead.
|
|
for (Use &U : I->operands())
|
|
if (auto *J = dyn_cast<Instruction>(U.get())) {
|
|
assert(VectorType::isValidElementType(J->getType()) &&
|
|
"Instruction has non-scalar type");
|
|
if (canBeScalarized(J))
|
|
Worklist.push_back(J);
|
|
else if (needsExtract(J))
|
|
ScalarCost += TTI.getScalarizationOverhead(
|
|
ToVectorTy(J->getType(),VF), false, true);
|
|
}
|
|
|
|
// Scale the total scalar cost by block probability.
|
|
ScalarCost /= getReciprocalPredBlockProb();
|
|
|
|
// Compute the discount. A non-negative discount means the vector version
|
|
// of the instruction costs more, and scalarizing would be beneficial.
|
|
Discount += VectorCost - ScalarCost;
|
|
ScalarCosts[I] = ScalarCost;
|
|
}
|
|
|
|
return Discount;
|
|
}
|
|
|
|
LoopVectorizationCostModel::VectorizationCostTy
|
|
LoopVectorizationCostModel::expectedCost(unsigned VF) {
|
|
VectorizationCostTy Cost;
|
|
|
|
// For each block.
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
VectorizationCostTy BlockCost;
|
|
|
|
// For each instruction in the old loop.
|
|
for (Instruction &I : *BB) {
|
|
// Skip dbg intrinsics.
|
|
if (isa<DbgInfoIntrinsic>(I))
|
|
continue;
|
|
|
|
// Skip ignored values.
|
|
if (ValuesToIgnore.count(&I))
|
|
continue;
|
|
|
|
VectorizationCostTy C = getInstructionCost(&I, VF);
|
|
|
|
// Check if we should override the cost.
|
|
if (ForceTargetInstructionCost.getNumOccurrences() > 0)
|
|
C.first = ForceTargetInstructionCost;
|
|
|
|
BlockCost.first += C.first;
|
|
BlockCost.second |= C.second;
|
|
DEBUG(dbgs() << "LV: Found an estimated cost of " << C.first << " for VF "
|
|
<< VF << " For instruction: " << I << '\n');
|
|
}
|
|
|
|
// If we are vectorizing a predicated block, it will have been
|
|
// if-converted. This means that the block's instructions (aside from
|
|
// stores and instructions that may divide by zero) will now be
|
|
// unconditionally executed. For the scalar case, we may not always execute
|
|
// the predicated block. Thus, scale the block's cost by the probability of
|
|
// executing it.
|
|
if (VF == 1 && Legal->blockNeedsPredication(BB))
|
|
BlockCost.first /= getReciprocalPredBlockProb();
|
|
|
|
Cost.first += BlockCost.first;
|
|
Cost.second |= BlockCost.second;
|
|
}
|
|
|
|
return Cost;
|
|
}
|
|
|
|
/// \brief Gets Address Access SCEV after verifying that the access pattern
|
|
/// is loop invariant except the induction variable dependence.
|
|
///
|
|
/// This SCEV can be sent to the Target in order to estimate the address
|
|
/// calculation cost.
|
|
static const SCEV *getAddressAccessSCEV(
|
|
Value *Ptr,
|
|
LoopVectorizationLegality *Legal,
|
|
ScalarEvolution *SE,
|
|
const Loop *TheLoop) {
|
|
auto *Gep = dyn_cast<GetElementPtrInst>(Ptr);
|
|
if (!Gep)
|
|
return nullptr;
|
|
|
|
// We are looking for a gep with all loop invariant indices except for one
|
|
// which should be an induction variable.
|
|
unsigned NumOperands = Gep->getNumOperands();
|
|
for (unsigned i = 1; i < NumOperands; ++i) {
|
|
Value *Opd = Gep->getOperand(i);
|
|
if (!SE->isLoopInvariant(SE->getSCEV(Opd), TheLoop) &&
|
|
!Legal->isInductionVariable(Opd))
|
|
return nullptr;
|
|
}
|
|
|
|
// Now we know we have a GEP ptr, %inv, %ind, %inv. return the Ptr SCEV.
|
|
return SE->getSCEV(Ptr);
|
|
}
|
|
|
|
static bool isStrideMul(Instruction *I, LoopVectorizationLegality *Legal) {
|
|
return Legal->hasStride(I->getOperand(0)) ||
|
|
Legal->hasStride(I->getOperand(1));
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getMemInstScalarizationCost(Instruction *I,
|
|
unsigned VF) {
|
|
Type *ValTy = getMemInstValueType(I);
|
|
auto SE = PSE.getSE();
|
|
|
|
unsigned Alignment = getMemInstAlignment(I);
|
|
unsigned AS = getMemInstAddressSpace(I);
|
|
Value *Ptr = getPointerOperand(I);
|
|
Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
|
|
|
|
// Figure out whether the access is strided and get the stride value
|
|
// if it's known in compile time
|
|
const SCEV *PtrSCEV = getAddressAccessSCEV(Ptr, Legal, SE, TheLoop);
|
|
|
|
// Get the cost of the scalar memory instruction and address computation.
|
|
unsigned Cost = VF * TTI.getAddressComputationCost(PtrTy, SE, PtrSCEV);
|
|
|
|
Cost += VF *
|
|
TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(), Alignment,
|
|
AS, I);
|
|
|
|
// Get the overhead of the extractelement and insertelement instructions
|
|
// we might create due to scalarization.
|
|
Cost += getScalarizationOverhead(I, VF, TTI);
|
|
|
|
// If we have a predicated store, it may not be executed for each vector
|
|
// lane. Scale the cost by the probability of executing the predicated
|
|
// block.
|
|
if (Legal->isScalarWithPredication(I))
|
|
Cost /= getReciprocalPredBlockProb();
|
|
|
|
return Cost;
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getConsecutiveMemOpCost(Instruction *I,
|
|
unsigned VF) {
|
|
Type *ValTy = getMemInstValueType(I);
|
|
Type *VectorTy = ToVectorTy(ValTy, VF);
|
|
unsigned Alignment = getMemInstAlignment(I);
|
|
Value *Ptr = getPointerOperand(I);
|
|
unsigned AS = getMemInstAddressSpace(I);
|
|
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
|
|
|
|
assert((ConsecutiveStride == 1 || ConsecutiveStride == -1) &&
|
|
"Stride should be 1 or -1 for consecutive memory access");
|
|
unsigned Cost = 0;
|
|
if (Legal->isMaskRequired(I))
|
|
Cost += TTI.getMaskedMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
|
|
else
|
|
Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS, I);
|
|
|
|
bool Reverse = ConsecutiveStride < 0;
|
|
if (Reverse)
|
|
Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
|
|
return Cost;
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getUniformMemOpCost(Instruction *I,
|
|
unsigned VF) {
|
|
LoadInst *LI = cast<LoadInst>(I);
|
|
Type *ValTy = LI->getType();
|
|
Type *VectorTy = ToVectorTy(ValTy, VF);
|
|
unsigned Alignment = LI->getAlignment();
|
|
unsigned AS = LI->getPointerAddressSpace();
|
|
|
|
return TTI.getAddressComputationCost(ValTy) +
|
|
TTI.getMemoryOpCost(Instruction::Load, ValTy, Alignment, AS) +
|
|
TTI.getShuffleCost(TargetTransformInfo::SK_Broadcast, VectorTy);
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getGatherScatterCost(Instruction *I,
|
|
unsigned VF) {
|
|
Type *ValTy = getMemInstValueType(I);
|
|
Type *VectorTy = ToVectorTy(ValTy, VF);
|
|
unsigned Alignment = getMemInstAlignment(I);
|
|
Value *Ptr = getPointerOperand(I);
|
|
|
|
return TTI.getAddressComputationCost(VectorTy) +
|
|
TTI.getGatherScatterOpCost(I->getOpcode(), VectorTy, Ptr,
|
|
Legal->isMaskRequired(I), Alignment);
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getInterleaveGroupCost(Instruction *I,
|
|
unsigned VF) {
|
|
Type *ValTy = getMemInstValueType(I);
|
|
Type *VectorTy = ToVectorTy(ValTy, VF);
|
|
unsigned AS = getMemInstAddressSpace(I);
|
|
|
|
auto Group = Legal->getInterleavedAccessGroup(I);
|
|
assert(Group && "Fail to get an interleaved access group.");
|
|
|
|
unsigned InterleaveFactor = Group->getFactor();
|
|
Type *WideVecTy = VectorType::get(ValTy, VF * InterleaveFactor);
|
|
|
|
// Holds the indices of existing members in an interleaved load group.
|
|
// An interleaved store group doesn't need this as it doesn't allow gaps.
|
|
SmallVector<unsigned, 4> Indices;
|
|
if (isa<LoadInst>(I)) {
|
|
for (unsigned i = 0; i < InterleaveFactor; i++)
|
|
if (Group->getMember(i))
|
|
Indices.push_back(i);
|
|
}
|
|
|
|
// Calculate the cost of the whole interleaved group.
|
|
unsigned Cost = TTI.getInterleavedMemoryOpCost(I->getOpcode(), WideVecTy,
|
|
Group->getFactor(), Indices,
|
|
Group->getAlignment(), AS);
|
|
|
|
if (Group->isReverse())
|
|
Cost += Group->getNumMembers() *
|
|
TTI.getShuffleCost(TargetTransformInfo::SK_Reverse, VectorTy, 0);
|
|
return Cost;
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getMemoryInstructionCost(Instruction *I,
|
|
unsigned VF) {
|
|
|
|
// Calculate scalar cost only. Vectorization cost should be ready at this
|
|
// moment.
|
|
if (VF == 1) {
|
|
Type *ValTy = getMemInstValueType(I);
|
|
unsigned Alignment = getMemInstAlignment(I);
|
|
unsigned AS = getMemInstAddressSpace(I);
|
|
|
|
return TTI.getAddressComputationCost(ValTy) +
|
|
TTI.getMemoryOpCost(I->getOpcode(), ValTy, Alignment, AS, I);
|
|
}
|
|
return getWideningCost(I, VF);
|
|
}
|
|
|
|
LoopVectorizationCostModel::VectorizationCostTy
|
|
LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
|
|
// If we know that this instruction will remain uniform, check the cost of
|
|
// the scalar version.
|
|
if (isUniformAfterVectorization(I, VF))
|
|
VF = 1;
|
|
|
|
if (VF > 1 && isProfitableToScalarize(I, VF))
|
|
return VectorizationCostTy(InstsToScalarize[VF][I], false);
|
|
|
|
// Forced scalars do not have any scalarization overhead.
|
|
if (VF > 1 && ForcedScalars.count(VF) &&
|
|
ForcedScalars.find(VF)->second.count(I))
|
|
return VectorizationCostTy((getInstructionCost(I, 1).first * VF), false);
|
|
|
|
Type *VectorTy;
|
|
unsigned C = getInstructionCost(I, VF, VectorTy);
|
|
|
|
bool TypeNotScalarized =
|
|
VF > 1 && VectorTy->isVectorTy() && TTI.getNumberOfParts(VectorTy) < VF;
|
|
return VectorizationCostTy(C, TypeNotScalarized);
|
|
}
|
|
|
|
void LoopVectorizationCostModel::setCostBasedWideningDecision(unsigned VF) {
|
|
if (VF == 1)
|
|
return;
|
|
for (BasicBlock *BB : TheLoop->blocks()) {
|
|
// For each instruction in the old loop.
|
|
for (Instruction &I : *BB) {
|
|
Value *Ptr = getPointerOperand(&I);
|
|
if (!Ptr)
|
|
continue;
|
|
|
|
if (isa<LoadInst>(&I) && Legal->isUniform(Ptr)) {
|
|
// Scalar load + broadcast
|
|
unsigned Cost = getUniformMemOpCost(&I, VF);
|
|
setWideningDecision(&I, VF, CM_Scalarize, Cost);
|
|
continue;
|
|
}
|
|
|
|
// We assume that widening is the best solution when possible.
|
|
if (Legal->memoryInstructionCanBeWidened(&I, VF)) {
|
|
unsigned Cost = getConsecutiveMemOpCost(&I, VF);
|
|
setWideningDecision(&I, VF, CM_Widen, Cost);
|
|
continue;
|
|
}
|
|
|
|
// Choose between Interleaving, Gather/Scatter or Scalarization.
|
|
unsigned InterleaveCost = UINT_MAX;
|
|
unsigned NumAccesses = 1;
|
|
if (Legal->isAccessInterleaved(&I)) {
|
|
auto Group = Legal->getInterleavedAccessGroup(&I);
|
|
assert(Group && "Fail to get an interleaved access group.");
|
|
|
|
// Make one decision for the whole group.
|
|
if (getWideningDecision(&I, VF) != CM_Unknown)
|
|
continue;
|
|
|
|
NumAccesses = Group->getNumMembers();
|
|
InterleaveCost = getInterleaveGroupCost(&I, VF);
|
|
}
|
|
|
|
unsigned GatherScatterCost =
|
|
Legal->isLegalGatherOrScatter(&I)
|
|
? getGatherScatterCost(&I, VF) * NumAccesses
|
|
: UINT_MAX;
|
|
|
|
unsigned ScalarizationCost =
|
|
getMemInstScalarizationCost(&I, VF) * NumAccesses;
|
|
|
|
// Choose better solution for the current VF,
|
|
// write down this decision and use it during vectorization.
|
|
unsigned Cost;
|
|
InstWidening Decision;
|
|
if (InterleaveCost <= GatherScatterCost &&
|
|
InterleaveCost < ScalarizationCost) {
|
|
Decision = CM_Interleave;
|
|
Cost = InterleaveCost;
|
|
} else if (GatherScatterCost < ScalarizationCost) {
|
|
Decision = CM_GatherScatter;
|
|
Cost = GatherScatterCost;
|
|
} else {
|
|
Decision = CM_Scalarize;
|
|
Cost = ScalarizationCost;
|
|
}
|
|
// If the instructions belongs to an interleave group, the whole group
|
|
// receives the same decision. The whole group receives the cost, but
|
|
// the cost will actually be assigned to one instruction.
|
|
if (auto Group = Legal->getInterleavedAccessGroup(&I))
|
|
setWideningDecision(Group, VF, Decision, Cost);
|
|
else
|
|
setWideningDecision(&I, VF, Decision, Cost);
|
|
}
|
|
}
|
|
|
|
// Make sure that any load of address and any other address computation
|
|
// remains scalar unless there is gather/scatter support. This avoids
|
|
// inevitable extracts into address registers, and also has the benefit of
|
|
// activating LSR more, since that pass can't optimize vectorized
|
|
// addresses.
|
|
if (TTI.prefersVectorizedAddressing())
|
|
return;
|
|
|
|
// Start with all scalar pointer uses.
|
|
SmallPtrSet<Instruction *, 8> AddrDefs;
|
|
for (BasicBlock *BB : TheLoop->blocks())
|
|
for (Instruction &I : *BB) {
|
|
Instruction *PtrDef =
|
|
dyn_cast_or_null<Instruction>(getPointerOperand(&I));
|
|
if (PtrDef && TheLoop->contains(PtrDef) &&
|
|
getWideningDecision(&I, VF) != CM_GatherScatter)
|
|
AddrDefs.insert(PtrDef);
|
|
}
|
|
|
|
// Add all instructions used to generate the addresses.
|
|
SmallVector<Instruction *, 4> Worklist;
|
|
for (auto *I : AddrDefs)
|
|
Worklist.push_back(I);
|
|
while (!Worklist.empty()) {
|
|
Instruction *I = Worklist.pop_back_val();
|
|
for (auto &Op : I->operands())
|
|
if (auto *InstOp = dyn_cast<Instruction>(Op))
|
|
if ((InstOp->getParent() == I->getParent()) && !isa<PHINode>(InstOp) &&
|
|
AddrDefs.insert(InstOp).second == true)
|
|
Worklist.push_back(InstOp);
|
|
}
|
|
|
|
for (auto *I : AddrDefs) {
|
|
if (isa<LoadInst>(I)) {
|
|
// Setting the desired widening decision should ideally be handled in
|
|
// by cost functions, but since this involves the task of finding out
|
|
// if the loaded register is involved in an address computation, it is
|
|
// instead changed here when we know this is the case.
|
|
if (getWideningDecision(I, VF) == CM_Widen)
|
|
// Scalarize a widened load of address.
|
|
setWideningDecision(I, VF, CM_Scalarize,
|
|
(VF * getMemoryInstructionCost(I, 1)));
|
|
else if (auto Group = Legal->getInterleavedAccessGroup(I)) {
|
|
// Scalarize an interleave group of address loads.
|
|
for (unsigned I = 0; I < Group->getFactor(); ++I) {
|
|
if (Instruction *Member = Group->getMember(I))
|
|
setWideningDecision(Member, VF, CM_Scalarize,
|
|
(VF * getMemoryInstructionCost(Member, 1)));
|
|
}
|
|
}
|
|
} else
|
|
// Make sure I gets scalarized and a cost estimate without
|
|
// scalarization overhead.
|
|
ForcedScalars[VF].insert(I);
|
|
}
|
|
}
|
|
|
|
unsigned LoopVectorizationCostModel::getInstructionCost(Instruction *I,
|
|
unsigned VF,
|
|
Type *&VectorTy) {
|
|
Type *RetTy = I->getType();
|
|
if (canTruncateToMinimalBitwidth(I, VF))
|
|
RetTy = IntegerType::get(RetTy->getContext(), MinBWs[I]);
|
|
VectorTy = isScalarAfterVectorization(I, VF) ? RetTy : ToVectorTy(RetTy, VF);
|
|
auto SE = PSE.getSE();
|
|
|
|
// TODO: We need to estimate the cost of intrinsic calls.
|
|
switch (I->getOpcode()) {
|
|
case Instruction::GetElementPtr:
|
|
// We mark this instruction as zero-cost because the cost of GEPs in
|
|
// vectorized code depends on whether the corresponding memory instruction
|
|
// is scalarized or not. Therefore, we handle GEPs with the memory
|
|
// instruction cost.
|
|
return 0;
|
|
case Instruction::Br: {
|
|
// In cases of scalarized and predicated instructions, there will be VF
|
|
// predicated blocks in the vectorized loop. Each branch around these
|
|
// blocks requires also an extract of its vector compare i1 element.
|
|
bool ScalarPredicatedBB = false;
|
|
BranchInst *BI = cast<BranchInst>(I);
|
|
if (VF > 1 && BI->isConditional() &&
|
|
(PredicatedBBsAfterVectorization.count(BI->getSuccessor(0)) ||
|
|
PredicatedBBsAfterVectorization.count(BI->getSuccessor(1))))
|
|
ScalarPredicatedBB = true;
|
|
|
|
if (ScalarPredicatedBB) {
|
|
// Return cost for branches around scalarized and predicated blocks.
|
|
Type *Vec_i1Ty =
|
|
VectorType::get(IntegerType::getInt1Ty(RetTy->getContext()), VF);
|
|
return (TTI.getScalarizationOverhead(Vec_i1Ty, false, true) +
|
|
(TTI.getCFInstrCost(Instruction::Br) * VF));
|
|
} else if (I->getParent() == TheLoop->getLoopLatch() || VF == 1)
|
|
// The back-edge branch will remain, as will all scalar branches.
|
|
return TTI.getCFInstrCost(Instruction::Br);
|
|
else
|
|
// This branch will be eliminated by if-conversion.
|
|
return 0;
|
|
// Note: We currently assume zero cost for an unconditional branch inside
|
|
// a predicated block since it will become a fall-through, although we
|
|
// may decide in the future to call TTI for all branches.
|
|
}
|
|
case Instruction::PHI: {
|
|
auto *Phi = cast<PHINode>(I);
|
|
|
|
// First-order recurrences are replaced by vector shuffles inside the loop.
|
|
if (VF > 1 && Legal->isFirstOrderRecurrence(Phi))
|
|
return TTI.getShuffleCost(TargetTransformInfo::SK_ExtractSubvector,
|
|
VectorTy, VF - 1, VectorTy);
|
|
|
|
// Phi nodes in non-header blocks (not inductions, reductions, etc.) are
|
|
// converted into select instructions. We require N - 1 selects per phi
|
|
// node, where N is the number of incoming values.
|
|
if (VF > 1 && Phi->getParent() != TheLoop->getHeader())
|
|
return (Phi->getNumIncomingValues() - 1) *
|
|
TTI.getCmpSelInstrCost(
|
|
Instruction::Select, ToVectorTy(Phi->getType(), VF),
|
|
ToVectorTy(Type::getInt1Ty(Phi->getContext()), VF));
|
|
|
|
return TTI.getCFInstrCost(Instruction::PHI);
|
|
}
|
|
case Instruction::UDiv:
|
|
case Instruction::SDiv:
|
|
case Instruction::URem:
|
|
case Instruction::SRem:
|
|
// If we have a predicated instruction, it may not be executed for each
|
|
// vector lane. Get the scalarization cost and scale this amount by the
|
|
// probability of executing the predicated block. If the instruction is not
|
|
// predicated, we fall through to the next case.
|
|
if (VF > 1 && Legal->isScalarWithPredication(I)) {
|
|
unsigned Cost = 0;
|
|
|
|
// These instructions have a non-void type, so account for the phi nodes
|
|
// that we will create. This cost is likely to be zero. The phi node
|
|
// cost, if any, should be scaled by the block probability because it
|
|
// models a copy at the end of each predicated block.
|
|
Cost += VF * TTI.getCFInstrCost(Instruction::PHI);
|
|
|
|
// The cost of the non-predicated instruction.
|
|
Cost += VF * TTI.getArithmeticInstrCost(I->getOpcode(), RetTy);
|
|
|
|
// The cost of insertelement and extractelement instructions needed for
|
|
// scalarization.
|
|
Cost += getScalarizationOverhead(I, VF, TTI);
|
|
|
|
// Scale the cost by the probability of executing the predicated blocks.
|
|
// This assumes the predicated block for each vector lane is equally
|
|
// likely.
|
|
return Cost / getReciprocalPredBlockProb();
|
|
}
|
|
LLVM_FALLTHROUGH;
|
|
case Instruction::Add:
|
|
case Instruction::FAdd:
|
|
case Instruction::Sub:
|
|
case Instruction::FSub:
|
|
case Instruction::Mul:
|
|
case Instruction::FMul:
|
|
case Instruction::FDiv:
|
|
case Instruction::FRem:
|
|
case Instruction::Shl:
|
|
case Instruction::LShr:
|
|
case Instruction::AShr:
|
|
case Instruction::And:
|
|
case Instruction::Or:
|
|
case Instruction::Xor: {
|
|
// Since we will replace the stride by 1 the multiplication should go away.
|
|
if (I->getOpcode() == Instruction::Mul && isStrideMul(I, Legal))
|
|
return 0;
|
|
// Certain instructions can be cheaper to vectorize if they have a constant
|
|
// second vector operand. One example of this are shifts on x86.
|
|
TargetTransformInfo::OperandValueKind Op1VK =
|
|
TargetTransformInfo::OK_AnyValue;
|
|
TargetTransformInfo::OperandValueKind Op2VK =
|
|
TargetTransformInfo::OK_AnyValue;
|
|
TargetTransformInfo::OperandValueProperties Op1VP =
|
|
TargetTransformInfo::OP_None;
|
|
TargetTransformInfo::OperandValueProperties Op2VP =
|
|
TargetTransformInfo::OP_None;
|
|
Value *Op2 = I->getOperand(1);
|
|
|
|
// Check for a splat or for a non uniform vector of constants.
|
|
if (isa<ConstantInt>(Op2)) {
|
|
ConstantInt *CInt = cast<ConstantInt>(Op2);
|
|
if (CInt && CInt->getValue().isPowerOf2())
|
|
Op2VP = TargetTransformInfo::OP_PowerOf2;
|
|
Op2VK = TargetTransformInfo::OK_UniformConstantValue;
|
|
} else if (isa<ConstantVector>(Op2) || isa<ConstantDataVector>(Op2)) {
|
|
Op2VK = TargetTransformInfo::OK_NonUniformConstantValue;
|
|
Constant *SplatValue = cast<Constant>(Op2)->getSplatValue();
|
|
if (SplatValue) {
|
|
ConstantInt *CInt = dyn_cast<ConstantInt>(SplatValue);
|
|
if (CInt && CInt->getValue().isPowerOf2())
|
|
Op2VP = TargetTransformInfo::OP_PowerOf2;
|
|
Op2VK = TargetTransformInfo::OK_UniformConstantValue;
|
|
}
|
|
} else if (Legal->isUniform(Op2)) {
|
|
Op2VK = TargetTransformInfo::OK_UniformValue;
|
|
}
|
|
SmallVector<const Value *, 4> Operands(I->operand_values());
|
|
unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
|
|
return N * TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy, Op1VK,
|
|
Op2VK, Op1VP, Op2VP, Operands);
|
|
}
|
|
case Instruction::Select: {
|
|
SelectInst *SI = cast<SelectInst>(I);
|
|
const SCEV *CondSCEV = SE->getSCEV(SI->getCondition());
|
|
bool ScalarCond = (SE->isLoopInvariant(CondSCEV, TheLoop));
|
|
Type *CondTy = SI->getCondition()->getType();
|
|
if (!ScalarCond)
|
|
CondTy = VectorType::get(CondTy, VF);
|
|
|
|
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, CondTy, I);
|
|
}
|
|
case Instruction::ICmp:
|
|
case Instruction::FCmp: {
|
|
Type *ValTy = I->getOperand(0)->getType();
|
|
Instruction *Op0AsInstruction = dyn_cast<Instruction>(I->getOperand(0));
|
|
if (canTruncateToMinimalBitwidth(Op0AsInstruction, VF))
|
|
ValTy = IntegerType::get(ValTy->getContext(), MinBWs[Op0AsInstruction]);
|
|
VectorTy = ToVectorTy(ValTy, VF);
|
|
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy, nullptr, I);
|
|
}
|
|
case Instruction::Store:
|
|
case Instruction::Load: {
|
|
unsigned Width = VF;
|
|
if (Width > 1) {
|
|
InstWidening Decision = getWideningDecision(I, Width);
|
|
assert(Decision != CM_Unknown &&
|
|
"CM decision should be taken at this point");
|
|
if (Decision == CM_Scalarize)
|
|
Width = 1;
|
|
}
|
|
VectorTy = ToVectorTy(getMemInstValueType(I), Width);
|
|
return getMemoryInstructionCost(I, VF);
|
|
}
|
|
case Instruction::ZExt:
|
|
case Instruction::SExt:
|
|
case Instruction::FPToUI:
|
|
case Instruction::FPToSI:
|
|
case Instruction::FPExt:
|
|
case Instruction::PtrToInt:
|
|
case Instruction::IntToPtr:
|
|
case Instruction::SIToFP:
|
|
case Instruction::UIToFP:
|
|
case Instruction::Trunc:
|
|
case Instruction::FPTrunc:
|
|
case Instruction::BitCast: {
|
|
// We optimize the truncation of induction variables having constant
|
|
// integer steps. The cost of these truncations is the same as the scalar
|
|
// operation.
|
|
if (isOptimizableIVTruncate(I, VF)) {
|
|
auto *Trunc = cast<TruncInst>(I);
|
|
return TTI.getCastInstrCost(Instruction::Trunc, Trunc->getDestTy(),
|
|
Trunc->getSrcTy(), Trunc);
|
|
}
|
|
|
|
Type *SrcScalarTy = I->getOperand(0)->getType();
|
|
Type *SrcVecTy =
|
|
VectorTy->isVectorTy() ? ToVectorTy(SrcScalarTy, VF) : SrcScalarTy;
|
|
if (canTruncateToMinimalBitwidth(I, VF)) {
|
|
// This cast is going to be shrunk. This may remove the cast or it might
|
|
// turn it into slightly different cast. For example, if MinBW == 16,
|
|
// "zext i8 %1 to i32" becomes "zext i8 %1 to i16".
|
|
//
|
|
// Calculate the modified src and dest types.
|
|
Type *MinVecTy = VectorTy;
|
|
if (I->getOpcode() == Instruction::Trunc) {
|
|
SrcVecTy = smallestIntegerVectorType(SrcVecTy, MinVecTy);
|
|
VectorTy =
|
|
largestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
|
|
} else if (I->getOpcode() == Instruction::ZExt ||
|
|
I->getOpcode() == Instruction::SExt) {
|
|
SrcVecTy = largestIntegerVectorType(SrcVecTy, MinVecTy);
|
|
VectorTy =
|
|
smallestIntegerVectorType(ToVectorTy(I->getType(), VF), MinVecTy);
|
|
}
|
|
}
|
|
|
|
unsigned N = isScalarAfterVectorization(I, VF) ? VF : 1;
|
|
return N * TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy, I);
|
|
}
|
|
case Instruction::Call: {
|
|
bool NeedToScalarize;
|
|
CallInst *CI = cast<CallInst>(I);
|
|
unsigned CallCost = getVectorCallCost(CI, VF, TTI, TLI, NeedToScalarize);
|
|
if (getVectorIntrinsicIDForCall(CI, TLI))
|
|
return std::min(CallCost, getVectorIntrinsicCost(CI, VF, TTI, TLI));
|
|
return CallCost;
|
|
}
|
|
default:
|
|
// The cost of executing VF copies of the scalar instruction. This opcode
|
|
// is unknown. Assume that it is the same as 'mul'.
|
|
return VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy) +
|
|
getScalarizationOverhead(I, VF, TTI);
|
|
} // end of switch.
|
|
}
|
|
|
|
char LoopVectorize::ID = 0;
|
|
static const char lv_name[] = "Loop Vectorization";
|
|
INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false)
|
|
INITIALIZE_PASS_DEPENDENCY(TargetTransformInfoWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(BasicAAWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(AAResultsWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(GlobalsAAWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(AssumptionCacheTracker)
|
|
INITIALIZE_PASS_DEPENDENCY(BlockFrequencyInfoWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(DominatorTreeWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(ScalarEvolutionWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(LoopInfoWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(LoopAccessLegacyAnalysis)
|
|
INITIALIZE_PASS_DEPENDENCY(DemandedBitsWrapperPass)
|
|
INITIALIZE_PASS_DEPENDENCY(OptimizationRemarkEmitterWrapperPass)
|
|
INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
|
|
|
|
namespace llvm {
|
|
Pass *createLoopVectorizePass(bool NoUnrolling, bool AlwaysVectorize) {
|
|
return new LoopVectorize(NoUnrolling, AlwaysVectorize);
|
|
}
|
|
}
|
|
|
|
bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) {
|
|
|
|
// Check if the pointer operand of a load or store instruction is
|
|
// consecutive.
|
|
if (auto *Ptr = getPointerOperand(Inst))
|
|
return Legal->isConsecutivePtr(Ptr);
|
|
return false;
|
|
}
|
|
|
|
void LoopVectorizationCostModel::collectValuesToIgnore() {
|
|
// Ignore ephemeral values.
|
|
CodeMetrics::collectEphemeralValues(TheLoop, AC, ValuesToIgnore);
|
|
|
|
// Ignore type-promoting instructions we identified during reduction
|
|
// detection.
|
|
for (auto &Reduction : *Legal->getReductionVars()) {
|
|
RecurrenceDescriptor &RedDes = Reduction.second;
|
|
SmallPtrSetImpl<Instruction *> &Casts = RedDes.getCastInsts();
|
|
VecValuesToIgnore.insert(Casts.begin(), Casts.end());
|
|
}
|
|
}
|
|
|
|
LoopVectorizationCostModel::VectorizationFactor
|
|
LoopVectorizationPlanner::plan(bool OptForSize, unsigned UserVF) {
|
|
|
|
// Width 1 means no vectorize, cost 0 means uncomputed cost.
|
|
const LoopVectorizationCostModel::VectorizationFactor NoVectorization = {1U,
|
|
0U};
|
|
Optional<unsigned> MaybeMaxVF = CM.computeMaxVF(OptForSize);
|
|
if (!MaybeMaxVF.hasValue()) // Cases considered too costly to vectorize.
|
|
return NoVectorization;
|
|
|
|
if (UserVF) {
|
|
DEBUG(dbgs() << "LV: Using user VF " << UserVF << ".\n");
|
|
assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two");
|
|
// Collect the instructions (and their associated costs) that will be more
|
|
// profitable to scalarize.
|
|
CM.selectUserVectorizationFactor(UserVF);
|
|
buildVPlans(UserVF, UserVF);
|
|
DEBUG(printPlans(dbgs()));
|
|
return {UserVF, 0};
|
|
}
|
|
|
|
unsigned MaxVF = MaybeMaxVF.getValue();
|
|
assert(MaxVF != 0 && "MaxVF is zero.");
|
|
|
|
for (unsigned VF = 1; VF <= MaxVF; VF *= 2) {
|
|
// Collect Uniform and Scalar instructions after vectorization with VF.
|
|
CM.collectUniformsAndScalars(VF);
|
|
|
|
// Collect the instructions (and their associated costs) that will be more
|
|
// profitable to scalarize.
|
|
if (VF > 1)
|
|
CM.collectInstsToScalarize(VF);
|
|
}
|
|
|
|
buildVPlans(1, MaxVF);
|
|
DEBUG(printPlans(dbgs()));
|
|
if (MaxVF == 1)
|
|
return NoVectorization;
|
|
|
|
// Select the optimal vectorization factor.
|
|
return CM.selectVectorizationFactor(MaxVF);
|
|
}
|
|
|
|
void LoopVectorizationPlanner::setBestPlan(unsigned VF, unsigned UF) {
|
|
DEBUG(dbgs() << "Setting best plan to VF=" << VF << ", UF=" << UF << '\n');
|
|
BestVF = VF;
|
|
BestUF = UF;
|
|
|
|
for (auto *VPlanIter = VPlans.begin(); VPlanIter != VPlans.end();) {
|
|
VPlan *Plan = *VPlanIter;
|
|
if (Plan->hasVF(VF))
|
|
++VPlanIter;
|
|
else {
|
|
VPlanIter = VPlans.erase(VPlanIter);
|
|
delete Plan;
|
|
}
|
|
}
|
|
assert(VPlans.size() == 1 && "Best VF has not a single VPlan.");
|
|
}
|
|
|
|
void LoopVectorizationPlanner::executePlan(InnerLoopVectorizer &ILV,
|
|
DominatorTree *DT) {
|
|
// Perform the actual loop transformation.
|
|
|
|
// 1. Create a new empty loop. Unlink the old loop and connect the new one.
|
|
VPTransformState State{
|
|
BestVF, BestUF, LI, DT, ILV.Builder, ILV.VectorLoopValueMap, &ILV};
|
|
State.CFG.PrevBB = ILV.createVectorizedLoopSkeleton();
|
|
|
|
//===------------------------------------------------===//
|
|
//
|
|
// Notice: any optimization or new instruction that go
|
|
// into the code below should also be implemented in
|
|
// the cost-model.
|
|
//
|
|
//===------------------------------------------------===//
|
|
|
|
// 2. Copy and widen instructions from the old loop into the new loop.
|
|
assert(VPlans.size() == 1 && "Not a single VPlan to execute.");
|
|
VPlan *Plan = *VPlans.begin();
|
|
Plan->execute(&State);
|
|
|
|
// 3. Fix the vectorized code: take care of header phi's, live-outs,
|
|
// predication, updating analyses.
|
|
ILV.fixVectorizedLoop();
|
|
}
|
|
|
|
void LoopVectorizationPlanner::collectTriviallyDeadInstructions(
|
|
SmallPtrSetImpl<Instruction *> &DeadInstructions) {
|
|
BasicBlock *Latch = OrigLoop->getLoopLatch();
|
|
|
|
// We create new control-flow for the vectorized loop, so the original
|
|
// condition will be dead after vectorization if it's only used by the
|
|
// branch.
|
|
auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0));
|
|
if (Cmp && Cmp->hasOneUse())
|
|
DeadInstructions.insert(Cmp);
|
|
|
|
// We create new "steps" for induction variable updates to which the original
|
|
// induction variables map. An original update instruction will be dead if
|
|
// all its users except the induction variable are dead.
|
|
for (auto &Induction : *Legal->getInductionVars()) {
|
|
PHINode *Ind = Induction.first;
|
|
auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch));
|
|
if (all_of(IndUpdate->users(), [&](User *U) -> bool {
|
|
return U == Ind || DeadInstructions.count(cast<Instruction>(U));
|
|
}))
|
|
DeadInstructions.insert(IndUpdate);
|
|
}
|
|
}
|
|
Value *InnerLoopUnroller::reverseVector(Value *Vec) { return Vec; }
|
|
|
|
Value *InnerLoopUnroller::getBroadcastInstrs(Value *V) { return V; }
|
|
|
|
Value *InnerLoopUnroller::getStepVector(Value *Val, int StartIdx, Value *Step,
|
|
Instruction::BinaryOps BinOp) {
|
|
// When unrolling and the VF is 1, we only need to add a simple scalar.
|
|
Type *Ty = Val->getType();
|
|
assert(!Ty->isVectorTy() && "Val must be a scalar");
|
|
|
|
if (Ty->isFloatingPointTy()) {
|
|
Constant *C = ConstantFP::get(Ty, (double)StartIdx);
|
|
|
|
// Floating point operations had to be 'fast' to enable the unrolling.
|
|
Value *MulOp = addFastMathFlag(Builder.CreateFMul(C, Step));
|
|
return addFastMathFlag(Builder.CreateBinOp(BinOp, Val, MulOp));
|
|
}
|
|
Constant *C = ConstantInt::get(Ty, StartIdx);
|
|
return Builder.CreateAdd(Val, Builder.CreateMul(C, Step), "induction");
|
|
}
|
|
|
|
static void AddRuntimeUnrollDisableMetaData(Loop *L) {
|
|
SmallVector<Metadata *, 4> MDs;
|
|
// Reserve first location for self reference to the LoopID metadata node.
|
|
MDs.push_back(nullptr);
|
|
bool IsUnrollMetadata = false;
|
|
MDNode *LoopID = L->getLoopID();
|
|
if (LoopID) {
|
|
// First find existing loop unrolling disable metadata.
|
|
for (unsigned i = 1, ie = LoopID->getNumOperands(); i < ie; ++i) {
|
|
auto *MD = dyn_cast<MDNode>(LoopID->getOperand(i));
|
|
if (MD) {
|
|
const auto *S = dyn_cast<MDString>(MD->getOperand(0));
|
|
IsUnrollMetadata =
|
|
S && S->getString().startswith("llvm.loop.unroll.disable");
|
|
}
|
|
MDs.push_back(LoopID->getOperand(i));
|
|
}
|
|
}
|
|
|
|
if (!IsUnrollMetadata) {
|
|
// Add runtime unroll disable metadata.
|
|
LLVMContext &Context = L->getHeader()->getContext();
|
|
SmallVector<Metadata *, 1> DisableOperands;
|
|
DisableOperands.push_back(
|
|
MDString::get(Context, "llvm.loop.unroll.runtime.disable"));
|
|
MDNode *DisableNode = MDNode::get(Context, DisableOperands);
|
|
MDs.push_back(DisableNode);
|
|
MDNode *NewLoopID = MDNode::get(Context, MDs);
|
|
// Set operand 0 to refer to the loop id itself.
|
|
NewLoopID->replaceOperandWith(0, NewLoopID);
|
|
L->setLoopID(NewLoopID);
|
|
}
|
|
}
|
|
|
|
namespace {
|
|
/// VPWidenRecipe is a recipe for producing a copy of vector type for each
|
|
/// Instruction in its ingredients independently, in order. This recipe covers
|
|
/// most of the traditional vectorization cases where each ingredient transforms
|
|
/// into a vectorized version of itself.
|
|
class VPWidenRecipe : public VPRecipeBase {
|
|
private:
|
|
/// Hold the ingredients by pointing to their original BasicBlock location.
|
|
BasicBlock::iterator Begin;
|
|
BasicBlock::iterator End;
|
|
|
|
public:
|
|
VPWidenRecipe(Instruction *I) : VPRecipeBase(VPWidenSC) {
|
|
End = I->getIterator();
|
|
Begin = End++;
|
|
}
|
|
|
|
~VPWidenRecipe() {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPWidenSC;
|
|
}
|
|
|
|
/// Produce widened copies of all Ingredients.
|
|
void execute(VPTransformState &State) override {
|
|
for (auto &Instr : make_range(Begin, End))
|
|
State.ILV->widenInstruction(Instr);
|
|
}
|
|
|
|
/// Augment the recipe to include Instr, if it lies at its End.
|
|
bool appendInstruction(Instruction *Instr) {
|
|
if (End != Instr->getIterator())
|
|
return false;
|
|
End++;
|
|
return true;
|
|
}
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override {
|
|
O << " +\n" << Indent << "\"WIDEN\\l\"";
|
|
for (auto &Instr : make_range(Begin, End))
|
|
O << " +\n" << Indent << "\" " << VPlanIngredient(&Instr) << "\\l\"";
|
|
}
|
|
};
|
|
|
|
/// A recipe for handling phi nodes of integer and floating-point inductions,
|
|
/// producing their vector and scalar values.
|
|
class VPWidenIntOrFpInductionRecipe : public VPRecipeBase {
|
|
private:
|
|
PHINode *IV;
|
|
TruncInst *Trunc;
|
|
|
|
public:
|
|
VPWidenIntOrFpInductionRecipe(PHINode *IV, TruncInst *Trunc = nullptr)
|
|
: VPRecipeBase(VPWidenIntOrFpInductionSC), IV(IV), Trunc(Trunc) {}
|
|
|
|
~VPWidenIntOrFpInductionRecipe() {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPWidenIntOrFpInductionSC;
|
|
}
|
|
|
|
/// Generate the vectorized and scalarized versions of the phi node as
|
|
/// needed by their users.
|
|
void execute(VPTransformState &State) override {
|
|
assert(!State.Instance && "Int or FP induction being replicated.");
|
|
State.ILV->widenIntOrFpInduction(IV, Trunc);
|
|
}
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override {
|
|
O << " +\n" << Indent << "\"WIDEN-INDUCTION";
|
|
if (Trunc) {
|
|
O << "\\l\"";
|
|
O << " +\n" << Indent << "\" " << VPlanIngredient(IV) << "\\l\"";
|
|
O << " +\n" << Indent << "\" " << VPlanIngredient(Trunc) << "\\l\"";
|
|
} else
|
|
O << " " << VPlanIngredient(IV) << "\\l\"";
|
|
}
|
|
};
|
|
|
|
/// A recipe for handling all phi nodes except for integer and FP inductions.
|
|
class VPWidenPHIRecipe : public VPRecipeBase {
|
|
private:
|
|
PHINode *Phi;
|
|
|
|
public:
|
|
VPWidenPHIRecipe(PHINode *Phi) : VPRecipeBase(VPWidenPHISC), Phi(Phi) {}
|
|
|
|
~VPWidenPHIRecipe() {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPWidenPHISC;
|
|
}
|
|
|
|
/// Generate the phi/select nodes.
|
|
void execute(VPTransformState &State) override {
|
|
State.ILV->widenPHIInstruction(Phi, State.UF, State.VF);
|
|
}
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override {
|
|
O << " +\n" << Indent << "\"WIDEN-PHI " << VPlanIngredient(Phi) << "\\l\"";
|
|
}
|
|
};
|
|
|
|
/// VPInterleaveRecipe is a recipe for transforming an interleave group of load
|
|
/// or stores into one wide load/store and shuffles.
|
|
class VPInterleaveRecipe : public VPRecipeBase {
|
|
private:
|
|
const InterleaveGroup *IG;
|
|
|
|
public:
|
|
VPInterleaveRecipe(const InterleaveGroup *IG)
|
|
: VPRecipeBase(VPInterleaveSC), IG(IG) {}
|
|
|
|
~VPInterleaveRecipe() {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPInterleaveSC;
|
|
}
|
|
|
|
/// Generate the wide load or store, and shuffles.
|
|
void execute(VPTransformState &State) override {
|
|
assert(!State.Instance && "Interleave group being replicated.");
|
|
State.ILV->vectorizeInterleaveGroup(IG->getInsertPos());
|
|
}
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override;
|
|
|
|
const InterleaveGroup *getInterleaveGroup() { return IG; }
|
|
};
|
|
|
|
/// VPReplicateRecipe replicates a given instruction producing multiple scalar
|
|
/// copies of the original scalar type, one per lane, instead of producing a
|
|
/// single copy of widened type for all lanes. If the instruction is known to be
|
|
/// uniform only one copy, per lane zero, will be generated.
|
|
class VPReplicateRecipe : public VPRecipeBase {
|
|
private:
|
|
/// The instruction being replicated.
|
|
Instruction *Ingredient;
|
|
|
|
/// Indicator if only a single replica per lane is needed.
|
|
bool IsUniform;
|
|
|
|
/// Indicator if the replicas are also predicated.
|
|
bool IsPredicated;
|
|
|
|
/// Indicator if the scalar values should also be packed into a vector.
|
|
bool AlsoPack;
|
|
|
|
public:
|
|
VPReplicateRecipe(Instruction *I, bool IsUniform, bool IsPredicated = false)
|
|
: VPRecipeBase(VPReplicateSC), Ingredient(I), IsUniform(IsUniform),
|
|
IsPredicated(IsPredicated) {
|
|
// Retain the previous behavior of predicateInstructions(), where an
|
|
// insert-element of a predicated instruction got hoisted into the
|
|
// predicated basic block iff it was its only user. This is achieved by
|
|
// having predicated instructions also pack their values into a vector by
|
|
// default unless they have a replicated user which uses their scalar value.
|
|
AlsoPack = IsPredicated && !I->use_empty();
|
|
}
|
|
|
|
~VPReplicateRecipe() {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPReplicateSC;
|
|
}
|
|
|
|
/// Generate replicas of the desired Ingredient. Replicas will be generated
|
|
/// for all parts and lanes unless a specific part and lane are specified in
|
|
/// the \p State.
|
|
void execute(VPTransformState &State) override;
|
|
|
|
void setAlsoPack(bool Pack) { AlsoPack = Pack; }
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override {
|
|
O << " +\n"
|
|
<< Indent << "\"" << (IsUniform ? "CLONE " : "REPLICATE ")
|
|
<< VPlanIngredient(Ingredient);
|
|
if (AlsoPack)
|
|
O << " (S->V)";
|
|
O << "\\l\"";
|
|
}
|
|
};
|
|
|
|
/// A recipe for generating conditional branches on the bits of a mask.
|
|
class VPBranchOnMaskRecipe : public VPRecipeBase {
|
|
private:
|
|
/// The input IR basic block used to obtain the mask providing the condition
|
|
/// bits for the branch.
|
|
BasicBlock *MaskedBasicBlock;
|
|
|
|
public:
|
|
VPBranchOnMaskRecipe(BasicBlock *BB)
|
|
: VPRecipeBase(VPBranchOnMaskSC), MaskedBasicBlock(BB) {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPBranchOnMaskSC;
|
|
}
|
|
|
|
/// Generate the extraction of the appropriate bit from the block mask and the
|
|
/// conditional branch.
|
|
void execute(VPTransformState &State) override;
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override {
|
|
O << " +\n"
|
|
<< Indent << "\"BRANCH-ON-MASK-OF " << MaskedBasicBlock->getName()
|
|
<< "\\l\"";
|
|
}
|
|
};
|
|
|
|
/// VPPredInstPHIRecipe is a recipe for generating the phi nodes needed when
|
|
/// control converges back from a Branch-on-Mask. The phi nodes are needed in
|
|
/// order to merge values that are set under such a branch and feed their uses.
|
|
/// The phi nodes can be scalar or vector depending on the users of the value.
|
|
/// This recipe works in concert with VPBranchOnMaskRecipe.
|
|
class VPPredInstPHIRecipe : public VPRecipeBase {
|
|
private:
|
|
Instruction *PredInst;
|
|
|
|
public:
|
|
/// Construct a VPPredInstPHIRecipe given \p PredInst whose value needs a phi
|
|
/// nodes after merging back from a Branch-on-Mask.
|
|
VPPredInstPHIRecipe(Instruction *PredInst)
|
|
: VPRecipeBase(VPPredInstPHISC), PredInst(PredInst) {}
|
|
|
|
~VPPredInstPHIRecipe() {}
|
|
|
|
/// Method to support type inquiry through isa, cast, and dyn_cast.
|
|
static inline bool classof(const VPRecipeBase *V) {
|
|
return V->getVPRecipeID() == VPRecipeBase::VPPredInstPHISC;
|
|
}
|
|
|
|
/// Generates phi nodes for live-outs as needed to retain SSA form.
|
|
void execute(VPTransformState &State) override;
|
|
|
|
/// Print the recipe.
|
|
void print(raw_ostream &O, const Twine &Indent) const override {
|
|
O << " +\n"
|
|
<< Indent << "\"PHI-PREDICATED-INSTRUCTION " << VPlanIngredient(PredInst)
|
|
<< "\\l\"";
|
|
}
|
|
};
|
|
} // end anonymous namespace
|
|
|
|
bool LoopVectorizationPlanner::getDecisionAndClampRange(
|
|
const std::function<bool(unsigned)> &Predicate, VFRange &Range) {
|
|
assert(Range.End > Range.Start && "Trying to test an empty VF range.");
|
|
bool PredicateAtRangeStart = Predicate(Range.Start);
|
|
|
|
for (unsigned TmpVF = Range.Start * 2; TmpVF < Range.End; TmpVF *= 2)
|
|
if (Predicate(TmpVF) != PredicateAtRangeStart) {
|
|
Range.End = TmpVF;
|
|
break;
|
|
}
|
|
|
|
return PredicateAtRangeStart;
|
|
}
|
|
|
|
/// Build VPlans for the full range of feasible VF's = {\p MinVF, 2 * \p MinVF,
|
|
/// 4 * \p MinVF, ..., \p MaxVF} by repeatedly building a VPlan for a sub-range
|
|
/// of VF's starting at a given VF and extending it as much as possible. Each
|
|
/// vectorization decision can potentially shorten this sub-range during
|
|
/// buildVPlan().
|
|
void LoopVectorizationPlanner::buildVPlans(unsigned MinVF, unsigned MaxVF) {
|
|
for (unsigned VF = MinVF; VF < MaxVF + 1;) {
|
|
VFRange SubRange = {VF, MaxVF + 1};
|
|
VPlan *Plan = buildVPlan(SubRange);
|
|
VPlans.push_back(Plan);
|
|
VF = SubRange.End;
|
|
}
|
|
}
|
|
|
|
VPInterleaveRecipe *
|
|
LoopVectorizationPlanner::tryToInterleaveMemory(Instruction *I,
|
|
VFRange &Range) {
|
|
const InterleaveGroup *IG = Legal->getInterleavedAccessGroup(I);
|
|
if (!IG)
|
|
return nullptr;
|
|
|
|
// Now check if IG is relevant for VF's in the given range.
|
|
auto isIGMember = [&](Instruction *I) -> std::function<bool(unsigned)> {
|
|
return [=](unsigned VF) -> bool {
|
|
return (VF >= 2 && // Query is illegal for VF == 1
|
|
CM.getWideningDecision(I, VF) ==
|
|
LoopVectorizationCostModel::CM_Interleave);
|
|
};
|
|
};
|
|
if (!getDecisionAndClampRange(isIGMember(I), Range))
|
|
return nullptr;
|
|
|
|
// I is a member of an InterleaveGroup for VF's in the (possibly trimmed)
|
|
// range. If it's the primary member of the IG construct a VPInterleaveRecipe.
|
|
// Otherwise, it's an adjunct member of the IG, do not construct any Recipe.
|
|
assert(I == IG->getInsertPos() &&
|
|
"Generating a recipe for an adjunct member of an interleave group");
|
|
|
|
return new VPInterleaveRecipe(IG);
|
|
}
|
|
|
|
VPWidenIntOrFpInductionRecipe *
|
|
LoopVectorizationPlanner::tryToOptimizeInduction(Instruction *I,
|
|
VFRange &Range) {
|
|
if (PHINode *Phi = dyn_cast<PHINode>(I)) {
|
|
// Check if this is an integer or fp induction. If so, build the recipe that
|
|
// produces its scalar and vector values.
|
|
InductionDescriptor II = Legal->getInductionVars()->lookup(Phi);
|
|
if (II.getKind() == InductionDescriptor::IK_IntInduction ||
|
|
II.getKind() == InductionDescriptor::IK_FpInduction)
|
|
return new VPWidenIntOrFpInductionRecipe(Phi);
|
|
|
|
return nullptr;
|
|
}
|
|
|
|
// Optimize the special case where the source is a constant integer
|
|
// induction variable. Notice that we can only optimize the 'trunc' case
|
|
// because (a) FP conversions lose precision, (b) sext/zext may wrap, and
|
|
// (c) other casts depend on pointer size.
|
|
|
|
// Determine whether \p K is a truncation based on an induction variable that
|
|
// can be optimized.
|
|
auto isOptimizableIVTruncate =
|
|
[&](Instruction *K) -> std::function<bool(unsigned)> {
|
|
return
|
|
[=](unsigned VF) -> bool { return CM.isOptimizableIVTruncate(K, VF); };
|
|
};
|
|
|
|
if (isa<TruncInst>(I) &&
|
|
getDecisionAndClampRange(isOptimizableIVTruncate(I), Range))
|
|
return new VPWidenIntOrFpInductionRecipe(cast<PHINode>(I->getOperand(0)),
|
|
cast<TruncInst>(I));
|
|
return nullptr;
|
|
}
|
|
|
|
VPWidenRecipe *LoopVectorizationPlanner::tryToWiden(
|
|
Instruction *I, VPWidenRecipe *LastWidenRecipe, VFRange &Range) {
|
|
|
|
if (Legal->isScalarWithPredication(I))
|
|
return nullptr;
|
|
|
|
auto IsVectorizableOpcode = [](unsigned Opcode) {
|
|
switch (Opcode) {
|
|
case Instruction::Add:
|
|
case Instruction::And:
|
|
case Instruction::AShr:
|
|
case Instruction::BitCast:
|
|
case Instruction::Br:
|
|
case Instruction::Call:
|
|
case Instruction::FAdd:
|
|
case Instruction::FCmp:
|
|
case Instruction::FDiv:
|
|
case Instruction::FMul:
|
|
case Instruction::FPExt:
|
|
case Instruction::FPToSI:
|
|
case Instruction::FPToUI:
|
|
case Instruction::FPTrunc:
|
|
case Instruction::FRem:
|
|
case Instruction::FSub:
|
|
case Instruction::GetElementPtr:
|
|
case Instruction::ICmp:
|
|
case Instruction::IntToPtr:
|
|
case Instruction::Load:
|
|
case Instruction::LShr:
|
|
case Instruction::Mul:
|
|
case Instruction::Or:
|
|
case Instruction::PHI:
|
|
case Instruction::PtrToInt:
|
|
case Instruction::SDiv:
|
|
case Instruction::Select:
|
|
case Instruction::SExt:
|
|
case Instruction::Shl:
|
|
case Instruction::SIToFP:
|
|
case Instruction::SRem:
|
|
case Instruction::Store:
|
|
case Instruction::Sub:
|
|
case Instruction::Trunc:
|
|
case Instruction::UDiv:
|
|
case Instruction::UIToFP:
|
|
case Instruction::URem:
|
|
case Instruction::Xor:
|
|
case Instruction::ZExt:
|
|
return true;
|
|
}
|
|
return false;
|
|
};
|
|
|
|
if (!IsVectorizableOpcode(I->getOpcode()))
|
|
return nullptr;
|
|
|
|
if (CallInst *CI = dyn_cast<CallInst>(I)) {
|
|
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
|
|
if (ID && (ID == Intrinsic::assume || ID == Intrinsic::lifetime_end ||
|
|
ID == Intrinsic::lifetime_start))
|
|
return nullptr;
|
|
}
|
|
|
|
auto willWiden = [&](unsigned VF) -> bool {
|
|
if (!isa<PHINode>(I) && (CM.isScalarAfterVectorization(I, VF) ||
|
|
CM.isProfitableToScalarize(I, VF)))
|
|
return false;
|
|
if (CallInst *CI = dyn_cast<CallInst>(I)) {
|
|
Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI);
|
|
// The following case may be scalarized depending on the VF.
|
|
// The flag shows whether we use Intrinsic or a usual Call for vectorized
|
|
// version of the instruction.
|
|
// Is it beneficial to perform intrinsic call compared to lib call?
|
|
bool NeedToScalarize;
|
|
unsigned CallCost = getVectorCallCost(CI, VF, *TTI, TLI, NeedToScalarize);
|
|
bool UseVectorIntrinsic =
|
|
ID && getVectorIntrinsicCost(CI, VF, *TTI, TLI) <= CallCost;
|
|
return UseVectorIntrinsic || !NeedToScalarize;
|
|
}
|
|
if (isa<LoadInst>(I) || isa<StoreInst>(I)) {
|
|
LoopVectorizationCostModel::InstWidening Decision =
|
|
CM.getWideningDecision(I, VF);
|
|
assert(Decision != LoopVectorizationCostModel::CM_Unknown &&
|
|
"CM decision should be taken at this point.");
|
|
assert(Decision != LoopVectorizationCostModel::CM_Interleave &&
|
|
"Interleave memory opportunity should be caught earlier.");
|
|
return Decision != LoopVectorizationCostModel::CM_Scalarize;
|
|
}
|
|
return true;
|
|
};
|
|
|
|
if (!getDecisionAndClampRange(willWiden, Range))
|
|
return nullptr;
|
|
|
|
// Success: widen this instruction. We optimize the common case where
|
|
// consecutive instructions can be represented by a single recipe.
|
|
if (LastWidenRecipe && LastWidenRecipe->appendInstruction(I))
|
|
return LastWidenRecipe;
|
|
return new VPWidenRecipe(I);
|
|
}
|
|
|
|
VPBasicBlock *LoopVectorizationPlanner::handleReplication(
|
|
Instruction *I, VFRange &Range, VPBasicBlock *VPBB,
|
|
DenseMap<Instruction *, VPReplicateRecipe *> &PredInst2Recipe) {
|
|
|
|
bool IsUniform = getDecisionAndClampRange(
|
|
[&](unsigned VF) { return CM.isUniformAfterVectorization(I, VF); },
|
|
Range);
|
|
|
|
bool IsPredicated = Legal->isScalarWithPredication(I);
|
|
auto *Recipe = new VPReplicateRecipe(I, IsUniform, IsPredicated);
|
|
|
|
// Find if I uses a predicated instruction. If so, it will use its scalar
|
|
// value. Avoid hoisting the insert-element which packs the scalar value into
|
|
// a vector value, as that happens iff all users use the vector value.
|
|
for (auto &Op : I->operands())
|
|
if (auto *PredInst = dyn_cast<Instruction>(Op))
|
|
if (PredInst2Recipe.find(PredInst) != PredInst2Recipe.end())
|
|
PredInst2Recipe[PredInst]->setAlsoPack(false);
|
|
|
|
// Finalize the recipe for Instr, first if it is not predicated.
|
|
if (!IsPredicated) {
|
|
DEBUG(dbgs() << "LV: Scalarizing:" << *I << "\n");
|
|
VPBB->appendRecipe(Recipe);
|
|
return VPBB;
|
|
}
|
|
DEBUG(dbgs() << "LV: Scalarizing and predicating:" << *I << "\n");
|
|
assert(VPBB->getSuccessors().empty() &&
|
|
"VPBB has successors when handling predicated replication.");
|
|
// Record predicated instructions for above packing optimizations.
|
|
PredInst2Recipe[I] = Recipe;
|
|
VPBlockBase *Region = VPBB->setOneSuccessor(createReplicateRegion(I, Recipe));
|
|
return cast<VPBasicBlock>(Region->setOneSuccessor(new VPBasicBlock()));
|
|
}
|
|
|
|
VPRegionBlock *
|
|
LoopVectorizationPlanner::createReplicateRegion(Instruction *Instr,
|
|
VPRecipeBase *PredRecipe) {
|
|
// Instructions marked for predication are replicated and placed under an
|
|
// if-then construct to prevent side-effects.
|
|
|
|
// Build the triangular if-then region.
|
|
std::string RegionName = (Twine("pred.") + Instr->getOpcodeName()).str();
|
|
assert(Instr->getParent() && "Predicated instruction not in any basic block");
|
|
auto *BOMRecipe = new VPBranchOnMaskRecipe(Instr->getParent());
|
|
auto *Entry = new VPBasicBlock(Twine(RegionName) + ".entry", BOMRecipe);
|
|
auto *PHIRecipe =
|
|
Instr->getType()->isVoidTy() ? nullptr : new VPPredInstPHIRecipe(Instr);
|
|
auto *Exit = new VPBasicBlock(Twine(RegionName) + ".continue", PHIRecipe);
|
|
auto *Pred = new VPBasicBlock(Twine(RegionName) + ".if", PredRecipe);
|
|
VPRegionBlock *Region = new VPRegionBlock(Entry, Exit, RegionName, true);
|
|
|
|
// Note: first set Entry as region entry and then connect successors starting
|
|
// from it in order, to propagate the "parent" of each VPBasicBlock.
|
|
Entry->setTwoSuccessors(Pred, Exit);
|
|
Pred->setOneSuccessor(Exit);
|
|
|
|
return Region;
|
|
}
|
|
|
|
VPlan *LoopVectorizationPlanner::buildVPlan(VFRange &Range) {
|
|
|
|
DenseMap<Instruction *, Instruction *> &SinkAfter = Legal->getSinkAfter();
|
|
DenseMap<Instruction *, Instruction *> SinkAfterInverse;
|
|
|
|
// Collect instructions from the original loop that will become trivially dead
|
|
// in the vectorized loop. We don't need to vectorize these instructions. For
|
|
// example, original induction update instructions can become dead because we
|
|
// separately emit induction "steps" when generating code for the new loop.
|
|
// Similarly, we create a new latch condition when setting up the structure
|
|
// of the new loop, so the old one can become dead.
|
|
SmallPtrSet<Instruction *, 4> DeadInstructions;
|
|
collectTriviallyDeadInstructions(DeadInstructions);
|
|
|
|
// Hold a mapping from predicated instructions to their recipes, in order to
|
|
// fix their AlsoPack behavior if a user is determined to replicate and use a
|
|
// scalar instead of vector value.
|
|
DenseMap<Instruction *, VPReplicateRecipe *> PredInst2Recipe;
|
|
|
|
// Create a dummy pre-entry VPBasicBlock to start building the VPlan.
|
|
VPBasicBlock *VPBB = new VPBasicBlock("Pre-Entry");
|
|
VPlan *Plan = new VPlan(VPBB);
|
|
|
|
// Scan the body of the loop in a topological order to visit each basic block
|
|
// after having visited its predecessor basic blocks.
|
|
LoopBlocksDFS DFS(OrigLoop);
|
|
DFS.perform(LI);
|
|
|
|
for (BasicBlock *BB : make_range(DFS.beginRPO(), DFS.endRPO())) {
|
|
// Relevant instructions from basic block BB will be grouped into VPRecipe
|
|
// ingredients and fill a new VPBasicBlock.
|
|
unsigned VPBBsForBB = 0;
|
|
auto *FirstVPBBForBB = new VPBasicBlock(BB->getName());
|
|
VPBB->setOneSuccessor(FirstVPBBForBB);
|
|
VPBB = FirstVPBBForBB;
|
|
VPWidenRecipe *LastWidenRecipe = nullptr;
|
|
|
|
std::vector<Instruction *> Ingredients;
|
|
|
|
// Organize the ingredients to vectorize from current basic block in the
|
|
// right order.
|
|
for (Instruction &I : *BB) {
|
|
Instruction *Instr = &I;
|
|
|
|
// First filter out irrelevant instructions, to ensure no recipes are
|
|
// built for them.
|
|
if (isa<BranchInst>(Instr) || isa<DbgInfoIntrinsic>(Instr) ||
|
|
DeadInstructions.count(Instr))
|
|
continue;
|
|
|
|
// I is a member of an InterleaveGroup for Range.Start. If it's an adjunct
|
|
// member of the IG, do not construct any Recipe for it.
|
|
const InterleaveGroup *IG = Legal->getInterleavedAccessGroup(Instr);
|
|
if (IG && Instr != IG->getInsertPos() &&
|
|
Range.Start >= 2 && // Query is illegal for VF == 1
|
|
CM.getWideningDecision(Instr, Range.Start) ==
|
|
LoopVectorizationCostModel::CM_Interleave)
|
|
continue;
|
|
|
|
// Move instructions to handle first-order recurrences, step 1: avoid
|
|
// handling this instruction until after we've handled the instruction it
|
|
// should follow.
|
|
auto SAIt = SinkAfter.find(Instr);
|
|
if (SAIt != SinkAfter.end()) {
|
|
DEBUG(dbgs() << "Sinking" << *SAIt->first << " after" << *SAIt->second
|
|
<< " to vectorize a 1st order recurrence.\n");
|
|
SinkAfterInverse[SAIt->second] = Instr;
|
|
continue;
|
|
}
|
|
|
|
Ingredients.push_back(Instr);
|
|
|
|
// Move instructions to handle first-order recurrences, step 2: push the
|
|
// instruction to be sunk at its insertion point.
|
|
auto SAInvIt = SinkAfterInverse.find(Instr);
|
|
if (SAInvIt != SinkAfterInverse.end())
|
|
Ingredients.push_back(SAInvIt->second);
|
|
}
|
|
|
|
// Introduce each ingredient into VPlan.
|
|
for (Instruction *Instr : Ingredients) {
|
|
VPRecipeBase *Recipe = nullptr;
|
|
|
|
// Check if Instr should belong to an interleave memory recipe, or already
|
|
// does. In the latter case Instr is irrelevant.
|
|
if ((Recipe = tryToInterleaveMemory(Instr, Range))) {
|
|
VPBB->appendRecipe(Recipe);
|
|
continue;
|
|
}
|
|
|
|
// Check if Instr should form some PHI recipe.
|
|
if ((Recipe = tryToOptimizeInduction(Instr, Range))) {
|
|
VPBB->appendRecipe(Recipe);
|
|
continue;
|
|
}
|
|
if (PHINode *Phi = dyn_cast<PHINode>(Instr)) {
|
|
VPBB->appendRecipe(new VPWidenPHIRecipe(Phi));
|
|
continue;
|
|
}
|
|
|
|
// Check if Instr is to be widened by a general VPWidenRecipe, after
|
|
// having first checked for specific widening recipes that deal with
|
|
// Interleave Groups, Inductions and Phi nodes.
|
|
if ((Recipe = tryToWiden(Instr, LastWidenRecipe, Range))) {
|
|
if (Recipe != LastWidenRecipe)
|
|
VPBB->appendRecipe(Recipe);
|
|
LastWidenRecipe = cast<VPWidenRecipe>(Recipe);
|
|
continue;
|
|
}
|
|
|
|
// Otherwise, if all widening options failed, Instruction is to be
|
|
// replicated. This may create a successor for VPBB.
|
|
VPBasicBlock *NextVPBB =
|
|
handleReplication(Instr, Range, VPBB, PredInst2Recipe);
|
|
if (NextVPBB != VPBB) {
|
|
VPBB = NextVPBB;
|
|
VPBB->setName(BB->hasName() ? BB->getName() + "." + Twine(VPBBsForBB++)
|
|
: "");
|
|
}
|
|
}
|
|
}
|
|
|
|
// Discard empty dummy pre-entry VPBasicBlock. Note that other VPBasicBlocks
|
|
// may also be empty, such as the last one VPBB, reflecting original
|
|
// basic-blocks with no recipes.
|
|
VPBasicBlock *PreEntry = cast<VPBasicBlock>(Plan->getEntry());
|
|
assert(PreEntry->empty() && "Expecting empty pre-entry block.");
|
|
VPBlockBase *Entry = Plan->setEntry(PreEntry->getSingleSuccessor());
|
|
PreEntry->disconnectSuccessor(Entry);
|
|
delete PreEntry;
|
|
|
|
std::string PlanName;
|
|
raw_string_ostream RSO(PlanName);
|
|
unsigned VF = Range.Start;
|
|
Plan->addVF(VF);
|
|
RSO << "Initial VPlan for VF={" << VF;
|
|
for (VF *= 2; VF < Range.End; VF *= 2) {
|
|
Plan->addVF(VF);
|
|
RSO << "," << VF;
|
|
}
|
|
RSO << "},UF>=1";
|
|
RSO.flush();
|
|
Plan->setName(PlanName);
|
|
|
|
return Plan;
|
|
}
|
|
|
|
void VPInterleaveRecipe::print(raw_ostream &O, const Twine &Indent) const {
|
|
O << " +\n"
|
|
<< Indent << "\"INTERLEAVE-GROUP with factor " << IG->getFactor() << " at ";
|
|
IG->getInsertPos()->printAsOperand(O, false);
|
|
O << "\\l\"";
|
|
for (unsigned i = 0; i < IG->getFactor(); ++i)
|
|
if (Instruction *I = IG->getMember(i))
|
|
O << " +\n"
|
|
<< Indent << "\" " << VPlanIngredient(I) << " " << i << "\\l\"";
|
|
}
|
|
|
|
void VPReplicateRecipe::execute(VPTransformState &State) {
|
|
|
|
if (State.Instance) { // Generate a single instance.
|
|
State.ILV->scalarizeInstruction(Ingredient, *State.Instance, IsPredicated);
|
|
// Insert scalar instance packing it into a vector.
|
|
if (AlsoPack && State.VF > 1) {
|
|
// If we're constructing lane 0, initialize to start from undef.
|
|
if (State.Instance->Lane == 0) {
|
|
Value *Undef =
|
|
UndefValue::get(VectorType::get(Ingredient->getType(), State.VF));
|
|
State.ValueMap.setVectorValue(Ingredient, State.Instance->Part, Undef);
|
|
}
|
|
State.ILV->packScalarIntoVectorValue(Ingredient, *State.Instance);
|
|
}
|
|
return;
|
|
}
|
|
|
|
// Generate scalar instances for all VF lanes of all UF parts, unless the
|
|
// instruction is uniform inwhich case generate only the first lane for each
|
|
// of the UF parts.
|
|
unsigned EndLane = IsUniform ? 1 : State.VF;
|
|
for (unsigned Part = 0; Part < State.UF; ++Part)
|
|
for (unsigned Lane = 0; Lane < EndLane; ++Lane)
|
|
State.ILV->scalarizeInstruction(Ingredient, {Part, Lane}, IsPredicated);
|
|
}
|
|
|
|
void VPBranchOnMaskRecipe::execute(VPTransformState &State) {
|
|
assert(State.Instance && "Branch on Mask works only on single instance.");
|
|
|
|
unsigned Part = State.Instance->Part;
|
|
unsigned Lane = State.Instance->Lane;
|
|
|
|
auto Cond = State.ILV->createBlockInMask(MaskedBasicBlock);
|
|
|
|
Value *ConditionBit = Cond[Part];
|
|
if (!ConditionBit) // Block in mask is all-one.
|
|
ConditionBit = State.Builder.getTrue();
|
|
else if (ConditionBit->getType()->isVectorTy())
|
|
ConditionBit = State.Builder.CreateExtractElement(
|
|
ConditionBit, State.Builder.getInt32(Lane));
|
|
|
|
// Replace the temporary unreachable terminator with a new conditional branch,
|
|
// whose two destinations will be set later when they are created.
|
|
auto *CurrentTerminator = State.CFG.PrevBB->getTerminator();
|
|
assert(isa<UnreachableInst>(CurrentTerminator) &&
|
|
"Expected to replace unreachable terminator with conditional branch.");
|
|
auto *CondBr = BranchInst::Create(State.CFG.PrevBB, nullptr, ConditionBit);
|
|
CondBr->setSuccessor(0, nullptr);
|
|
ReplaceInstWithInst(CurrentTerminator, CondBr);
|
|
|
|
DEBUG(dbgs() << "\nLV: vectorizing BranchOnMask recipe "
|
|
<< MaskedBasicBlock->getName());
|
|
}
|
|
|
|
void VPPredInstPHIRecipe::execute(VPTransformState &State) {
|
|
assert(State.Instance && "Predicated instruction PHI works per instance.");
|
|
Instruction *ScalarPredInst = cast<Instruction>(
|
|
State.ValueMap.getScalarValue(PredInst, *State.Instance));
|
|
BasicBlock *PredicatedBB = ScalarPredInst->getParent();
|
|
BasicBlock *PredicatingBB = PredicatedBB->getSinglePredecessor();
|
|
assert(PredicatingBB && "Predicated block has no single predecessor.");
|
|
|
|
// By current pack/unpack logic we need to generate only a single phi node: if
|
|
// a vector value for the predicated instruction exists at this point it means
|
|
// the instruction has vector users only, and a phi for the vector value is
|
|
// needed. In this case the recipe of the predicated instruction is marked to
|
|
// also do that packing, thereby "hoisting" the insert-element sequence.
|
|
// Otherwise, a phi node for the scalar value is needed.
|
|
unsigned Part = State.Instance->Part;
|
|
if (State.ValueMap.hasVectorValue(PredInst, Part)) {
|
|
Value *VectorValue = State.ValueMap.getVectorValue(PredInst, Part);
|
|
InsertElementInst *IEI = cast<InsertElementInst>(VectorValue);
|
|
PHINode *VPhi = State.Builder.CreatePHI(IEI->getType(), 2);
|
|
VPhi->addIncoming(IEI->getOperand(0), PredicatingBB); // Unmodified vector.
|
|
VPhi->addIncoming(IEI, PredicatedBB); // New vector with inserted element.
|
|
State.ValueMap.resetVectorValue(PredInst, Part, VPhi); // Update cache.
|
|
} else {
|
|
Type *PredInstType = PredInst->getType();
|
|
PHINode *Phi = State.Builder.CreatePHI(PredInstType, 2);
|
|
Phi->addIncoming(UndefValue::get(ScalarPredInst->getType()), PredicatingBB);
|
|
Phi->addIncoming(ScalarPredInst, PredicatedBB);
|
|
State.ValueMap.resetScalarValue(PredInst, *State.Instance, Phi);
|
|
}
|
|
}
|
|
|
|
bool LoopVectorizePass::processLoop(Loop *L) {
|
|
assert(L->empty() && "Only process inner loops.");
|
|
|
|
#ifndef NDEBUG
|
|
const std::string DebugLocStr = getDebugLocString(L);
|
|
#endif /* NDEBUG */
|
|
|
|
DEBUG(dbgs() << "\nLV: Checking a loop in \""
|
|
<< L->getHeader()->getParent()->getName() << "\" from "
|
|
<< DebugLocStr << "\n");
|
|
|
|
LoopVectorizeHints Hints(L, DisableUnrolling, *ORE);
|
|
|
|
DEBUG(dbgs() << "LV: Loop hints:"
|
|
<< " force="
|
|
<< (Hints.getForce() == LoopVectorizeHints::FK_Disabled
|
|
? "disabled"
|
|
: (Hints.getForce() == LoopVectorizeHints::FK_Enabled
|
|
? "enabled"
|
|
: "?"))
|
|
<< " width=" << Hints.getWidth()
|
|
<< " unroll=" << Hints.getInterleave() << "\n");
|
|
|
|
// Function containing loop
|
|
Function *F = L->getHeader()->getParent();
|
|
|
|
// Looking at the diagnostic output is the only way to determine if a loop
|
|
// was vectorized (other than looking at the IR or machine code), so it
|
|
// is important to generate an optimization remark for each loop. Most of
|
|
// these messages are generated as OptimizationRemarkAnalysis. Remarks
|
|
// generated as OptimizationRemark and OptimizationRemarkMissed are
|
|
// less verbose reporting vectorized loops and unvectorized loops that may
|
|
// benefit from vectorization, respectively.
|
|
|
|
if (!Hints.allowVectorization(F, L, AlwaysVectorize)) {
|
|
DEBUG(dbgs() << "LV: Loop hints prevent vectorization.\n");
|
|
return false;
|
|
}
|
|
|
|
PredicatedScalarEvolution PSE(*SE, *L);
|
|
|
|
// Check if it is legal to vectorize the loop.
|
|
LoopVectorizationRequirements Requirements(*ORE);
|
|
LoopVectorizationLegality LVL(L, PSE, DT, TLI, AA, F, TTI, GetLAA, LI, ORE,
|
|
&Requirements, &Hints);
|
|
if (!LVL.canVectorize()) {
|
|
DEBUG(dbgs() << "LV: Not vectorizing: Cannot prove legality.\n");
|
|
emitMissedWarning(F, L, Hints, ORE);
|
|
return false;
|
|
}
|
|
|
|
// Check the function attributes to find out if this function should be
|
|
// optimized for size.
|
|
bool OptForSize =
|
|
Hints.getForce() != LoopVectorizeHints::FK_Enabled && F->optForSize();
|
|
|
|
// Check the loop for a trip count threshold: vectorize loops with a tiny trip
|
|
// count by optimizing for size, to minimize overheads.
|
|
unsigned ExpectedTC = SE->getSmallConstantMaxTripCount(L);
|
|
bool HasExpectedTC = (ExpectedTC > 0);
|
|
|
|
if (!HasExpectedTC && LoopVectorizeWithBlockFrequency) {
|
|
auto EstimatedTC = getLoopEstimatedTripCount(L);
|
|
if (EstimatedTC) {
|
|
ExpectedTC = *EstimatedTC;
|
|
HasExpectedTC = true;
|
|
}
|
|
}
|
|
|
|
if (HasExpectedTC && ExpectedTC < TinyTripCountVectorThreshold) {
|
|
DEBUG(dbgs() << "LV: Found a loop with a very small trip count. "
|
|
<< "This loop is worth vectorizing only if no scalar "
|
|
<< "iteration overheads are incurred.");
|
|
if (Hints.getForce() == LoopVectorizeHints::FK_Enabled)
|
|
DEBUG(dbgs() << " But vectorizing was explicitly forced.\n");
|
|
else {
|
|
DEBUG(dbgs() << "\n");
|
|
// Loops with a very small trip count are considered for vectorization
|
|
// under OptForSize, thereby making sure the cost of their loop body is
|
|
// dominant, free of runtime guards and scalar iteration overheads.
|
|
OptForSize = true;
|
|
}
|
|
}
|
|
|
|
// Check the function attributes to see if implicit floats are allowed.
|
|
// FIXME: This check doesn't seem possibly correct -- what if the loop is
|
|
// an integer loop and the vector instructions selected are purely integer
|
|
// vector instructions?
|
|
if (F->hasFnAttribute(Attribute::NoImplicitFloat)) {
|
|
DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat"
|
|
"attribute is used.\n");
|
|
ORE->emit(createMissedAnalysis(Hints.vectorizeAnalysisPassName(),
|
|
"NoImplicitFloat", L)
|
|
<< "loop not vectorized due to NoImplicitFloat attribute");
|
|
emitMissedWarning(F, L, Hints, ORE);
|
|
return false;
|
|
}
|
|
|
|
// Check if the target supports potentially unsafe FP vectorization.
|
|
// FIXME: Add a check for the type of safety issue (denormal, signaling)
|
|
// for the target we're vectorizing for, to make sure none of the
|
|
// additional fp-math flags can help.
|
|
if (Hints.isPotentiallyUnsafe() &&
|
|
TTI->isFPVectorizationPotentiallyUnsafe()) {
|
|
DEBUG(dbgs() << "LV: Potentially unsafe FP op prevents vectorization.\n");
|
|
ORE->emit(
|
|
createMissedAnalysis(Hints.vectorizeAnalysisPassName(), "UnsafeFP", L)
|
|
<< "loop not vectorized due to unsafe FP support.");
|
|
emitMissedWarning(F, L, Hints, ORE);
|
|
return false;
|
|
}
|
|
|
|
// Use the cost model.
|
|
LoopVectorizationCostModel CM(L, PSE, LI, &LVL, *TTI, TLI, DB, AC, ORE, F,
|
|
&Hints);
|
|
CM.collectValuesToIgnore();
|
|
|
|
// Use the planner for vectorization.
|
|
LoopVectorizationPlanner LVP(L, LI, TLI, TTI, &LVL, CM);
|
|
|
|
// Get user vectorization factor.
|
|
unsigned UserVF = Hints.getWidth();
|
|
|
|
// Plan how to best vectorize, return the best VF and its cost.
|
|
LoopVectorizationCostModel::VectorizationFactor VF =
|
|
LVP.plan(OptForSize, UserVF);
|
|
|
|
// Select the interleave count.
|
|
unsigned IC = CM.selectInterleaveCount(OptForSize, VF.Width, VF.Cost);
|
|
|
|
// Get user interleave count.
|
|
unsigned UserIC = Hints.getInterleave();
|
|
|
|
// Identify the diagnostic messages that should be produced.
|
|
std::pair<StringRef, std::string> VecDiagMsg, IntDiagMsg;
|
|
bool VectorizeLoop = true, InterleaveLoop = true;
|
|
if (Requirements.doesNotMeet(F, L, Hints)) {
|
|
DEBUG(dbgs() << "LV: Not vectorizing: loop did not meet vectorization "
|
|
"requirements.\n");
|
|
emitMissedWarning(F, L, Hints, ORE);
|
|
return false;
|
|
}
|
|
|
|
if (VF.Width == 1) {
|
|
DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
|
|
VecDiagMsg = std::make_pair(
|
|
"VectorizationNotBeneficial",
|
|
"the cost-model indicates that vectorization is not beneficial");
|
|
VectorizeLoop = false;
|
|
}
|
|
|
|
if (IC == 1 && UserIC <= 1) {
|
|
// Tell the user interleaving is not beneficial.
|
|
DEBUG(dbgs() << "LV: Interleaving is not beneficial.\n");
|
|
IntDiagMsg = std::make_pair(
|
|
"InterleavingNotBeneficial",
|
|
"the cost-model indicates that interleaving is not beneficial");
|
|
InterleaveLoop = false;
|
|
if (UserIC == 1) {
|
|
IntDiagMsg.first = "InterleavingNotBeneficialAndDisabled";
|
|
IntDiagMsg.second +=
|
|
" and is explicitly disabled or interleave count is set to 1";
|
|
}
|
|
} else if (IC > 1 && UserIC == 1) {
|
|
// Tell the user interleaving is beneficial, but it explicitly disabled.
|
|
DEBUG(dbgs()
|
|
<< "LV: Interleaving is beneficial but is explicitly disabled.");
|
|
IntDiagMsg = std::make_pair(
|
|
"InterleavingBeneficialButDisabled",
|
|
"the cost-model indicates that interleaving is beneficial "
|
|
"but is explicitly disabled or interleave count is set to 1");
|
|
InterleaveLoop = false;
|
|
}
|
|
|
|
// Override IC if user provided an interleave count.
|
|
IC = UserIC > 0 ? UserIC : IC;
|
|
|
|
// Emit diagnostic messages, if any.
|
|
const char *VAPassName = Hints.vectorizeAnalysisPassName();
|
|
if (!VectorizeLoop && !InterleaveLoop) {
|
|
// Do not vectorize or interleaving the loop.
|
|
ORE->emit(OptimizationRemarkMissed(VAPassName, VecDiagMsg.first,
|
|
L->getStartLoc(), L->getHeader())
|
|
<< VecDiagMsg.second);
|
|
ORE->emit(OptimizationRemarkMissed(LV_NAME, IntDiagMsg.first,
|
|
L->getStartLoc(), L->getHeader())
|
|
<< IntDiagMsg.second);
|
|
return false;
|
|
} else if (!VectorizeLoop && InterleaveLoop) {
|
|
DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
|
|
ORE->emit(OptimizationRemarkAnalysis(VAPassName, VecDiagMsg.first,
|
|
L->getStartLoc(), L->getHeader())
|
|
<< VecDiagMsg.second);
|
|
} else if (VectorizeLoop && !InterleaveLoop) {
|
|
DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width << ") in "
|
|
<< DebugLocStr << '\n');
|
|
ORE->emit(OptimizationRemarkAnalysis(LV_NAME, IntDiagMsg.first,
|
|
L->getStartLoc(), L->getHeader())
|
|
<< IntDiagMsg.second);
|
|
} else if (VectorizeLoop && InterleaveLoop) {
|
|
DEBUG(dbgs() << "LV: Found a vectorizable loop (" << VF.Width << ") in "
|
|
<< DebugLocStr << '\n');
|
|
DEBUG(dbgs() << "LV: Interleave Count is " << IC << '\n');
|
|
}
|
|
|
|
LVP.setBestPlan(VF.Width, IC);
|
|
|
|
using namespace ore;
|
|
if (!VectorizeLoop) {
|
|
assert(IC > 1 && "interleave count should not be 1 or 0");
|
|
// If we decided that it is not legal to vectorize the loop, then
|
|
// interleave it.
|
|
InnerLoopUnroller Unroller(L, PSE, LI, DT, TLI, TTI, AC, ORE, IC, &LVL,
|
|
&CM);
|
|
LVP.executePlan(Unroller, DT);
|
|
|
|
ORE->emit(OptimizationRemark(LV_NAME, "Interleaved", L->getStartLoc(),
|
|
L->getHeader())
|
|
<< "interleaved loop (interleaved count: "
|
|
<< NV("InterleaveCount", IC) << ")");
|
|
} else {
|
|
// If we decided that it is *legal* to vectorize the loop, then do it.
|
|
InnerLoopVectorizer LB(L, PSE, LI, DT, TLI, TTI, AC, ORE, VF.Width, IC,
|
|
&LVL, &CM);
|
|
LVP.executePlan(LB, DT);
|
|
++LoopsVectorized;
|
|
|
|
// Add metadata to disable runtime unrolling a scalar loop when there are
|
|
// no runtime checks about strides and memory. A scalar loop that is
|
|
// rarely used is not worth unrolling.
|
|
if (!LB.areSafetyChecksAdded())
|
|
AddRuntimeUnrollDisableMetaData(L);
|
|
|
|
// Report the vectorization decision.
|
|
ORE->emit(OptimizationRemark(LV_NAME, "Vectorized", L->getStartLoc(),
|
|
L->getHeader())
|
|
<< "vectorized loop (vectorization width: "
|
|
<< NV("VectorizationFactor", VF.Width)
|
|
<< ", interleaved count: " << NV("InterleaveCount", IC) << ")");
|
|
}
|
|
|
|
// Mark the loop as already vectorized to avoid vectorizing again.
|
|
Hints.setAlreadyVectorized();
|
|
|
|
DEBUG(verifyFunction(*L->getHeader()->getParent()));
|
|
return true;
|
|
}
|
|
|
|
bool LoopVectorizePass::runImpl(
|
|
Function &F, ScalarEvolution &SE_, LoopInfo &LI_, TargetTransformInfo &TTI_,
|
|
DominatorTree &DT_, BlockFrequencyInfo &BFI_, TargetLibraryInfo *TLI_,
|
|
DemandedBits &DB_, AliasAnalysis &AA_, AssumptionCache &AC_,
|
|
std::function<const LoopAccessInfo &(Loop &)> &GetLAA_,
|
|
OptimizationRemarkEmitter &ORE_) {
|
|
|
|
SE = &SE_;
|
|
LI = &LI_;
|
|
TTI = &TTI_;
|
|
DT = &DT_;
|
|
BFI = &BFI_;
|
|
TLI = TLI_;
|
|
AA = &AA_;
|
|
AC = &AC_;
|
|
GetLAA = &GetLAA_;
|
|
DB = &DB_;
|
|
ORE = &ORE_;
|
|
|
|
// Don't attempt if
|
|
// 1. the target claims to have no vector registers, and
|
|
// 2. interleaving won't help ILP.
|
|
//
|
|
// The second condition is necessary because, even if the target has no
|
|
// vector registers, loop vectorization may still enable scalar
|
|
// interleaving.
|
|
if (!TTI->getNumberOfRegisters(true) && TTI->getMaxInterleaveFactor(1) < 2)
|
|
return false;
|
|
|
|
bool Changed = false;
|
|
|
|
// The vectorizer requires loops to be in simplified form.
|
|
// Since simplification may add new inner loops, it has to run before the
|
|
// legality and profitability checks. This means running the loop vectorizer
|
|
// will simplify all loops, regardless of whether anything end up being
|
|
// vectorized.
|
|
for (auto &L : *LI)
|
|
Changed |= simplifyLoop(L, DT, LI, SE, AC, false /* PreserveLCSSA */);
|
|
|
|
// Build up a worklist of inner-loops to vectorize. This is necessary as
|
|
// the act of vectorizing or partially unrolling a loop creates new loops
|
|
// and can invalidate iterators across the loops.
|
|
SmallVector<Loop *, 8> Worklist;
|
|
|
|
for (Loop *L : *LI)
|
|
addAcyclicInnerLoop(*L, Worklist);
|
|
|
|
LoopsAnalyzed += Worklist.size();
|
|
|
|
// Now walk the identified inner loops.
|
|
while (!Worklist.empty()) {
|
|
Loop *L = Worklist.pop_back_val();
|
|
|
|
// For the inner loops we actually process, form LCSSA to simplify the
|
|
// transform.
|
|
Changed |= formLCSSARecursively(*L, *DT, LI, SE);
|
|
|
|
Changed |= processLoop(L);
|
|
}
|
|
|
|
// Process each loop nest in the function.
|
|
return Changed;
|
|
|
|
}
|
|
|
|
|
|
PreservedAnalyses LoopVectorizePass::run(Function &F,
|
|
FunctionAnalysisManager &AM) {
|
|
auto &SE = AM.getResult<ScalarEvolutionAnalysis>(F);
|
|
auto &LI = AM.getResult<LoopAnalysis>(F);
|
|
auto &TTI = AM.getResult<TargetIRAnalysis>(F);
|
|
auto &DT = AM.getResult<DominatorTreeAnalysis>(F);
|
|
auto &BFI = AM.getResult<BlockFrequencyAnalysis>(F);
|
|
auto &TLI = AM.getResult<TargetLibraryAnalysis>(F);
|
|
auto &AA = AM.getResult<AAManager>(F);
|
|
auto &AC = AM.getResult<AssumptionAnalysis>(F);
|
|
auto &DB = AM.getResult<DemandedBitsAnalysis>(F);
|
|
auto &ORE = AM.getResult<OptimizationRemarkEmitterAnalysis>(F);
|
|
|
|
auto &LAM = AM.getResult<LoopAnalysisManagerFunctionProxy>(F).getManager();
|
|
std::function<const LoopAccessInfo &(Loop &)> GetLAA =
|
|
[&](Loop &L) -> const LoopAccessInfo & {
|
|
LoopStandardAnalysisResults AR = {AA, AC, DT, LI, SE, TLI, TTI};
|
|
return LAM.getResult<LoopAccessAnalysis>(L, AR);
|
|
};
|
|
bool Changed =
|
|
runImpl(F, SE, LI, TTI, DT, BFI, &TLI, DB, AA, AC, GetLAA, ORE);
|
|
if (!Changed)
|
|
return PreservedAnalyses::all();
|
|
PreservedAnalyses PA;
|
|
PA.preserve<LoopAnalysis>();
|
|
PA.preserve<DominatorTreeAnalysis>();
|
|
PA.preserve<BasicAA>();
|
|
PA.preserve<GlobalsAA>();
|
|
return PA;
|
|
}
|