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//===- 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.
//
// 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.
//
//===----------------------------------------------------------------------===//
#define LV_NAME "loop-vectorize"
#define DEBUG_TYPE LV_NAME
#include "llvm/Transforms/Vectorize.h"
#include "llvm/ADT/DenseMap.h"
#include "llvm/ADT/MapVector.h"
#include "llvm/ADT/SmallPtrSet.h"
#include "llvm/ADT/SmallSet.h"
#include "llvm/ADT/SmallVector.h"
#include "llvm/ADT/StringExtras.h"
#include "llvm/Analysis/AliasAnalysis.h"
#include "llvm/Analysis/AliasSetTracker.h"
#include "llvm/Analysis/Dominators.h"
#include "llvm/Analysis/LoopInfo.h"
#include "llvm/Analysis/LoopIterator.h"
#include "llvm/Analysis/LoopPass.h"
#include "llvm/Analysis/ScalarEvolution.h"
#include "llvm/Analysis/ScalarEvolutionExpander.h"
#include "llvm/Analysis/ScalarEvolutionExpressions.h"
#include "llvm/Analysis/TargetTransformInfo.h"
#include "llvm/Analysis/ValueTracking.h"
#include "llvm/Analysis/Verifier.h"
#include "llvm/IR/Constants.h"
#include "llvm/IR/DataLayout.h"
#include "llvm/IR/DerivedTypes.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/Type.h"
#include "llvm/IR/Value.h"
#include "llvm/Pass.h"
#include "llvm/Support/CommandLine.h"
#include "llvm/Support/Debug.h"
#include "llvm/Support/PatternMatch.h"
#include "llvm/Support/raw_ostream.h"
#include "llvm/Support/ValueHandle.h"
#include "llvm/Target/TargetLibraryInfo.h"
#include "llvm/Transforms/Scalar.h"
#include "llvm/Transforms/Utils/BasicBlockUtils.h"
#include "llvm/Transforms/Utils/Local.h"
#include <algorithm>
#include <map>
using namespace llvm;
using namespace llvm::PatternMatch;
static cl::opt<unsigned>
VectorizationFactor("force-vector-width", cl::init(0), cl::Hidden,
cl::desc("Sets the SIMD width. Zero is autoselect."));
static cl::opt<unsigned>
VectorizationUnroll("force-vector-unroll", cl::init(0), cl::Hidden,
cl::desc("Sets the vectorization unroll count. "
"Zero is autoselect."));
static cl::opt<bool>
EnableIfConversion("enable-if-conversion", cl::init(true), cl::Hidden,
cl::desc("Enable if-conversion during vectorization."));
/// We don't vectorize loops with a known constant trip count below this number.
static cl::opt<unsigned>
TinyTripCountVectorThreshold("vectorizer-min-trip-count", cl::init(16),
cl::Hidden,
cl::desc("Don't vectorize loops with a constant "
"trip count that is smaller than this "
"value."));
/// We don't unroll loops with a known constant trip count below this number.
static const unsigned TinyTripCountUnrollThreshold = 128;
/// When performing memory disambiguation checks at runtime do not make more
/// than this number of comparisons.
static const unsigned RuntimeMemoryCheckThreshold = 8;
/// We use a metadata with this name to indicate that a scalar loop was
/// vectorized and that we don't need to re-vectorize it if we run into it
/// again.
static const char*
AlreadyVectorizedMDName = "llvm.vectorizer.already_vectorized";
namespace {
// Forward declarations.
class LoopVectorizationLegality;
class LoopVectorizationCostModel;
/// 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, ScalarEvolution *SE, LoopInfo *LI,
DominatorTree *DT, DataLayout *DL,
const TargetLibraryInfo *TLI, unsigned VecWidth,
unsigned UnrollFactor)
: OrigLoop(OrigLoop), SE(SE), LI(LI), DT(DT), DL(DL), TLI(TLI),
VF(VecWidth), UF(UnrollFactor), Builder(SE->getContext()), Induction(0),
OldInduction(0), WidenMap(UnrollFactor) {}
// Perform the actual loop widening (vectorization).
void vectorize(LoopVectorizationLegality *Legal) {
// Create a new empty loop. Unlink the old loop and connect the new one.
createEmptyLoop(Legal);
// Widen each instruction in the old loop to a new one in the new loop.
// Use the Legality module to find the induction and reduction variables.
vectorizeLoop(Legal);
// Register the new loop and update the analysis passes.
updateAnalysis();
}
private:
/// A small list of PHINodes.
typedef SmallVector<PHINode*, 4> PhiVector;
/// When we unroll loops we have multiple vector values for each scalar.
/// This data structure holds the unrolled and vectorized values that
/// originated from one scalar instruction.
typedef SmallVector<Value*, 2> VectorParts;
/// Add code that checks at runtime if the accessed arrays overlap.
/// Returns the comparator value or NULL if no check is needed.
Instruction *addRuntimeCheck(LoopVectorizationLegality *Legal,
Instruction *Loc);
/// Create an empty loop, based on the loop ranges of the old loop.
void createEmptyLoop(LoopVectorizationLegality *Legal);
/// Copy and widen the instructions from the old loop.
void vectorizeLoop(LoopVectorizationLegality *Legal);
/// 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);
/// A helper function that computes the predicate of the edge between SRC
/// and DST.
VectorParts createEdgeMask(BasicBlock *Src, BasicBlock *Dst);
/// A helper function to vectorize a single BB within the innermost loop.
void vectorizeBlockInLoop(LoopVectorizationLegality *Legal, BasicBlock *BB,
PhiVector *PV);
/// Insert the new loop to the loop hierarchy and pass manager
/// and update the analysis passes.
void updateAnalysis();
/// This instruction is un-vectorizable. Implement it as a sequence
/// of scalars.
void scalarizeInstruction(Instruction *Instr);
/// Vectorize Load and Store instructions,
void vectorizeMemoryInstruction(Instruction *Instr,
LoopVectorizationLegality *Legal);
/// 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.
Value *getBroadcastInstrs(Value *V);
/// This function adds 0, 1, 2 ... to each vector element, starting at zero.
/// If Negate is set then negative numbers are added e.g. (0, -1, -2, ...).
/// The sequence starts at StartIndex.
Value *getConsecutiveVector(Value* Val, int StartIdx, bool Negate);
/// When we go over instructions in the basic block we rely on previous
/// values within the current basic block or on loop invariant values.
/// When we widen (vectorize) values we place them in the map. If the values
/// are not within the map, they have to be loop invariant, so we simply
/// broadcast them into a vector.
VectorParts &getVectorValue(Value *V);
/// Generate a shuffle sequence that will reverse the vector Vec.
Value *reverseVector(Value *Vec);
/// This is a helper class that holds the vectorizer state. It maps scalar
/// instructions to vector instructions. When the code is 'unrolled' then
/// then a single scalar value is mapped to multiple vector parts. The parts
/// are stored in the VectorPart type.
struct ValueMap {
/// C'tor. UnrollFactor controls the number of vectors ('parts') that
/// are mapped.
ValueMap(unsigned UnrollFactor) : UF(UnrollFactor) {}
/// \return True if 'Key' is saved in the Value Map.
bool has(Value *Key) const { return MapStorage.count(Key); }
/// Initializes a new entry in the map. Sets all of the vector parts to the
/// save value in 'Val'.
/// \return A reference to a vector with splat values.
VectorParts &splat(Value *Key, Value *Val) {
VectorParts &Entry = MapStorage[Key];
Entry.assign(UF, Val);
return Entry;
}
///\return A reference to the value that is stored at 'Key'.
VectorParts &get(Value *Key) {
VectorParts &Entry = MapStorage[Key];
if (Entry.empty())
Entry.resize(UF);
assert(Entry.size() == UF);
return Entry;
}
private:
/// The unroll factor. Each entry in the map stores this number of vector
/// elements.
unsigned UF;
/// Map storage. We use std::map and not DenseMap because insertions to a
/// dense map invalidates its iterators.
std::map<Value *, VectorParts> MapStorage;
};
/// The original loop.
Loop *OrigLoop;
/// Scev analysis to use.
ScalarEvolution *SE;
/// Loop Info.
LoopInfo *LI;
/// Dominator Tree.
DominatorTree *DT;
/// Data Layout.
DataLayout *DL;
/// Target Library Info.
const TargetLibraryInfo *TLI;
/// 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 scalars to widened vectors.
ValueMap WidenMap;
};
/// 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, ScalarEvolution *SE, DataLayout *DL,
DominatorTree *DT, TargetTransformInfo* TTI,
AliasAnalysis *AA, TargetLibraryInfo *TLI)
: TheLoop(L), SE(SE), DL(DL), DT(DT), TTI(TTI), AA(AA), TLI(TLI),
Induction(0), HasFunNoNaNAttr(false) {}
/// This enum represents the kinds of reductions that we support.
enum ReductionKind {
RK_NoReduction, ///< Not a reduction.
RK_IntegerAdd, ///< Sum of integers.
RK_IntegerMult, ///< Product of integers.
RK_IntegerOr, ///< Bitwise or logical OR of numbers.
RK_IntegerAnd, ///< Bitwise or logical AND of numbers.
RK_IntegerXor, ///< Bitwise or logical XOR of numbers.
RK_IntegerMinMax, ///< Min/max implemented in terms of select(cmp()).
RK_FloatAdd, ///< Sum of floats.
RK_FloatMult, ///< Product of floats.
RK_FloatMinMax ///< Min/max implemented in terms of select(cmp()).
};
/// This enum represents the kinds of inductions that we support.
enum InductionKind {
IK_NoInduction, ///< Not an induction variable.
IK_IntInduction, ///< Integer induction variable. Step = 1.
IK_ReverseIntInduction, ///< Reverse int induction variable. Step = -1.
IK_PtrInduction, ///< Pointer induction var. Step = sizeof(elem).
IK_ReversePtrInduction ///< Reverse ptr indvar. Step = - sizeof(elem).
};
// This enum represents the kind of minmax reduction.
enum MinMaxReductionKind {
MRK_Invalid,
MRK_UIntMin,
MRK_UIntMax,
MRK_SIntMin,
MRK_SIntMax,
MRK_FloatMin,
MRK_FloatMax
};
/// This POD struct holds information about reduction variables.
struct ReductionDescriptor {
ReductionDescriptor() : StartValue(0), LoopExitInstr(0),
Kind(RK_NoReduction), MinMaxKind(MRK_Invalid) {}
ReductionDescriptor(Value *Start, Instruction *Exit, ReductionKind K,
MinMaxReductionKind MK)
: StartValue(Start), LoopExitInstr(Exit), Kind(K), MinMaxKind(MK) {}
// The starting value of the reduction.
// It does not have to be zero!
TrackingVH<Value> StartValue;
// The instruction who's value is used outside the loop.
Instruction *LoopExitInstr;
// The kind of the reduction.
ReductionKind Kind;
// If this a min/max reduction the kind of reduction.
MinMaxReductionKind MinMaxKind;
};
/// This POD struct holds information about a potential reduction operation.
struct ReductionInstDesc {
ReductionInstDesc(bool IsRedux, Instruction *I) :
IsReduction(IsRedux), PatternLastInst(I), MinMaxKind(MRK_Invalid) {}
ReductionInstDesc(Instruction *I, MinMaxReductionKind K) :
IsReduction(true), PatternLastInst(I), MinMaxKind(K) {}
// Is this instruction a reduction candidate.
bool IsReduction;
// The last instruction in a min/max pattern (select of the select(icmp())
// pattern), or the current reduction instruction otherwise.
Instruction *PatternLastInst;
// If this is a min/max pattern the comparison predicate.
MinMaxReductionKind MinMaxKind;
};
// This POD struct holds information about the memory runtime legality
// check that a group of pointers do not overlap.
struct RuntimePointerCheck {
RuntimePointerCheck() : Need(false) {}
/// Reset the state of the pointer runtime information.
void reset() {
Need = false;
Pointers.clear();
Starts.clear();
Ends.clear();
}
/// Insert a pointer and calculate the start and end SCEVs.
void insert(ScalarEvolution *SE, Loop *Lp, Value *Ptr, bool WritePtr);
/// This flag indicates if we need to add the runtime check.
bool Need;
/// Holds the pointers that we need to check.
SmallVector<TrackingVH<Value>, 2> Pointers;
/// Holds the pointer value at the beginning of the loop.
SmallVector<const SCEV*, 2> Starts;
/// Holds the pointer value at the end of the loop.
SmallVector<const SCEV*, 2> Ends;
/// Holds the information if this pointer is used for writing to memory.
SmallVector<bool, 2> IsWritePtr;
};
/// A POD for saving information about induction variables.
struct InductionInfo {
InductionInfo(Value *Start, InductionKind K) : StartValue(Start), IK(K) {}
InductionInfo() : StartValue(0), IK(IK_NoInduction) {}
/// Start value.
TrackingVH<Value> StartValue;
/// Induction kind.
InductionKind IK;
};
/// ReductionList contains the reduction descriptors for all
/// of the reductions that were found in the loop.
typedef DenseMap<PHINode*, ReductionDescriptor> ReductionList;
/// InductionList saves induction variables and maps them to the
/// induction descriptor.
typedef MapVector<PHINode*, InductionInfo> InductionList;
/// Alias(Multi)Map stores the values (GEPs or underlying objects and their
/// respective Store/Load instruction(s) to calculate aliasing.
typedef MapVector<Value*, Instruction* > AliasMap;
typedef DenseMap<Value*, std::vector<Instruction*> > AliasMultiMap;
/// 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 Induction variable.
PHINode *getInduction() { return Induction; }
/// Returns the reduction variables found in the loop.
ReductionList *getReductionVars() { return &Reductions; }
/// Returns the induction variables found in the loop.
InductionList *getInductionVars() { return &Inductions; }
/// Returns True if V is an induction variable in this loop.
bool isInductionVariable(const Value *V);
/// 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 true if this instruction will remain scalar after vectorization.
bool isUniformAfterVectorization(Instruction* I) { return Uniforms.count(I); }
/// Returns the information that we collected about runtime memory check.
RuntimePointerCheck *getRuntimePointerCheck() { return &PtrRtCheck; }
/// This function returns the identity element (or neutral element) for
/// the operation K.
static Constant *getReductionIdentity(ReductionKind K, Type *Tp);
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();
/// Collect the variables that need to stay uniform after vectorization.
void collectLoopUniforms();
/// Return true if all of the instructions in the block can be speculatively
/// executed.
bool blockCanBePredicated(BasicBlock *BB);
/// Returns True, if 'Phi' is the kind of reduction variable for type
/// 'Kind'. If this is a reduction variable, it adds it to ReductionList.
bool AddReductionVar(PHINode *Phi, ReductionKind Kind);
/// Returns a struct describing if the instruction 'I' can be a reduction
/// variable of type 'Kind'. If the reduction is a min/max pattern of
/// select(icmp()) this function advances the instruction pointer 'I' from the
/// compare instruction to the select instruction and stores this pointer in
/// 'PatternLastInst' member of the returned struct.
ReductionInstDesc isReductionInstr(Instruction *I, ReductionKind Kind,
ReductionInstDesc &Desc);
/// Returns true if the instruction is a Select(ICmp(X, Y), X, Y) instruction
/// pattern corresponding to a min(X, Y) or max(X, Y).
static ReductionInstDesc isMinMaxSelectCmpPattern(Instruction *I,
ReductionInstDesc &Prev);
/// Returns the induction kind of Phi. This function may return NoInduction
/// if the PHI is not an induction variable.
InductionKind isInductionVariable(PHINode *Phi);
/// Return true if can compute the address bounds of Ptr within the loop.
bool hasComputableBounds(Value *Ptr);
/// Return true if there is the chance of write reorder.
bool hasPossibleGlobalWriteReorder(Value *Object,
Instruction *Inst,
AliasMultiMap &WriteObjects,
unsigned MaxByteWidth);
/// Return the AA location for a load or a store.
AliasAnalysis::Location getLoadStoreLocation(Instruction *Inst);
/// The loop that we evaluate.
Loop *TheLoop;
/// Scev analysis.
ScalarEvolution *SE;
/// DataLayout analysis.
DataLayout *DL;
/// Dominators.
DominatorTree *DT;
/// Target Info.
TargetTransformInfo *TTI;
/// Alias Analysis.
AliasAnalysis *AA;
/// Target Library Info.
TargetLibraryInfo *TLI;
// --- vectorization state --- //
/// Holds the integer induction variable. This is the counter of the
/// loop.
PHINode *Induction;
/// 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;
/// Allowed outside users. This holds the reduction
/// vars which can be accessed from outside the loop.
SmallPtrSet<Value*, 4> AllowedExit;
/// This set holds the variables which are known to be uniform after
/// vectorization.
SmallPtrSet<Instruction*, 4> Uniforms;
/// We need to check that all of the pointers in this list are disjoint
/// at runtime.
RuntimePointerCheck PtrRtCheck;
/// Can we assume the absence of NaNs.
bool HasFunNoNaNAttr;
};
/// 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, ScalarEvolution *SE, LoopInfo *LI,
LoopVectorizationLegality *Legal,
const TargetTransformInfo &TTI,
DataLayout *DL, const TargetLibraryInfo *TLI)
: TheLoop(L), SE(SE), LI(LI), Legal(Legal), TTI(TTI), DL(DL), TLI(TLI) {}
/// 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 VF. If UserVF is not ZERO
/// then this vectorization factor will be selected if vectorization is
/// possible.
VectorizationFactor selectVectorizationFactor(bool OptForSize,
unsigned UserVF);
/// \return The size (in bits) of the widest type in the code that
/// needs to be vectorized. We ignore values that remain scalar such as
/// 64 bit loop indices.
unsigned getWidestType();
/// \return The most profitable unroll factor.
/// If UserUF is non-zero then this method finds the best unroll-factor
/// based on register pressure and other parameters.
/// VF and LoopCost are the selected vectorization factor and the cost of the
/// selected VF.
unsigned selectUnrollFactor(bool OptForSize, unsigned UserUF, unsigned VF,
unsigned LoopCost);
/// \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 information about the register usage of the loop.
RegisterUsage calculateRegisterUsage();
private:
/// 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.
unsigned expectedCost(unsigned VF);
/// Returns the execution time cost of an instruction for a given vector
/// width. Vector width of one means scalar.
unsigned getInstructionCost(Instruction *I, unsigned VF);
/// 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);
/// Returns whether the instruction is a load or store and will be a emitted
/// as a vector operation.
bool isConsecutiveLoadOrStore(Instruction *I);
/// The loop that we evaluate.
Loop *TheLoop;
/// Scev analysis.
ScalarEvolution *SE;
/// Loop Info analysis.
LoopInfo *LI;
/// Vectorization legality.
LoopVectorizationLegality *Legal;
/// Vector target information.
const TargetTransformInfo &TTI;
/// Target data layout information.
DataLayout *DL;
/// Target Library Info.
const TargetLibraryInfo *TLI;
};
/// The LoopVectorize Pass.
struct LoopVectorize : public LoopPass {
/// Pass identification, replacement for typeid
static char ID;
explicit LoopVectorize() : LoopPass(ID) {
initializeLoopVectorizePass(*PassRegistry::getPassRegistry());
}
ScalarEvolution *SE;
DataLayout *DL;
LoopInfo *LI;
TargetTransformInfo *TTI;
DominatorTree *DT;
AliasAnalysis *AA;
TargetLibraryInfo *TLI;
virtual bool runOnLoop(Loop *L, LPPassManager &LPM) {
// We only vectorize innermost loops.
if (!L->empty())
return false;
SE = &getAnalysis<ScalarEvolution>();
DL = getAnalysisIfAvailable<DataLayout>();
LI = &getAnalysis<LoopInfo>();
TTI = &getAnalysis<TargetTransformInfo>();
DT = &getAnalysis<DominatorTree>();
AA = getAnalysisIfAvailable<AliasAnalysis>();
TLI = getAnalysisIfAvailable<TargetLibraryInfo>();
if (DL == NULL) {
DEBUG(dbgs() << "LV: Not vectorizing because of missing data layout");
return false;
}
DEBUG(dbgs() << "LV: Checking a loop in \"" <<
L->getHeader()->getParent()->getName() << "\"\n");
// Check if it is legal to vectorize the loop.
LoopVectorizationLegality LVL(L, SE, DL, DT, TTI, AA, TLI);
if (!LVL.canVectorize()) {
DEBUG(dbgs() << "LV: Not vectorizing.\n");
return false;
}
// Use the cost model.
LoopVectorizationCostModel CM(L, SE, LI, &LVL, *TTI, DL, TLI);
// Check the function attributes to find out if this function should be
// optimized for size.
Function *F = L->getHeader()->getParent();
Attribute::AttrKind SzAttr = Attribute::OptimizeForSize;
Attribute::AttrKind FlAttr = Attribute::NoImplicitFloat;
unsigned FnIndex = AttributeSet::FunctionIndex;
bool OptForSize = F->getAttributes().hasAttribute(FnIndex, SzAttr);
bool NoFloat = F->getAttributes().hasAttribute(FnIndex, FlAttr);
if (NoFloat) {
DEBUG(dbgs() << "LV: Can't vectorize when the NoImplicitFloat"
"attribute is used.\n");
return false;
}
// Select the optimal vectorization factor.
LoopVectorizationCostModel::VectorizationFactor VF;
VF = CM.selectVectorizationFactor(OptForSize, VectorizationFactor);
// Select the unroll factor.
unsigned UF = CM.selectUnrollFactor(OptForSize, VectorizationUnroll,
VF.Width, VF.Cost);
if (VF.Width == 1) {
DEBUG(dbgs() << "LV: Vectorization is possible but not beneficial.\n");
return false;
}
DEBUG(dbgs() << "LV: Found a vectorizable loop ("<< VF.Width << ") in "<<
F->getParent()->getModuleIdentifier()<<"\n");
DEBUG(dbgs() << "LV: Unroll Factor is " << UF << "\n");
// If we decided that it is *legal* to vectorize the loop then do it.
InnerLoopVectorizer LB(L, SE, LI, DT, DL, TLI, VF.Width, UF);
LB.vectorize(&LVL);
DEBUG(verifyFunction(*L->getHeader()->getParent()));
return true;
}
virtual void getAnalysisUsage(AnalysisUsage &AU) const {
LoopPass::getAnalysisUsage(AU);
AU.addRequiredID(LoopSimplifyID);
AU.addRequiredID(LCSSAID);
AU.addRequired<DominatorTree>();
AU.addRequired<LoopInfo>();
AU.addRequired<ScalarEvolution>();
AU.addRequired<TargetTransformInfo>();
AU.addPreserved<LoopInfo>();
AU.addPreserved<DominatorTree>();
}
};
} // end anonymous namespace
//===----------------------------------------------------------------------===//
// Implementation of LoopVectorizationLegality, InnerLoopVectorizer and
// LoopVectorizationCostModel.
//===----------------------------------------------------------------------===//
void
LoopVectorizationLegality::RuntimePointerCheck::insert(ScalarEvolution *SE,
Loop *Lp, Value *Ptr,
bool WritePtr) {
const SCEV *Sc = SE->getSCEV(Ptr);
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Sc);
assert(AR && "Invalid addrec expression");
const SCEV *Ex = SE->getExitCount(Lp, Lp->getLoopLatch());
const SCEV *ScEnd = AR->evaluateAtIteration(Ex, *SE);
Pointers.push_back(Ptr);
Starts.push_back(AR->getStart());
Ends.push_back(ScEnd);
IsWritePtr.push_back(WritePtr);
}
Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) {
// Save the current insertion location.
Instruction *Loc = Builder.GetInsertPoint();
// 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.
if (Invariant)
Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator());
// Broadcast the scalar into all locations in the vector.
Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast");
// Restore the builder insertion point.
if (Invariant)
Builder.SetInsertPoint(Loc);
return Shuf;
}
Value *InnerLoopVectorizer::getConsecutiveVector(Value* Val, int StartIdx,
bool Negate) {
assert(Val->getType()->isVectorTy() && "Must be a vector");
assert(Val->getType()->getScalarType()->isIntegerTy() &&
"Elem must be an integer");
// Create the types.
Type *ITy = Val->getType()->getScalarType();
VectorType *Ty = cast<VectorType>(Val->getType());
int VLen = Ty->getNumElements();
SmallVector<Constant*, 8> Indices;
// Create a vector of consecutive numbers from zero to VF.
for (int i = 0; i < VLen; ++i) {
int64_t Idx = Negate ? (-i) : i;
Indices.push_back(ConstantInt::get(ITy, StartIdx + Idx, Negate));
}
// Add the consecutive indices to the vector value.
Constant *Cv = ConstantVector::get(Indices);
assert(Cv->getType() == Val->getType() && "Invalid consecutive vec");
return Builder.CreateAdd(Val, Cv, "induction");
}
int LoopVectorizationLegality::isConsecutivePtr(Value *Ptr) {
assert(Ptr->getType()->isPointerTy() && "Unexpected non ptr");
// Make sure that the pointer does not point to structs.
if (cast<PointerType>(Ptr->getType())->getElementType()->isAggregateType())
return 0;
// If this value is a pointer induction variable we know it is consecutive.
PHINode *Phi = dyn_cast_or_null<PHINode>(Ptr);
if (Phi && Inductions.count(Phi)) {
InductionInfo II = Inductions[Phi];
if (IK_PtrInduction == II.IK)
return 1;
else if (IK_ReversePtrInduction == II.IK)
return -1;
}
GetElementPtrInst *Gep = dyn_cast_or_null<GetElementPtrInst>(Ptr);
if (!Gep)
return 0;
unsigned NumOperands = Gep->getNumOperands();
Value *LastIndex = Gep->getOperand(NumOperands - 1);
Value *GpPtr = Gep->getPointerOperand();
// If this GEP value is a consecutive pointer induction variable and all of
// the indices are constant then we know it is consecutive. We can
Phi = dyn_cast<PHINode>(GpPtr);
if (Phi && Inductions.count(Phi)) {
// Make sure that the pointer does not point to structs.
PointerType *GepPtrType = cast<PointerType>(GpPtr->getType());
if (GepPtrType->getElementType()->isAggregateType())
return 0;
// Make sure that all of the index operands are loop invariant.
for (unsigned i = 1; i < NumOperands; ++i)
if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
return 0;
InductionInfo II = Inductions[Phi];
if (IK_PtrInduction == II.IK)
return 1;
else if (IK_ReversePtrInduction == II.IK)
return -1;
}
// Check that all of the gep indices are uniform except for the last.
for (unsigned i = 0; i < NumOperands - 1; ++i)
if (!SE->isLoopInvariant(SE->getSCEV(Gep->getOperand(i)), TheLoop))
return 0;
// We can emit wide load/stores only if the last index is the induction
// variable.
const SCEV *Last = SE->getSCEV(LastIndex);
if (const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(Last)) {
const SCEV *Step = AR->getStepRecurrence(*SE);
// The memory is consecutive because the last index is consecutive
// and all other indices are loop invariant.
if (Step->isOne())
return 1;
if (Step->isAllOnesValue())
return -1;
}
return 0;
}
bool LoopVectorizationLegality::isUniform(Value *V) {
return (SE->isLoopInvariant(SE->getSCEV(V), TheLoop));
}
InnerLoopVectorizer::VectorParts&
InnerLoopVectorizer::getVectorValue(Value *V) {
assert(V != Induction && "The new induction variable should not be used.");
assert(!V->getType()->isVectorTy() && "Can't widen a vector");
// If we have this scalar in the map, return it.
if (WidenMap.has(V))
return WidenMap.get(V);
// 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);
return WidenMap.splat(V, B);
}
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");
}
void InnerLoopVectorizer::vectorizeMemoryInstruction(Instruction *Instr,
LoopVectorizationLegality *Legal) {
// 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");
Type *ScalarDataTy = LI ? LI->getType() : SI->getValueOperand()->getType();
Type *DataTy = VectorType::get(ScalarDataTy, VF);
Value *Ptr = LI ? LI->getPointerOperand() : SI->getPointerOperand();
unsigned Alignment = LI ? LI->getAlignment() : SI->getAlignment();
unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace();
unsigned ScalarAllocatedSize = DL->getTypeAllocSize(ScalarDataTy);
unsigned VectorElementSize = DL->getTypeStoreSize(DataTy)/VF;
if (ScalarAllocatedSize != VectorElementSize)
return scalarizeInstruction(Instr);
// If the pointer is loop invariant or if it is non consecutive,
// scalarize the load.
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
bool Reverse = ConsecutiveStride < 0;
bool UniformLoad = LI && Legal->isUniform(Ptr);
if (!ConsecutiveStride || UniformLoad)
return scalarizeInstruction(Instr);
Constant *Zero = Builder.getInt32(0);
VectorParts &Entry = WidenMap.get(Instr);
// Handle consecutive loads/stores.
GetElementPtrInst *Gep = dyn_cast<GetElementPtrInst>(Ptr);
if (Gep && Legal->isInductionVariable(Gep->getPointerOperand())) {
Value *PtrOperand = Gep->getPointerOperand();
Value *FirstBasePtr = getVectorValue(PtrOperand)[0];
FirstBasePtr = Builder.CreateExtractElement(FirstBasePtr, Zero);
// Create the new GEP with the new induction variable.
GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
Gep2->setOperand(0, FirstBasePtr);
Gep2->setName("gep.indvar.base");
Ptr = Builder.Insert(Gep2);
} else if (Gep) {
assert(SE->isLoopInvariant(SE->getSCEV(Gep->getPointerOperand()),
OrigLoop) && "Base ptr must be invariant");
// The last index does not have to be the induction. It can be
// consecutive and be a function of the index. For example A[I+1];
unsigned NumOperands = Gep->getNumOperands();
Value *LastGepOperand = Gep->getOperand(NumOperands - 1);
VectorParts &GEPParts = getVectorValue(LastGepOperand);
Value *LastIndex = GEPParts[0];
LastIndex = Builder.CreateExtractElement(LastIndex, Zero);
// Create the new GEP with the new induction variable.
GetElementPtrInst *Gep2 = cast<GetElementPtrInst>(Gep->clone());
Gep2->setOperand(NumOperands - 1, LastIndex);
Gep2->setName("gep.indvar.idx");
Ptr = Builder.Insert(Gep2);
} else {
// Use the induction element ptr.
assert(isa<PHINode>(Ptr) && "Invalid induction ptr");
VectorParts &PtrVal = getVectorValue(Ptr);
Ptr = Builder.CreateExtractElement(PtrVal[0], Zero);
}
// Handle Stores:
if (SI) {
assert(!Legal->isUniform(SI->getPointerOperand()) &&
"We do not allow storing to uniform addresses");
VectorParts &StoredVal = getVectorValue(SI->getValueOperand());
for (unsigned Part = 0; Part < UF; ++Part) {
// Calculate the pointer for the specific unroll-part.
Value *PartPtr = Builder.CreateGEP(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[Part] = reverseVector(StoredVal[Part]);
// If the address is consecutive but reversed, then the
// wide store needs to start at the last vector element.
PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF));
PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF));
}
Value *VecPtr = Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
Builder.CreateStore(StoredVal[Part], VecPtr)->setAlignment(Alignment);
}
}
for (unsigned Part = 0; Part < UF; ++Part) {
// Calculate the pointer for the specific unroll-part.
Value *PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(Part * VF));
if (Reverse) {
// If the address is consecutive but reversed, then the
// wide store needs to start at the last vector element.
PartPtr = Builder.CreateGEP(Ptr, Builder.getInt32(-Part * VF));
PartPtr = Builder.CreateGEP(PartPtr, Builder.getInt32(1 - VF));
}
Value *VecPtr = Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace));
Value *LI = Builder.CreateLoad(VecPtr, "wide.load");
cast<LoadInst>(LI)->setAlignment(Alignment);
Entry[Part] = Reverse ? reverseVector(LI) : LI;
}
}
void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr) {
assert(!Instr->getType()->isAggregateType() && "Can't handle vectors");
// Holds vector parameters or scalars, in case of uniform vals.
SmallVector<VectorParts, 4> Params;
// Find all of the vectorized parameters.
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
Value *SrcOp = Instr->getOperand(op);
// If we are accessing the old induction variable, use the new one.
if (SrcOp == OldInduction) {
Params.push_back(getVectorValue(SrcOp));
continue;
}
// Try using previously calculated values.
Instruction *SrcInst = dyn_cast<Instruction>(SrcOp);
// If the src is an instruction that appeared earlier in the basic block
// then it should already be vectorized.
if (SrcInst && OrigLoop->contains(SrcInst)) {
assert(WidenMap.has(SrcInst) && "Source operand is unavailable");
// The parameter is a vector value from earlier.
Params.push_back(WidenMap.get(SrcInst));
} else {
// The parameter is a scalar from outside the loop. Maybe even a constant.
VectorParts Scalars;
Scalars.append(UF, SrcOp);
Params.push_back(Scalars);
}
}
assert(Params.size() == Instr->getNumOperands() &&
"Invalid number of operands");
// Does this instruction return a value ?
bool IsVoidRetTy = Instr->getType()->isVoidTy();
Value *UndefVec = IsVoidRetTy ? 0 :
UndefValue::get(VectorType::get(Instr->getType(), VF));
// Create a new entry in the WidenMap and initialize it to Undef or Null.
VectorParts &VecResults = WidenMap.splat(Instr, UndefVec);
// For each vector unroll 'part':
for (unsigned Part = 0; Part < UF; ++Part) {
// For each scalar that we create:
for (unsigned Width = 0; Width < VF; ++Width) {
Instruction *Cloned = Instr->clone();
if (!IsVoidRetTy)
Cloned->setName(Instr->getName() + ".cloned");
// Replace the operands of the cloned instrucions with extracted scalars.
for (unsigned op = 0, e = Instr->getNumOperands(); op != e; ++op) {
Value *Op = Params[op][Part];
// Param is a vector. Need to extract the right lane.
if (Op->getType()->isVectorTy())
Op = Builder.CreateExtractElement(Op, Builder.getInt32(Width));
Cloned->setOperand(op, Op);
}
// Place the cloned scalar in the new loop.
Builder.Insert(Cloned);
// If the original scalar returns a value we need to place it in a vector
// so that future users will be able to use it.
if (!IsVoidRetTy)
VecResults[Part] = Builder.CreateInsertElement(VecResults[Part], Cloned,
Builder.getInt32(Width));
}
}
}
Instruction *
InnerLoopVectorizer::addRuntimeCheck(LoopVectorizationLegality *Legal,
Instruction *Loc) {
LoopVectorizationLegality::RuntimePointerCheck *PtrRtCheck =
Legal->getRuntimePointerCheck();
if (!PtrRtCheck->Need)
return NULL;
Instruction *MemoryRuntimeCheck = 0;
unsigned NumPointers = PtrRtCheck->Pointers.size();
SmallVector<Value* , 2> Starts;
SmallVector<Value* , 2> Ends;
SCEVExpander Exp(*SE, "induction");
// Use this type for pointer arithmetic.
Type* PtrArithTy = Type::getInt8PtrTy(Loc->getContext(), 0);
for (unsigned i = 0; i < NumPointers; ++i) {
Value *Ptr = PtrRtCheck->Pointers[i];
const SCEV *Sc = SE->getSCEV(Ptr);
if (SE->isLoopInvariant(Sc, OrigLoop)) {
DEBUG(dbgs() << "LV: Adding RT check for a loop invariant ptr:" <<
*Ptr <<"\n");
Starts.push_back(Ptr);
Ends.push_back(Ptr);
} else {
DEBUG(dbgs() << "LV: Adding RT check for range:" << *Ptr <<"\n");
Value *Start = Exp.expandCodeFor(PtrRtCheck->Starts[i], PtrArithTy, Loc);
Value *End = Exp.expandCodeFor(PtrRtCheck->Ends[i], PtrArithTy, Loc);
Starts.push_back(Start);
Ends.push_back(End);
}
}
IRBuilder<> ChkBuilder(Loc);
for (unsigned i = 0; i < NumPointers; ++i) {
for (unsigned j = i+1; j < NumPointers; ++j) {
// No need to check if two readonly pointers intersect.
if (!PtrRtCheck->IsWritePtr[i] && !PtrRtCheck->IsWritePtr[j])
continue;
Value *Start0 = ChkBuilder.CreateBitCast(Starts[i], PtrArithTy, "bc");
Value *Start1 = ChkBuilder.CreateBitCast(Starts[j], PtrArithTy, "bc");
Value *End0 = ChkBuilder.CreateBitCast(Ends[i], PtrArithTy, "bc");
Value *End1 = ChkBuilder.CreateBitCast(Ends[j], PtrArithTy, "bc");
Value *Cmp0 = ChkBuilder.CreateICmpULE(Start0, End1, "bound0");
Value *Cmp1 = ChkBuilder.CreateICmpULE(Start1, End0, "bound1");
Value *IsConflict = ChkBuilder.CreateAnd(Cmp0, Cmp1, "found.conflict");
if (MemoryRuntimeCheck)
IsConflict = ChkBuilder.CreateOr(MemoryRuntimeCheck, IsConflict,
"conflict.rdx");
MemoryRuntimeCheck = cast<Instruction>(IsConflict);
}
}
return MemoryRuntimeCheck;
}
void
InnerLoopVectorizer::createEmptyLoop(LoopVectorizationLegality *Legal) {
/*
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.
[ ] <-- 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 *BypassBlock = OrigLoop->getLoopPreheader();
BasicBlock *ExitBlock = OrigLoop->getExitBlock();
assert(ExitBlock && "Must have an exit block");
// Mark the old scalar loop with metadata that tells us not to vectorize this
// loop again if we run into it.
MDNode *MD = MDNode::get(OldBasicBlock->getContext(), None);
OldBasicBlock->getTerminator()->setMetadata(AlreadyVectorizedMDName, MD);
// 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.
OldInduction = Legal->getInduction();
Type *IdxTy = OldInduction ? OldInduction->getType() :
DL->getIntPtrType(SE->getContext());
// Find the loop boundaries.
const SCEV *ExitCount = SE->getExitCount(OrigLoop, OrigLoop->getLoopLatch());
assert(ExitCount != SE->getCouldNotCompute() && "Invalid loop count");
// Get the total trip count from the count by adding 1.
ExitCount = SE->getAddExpr(ExitCount,
SE->getConstant(ExitCount->getType(), 1));
// 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, "induction");
// Count holds the overall loop count (N).
Value *Count = Exp.expandCodeFor(ExitCount, ExitCount->getType(),
BypassBlock->getTerminator());
// The loop index does not have to start at Zero. Find the original start
// value from the induction PHI node. If we don't have an induction variable
// then we know that it starts at zero.
Value *StartIdx = OldInduction ?
OldInduction->getIncomingValueForBlock(BypassBlock):
ConstantInt::get(IdxTy, 0);
assert(BypassBlock && "Invalid loop structure");
LoopBypassBlocks.push_back(BypassBlock);
// Split the single block loop into the two loop structure described above.
BasicBlock *VectorPH =
BypassBlock->splitBasicBlock(BypassBlock->getTerminator(), "vector.ph");
BasicBlock *VecBody =
VectorPH->splitBasicBlock(VectorPH->getTerminator(), "vector.body");
BasicBlock *MiddleBlock =
VecBody->splitBasicBlock(VecBody->getTerminator(), "middle.block");
BasicBlock *ScalarPH =
MiddleBlock->splitBasicBlock(MiddleBlock->getTerminator(), "scalar.ph");
// Use this IR builder to create the loop instructions (Phi, Br, Cmp)
// inside the loop.
Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
// Generate the induction variable.
Induction = Builder.CreatePHI(IdxTy, 2, "index");
// The loop step is equal to the vectorization factor (num of SIMD elements)
// times the unroll factor (num of SIMD instructions).
Constant *Step = ConstantInt::get(IdxTy, VF * UF);
// This is the IR builder that we use to add all of the logic for bypassing
// the new vector loop.
IRBuilder<> BypassBuilder(BypassBlock->getTerminator());
// We may need to extend the index in case there is a type mismatch.
// We know that the count starts at zero and does not overflow.
if (Count->getType() != IdxTy) {
// The exit count can be of pointer type. Convert it to the correct
// integer type.
if (ExitCount->getType()->isPointerTy())
Count = BypassBuilder.CreatePointerCast(Count, IdxTy, "ptrcnt.to.int");
else
Count = BypassBuilder.CreateZExtOrTrunc(Count, IdxTy, "cnt.cast");
}
// Add the start index to the loop count to get the new end index.
Value *IdxEnd = BypassBuilder.CreateAdd(Count, StartIdx, "end.idx");
// Now we need to generate the expression for N - (N % VF), which is
// the part that the vectorized body will execute.
Value *R = BypassBuilder.CreateURem(Count, Step, "n.mod.vf");
Value *CountRoundDown = BypassBuilder.CreateSub(Count, R, "n.vec");
Value *IdxEndRoundDown = BypassBuilder.CreateAdd(CountRoundDown, StartIdx,
"end.idx.rnd.down");
// Now, compare the new count to zero. If it is zero skip the vector loop and
// jump to the scalar loop.
Value *Cmp = BypassBuilder.CreateICmpEQ(IdxEndRoundDown, StartIdx,
"cmp.zero");
BasicBlock *LastBypassBlock = BypassBlock;
// 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 *MemRuntimeCheck = addRuntimeCheck(Legal,
BypassBlock->getTerminator());
if (MemRuntimeCheck) {
// Create a new block containing the memory check.
BasicBlock *CheckBlock = BypassBlock->splitBasicBlock(MemRuntimeCheck,
"vector.memcheck");
LoopBypassBlocks.push_back(CheckBlock);
// Replace the branch into the memory check block with a conditional branch
// for the "few elements case".
Instruction *OldTerm = BypassBlock->getTerminator();
BranchInst::Create(MiddleBlock, CheckBlock, Cmp, OldTerm);
OldTerm->eraseFromParent();
Cmp = MemRuntimeCheck;
LastBypassBlock = CheckBlock;
}
LastBypassBlock->getTerminator()->eraseFromParent();
BranchInst::Create(MiddleBlock, VectorPH, Cmp,
LastBypassBlock);
// 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.
PHINode *ResumeIndex = 0;
LoopVectorizationLegality::InductionList::iterator I, E;
LoopVectorizationLegality::InductionList *List = Legal->getInductionVars();
for (I = List->begin(), E = List->end(); I != E; ++I) {
PHINode *OrigPhi = I->first;
LoopVectorizationLegality::InductionInfo II = I->second;
PHINode *ResumeVal = PHINode::Create(OrigPhi->getType(), 2, "resume.val",
MiddleBlock->getTerminator());
Value *EndValue = 0;
switch (II.IK) {
case LoopVectorizationLegality::IK_NoInduction:
llvm_unreachable("Unknown induction");
case LoopVectorizationLegality::IK_IntInduction: {
// Handle the integer induction counter:
assert(OrigPhi->getType()->isIntegerTy() && "Invalid type");
assert(OrigPhi == OldInduction && "Unknown integer PHI");
// We know what the end value is.
EndValue = IdxEndRoundDown;
// We also know which PHI node holds it.
ResumeIndex = ResumeVal;
break;
}
case LoopVectorizationLegality::IK_ReverseIntInduction: {
// Convert the CountRoundDown variable to the PHI size.
unsigned CRDSize = CountRoundDown->getType()->getScalarSizeInBits();
unsigned IISize = II.StartValue->getType()->getScalarSizeInBits();
Value *CRD = CountRoundDown;
if (CRDSize > IISize)
CRD = CastInst::Create(Instruction::Trunc, CountRoundDown,
II.StartValue->getType(), "tr.crd",
LoopBypassBlocks.back()->getTerminator());
else if (CRDSize < IISize)
CRD = CastInst::Create(Instruction::SExt, CountRoundDown,
II.StartValue->getType(),
"sext.crd",
LoopBypassBlocks.back()->getTerminator());
// Handle reverse integer induction counter:
EndValue =
BinaryOperator::CreateSub(II.StartValue, CRD, "rev.ind.end",
LoopBypassBlocks.back()->getTerminator());
break;
}
case LoopVectorizationLegality::IK_PtrInduction: {
// For pointer induction variables, calculate the offset using
// the end index.
EndValue =
GetElementPtrInst::Create(II.StartValue, CountRoundDown, "ptr.ind.end",
LoopBypassBlocks.back()->getTerminator());
break;
}
case LoopVectorizationLegality::IK_ReversePtrInduction: {
// The value at the end of the loop for the reverse pointer is calculated
// by creating a GEP with a negative index starting from the start value.
Value *Zero = ConstantInt::get(CountRoundDown->getType(), 0);
Value *NegIdx = BinaryOperator::CreateSub(Zero, CountRoundDown,
"rev.ind.end",
LoopBypassBlocks.back()->getTerminator());
EndValue = GetElementPtrInst::Create(II.StartValue, NegIdx,
"rev.ptr.ind.end",
LoopBypassBlocks.back()->getTerminator());
break;
}
}// end of case
// The new PHI merges the original incoming value, in case of a bypass,
// or the value at the end of the vectorized loop.
for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
ResumeVal->addIncoming(II.StartValue, LoopBypassBlocks[I]);
ResumeVal->addIncoming(EndValue, VecBody);
// Fix the scalar body counter (PHI node).
unsigned BlockIdx = OrigPhi->getBasicBlockIndex(ScalarPH);
OrigPhi->setIncomingValue(BlockIdx, ResumeVal);
}
// If we are generating a new induction variable then we also need to
// generate the code that calculates the exit value. This value is not
// simply the end of the counter because we may skip the vectorized body
// in case of a runtime check.
if (!OldInduction){
assert(!ResumeIndex && "Unexpected resume value found");
ResumeIndex = PHINode::Create(IdxTy, 2, "new.indc.resume.val",
MiddleBlock->getTerminator());
for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
ResumeIndex->addIncoming(StartIdx, LoopBypassBlocks[I]);
ResumeIndex->addIncoming(IdxEndRoundDown, VecBody);
}
// Make sure that we found the index where scalar loop needs to continue.
assert(ResumeIndex && ResumeIndex->getType()->isIntegerTy() &&
"Invalid resume Index");
// 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, IdxEnd,
ResumeIndex, "cmp.n",
MiddleBlock->getTerminator());
BranchInst::Create(ExitBlock, ScalarPH, CmpN, MiddleBlock->getTerminator());
// Remove the old terminator.
MiddleBlock->getTerminator()->eraseFromParent();
// Create i+1 and fill the PHINode.
Value *NextIdx = Builder.CreateAdd(Induction, Step, "index.next");
Induction->addIncoming(StartIdx, VectorPH);
Induction->addIncoming(NextIdx, VecBody);
// Create the compare.
Value *ICmp = Builder.CreateICmpEQ(NextIdx, IdxEndRoundDown);
Builder.CreateCondBr(ICmp, MiddleBlock, VecBody);
// Now we have two terminators. Remove the old one from the block.
VecBody->getTerminator()->eraseFromParent();
// Get ready to start creating new instructions into the vectorized body.
Builder.SetInsertPoint(VecBody->getFirstInsertionPt());
// 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.
if (ParentLoop) {
ParentLoop->addChildLoop(Lp);
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
ParentLoop->addBasicBlockToLoop(LoopBypassBlocks[I], LI->getBase());
ParentLoop->addBasicBlockToLoop(ScalarPH, LI->getBase());
ParentLoop->addBasicBlockToLoop(VectorPH, LI->getBase());
ParentLoop->addBasicBlockToLoop(MiddleBlock, LI->getBase());
} else {
LI->addTopLevelLoop(Lp);
}
Lp->addBasicBlockToLoop(VecBody, LI->getBase());
// Save the state.
LoopVectorPreHeader = VectorPH;
LoopScalarPreHeader = ScalarPH;
LoopMiddleBlock = MiddleBlock;
LoopExitBlock = ExitBlock;
LoopVectorBody = VecBody;
LoopScalarBody = OldBasicBlock;
}
/// This function returns the identity element (or neutral element) for
/// the operation K.
Constant*
LoopVectorizationLegality::getReductionIdentity(ReductionKind K, Type *Tp) {
switch (K) {
case RK_IntegerXor:
case RK_IntegerAdd:
case RK_IntegerOr:
// Adding, Xoring, Oring zero to a number does not change it.
return ConstantInt::get(Tp, 0);
case RK_IntegerMult:
// Multiplying a number by 1 does not change it.
return ConstantInt::get(Tp, 1);
case RK_IntegerAnd:
// AND-ing a number with an all-1 value does not change it.
return ConstantInt::get(Tp, -1, true);
case RK_FloatMult:
// Multiplying a number by 1 does not change it.
return ConstantFP::get(Tp, 1.0L);
case RK_FloatAdd:
// Adding zero to a number does not change it.
return ConstantFP::get(Tp, 0.0L);
default:
llvm_unreachable("Unknown reduction kind");
}
}
static Intrinsic::ID
getIntrinsicIDForCall(CallInst *CI, const TargetLibraryInfo *TLI) {
// If we have an intrinsic call, check if it is trivially vectorizable.
if (IntrinsicInst *II = dyn_cast<IntrinsicInst>(CI)) {
switch (II->getIntrinsicID()) {
case Intrinsic::sqrt:
case Intrinsic::sin:
case Intrinsic::cos:
case Intrinsic::exp:
case Intrinsic::exp2:
case Intrinsic::log:
case Intrinsic::log10:
case Intrinsic::log2:
case Intrinsic::fabs:
case Intrinsic::floor:
case Intrinsic::ceil:
case Intrinsic::trunc:
case Intrinsic::rint:
case Intrinsic::nearbyint:
case Intrinsic::pow:
case Intrinsic::fma:
case Intrinsic::fmuladd:
return II->getIntrinsicID();
default:
return Intrinsic::not_intrinsic;
}
}
if (!TLI)
return Intrinsic::not_intrinsic;
LibFunc::Func Func;
Function *F = CI->getCalledFunction();
// We're going to make assumptions on the semantics of the functions, check
// that the target knows that it's available in this environment.
if (!F || !TLI->getLibFunc(F->getName(), Func))
return Intrinsic::not_intrinsic;
// Otherwise check if we have a call to a function that can be turned into a
// vector intrinsic.
switch (Func) {
default:
break;
case LibFunc::sin:
case LibFunc::sinf:
case LibFunc::sinl:
return Intrinsic::sin;
case LibFunc::cos:
case LibFunc::cosf:
case LibFunc::cosl:
return Intrinsic::cos;
case LibFunc::exp:
case LibFunc::expf:
case LibFunc::expl:
return Intrinsic::exp;
case LibFunc::exp2:
case LibFunc::exp2f:
case LibFunc::exp2l:
return Intrinsic::exp2;
case LibFunc::log:
case LibFunc::logf:
case LibFunc::logl:
return Intrinsic::log;
case LibFunc::log10:
case LibFunc::log10f:
case LibFunc::log10l:
return Intrinsic::log10;
case LibFunc::log2:
case LibFunc::log2f:
case LibFunc::log2l:
return Intrinsic::log2;
case LibFunc::fabs:
case LibFunc::fabsf:
case LibFunc::fabsl:
return Intrinsic::fabs;
case LibFunc::floor:
case LibFunc::floorf:
case LibFunc::floorl:
return Intrinsic::floor;
case LibFunc::ceil:
case LibFunc::ceilf:
case LibFunc::ceill:
return Intrinsic::ceil;
case LibFunc::trunc:
case LibFunc::truncf:
case LibFunc::truncl:
return Intrinsic::trunc;
case LibFunc::rint:
case LibFunc::rintf:
case LibFunc::rintl:
return Intrinsic::rint;
case LibFunc::nearbyint:
case LibFunc::nearbyintf:
case LibFunc::nearbyintl:
return Intrinsic::nearbyint;
case LibFunc::pow:
case LibFunc::powf:
case LibFunc::powl:
return Intrinsic::pow;
}
return Intrinsic::not_intrinsic;
}
/// This function translates the reduction kind to an LLVM binary operator.
static unsigned
getReductionBinOp(LoopVectorizationLegality::ReductionKind Kind) {
switch (Kind) {
case LoopVectorizationLegality::RK_IntegerAdd:
return Instruction::Add;
case LoopVectorizationLegality::RK_IntegerMult:
return Instruction::Mul;
case LoopVectorizationLegality::RK_IntegerOr:
return Instruction::Or;
case LoopVectorizationLegality::RK_IntegerAnd:
return Instruction::And;
case LoopVectorizationLegality::RK_IntegerXor:
return Instruction::Xor;
case LoopVectorizationLegality::RK_FloatMult:
return Instruction::FMul;
case LoopVectorizationLegality::RK_FloatAdd:
return Instruction::FAdd;
case LoopVectorizationLegality::RK_IntegerMinMax:
return Instruction::ICmp;
case LoopVectorizationLegality::RK_FloatMinMax:
return Instruction::FCmp;
default:
llvm_unreachable("Unknown reduction operation");
}
}
Value *createMinMaxOp(IRBuilder<> &Builder,
LoopVectorizationLegality::MinMaxReductionKind RK,
Value *Left,
Value *Right) {
CmpInst::Predicate P = CmpInst::ICMP_NE;
switch (RK) {
default:
llvm_unreachable("Unknown min/max reduction kind");
case LoopVectorizationLegality::MRK_UIntMin:
P = CmpInst::ICMP_ULT;
break;
case LoopVectorizationLegality::MRK_UIntMax:
P = CmpInst::ICMP_UGT;
break;
case LoopVectorizationLegality::MRK_SIntMin:
P = CmpInst::ICMP_SLT;
break;
case LoopVectorizationLegality::MRK_SIntMax:
P = CmpInst::ICMP_SGT;
break;
case LoopVectorizationLegality::MRK_FloatMin:
P = CmpInst::FCMP_OLT;
break;
case LoopVectorizationLegality::MRK_FloatMax:
P = CmpInst::FCMP_OGT;
break;
}
Value *Cmp;
if (RK == LoopVectorizationLegality::MRK_FloatMin || RK == LoopVectorizationLegality::MRK_FloatMax)
Cmp = Builder.CreateFCmp(P, Left, Right, "rdx.minmax.cmp");
else
Cmp = Builder.CreateICmp(P, Left, Right, "rdx.minmax.cmp");
Value *Select = Builder.CreateSelect(Cmp, Left, Right, "rdx.minmax.select");
return Select;
}
void
InnerLoopVectorizer::vectorizeLoop(LoopVectorizationLegality *Legal) {
//===------------------------------------------------===//
//
// Notice: any optimization or new instruction that go
// into the code below should be also be implemented in
// the cost-model.
//
//===------------------------------------------------===//
Constant *Zero = Builder.getInt32(0);
// In order to support reduction variables we need to be able to vectorize
// Phi nodes. Phi nodes have cycles, so we need to vectorize them in two
// stages. First, we create a new vector PHI node with no incoming edges.
// We use this value when we vectorize all of the instructions that use the
// PHI. Next, after all of the instructions in the block are complete we
// add the new incoming edges to the PHI. At this point all of the
// instructions in the basic block are vectorized, so we can use them to
// construct the PHI.
PhiVector RdxPHIsToFix;
// Scan the loop in a topological order to ensure that defs are vectorized
// before users.
LoopBlocksDFS DFS(OrigLoop);
DFS.perform(LI);
// Vectorize all of the blocks in the original loop.
for (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(),
be = DFS.endRPO(); bb != be; ++bb)
vectorizeBlockInLoop(Legal, *bb, &RdxPHIsToFix);
// At this point every instruction in the original loop is widened to
// a vector form. We are almost done. Now, we need to fix the PHI nodes
// that we vectorized. The PHI nodes are currently empty because we did
// not want to introduce cycles. Notice that the remaining PHI nodes
// that we need to fix are reduction variables.
// Create the 'reduced' values for each of the induction vars.
// The reduced values are the vector values that we scalarize and combine
// after the loop is finished.
for (PhiVector::iterator it = RdxPHIsToFix.begin(), e = RdxPHIsToFix.end();
it != e; ++it) {
PHINode *RdxPhi = *it;
assert(RdxPhi && "Unable to recover vectorized PHI");
// Find the reduction variable descriptor.
assert(Legal->getReductionVars()->count(RdxPhi) &&
"Unable to find the reduction variable");
LoopVectorizationLegality::ReductionDescriptor RdxDesc =
(*Legal->getReductionVars())[RdxPhi];
// We need to generate a reduction vector from the incoming scalar.
// To do so, we need to generate the 'identity' vector and overide
// one of the elements with the incoming scalar reduction. We need
// to do it in the vector-loop preheader.
Builder.SetInsertPoint(LoopBypassBlocks.front()->getTerminator());
// This is the vector-clone of the value that leaves the loop.
VectorParts &VectorExit = getVectorValue(RdxDesc.LoopExitInstr);
Type *VecTy = VectorExit[0]->getType();
// Find the reduction identity variable. Zero for addition, or, xor,
// one for multiplication, -1 for And.
Value *Identity;
Value *VectorStart;
if (RdxDesc.Kind == LoopVectorizationLegality::RK_IntegerMinMax ||
RdxDesc.Kind == LoopVectorizationLegality::RK_FloatMinMax) {
// MinMax reduction have the start value as their identify.
VectorStart = Identity = Builder.CreateVectorSplat(VF, RdxDesc.StartValue,
"minmax.ident");
} else {
Constant *Iden =
LoopVectorizationLegality::getReductionIdentity(RdxDesc.Kind,
VecTy->getScalarType());
Identity = ConstantVector::getSplat(VF, Iden);
// This vector is the Identity vector where the first element is the
// incoming scalar reduction.
VectorStart = Builder.CreateInsertElement(Identity,
RdxDesc.StartValue, Zero);
}
// Fix the vector-loop phi.
// We created the induction variable so we know that the
// preheader is the first entry.
BasicBlock *VecPreheader = Induction->getIncomingBlock(0);
// Reductions do not have to start at zero. They can start with
// any loop invariant values.
VectorParts &VecRdxPhi = WidenMap.get(RdxPhi);
BasicBlock *Latch = OrigLoop->getLoopLatch();
Value *LoopVal = RdxPhi->getIncomingValueForBlock(Latch);
VectorParts &Val = getVectorValue(LoopVal);
for (unsigned part = 0; part < UF; ++part) {
// Make sure to add the reduction stat value only to the
// first unroll part.
Value *StartVal = (part == 0) ? VectorStart : Identity;
cast<PHINode>(VecRdxPhi[part])->addIncoming(StartVal, VecPreheader);
cast<PHINode>(VecRdxPhi[part])->addIncoming(Val[part], LoopVectorBody);
}
// 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());
VectorParts RdxParts;
for (unsigned part = 0; part < UF; ++part) {
// This PHINode contains the vectorized reduction variable, or
// the initial value vector, if we bypass the vector loop.
VectorParts &RdxExitVal = getVectorValue(RdxDesc.LoopExitInstr);
PHINode *NewPhi = Builder.CreatePHI(VecTy, 2, "rdx.vec.exit.phi");
Value *StartVal = (part == 0) ? VectorStart : Identity;
for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I)
NewPhi->addIncoming(StartVal, LoopBypassBlocks[I]);
NewPhi->addIncoming(RdxExitVal[part], LoopVectorBody);
RdxParts.push_back(NewPhi);
}
// Reduce all of the unrolled parts into a single vector.
Value *ReducedPartRdx = RdxParts[0];
unsigned Op = getReductionBinOp(RdxDesc.Kind);
for (unsigned part = 1; part < UF; ++part) {
if (Op != Instruction::ICmp && Op != Instruction::FCmp)
ReducedPartRdx = Builder.CreateBinOp((Instruction::BinaryOps)Op,
RdxParts[part], ReducedPartRdx,
"bin.rdx");
else
ReducedPartRdx = createMinMaxOp(Builder, RdxDesc.MinMaxKind,
ReducedPartRdx, RdxParts[part]);
}
// VF is a power of 2 so we can emit the reduction using log2(VF) shuffles
// and vector ops, reducing the set of values being computed by half each
// round.
assert(isPowerOf2_32(VF) &&
"Reduction emission only supported for pow2 vectors!");
Value *TmpVec = ReducedPartRdx;
SmallVector<Constant*, 32> ShuffleMask(VF, 0);
for (unsigned i = VF; i != 1; i >>= 1) {
// Move the upper half of the vector to the lower half.
for (unsigned j = 0; j != i/2; ++j)
ShuffleMask[j] = Builder.getInt32(i/2 + j);
// Fill the rest of the mask with undef.
std::fill(&ShuffleMask[i/2], ShuffleMask.end(),
UndefValue::get(Builder.getInt32Ty()));
Value *Shuf =
Builder.CreateShuffleVector(TmpVec,
UndefValue::get(TmpVec->getType()),
ConstantVector::get(ShuffleMask),
"rdx.shuf");
if (Op != Instruction::ICmp && Op != Instruction::FCmp)
TmpVec = Builder.CreateBinOp((Instruction::BinaryOps)Op, TmpVec, Shuf,
"bin.rdx");
else
TmpVec = createMinMaxOp(Builder, RdxDesc.MinMaxKind, TmpVec, Shuf);
}
// The result is in the first element of the vector.
Value *Scalar0 = Builder.CreateExtractElement(TmpVec, Builder.getInt32(0));
// 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) continue;
// 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 our reduction value exit-PHI. Update it with the
// incoming bypass edge.
if (LCSSAPhi->getIncomingValue(0) == RdxDesc.LoopExitInstr) {
// Add an edge coming from the bypass.
LCSSAPhi->addIncoming(Scalar0, LoopMiddleBlock);
break;
}
}// 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 =
(RdxPhi)->getBasicBlockIndex(OrigLoop->getLoopLatch());
assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index");
// Pick the other block.
int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1);
(RdxPhi)->setIncomingValue(SelfEdgeBlockIdx, Scalar0);
(RdxPhi)->setIncomingValue(IncomingEdgeBlockIdx, RdxDesc.LoopExitInstr);
}// end of for each redux variable.
// The Loop exit block may have single value PHI nodes where the incoming
// value is 'undef'. While vectorizing we only handled real values that
// were defined inside the loop. Here we handle the 'undef case'.
// See PR14725.
for (BasicBlock::iterator LEI = LoopExitBlock->begin(),
LEE = LoopExitBlock->end(); LEI != LEE; ++LEI) {
PHINode *LCSSAPhi = dyn_cast<PHINode>(LEI);
if (!LCSSAPhi) continue;
if (LCSSAPhi->getNumIncomingValues() == 1)
LCSSAPhi->addIncoming(UndefValue::get(LCSSAPhi->getType()),
LoopMiddleBlock);
}
}
InnerLoopVectorizer::VectorParts
InnerLoopVectorizer::createEdgeMask(BasicBlock *Src, BasicBlock *Dst) {
assert(std::find(pred_begin(Dst), pred_end(Dst), Src) != pred_end(Dst) &&
"Invalid edge");
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()) {
VectorParts EdgeMask = getVectorValue(BI->getCondition());
if (BI->getSuccessor(0) != Dst)
for (unsigned part = 0; part < UF; ++part)
EdgeMask[part] = Builder.CreateNot(EdgeMask[part]);
for (unsigned part = 0; part < UF; ++part)
EdgeMask[part] = Builder.CreateAnd(EdgeMask[part], SrcMask[part]);
return EdgeMask;
}
return SrcMask;
}
InnerLoopVectorizer::VectorParts
InnerLoopVectorizer::createBlockInMask(BasicBlock *BB) {
assert(OrigLoop->contains(BB) && "Block is not a part of a loop");
// Loop incoming mask is all-one.
if (OrigLoop->getHeader() == BB) {
Value *C = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 1);
return getVectorValue(C);
}
// This is the block mask. We OR all incoming edges, and with zero.
Value *Zero = ConstantInt::get(IntegerType::getInt1Ty(BB->getContext()), 0);
VectorParts BlockMask = getVectorValue(Zero);
// For each pred:
for (pred_iterator it = pred_begin(BB), e = pred_end(BB); it != e; ++it) {
VectorParts EM = createEdgeMask(*it, BB);
for (unsigned part = 0; part < UF; ++part)
BlockMask[part] = Builder.CreateOr(BlockMask[part], EM[part]);
}
return BlockMask;
}
void
InnerLoopVectorizer::vectorizeBlockInLoop(LoopVectorizationLegality *Legal,
BasicBlock *BB, PhiVector *PV) {
// For each instruction in the old loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
VectorParts &Entry = WidenMap.get(it);
switch (it->getOpcode()) {
case Instruction::Br:
// Nothing to do for PHIs and BR, since we already took care of the
// loop control flow instructions.
continue;
case Instruction::PHI:{
PHINode* P = cast<PHINode>(it);
// Handle reduction variables:
if (Legal->getReductionVars()->count(P)) {
for (unsigned part = 0; part < UF; ++part) {
// This is phase one of vectorizing PHIs.
Type *VecTy = VectorType::get(it->getType(), VF);
Entry[part] = PHINode::Create(VecTy, 2, "vec.phi",
LoopVectorBody-> getFirstInsertionPt());
}
PV->push_back(P);
continue;
}
// 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();
assert(NumIncoming > 1 && "Invalid PHI");
// Generate a sequence of selects of the form:
// SELECT(Mask3, In3,
// SELECT(Mask2, In2,
// ( ...)))
for (unsigned In = 0; In < NumIncoming; In++) {
VectorParts Cond = createEdgeMask(P->getIncomingBlock(In),
P->getParent());
VectorParts &In0 = getVectorValue(P->getIncomingValue(In));
for (unsigned part = 0; part < UF; ++part) {
// We don't need to 'select' the first PHI operand because it is
// the default value if all of the other masks don't match.
if (In == 0)
Entry[part] = In0[part];
else
// Select between the current value and the previous incoming edge
// based on the incoming mask.
Entry[part] = Builder.CreateSelect(Cond[part], In0[part],
Entry[part], "predphi");
}
}
continue;
}
// This PHINode must be an induction variable.
// Make sure that we know about it.
assert(Legal->getInductionVars()->count(P) &&
"Not an induction variable");
LoopVectorizationLegality::InductionInfo II =
Legal->getInductionVars()->lookup(P);
switch (II.IK) {
case LoopVectorizationLegality::IK_NoInduction:
llvm_unreachable("Unknown induction");
case LoopVectorizationLegality::IK_IntInduction: {
assert(P == OldInduction && "Unexpected PHI");
Value *Broadcasted = getBroadcastInstrs(Induction);
// After broadcasting the induction variable we need to make the
// vector consecutive by adding 0, 1, 2 ...
for (unsigned part = 0; part < UF; ++part)
Entry[part] = getConsecutiveVector(Broadcasted, VF * part, false);
continue;
}
case LoopVectorizationLegality::IK_ReverseIntInduction:
case LoopVectorizationLegality::IK_PtrInduction:
case LoopVectorizationLegality::IK_ReversePtrInduction:
// Handle reverse integer and pointer inductions.
Value *StartIdx = 0;
// If we have a single integer induction variable then use it.
// Otherwise, start counting at zero.
if (OldInduction) {
LoopVectorizationLegality::InductionInfo OldII =
Legal->getInductionVars()->lookup(OldInduction);
StartIdx = OldII.StartValue;
} else {
StartIdx = ConstantInt::get(Induction->getType(), 0);
}
// This is the normalized GEP that starts counting at zero.
Value *NormalizedIdx = Builder.CreateSub(Induction, StartIdx,
"normalized.idx");
// Handle the reverse integer induction variable case.
if (LoopVectorizationLegality::IK_ReverseIntInduction == II.IK) {
IntegerType *DstTy = cast<IntegerType>(II.StartValue->getType());
Value *CNI = Builder.CreateSExtOrTrunc(NormalizedIdx, DstTy,
"resize.norm.idx");
Value *ReverseInd = Builder.CreateSub(II.StartValue, CNI,
"reverse.idx");
// This is a new value so do not hoist it out.
Value *Broadcasted = getBroadcastInstrs(ReverseInd);
// After broadcasting the induction variable we need to make the
// vector consecutive by adding ... -3, -2, -1, 0.
for (unsigned part = 0; part < UF; ++part)
Entry[part] = getConsecutiveVector(Broadcasted, -(int)VF * part,
true);
continue;
}
// Handle the pointer induction variable case.
assert(P->getType()->isPointerTy() && "Unexpected type.");
// Is this a reverse induction ptr or a consecutive induction ptr.
bool Reverse = (LoopVectorizationLegality::IK_ReversePtrInduction ==
II.IK);
// This is the vector of results. Notice that we don't generate
// vector geps because scalar geps result in better code.
for (unsigned part = 0; part < UF; ++part) {
Value *VecVal = UndefValue::get(VectorType::get(P->getType(), VF));
for (unsigned int i = 0; i < VF; ++i) {
int EltIndex = (i + part * VF) * (Reverse ? -1 : 1);
Constant *Idx = ConstantInt::get(Induction->getType(), EltIndex);
Value *GlobalIdx;
if (!Reverse)
GlobalIdx = Builder.CreateAdd(NormalizedIdx, Idx, "gep.idx");
else
GlobalIdx = Builder.CreateSub(Idx, NormalizedIdx, "gep.ridx");
Value *SclrGep = Builder.CreateGEP(II.StartValue, GlobalIdx,
"next.gep");
VecVal = Builder.CreateInsertElement(VecVal, SclrGep,
Builder.getInt32(i),
"insert.gep");
}
Entry[part] = VecVal;
}
continue;
}
}// End of PHI.
case Instruction::Add:
case Instruction::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::FDiv:
case Instruction::URem:
case Instruction::SRem:
case Instruction::FRem:
case Instruction::Shl:
case Instruction::LShr:
case Instruction::AShr:
case Instruction::And:
case Instruction::Or:
case Instruction::Xor: {
// Just widen binops.
BinaryOperator *BinOp = dyn_cast<BinaryOperator>(it);
VectorParts &A = getVectorValue(it->getOperand(0));
VectorParts &B = getVectorValue(it->getOperand(1));
// Use this vector value for all users of the original instruction.
for (unsigned Part = 0; Part < UF; ++Part) {
Value *V = Builder.CreateBinOp(BinOp->getOpcode(), A[Part], B[Part]);
// Update the NSW, NUW and Exact flags. Notice: V can be an Undef.
BinaryOperator *VecOp = dyn_cast<BinaryOperator>(V);
if (VecOp && isa<OverflowingBinaryOperator>(BinOp)) {
VecOp->setHasNoSignedWrap(BinOp->hasNoSignedWrap());
VecOp->setHasNoUnsignedWrap(BinOp->hasNoUnsignedWrap());
}
if (VecOp && isa<PossiblyExactOperator>(VecOp))
VecOp->setIsExact(BinOp->isExact());
Entry[Part] = V;
}
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.
bool InvariantCond = SE->isLoopInvariant(SE->getSCEV(it->getOperand(0)),
OrigLoop);
// 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.
VectorParts &Cond = getVectorValue(it->getOperand(0));
VectorParts &Op0 = getVectorValue(it->getOperand(1));
VectorParts &Op1 = getVectorValue(it->getOperand(2));
Value *ScalarCond = Builder.CreateExtractElement(Cond[0],
Builder.getInt32(0));
for (unsigned Part = 0; Part < UF; ++Part) {
Entry[Part] = Builder.CreateSelect(
InvariantCond ? ScalarCond : Cond[Part],
Op0[Part],
Op1[Part]);
}
break;
}
case Instruction::ICmp:
case Instruction::FCmp: {
// Widen compares. Generate vector compares.
bool FCmp = (it->getOpcode() == Instruction::FCmp);
CmpInst *Cmp = dyn_cast<CmpInst>(it);
VectorParts &A = getVectorValue(it->getOperand(0));
VectorParts &B = getVectorValue(it->getOperand(1));
for (unsigned Part = 0; Part < UF; ++Part) {
Value *C = 0;
if (FCmp)
C = Builder.CreateFCmp(Cmp->getPredicate(), A[Part], B[Part]);
else
C = Builder.CreateICmp(Cmp->getPredicate(), A[Part], B[Part]);
Entry[Part] = C;
}
break;
}
case Instruction::Store:
case Instruction::Load:
vectorizeMemoryInstruction(it, Legal);
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: {
CastInst *CI = dyn_cast<CastInst>(it);
/// Optimize the special case where the source is the induction
/// variable. Notice that we can only optimize the 'trunc' case
/// because: a. FP conversions lose precision, b. sext/zext may wrap,
/// c. other casts depend on pointer size.
if (CI->getOperand(0) == OldInduction &&
it->getOpcode() == Instruction::Trunc) {
Value *ScalarCast = Builder.CreateCast(CI->getOpcode(), Induction,
CI->getType());
Value *Broadcasted = getBroadcastInstrs(ScalarCast);
for (unsigned Part = 0; Part < UF; ++Part)
Entry[Part] = getConsecutiveVector(Broadcasted, VF * Part, false);
break;
}
/// Vectorize casts.
Type *DestTy = VectorType::get(CI->getType()->getScalarType(), VF);
VectorParts &A = getVectorValue(it->getOperand(0));
for (unsigned Part = 0; Part < UF; ++Part)
Entry[Part] = Builder.CreateCast(CI->getOpcode(), A[Part], DestTy);
break;
}
case Instruction::Call: {
// Ignore dbg intrinsics.
if (isa<DbgInfoIntrinsic>(it))
break;
Module *M = BB->getParent()->getParent();
CallInst *CI = cast<CallInst>(it);
Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI);
assert(ID && "Not an intrinsic call!");
for (unsigned Part = 0; Part < UF; ++Part) {
SmallVector<Value*, 4> Args;
for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i) {
VectorParts &Arg = getVectorValue(CI->getArgOperand(i));
Args.push_back(Arg[Part]);
}
Type *Tys[] = { VectorType::get(CI->getType()->getScalarType(), VF) };
Function *F = Intrinsic::getDeclaration(M, ID, Tys);
Entry[Part] = Builder.CreateCall(F, Args);
}
break;
}
default:
// All other instructions are unsupported. Scalarize them.
scalarizeInstruction(it);
break;
}// end of switch.
}// end of for_each instr.
}
void InnerLoopVectorizer::updateAnalysis() {
// Forget the original basic block.
SE->forgetLoop(OrigLoop);
// Update the dominator tree information.
assert(DT->properlyDominates(LoopBypassBlocks.front(), LoopExitBlock) &&
"Entry does not dominate exit.");
for (unsigned I = 1, E = LoopBypassBlocks.size(); I != E; ++I)
DT->addNewBlock(LoopBypassBlocks[I], LoopBypassBlocks[I-1]);
DT->addNewBlock(LoopVectorPreHeader, LoopBypassBlocks.back());
DT->addNewBlock(LoopVectorBody, LoopVectorPreHeader);
DT->addNewBlock(LoopMiddleBlock, LoopBypassBlocks.front());
DT->addNewBlock(LoopScalarPreHeader, LoopMiddleBlock);
DT->changeImmediateDominator(LoopScalarBody, LoopScalarPreHeader);
DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock);
DEBUG(DT->verifyAnalysis());
}
bool LoopVectorizationLegality::canVectorizeWithIfConvert() {
if (!EnableIfConversion)
return false;
assert(TheLoop->getNumBlocks() > 1 && "Single block loops are vectorizable");
std::vector<BasicBlock*> &LoopBlocks = TheLoop->getBlocksVector();
// Collect the blocks that need predication.
for (unsigned i = 0, e = LoopBlocks.size(); i < e; ++i) {
BasicBlock *BB = LoopBlocks[i];
// We don't support switch statements inside loops.
if (!isa<BranchInst>(BB->getTerminator()))
return false;
// We must be able to predicate all blocks that need to be predicated.
if (blockNeedsPredication(BB) && !blockCanBePredicated(BB))
return false;
}
// We can if-convert this loop.
return true;
}
bool LoopVectorizationLegality::canVectorize() {
// We must have a loop in canonical form. Loops with indirectbr in them cannot
// be canonicalized.
if (!TheLoop->getLoopPreheader())
return false;
// We can only vectorize innermost loops.
if (TheLoop->getSubLoopsVector().size())
return false;
// We must have a single backedge.
if (TheLoop->getNumBackEdges() != 1)
return false;
// We must have a single exiting block.
if (!TheLoop->getExitingBlock())
return false;
unsigned NumBlocks = TheLoop->getNumBlocks();
// Check if we can if-convert non single-bb loops.
if (NumBlocks != 1 && !canVectorizeWithIfConvert()) {
DEBUG(dbgs() << "LV: Can't if-convert the loop.\n");
return false;
}
// We need to have a loop header.
BasicBlock *Latch = TheLoop->getLoopLatch();
DEBUG(dbgs() << "LV: Found a loop: " <<
TheLoop->getHeader()->getName() << "\n");
// ScalarEvolution needs to be able to find the exit count.
const SCEV *ExitCount = SE->getExitCount(TheLoop, Latch);
if (ExitCount == SE->getCouldNotCompute()) {
DEBUG(dbgs() << "LV: SCEV could not compute the loop exit count.\n");
return false;
}
// Do not loop-vectorize loops with a tiny trip count.
unsigned TC = SE->getSmallConstantTripCount(TheLoop, Latch);
if (TC > 0u && TC < TinyTripCountVectorThreshold) {
DEBUG(dbgs() << "LV: Found a loop with a very small trip count. " <<
"This loop is not worth vectorizing.\n");
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");
return false;
}
// Go over each instruction and look at memory deps.
if (!canVectorizeMemory()) {
DEBUG(dbgs() << "LV: Can't vectorize due to memory conflicts\n");
return false;
}
// Collect all of the variables that remain uniform after vectorization.
collectLoopUniforms();
DEBUG(dbgs() << "LV: We can vectorize this loop" <<
(PtrRtCheck.Need ? " (with a runtime bound check)" : "")
<<"!\n");
// Okay! We can vectorize. 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 true;
}
/// \brief Check that the instruction has outside loop users and is not an
/// identified reduction variable.
static bool hasOutsideLoopUser(const Loop *TheLoop, Instruction *Inst,
SmallPtrSet<Value *, 4> &Reductions) {
// Reduction instructions are allowed to have exit users. All other
// instructions must not have external users.
if (!Reductions.count(Inst))
//Check that all of the users of the loop are inside the BB.
for (Value::use_iterator I = Inst->use_begin(), E = Inst->use_end();
I != E; ++I) {
Instruction *U = cast<Instruction>(*I);
// This user may be a reduction exit value.
if (!TheLoop->contains(U)) {
DEBUG(dbgs() << "LV: Found an outside user for : "<< *U << "\n");
return true;
}
}
return false;
}
bool LoopVectorizationLegality::canVectorizeInstrs() {
BasicBlock *PreHeader = TheLoop->getLoopPreheader();
BasicBlock *Header = TheLoop->getHeader();
// If we marked the scalar loop as "already vectorized" then no need
// to vectorize it again.
if (Header->getTerminator()->getMetadata(AlreadyVectorizedMDName)) {
DEBUG(dbgs() << "LV: This loop was vectorized before\n");
return false;
}
// Look for the attribute signaling the absence of NaNs.
Function &F = *Header->getParent();
if (F.hasFnAttribute("no-nans-fp-math"))
HasFunNoNaNAttr = F.getAttributes().getAttribute(
AttributeSet::FunctionIndex,
"no-nans-fp-math").getValueAsString() == "true";
// For each block in the loop.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
// Scan the instructions in the block and look for hazards.
for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
++it) {
if (PHINode *Phi = dyn_cast<PHINode>(it)) {
// Check that this PHI type is allowed.
if (!Phi->getType()->isIntegerTy() &&
!Phi->getType()->isFloatingPointTy() &&
!Phi->getType()->isPointerTy()) {
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, it, AllowedExit))
continue;
return false;
}
// We only allow if-converted PHIs with more than two incoming values.
if (Phi->getNumIncomingValues() != 2) {
DEBUG(dbgs() << "LV: Found an invalid PHI.\n");
return false;
}
// This is the value coming from the preheader.
Value *StartValue = Phi->getIncomingValueForBlock(PreHeader);
// Check if this is an induction variable.
InductionKind IK = isInductionVariable(Phi);
if (IK_NoInduction != IK) {
// Int inductions are special because we only allow one IV.
if (IK == IK_IntInduction) {
if (Induction) {
DEBUG(dbgs() << "LV: Found too many inductions."<< *Phi <<"\n");
return false;
}
Induction = Phi;
}
DEBUG(dbgs() << "LV: Found an induction variable.\n");
Inductions[Phi] = InductionInfo(StartValue, IK);
continue;
}
if (AddReductionVar(Phi, RK_IntegerAdd)) {
DEBUG(dbgs() << "LV: Found an ADD reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerMult)) {
DEBUG(dbgs() << "LV: Found a MUL reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerOr)) {
DEBUG(dbgs() << "LV: Found an OR reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerAnd)) {
DEBUG(dbgs() << "LV: Found an AND reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerXor)) {
DEBUG(dbgs() << "LV: Found a XOR reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_IntegerMinMax)) {
DEBUG(dbgs() << "LV: Found a MINMAX reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_FloatMult)) {
DEBUG(dbgs() << "LV: Found an FMult reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_FloatAdd)) {
DEBUG(dbgs() << "LV: Found an FAdd reduction PHI."<< *Phi <<"\n");
continue;
}
if (AddReductionVar(Phi, RK_FloatMinMax)) {
DEBUG(dbgs() << "LV: Found an float MINMAX reduction PHI."<< *Phi <<"\n");
continue;
}
DEBUG(dbgs() << "LV: Found an unidentified PHI."<< *Phi <<"\n");
return false;
}// end of PHI handling
// We still don't handle functions. However, we can ignore dbg intrinsic
// calls and we do handle certain intrinsic and libm functions.
CallInst *CI = dyn_cast<CallInst>(it);
if (CI && !getIntrinsicIDForCall(CI, TLI) && !isa<DbgInfoIntrinsic>(CI)) {
DEBUG(dbgs() << "LV: Found a call site.\n");
return false;
}
// Check that the instruction return type is vectorizable.
if (!VectorType::isValidElementType(it->getType()) &&
!it->getType()->isVoidTy()) {
DEBUG(dbgs() << "LV: Found unvectorizable type." << "\n");
return false;
}
// Check that the stored type is vectorizable.
if (StoreInst *ST = dyn_cast<StoreInst>(it)) {
Type *T = ST->getValueOperand()->getType();
if (!VectorType::isValidElementType(T))
return false;
}
// Reduction instructions are allowed to have exit users.
// All other instructions must not have external users.
if (hasOutsideLoopUser(TheLoop, it, AllowedExit))
return false;
} // next instr.
}
if (!Induction) {
DEBUG(dbgs() << "LV: Did not find one integer induction var.\n");
assert(getInductionVars()->size() && "No induction variables");
}
return true;
}
void LoopVectorizationLegality::collectLoopUniforms() {
// We now know that the loop is vectorizable!
// Collect variables that will remain uniform after vectorization.
std::vector<Value*> Worklist;
BasicBlock *Latch = TheLoop->getLoopLatch();
// Start with the conditional branch and walk up the block.
Worklist.push_back(Latch->getTerminator()->getOperand(0));
while (Worklist.size()) {
Instruction *I = dyn_cast<Instruction>(Worklist.back());
Worklist.pop_back();
// Look at instructions inside this loop.
// Stop when reaching PHI nodes.
// TODO: we need to follow values all over the loop, not only in this block.
if (!I || !TheLoop->contains(I) || isa<PHINode>(I))
continue;
// This is a known uniform.
Uniforms.insert(I);
// Insert all operands.
for (int i = 0, Op = I->getNumOperands(); i < Op; ++i) {
Worklist.push_back(I->getOperand(i));
}
}
}
AliasAnalysis::Location
LoopVectorizationLegality::getLoadStoreLocation(Instruction *Inst) {
if (StoreInst *Store = dyn_cast<StoreInst>(Inst))
return AA->getLocation(Store);
else if (LoadInst *Load = dyn_cast<LoadInst>(Inst))
return AA->getLocation(Load);
llvm_unreachable("Should be either load or store instruction");
}
bool
LoopVectorizationLegality::hasPossibleGlobalWriteReorder(
Value *Object,
Instruction *Inst,
AliasMultiMap& WriteObjects,
unsigned MaxByteWidth) {
AliasAnalysis::Location ThisLoc = getLoadStoreLocation(Inst);
std::vector<Instruction*>::iterator
it = WriteObjects[Object].begin(),
end = WriteObjects[Object].end();
for (; it != end; ++it) {
Instruction* I = *it;
if (I == Inst)
continue;
AliasAnalysis::Location ThatLoc = getLoadStoreLocation(I);
if (AA->alias(ThisLoc.getWithNewSize(MaxByteWidth),
ThatLoc.getWithNewSize(MaxByteWidth)))
return true;
}
return false;
}
bool LoopVectorizationLegality::canVectorizeMemory() {
typedef SmallVector<Value*, 16> ValueVector;
typedef SmallPtrSet<Value*, 16> ValueSet;
// Holds the Load and Store *instructions*.
ValueVector Loads;
ValueVector Stores;
PtrRtCheck.Pointers.clear();
PtrRtCheck.Need = false;
const bool IsAnnotatedParallel = TheLoop->isAnnotatedParallel();
// For each block.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
// Scan the BB and collect legal loads and stores.
for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
++it) {
// If this is a load, save it. If this instruction can read from memory
// but is not a load, then we quit. Notice that we don't handle function
// calls that read or write.
if (it->mayReadFromMemory()) {
LoadInst *Ld = dyn_cast<LoadInst>(it);
if (!Ld) return false;
if (!Ld->isSimple() && !IsAnnotatedParallel) {
DEBUG(dbgs() << "LV: Found a non-simple load.\n");
return false;
}
Loads.push_back(Ld);
continue;
}
// Save 'store' instructions. Abort if other instructions write to memory.
if (it->mayWriteToMemory()) {
StoreInst *St = dyn_cast<StoreInst>(it);
if (!St) return false;
if (!St->isSimple() && !IsAnnotatedParallel) {
DEBUG(dbgs() << "LV: Found a non-simple store.\n");
return false;
}
Stores.push_back(St);
}
} // next instr.
} // next block.
// Now we have two lists that hold the loads and the stores.
// Next, we find the pointers that they use.
// Check if we see any stores. If there are no stores, then we don't
// care if the pointers are *restrict*.
if (!Stores.size()) {
DEBUG(dbgs() << "LV: Found a read-only loop!\n");
return true;
}
// Holds the read and read-write *pointers* that we find. These maps hold
// unique values for pointers (so no need for multi-map).
AliasMap Reads;
AliasMap ReadWrites;
// Holds the analyzed pointers. We don't want to call GetUnderlyingObjects
// multiple times on the same object. If the ptr is accessed twice, once
// for read and once for write, it will only appear once (on the write
// list). This is okay, since we are going to check for conflicts between
// writes and between reads and writes, but not between reads and reads.
ValueSet Seen;
ValueVector::iterator I, IE;
for (I = Stores.begin(), IE = Stores.end(); I != IE; ++I) {
StoreInst *ST = cast<StoreInst>(*I);
Value* Ptr = ST->getPointerOperand();
if (isUniform(Ptr)) {
DEBUG(dbgs() << "LV: We don't allow storing to uniform addresses\n");
return false;
}
// If we did *not* see this pointer before, insert it to
// the read-write list. At this phase it is only a 'write' list.
if (Seen.insert(Ptr))
ReadWrites.insert(std::make_pair(Ptr, ST));
}
if (IsAnnotatedParallel) {
DEBUG(dbgs()
<< "LV: A loop annotated parallel, ignore memory dependency "
<< "checks.\n");
return true;
}
for (I = Loads.begin(), IE = Loads.end(); I != IE; ++I) {
LoadInst *LD = cast<LoadInst>(*I);
Value* Ptr = LD->getPointerOperand();
// If we did *not* see this pointer before, insert it to the
// read list. If we *did* see it before, then it is already in
// the read-write list. This allows us to vectorize expressions
// such as A[i] += x; Because the address of A[i] is a read-write
// pointer. This only works if the index of A[i] is consecutive.
// If the address of i is unknown (for example A[B[i]]) then we may
// read a few words, modify, and write a few words, and some of the
// words may be written to the same address.
if (Seen.insert(Ptr) || 0 == isConsecutivePtr(Ptr))
Reads.insert(std::make_pair(Ptr, LD));
}
// If we write (or read-write) to a single destination and there are no
// other reads in this loop then is it safe to vectorize.
if (ReadWrites.size() == 1 && Reads.size() == 0) {
DEBUG(dbgs() << "LV: Found a write-only loop!\n");
return true;
}
unsigned NumReadPtrs = 0;
unsigned NumWritePtrs = 0;
// Find pointers with computable bounds. We are going to use this information
// to place a runtime bound check.
bool CanDoRT = true;
AliasMap::iterator MI, ME;
for (MI = ReadWrites.begin(), ME = ReadWrites.end(); MI != ME; ++MI) {
Value *V = (*MI).first;
if (hasComputableBounds(V)) {
PtrRtCheck.insert(SE, TheLoop, V, true);
NumWritePtrs++;
DEBUG(dbgs() << "LV: Found a runtime check ptr:" << *V <<"\n");
} else {
CanDoRT = false;
break;
}
}
for (MI = Reads.begin(), ME = Reads.end(); MI != ME; ++MI) {
Value *V = (*MI).first;
if (hasComputableBounds(V)) {
PtrRtCheck.insert(SE, TheLoop, V, false);
NumReadPtrs++;
DEBUG(dbgs() << "LV: Found a runtime check ptr:" << *V <<"\n");
} else {
CanDoRT = false;
break;
}
}
// Check that we did not collect too many pointers or found a
// unsizeable pointer.
unsigned NumComparisons = (NumWritePtrs * (NumReadPtrs + NumWritePtrs - 1));
DEBUG(dbgs() << "LV: We need to compare " << NumComparisons << " ptrs.\n");
if (!CanDoRT || NumComparisons > RuntimeMemoryCheckThreshold) {
PtrRtCheck.reset();
CanDoRT = false;
}
if (CanDoRT) {
DEBUG(dbgs() << "LV: We can perform a memory runtime check if needed.\n");
}
bool NeedRTCheck = false;
// Biggest vectorized access possible, vector width * unroll factor.
// TODO: We're being very pessimistic here, find a way to know the
// real access width before getting here.
unsigned MaxByteWidth = (TTI->getRegisterBitWidth(true) / 8) *
TTI->getMaximumUnrollFactor();
// Now that the pointers are in two lists (Reads and ReadWrites), we
// can check that there are no conflicts between each of the writes and
// between the writes to the reads.
// Note that WriteObjects duplicates the stores (indexed now by underlying
// objects) to avoid pointing to elements inside ReadWrites.
// TODO: Maybe create a new type where they can interact without duplication.
AliasMultiMap WriteObjects;
ValueVector TempObjects;
// Check that the read-writes do not conflict with other read-write
// pointers.
bool AllWritesIdentified = true;
for (MI = ReadWrites.begin(), ME = ReadWrites.end(); MI != ME; ++MI) {
Value *Val = (*MI).first;
Instruction *Inst = (*MI).second;
GetUnderlyingObjects(Val, TempObjects, DL);
for (ValueVector::iterator UI=TempObjects.begin(), UE=TempObjects.end();
UI != UE; ++UI) {
if (!isIdentifiedObject(*UI)) {
DEBUG(dbgs() << "LV: Found an unidentified write ptr:"<< **UI <<"\n");
NeedRTCheck = true;
AllWritesIdentified = false;
}
// Never seen it before, can't alias.
if (WriteObjects[*UI].empty()) {
DEBUG(dbgs() << "LV: Adding Underlying value:" << **UI <<"\n");
WriteObjects[*UI].push_back(Inst);
continue;
}
// Direct alias found.
if (!AA || dyn_cast<GlobalValue>(*UI) == NULL) {
DEBUG(dbgs() << "LV: Found a possible write-write reorder:"
<< **UI <<"\n");
return false;
}
DEBUG(dbgs() << "LV: Found a conflicting global value:"
<< **UI <<"\n");
DEBUG(dbgs() << "LV: While examining store:" << *Inst <<"\n");
DEBUG(dbgs() << "LV: On value:" << *Val <<"\n");
// If global alias, make sure they do alias.
if (hasPossibleGlobalWriteReorder(*UI,
Inst,
WriteObjects,
MaxByteWidth)) {
DEBUG(dbgs() << "LV: Found a possible write-write reorder:" << **UI
<< "\n");
return false;
}
// Didn't alias, insert into map for further reference.
WriteObjects[*UI].push_back(Inst);
}
TempObjects.clear();
}
/// Check that the reads don't conflict with the read-writes.
for (MI = Reads.begin(), ME = Reads.end(); MI != ME; ++MI) {
Value *Val = (*MI).first;
GetUnderlyingObjects(Val, TempObjects, DL);
for (ValueVector::iterator UI=TempObjects.begin(), UE=TempObjects.end();
UI != UE; ++UI) {
// If all of the writes are identified then we don't care if the read
// pointer is identified or not.
if (!AllWritesIdentified && !isIdentifiedObject(*UI)) {
DEBUG(dbgs() << "LV: Found an unidentified read ptr:"<< **UI <<"\n");
NeedRTCheck = true;
}
// Never seen it before, can't alias.
if (WriteObjects[*UI].empty())
continue;
// Direct alias found.
if (!AA || dyn_cast<GlobalValue>(*UI) == NULL) {
DEBUG(dbgs() << "LV: Found a possible write-write reorder:"
<< **UI <<"\n");
return false;
}
DEBUG(dbgs() << "LV: Found a global value: "
<< **UI <<"\n");
Instruction *Inst = (*MI).second;
DEBUG(dbgs() << "LV: While examining load:" << *Inst <<"\n");
DEBUG(dbgs() << "LV: On value:" << *Val <<"\n");
// If global alias, make sure they do alias.
if (hasPossibleGlobalWriteReorder(*UI,
Inst,
WriteObjects,
MaxByteWidth)) {
DEBUG(dbgs() << "LV: Found a possible read-write reorder:" << **UI
<< "\n");
return false;
}
}
TempObjects.clear();
}
PtrRtCheck.Need = NeedRTCheck;
if (NeedRTCheck && !CanDoRT) {
DEBUG(dbgs() << "LV: We can't vectorize because we can't find " <<
"the array bounds.\n");
PtrRtCheck.reset();
return false;
}
DEBUG(dbgs() << "LV: We "<< (NeedRTCheck ? "" : "don't") <<
" need a runtime memory check.\n");
return true;
}
bool LoopVectorizationLegality::AddReductionVar(PHINode *Phi,
ReductionKind Kind) {
if (Phi->getNumIncomingValues() != 2)
return false;
// Reduction variables are only found in the loop header block.
if (Phi->getParent() != TheLoop->getHeader())
return false;
// Obtain the reduction start value from the value that comes from the loop
// preheader.
Value *RdxStart = Phi->getIncomingValueForBlock(TheLoop->getLoopPreheader());
// ExitInstruction is the single value which is used outside the loop.
// We only allow for a single reduction value to be used outside the loop.
// This includes users of the reduction, variables (which form a cycle
// which ends in the phi node).
Instruction *ExitInstruction = 0;
// Indicates that we found a binary operation in our scan.
bool FoundBinOp = false;
// Iter is our iterator. We start with the PHI node and scan for all of the
// users of this instruction. All users must be instructions that can be
// used as reduction variables (such as ADD). We may have a single
// out-of-block user. The cycle must end with the original PHI.
Instruction *Iter = Phi;
// To recognize min/max patterns formed by a icmp select sequence, we store
// the number of instruction we saw from the recognized min/max pattern,
// such that we don't stop when we see the phi has two uses (one by the select
// and one by the icmp) and to make sure we only see exactly the two
// instructions.
unsigned NumCmpSelectPatternInst = 0;
ReductionInstDesc ReduxDesc(false, 0);
// Avoid cycles in the chain.
SmallPtrSet<Instruction *, 8> VisitedInsts;
while (VisitedInsts.insert(Iter)) {
// If the instruction has no users then this is a broken
// chain and can't be a reduction variable.
if (Iter->use_empty())
return false;
// Did we find a user inside this loop already ?
bool FoundInBlockUser = false;
// Did we reach the initial PHI node already ?
bool FoundStartPHI = false;
// Is this a bin op ?
FoundBinOp |= !isa<PHINode>(Iter);
// For each of the *users* of iter.
for (Value::use_iterator it = Iter->use_begin(), e = Iter->use_end();
it != e; ++it) {
Instruction *U = cast<Instruction>(*it);
// We already know that the PHI is a user.
if (U == Phi) {
FoundStartPHI = true;
continue;
}
// Check if we found the exit user.
BasicBlock *Parent = U->getParent();
if (!TheLoop->contains(Parent)) {
// Exit if you find multiple outside users.
if (ExitInstruction != 0)
return false;
ExitInstruction = Iter;
}
// We allow in-loop PHINodes which are not the original reduction PHI
// node. If this PHI is the only user of Iter (happens in IF w/ no ELSE
// structure) then don't skip this PHI.
if (isa<PHINode>(Iter) && isa<PHINode>(U) &&
U->getParent() != TheLoop->getHeader() &&
TheLoop->contains(U) &&
Iter->hasNUsesOrMore(2))
continue;
// We can't have multiple inside users except for a combination of
// icmp/select both using the phi.
if (FoundInBlockUser && !NumCmpSelectPatternInst)
return false;
FoundInBlockUser = true;
// Any reduction instr must be of one of the allowed kinds.
ReduxDesc = isReductionInstr(U, Kind, ReduxDesc);
if (!ReduxDesc.IsReduction)
return false;
if (Kind == RK_IntegerMinMax && (isa<ICmpInst>(U) || isa<SelectInst>(U)))
++NumCmpSelectPatternInst;
if (Kind == RK_FloatMinMax && (isa<FCmpInst>(U) || isa<SelectInst>(U)))
++NumCmpSelectPatternInst;
// Reductions of instructions such as Div, and Sub is only
// possible if the LHS is the reduction variable.
if (!U->isCommutative() && !isa<PHINode>(U) && !isa<SelectInst>(U) &&
!isa<ICmpInst>(U) && !isa<FCmpInst>(U) && U->getOperand(0) != Iter)
return false;
Iter = ReduxDesc.PatternLastInst;
}
// This means we have seen one but not the other instruction of the
// pattern or more than just a select and cmp.
if ((Kind == RK_IntegerMinMax || Kind == RK_FloatMinMax) &&
NumCmpSelectPatternInst != 2)
return false;
// We found a reduction var if we have reached the original
// phi node and we only have a single instruction with out-of-loop
// users.
if (FoundStartPHI) {
// This instruction is allowed to have out-of-loop users.
AllowedExit.insert(ExitInstruction);
// Save the description of this reduction variable.
ReductionDescriptor RD(RdxStart, ExitInstruction, Kind,
ReduxDesc.MinMaxKind);
Reductions[Phi] = RD;
// We've ended the cycle. This is a reduction variable if we have an
// outside user and it has a binary op.
return FoundBinOp && ExitInstruction;
}
}
return false;
}
/// Returns true if the instruction is a Select(ICmp(X, Y), X, Y) instruction
/// pattern corresponding to a min(X, Y) or max(X, Y).
LoopVectorizationLegality::ReductionInstDesc
LoopVectorizationLegality::isMinMaxSelectCmpPattern(Instruction *I,
ReductionInstDesc &Prev) {
assert((isa<ICmpInst>(I) || isa<FCmpInst>(I) || isa<SelectInst>(I)) &&
"Expect a select instruction");
Instruction *Cmp = 0;
SelectInst *Select = 0;
// We must handle the select(cmp()) as a single instruction. Advance to the
// select.
if ((Cmp = dyn_cast<ICmpInst>(I)) || (Cmp = dyn_cast<FCmpInst>(I))) {
if (!Cmp->hasOneUse() || !(Select = dyn_cast<SelectInst>(*I->use_begin())))
return ReductionInstDesc(false, I);
return ReductionInstDesc(Select, Prev.MinMaxKind);
}
// Only handle single use cases for now.
if (!(Select = dyn_cast<SelectInst>(I)))
return ReductionInstDesc(false, I);
if (!(Cmp = dyn_cast<ICmpInst>(I->getOperand(0))) &&
!(Cmp = dyn_cast<FCmpInst>(I->getOperand(0))))
return ReductionInstDesc(false, I);
if (!Cmp->hasOneUse())
return ReductionInstDesc(false, I);
Value *CmpLeft;
Value *CmpRight;
// Look for a min/max pattern.
if (m_UMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_UIntMin);
else if (m_UMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_UIntMax);
else if (m_SMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_SIntMax);
else if (m_SMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_SIntMin);
else if (m_OrdFMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMin);
else if (m_OrdFMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMax);
else if (m_UnordFMin(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMin);
else if (m_UnordFMax(m_Value(CmpLeft), m_Value(CmpRight)).match(Select))
return ReductionInstDesc(Select, MRK_FloatMax);
return ReductionInstDesc(false, I);
}
LoopVectorizationLegality::ReductionInstDesc
LoopVectorizationLegality::isReductionInstr(Instruction *I,
ReductionKind Kind,
ReductionInstDesc &Prev) {
bool FP = I->getType()->isFloatingPointTy();
bool FastMath = (FP && I->isCommutative() && I->isAssociative());
switch (I->getOpcode()) {
default:
return ReductionInstDesc(false, I);
case Instruction::PHI:
if (FP && (Kind != RK_FloatMult && Kind != RK_FloatAdd &&
Kind != RK_FloatMinMax))
return ReductionInstDesc(false, I);
return ReductionInstDesc(I, Prev.MinMaxKind);
case Instruction::Sub:
case Instruction::Add:
return ReductionInstDesc(Kind == RK_IntegerAdd, I);
case Instruction::Mul:
return ReductionInstDesc(Kind == RK_IntegerMult, I);
case Instruction::And:
return ReductionInstDesc(Kind == RK_IntegerAnd, I);
case Instruction::Or:
return ReductionInstDesc(Kind == RK_IntegerOr, I);
case Instruction::Xor:
return ReductionInstDesc(Kind == RK_IntegerXor, I);
case Instruction::FMul:
return ReductionInstDesc(Kind == RK_FloatMult && FastMath, I);
case Instruction::FAdd:
return ReductionInstDesc(Kind == RK_FloatAdd && FastMath, I);
case Instruction::FCmp:
case Instruction::ICmp:
case Instruction::Select:
if (Kind != RK_IntegerMinMax &&
(!HasFunNoNaNAttr || Kind != RK_FloatMinMax))
return ReductionInstDesc(false, I);
return isMinMaxSelectCmpPattern(I, Prev);
}
}
LoopVectorizationLegality::InductionKind
LoopVectorizationLegality::isInductionVariable(PHINode *Phi) {
Type *PhiTy = Phi->getType();
// We only handle integer and pointer inductions variables.
if (!PhiTy->isIntegerTy() && !PhiTy->isPointerTy())
return IK_NoInduction;
// Check that the PHI is consecutive.
const SCEV *PhiScev = SE->getSCEV(Phi);
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev);
if (!AR) {
DEBUG(dbgs() << "LV: PHI is not a poly recurrence.\n");
return IK_NoInduction;
}
const SCEV *Step = AR->getStepRecurrence(*SE);
// Integer inductions need to have a stride of one.
if (PhiTy->isIntegerTy()) {
if (Step->isOne())
return IK_IntInduction;
if (Step->isAllOnesValue())
return IK_ReverseIntInduction;
return IK_NoInduction;
}
// Calculate the pointer stride and check if it is consecutive.
const SCEVConstant *C = dyn_cast<SCEVConstant>(Step);
if (!C)
return IK_NoInduction;
assert(PhiTy->isPointerTy() && "The PHI must be a pointer");
uint64_t Size = DL->getTypeAllocSize(PhiTy->getPointerElementType());
if (C->getValue()->equalsInt(Size))
return IK_PtrInduction;
else if (C->getValue()->equalsInt(0 - Size))
return IK_ReversePtrInduction;
return IK_NoInduction;
}
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::blockNeedsPredication(BasicBlock *BB) {
assert(TheLoop->contains(BB) && "Unknown block used");
// Blocks that do not dominate the latch need predication.
BasicBlock* Latch = TheLoop->getLoopLatch();
return !DT->dominates(BB, Latch);
}
bool LoopVectorizationLegality::blockCanBePredicated(BasicBlock *BB) {
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
// We don't predicate loads/stores at the moment.
if (it->mayReadFromMemory() || it->mayWriteToMemory() || it->mayThrow())
return false;
// The instructions below can trap.
switch (it->getOpcode()) {
default: continue;
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::URem:
case Instruction::SRem:
return false;
}
}
return true;
}
bool LoopVectorizationLegality::hasComputableBounds(Value *Ptr) {
const SCEV *PhiScev = SE->getSCEV(Ptr);
const SCEVAddRecExpr *AR = dyn_cast<SCEVAddRecExpr>(PhiScev);
if (!AR)
return false;
return AR->isAffine();
}
LoopVectorizationCostModel::VectorizationFactor
LoopVectorizationCostModel::selectVectorizationFactor(bool OptForSize,
unsigned UserVF) {
// Width 1 means no vectorize
VectorizationFactor Factor = { 1U, 0U };
if (OptForSize && Legal->getRuntimePointerCheck()->Need) {
DEBUG(dbgs() << "LV: Aborting. Runtime ptr check is required in Os.\n");
return Factor;
}
// Find the trip count.
unsigned TC = SE->getSmallConstantTripCount(TheLoop, TheLoop->getLoopLatch());
DEBUG(dbgs() << "LV: Found trip count:"<<TC<<"\n");
unsigned WidestType = getWidestType();
unsigned WidestRegister = TTI.getRegisterBitWidth(true);
unsigned MaxVectorSize = WidestRegister / WidestType;
DEBUG(dbgs() << "LV: The Widest type: " << 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;
}
assert(MaxVectorSize <= 32 && "Did not expect to pack so many elements"
" into one vector!");
unsigned VF = MaxVectorSize;
// If we optimize the program for size, avoid creating the tail loop.
if (OptForSize) {
// If we are unable to calculate the trip count then don't try to vectorize.
if (TC < 2) {
DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n");
return Factor;
}
// Find the maximum SIMD width that can fit within the trip count.
VF = TC % MaxVectorSize;
if (VF == 0)
VF = MaxVectorSize;
// If the trip count that we found modulo the vectorization factor is not
// zero then we require a tail.
if (VF < 2) {
DEBUG(dbgs() << "LV: Aborting. A tail loop is required in Os.\n");
return Factor;
}
}
if (UserVF != 0) {
assert(isPowerOf2_32(UserVF) && "VF needs to be a power of two");
DEBUG(dbgs() << "LV: Using user VF "<<UserVF<<".\n");
Factor.Width = UserVF;
return Factor;
}
float Cost = expectedCost(1);
unsigned Width = 1;
DEBUG(dbgs() << "LV: Scalar loop costs: "<< (int)Cost << ".\n");
for (unsigned i=2; i <= VF; 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.
float VectorCost = expectedCost(i) / (float)i;
DEBUG(dbgs() << "LV: Vector loop of width "<< i << " costs: " <<
(int)VectorCost << ".\n");
if (VectorCost < Cost) {
Cost = VectorCost;
Width = i;
}
}
DEBUG(dbgs() << "LV: Selecting VF = : "<< Width << ".\n");
Factor.Width = Width;
Factor.Cost = Width * Cost;
return Factor;
}
unsigned LoopVectorizationCostModel::getWidestType() {
unsigned MaxWidth = 8;
// For each block.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
BasicBlock *BB = *bb;
// For each instruction in the loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
Type *T = it->getType();
// Only examine Loads, Stores and PHINodes.
if (!isa<LoadInst>(it) && !isa<StoreInst>(it) && !isa<PHINode>(it))
continue;
// Examine PHI nodes that are reduction variables.
if (PHINode *PN = dyn_cast<PHINode>(it))
if (!Legal->getReductionVars()->count(PN))
continue;
// Examine the stored values.
if (StoreInst *ST = dyn_cast<StoreInst>(it))
T = ST->getValueOperand()->getType();
// Ignore loaded pointer types and stored pointer types that are not
// consecutive. However, we do want to take consecutive stores/loads of
// pointer vectors into account.
if (T->isPointerTy() && !isConsecutiveLoadOrStore(it))
continue;
MaxWidth = std::max(MaxWidth,
(unsigned)DL->getTypeSizeInBits(T->getScalarType()));
}
}
return MaxWidth;
}
unsigned
LoopVectorizationCostModel::selectUnrollFactor(bool OptForSize,
unsigned UserUF,
unsigned VF,
unsigned LoopCost) {
// -- The unroll heuristics --
// We unroll 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 unroll factor:
// 1. If the code has reductions the we unroll in order to break the cross
// iteration dependency.
// 2. If the loop is really small then we unroll in order to reduce the loop
// overhead.
// 3. We don't unroll if we think that we will spill registers to memory due
// to the increased register pressure.
// Use the user preference, unless 'auto' is selected.
if (UserUF != 0)
return UserUF;
// When we optimize for size we don't unroll.
if (OptForSize)
return 1;
// Do not unroll loops with a relatively small trip count.
unsigned TC = SE->getSmallConstantTripCount(TheLoop,
TheLoop->getLoopLatch());
if (TC > 1 && TC < TinyTripCountUnrollThreshold)
return 1;
unsigned TargetVectorRegisters = TTI.getNumberOfRegisters(true);
DEBUG(dbgs() << "LV: The target has " << TargetVectorRegisters <<
" vector registers\n");
LoopVectorizationCostModel::RegisterUsage R = calculateRegisterUsage();
// 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 unroll factor using the following formula.
// Subtract the number of loop invariants from the number of available
// registers. These registers are used by all of the unrolled 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.
unsigned UF = (TargetVectorRegisters - R.LoopInvariantRegs) / R.MaxLocalUsers;
// Clamp the unroll factor ranges to reasonable factors.
unsigned MaxUnrollSize = TTI.getMaximumUnrollFactor();
// 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);
// Clamp the calculated UF to be between the 1 and the max unroll factor
// that the target allows.
if (UF > MaxUnrollSize)
UF = MaxUnrollSize;
else if (UF < 1)
UF = 1;
if (Legal->getReductionVars()->size()) {
DEBUG(dbgs() << "LV: Unrolling because of reductions. \n");
return UF;
}
// We want to unroll tiny loops in order to reduce the loop overhead.
// We assume that the cost overhead is 1 and we use the cost model
// to estimate the cost of the loop and unroll until the cost of the
// loop overhead is about 5% of the cost of the loop.
DEBUG(dbgs() << "LV: Loop cost is "<< LoopCost <<" \n");
if (LoopCost < 20) {
DEBUG(dbgs() << "LV: Unrolling to reduce branch cost. \n");
unsigned NewUF = 20/LoopCost + 1;
return std::min(NewUF, UF);
}
DEBUG(dbgs() << "LV: Not Unrolling. \n");
return 1;
}
LoopVectorizationCostModel::RegisterUsage
LoopVectorizationCostModel::calculateRegisterUsage() {
// 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 R;
R.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 (LoopBlocksDFS::RPOIterator bb = DFS.beginRPO(),
be = DFS.endRPO(); bb != be; ++bb) {
R.NumInstructions += (*bb)->size();
for (BasicBlock::iterator it = (*bb)->begin(), e = (*bb)->end(); it != e;
++it) {
Instruction *I = it;
IdxToInstr[Index++] = I;
// Save the end location of each USE.
for (unsigned i = 0; i < I->getNumOperands(); ++i) {
Value *U = I->getOperand(i);
Instruction *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 (IntervalMap::iterator it = EndPoint.begin(), e = EndPoint.end();
it != e; ++it)
TransposeEnds[it->second].push_back(it->first);
SmallSet<Instruction*, 8> OpenIntervals;
unsigned MaxUsage = 0;
DEBUG(dbgs() << "LV(REG): Calculating max register usage:\n");
for (unsigned int i = 0; i < Index; ++i) {
Instruction *I = IdxToInstr[i];
// Ignore instructions that are never used within the loop.
if (!Ends.count(I)) continue;
// Remove all of the instructions that end at this location.
InstrList &List = TransposeEnds[i];
for (unsigned int j=0, e = List.size(); j < e; ++j)
OpenIntervals.erase(List[j]);
// Count the number of live interals.
MaxUsage = std::max(MaxUsage, OpenIntervals.size());
DEBUG(dbgs() << "LV(REG): At #" << i << " Interval # " <<
OpenIntervals.size() <<"\n");
// Add the current instruction to the list of open intervals.
OpenIntervals.insert(I);
}
unsigned Invariant = LoopInvariants.size();
DEBUG(dbgs() << "LV(REG): Found max usage: " << MaxUsage << " \n");
DEBUG(dbgs() << "LV(REG): Found invariant usage: " << Invariant << " \n");
DEBUG(dbgs() << "LV(REG): LoopSize: " << R.NumInstructions << " \n");
R.LoopInvariantRegs = Invariant;
R.MaxLocalUsers = MaxUsage;
return R;
}
unsigned LoopVectorizationCostModel::expectedCost(unsigned VF) {
unsigned Cost = 0;
// For each block.
for (Loop::block_iterator bb = TheLoop->block_begin(),
be = TheLoop->block_end(); bb != be; ++bb) {
unsigned BlockCost = 0;
BasicBlock *BB = *bb;
// For each instruction in the old loop.
for (BasicBlock::iterator it = BB->begin(), e = BB->end(); it != e; ++it) {
// Skip dbg intrinsics.
if (isa<DbgInfoIntrinsic>(it))
continue;
unsigned C = getInstructionCost(it, VF);
Cost += C;
DEBUG(dbgs() << "LV: Found an estimated cost of "<< C <<" for VF " <<
VF << " For instruction: "<< *it << "\n");
}
// We assume that if-converted blocks have a 50% chance of being executed.
// When the code is scalar then some of the blocks are avoided due to CF.
// When the code is vectorized we execute all code paths.
if (Legal->blockNeedsPredication(*bb) && VF == 1)
BlockCost /= 2;
Cost += BlockCost;
}
return Cost;
}
unsigned
LoopVectorizationCostModel::getInstructionCost(Instruction *I, unsigned VF) {
// If we know that this instruction will remain uniform, check the cost of
// the scalar version.
if (Legal->isUniformAfterVectorization(I))
VF = 1;
Type *RetTy = I->getType();
Type *VectorTy = ToVectorTy(RetTy, VF);
// TODO: We need to estimate the cost of intrinsic calls.
switch (I->getOpcode()) {
case Instruction::GetElementPtr:
// We mark this instruction as zero-cost because the cost of GEPs in
// vectorized code depends on whether the corresponding memory instruction
// is scalarized or not. Therefore, we handle GEPs with the memory
// instruction cost.
return 0;
case Instruction::Br: {
return TTI.getCFInstrCost(I->getOpcode());
}
case Instruction::PHI:
//TODO: IF-converted IFs become selects.
return 0;
case Instruction::Add:
case Instruction::FAdd:
case Instruction::Sub:
case Instruction::FSub:
case Instruction::Mul:
case Instruction::FMul:
case Instruction::UDiv:
case Instruction::SDiv:
case Instruction::FDiv:
case Instruction::URem:
case Instruction::SRem:
case Instruction::FRem:
case Instruction::Shl:
case Instruction::LShr:
case Instruction::AShr:
case Instruction::And:
case Instruction::Or:
case Instruction::Xor: {
// 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;
if (isa<ConstantInt>(I->getOperand(1)))
Op2VK = TargetTransformInfo::OK_UniformConstantValue;
return TTI.getArithmeticInstrCost(I->getOpcode(), VectorTy, Op1VK, Op2VK);
}
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);
}
case Instruction::ICmp:
case Instruction::FCmp: {
Type *ValTy = I->getOperand(0)->getType();
VectorTy = ToVectorTy(ValTy, VF);
return TTI.getCmpSelInstrCost(I->getOpcode(), VectorTy);
}
case Instruction::Store:
case Instruction::Load: {
StoreInst *SI = dyn_cast<StoreInst>(I);
LoadInst *LI = dyn_cast<LoadInst>(I);
Type *ValTy = (SI ? SI->getValueOperand()->getType() :
LI->getType());
VectorTy = ToVectorTy(ValTy, VF);
unsigned Alignment = SI ? SI->getAlignment() : LI->getAlignment();
unsigned AS = SI ? SI->getPointerAddressSpace() :
LI->getPointerAddressSpace();
Value *Ptr = SI ? SI->getPointerOperand() : LI->getPointerOperand();
// We add the cost of address computation here instead of with the gep
// instruction because only here we know whether the operation is
// scalarized.
if (VF == 1)
return TTI.getAddressComputationCost(VectorTy) +
TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
// Scalarized loads/stores.
int ConsecutiveStride = Legal->isConsecutivePtr(Ptr);
bool Reverse = ConsecutiveStride < 0;
unsigned ScalarAllocatedSize = DL->getTypeAllocSize(ValTy);
unsigned VectorElementSize = DL->getTypeStoreSize(VectorTy)/VF;
if (!ConsecutiveStride || ScalarAllocatedSize != VectorElementSize) {
unsigned Cost = 0;
// The cost of extracting from the value vector and pointer vector.
Type *PtrTy = ToVectorTy(Ptr->getType(), VF);
for (unsigned i = 0; i < VF; ++i) {
// The cost of extracting the pointer operand.
Cost += TTI.getVectorInstrCost(Instruction::ExtractElement, PtrTy, i);
// In case of STORE, the cost of ExtractElement from the vector.
// In case of LOAD, the cost of InsertElement into the returned
// vector.
Cost += TTI.getVectorInstrCost(SI ? Instruction::ExtractElement :
Instruction::InsertElement,
VectorTy, i);
}
// The cost of the scalar loads/stores.
Cost += VF * TTI.getAddressComputationCost(ValTy->getScalarType());
Cost += VF * TTI.getMemoryOpCost(I->getOpcode(), ValTy->getScalarType(),
Alignment, AS);
return Cost;
}
// Wide load/stores.
unsigned Cost = TTI.getAddressComputationCost(VectorTy);
Cost += TTI.getMemoryOpCost(I->getOpcode(), VectorTy, Alignment, AS);
if (Reverse)
Cost += TTI.getShuffleCost(TargetTransformInfo::SK_Reverse,
VectorTy, 0);
return Cost;
}
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 variable.
// The cost of these is the same as the scalar operation.
if (I->getOpcode() == Instruction::Trunc &&
Legal->isInductionVariable(I->getOperand(0)))
return TTI.getCastInstrCost(I->getOpcode(), I->getType(),
I->getOperand(0)->getType());
Type *SrcVecTy = ToVectorTy(I->getOperand(0)->getType(), VF);
return TTI.getCastInstrCost(I->getOpcode(), VectorTy, SrcVecTy);
}
case Instruction::Call: {
CallInst *CI = cast<CallInst>(I);
Intrinsic::ID ID = getIntrinsicIDForCall(CI, TLI);
assert(ID && "Not an intrinsic call!");
Type *RetTy = ToVectorTy(CI->getType(), VF);
SmallVector<Type*, 4> Tys;
for (unsigned i = 0, ie = CI->getNumArgOperands(); i != ie; ++i)
Tys.push_back(ToVectorTy(CI->getArgOperand(i)->getType(), VF));
return TTI.getIntrinsicInstrCost(ID, RetTy, Tys);
}
default: {
// We are scalarizing the instruction. Return the cost of the scalar
// instruction, plus the cost of insert and extract into vector
// elements, times the vector width.
unsigned Cost = 0;
if (!RetTy->isVoidTy() && VF != 1) {
unsigned InsCost = TTI.getVectorInstrCost(Instruction::InsertElement,
VectorTy);
unsigned ExtCost = TTI.getVectorInstrCost(Instruction::ExtractElement,
VectorTy);
// The cost of inserting the results plus extracting each one of the
// operands.
Cost += VF * (InsCost + ExtCost * I->getNumOperands());
}
// The cost of executing VF copies of the scalar instruction. This opcode
// is unknown. Assume that it is the same as 'mul'.
Cost += VF * TTI.getArithmeticInstrCost(Instruction::Mul, VectorTy);
return Cost;
}
}// end of switch.
}
Type* LoopVectorizationCostModel::ToVectorTy(Type *Scalar, unsigned VF) {
if (Scalar->isVoidTy() || VF == 1)
return Scalar;
return VectorType::get(Scalar, VF);
}
char LoopVectorize::ID = 0;
static const char lv_name[] = "Loop Vectorization";
INITIALIZE_PASS_BEGIN(LoopVectorize, LV_NAME, lv_name, false, false)
INITIALIZE_AG_DEPENDENCY(AliasAnalysis)
INITIALIZE_AG_DEPENDENCY(TargetTransformInfo)
INITIALIZE_PASS_DEPENDENCY(ScalarEvolution)
INITIALIZE_PASS_DEPENDENCY(LoopSimplify)
INITIALIZE_PASS_END(LoopVectorize, LV_NAME, lv_name, false, false)
namespace llvm {
Pass *createLoopVectorizePass() {
return new LoopVectorize();
}
}
bool LoopVectorizationCostModel::isConsecutiveLoadOrStore(Instruction *Inst) {
// Check for a store.
if (StoreInst *ST = dyn_cast<StoreInst>(Inst))
return Legal->isConsecutivePtr(ST->getPointerOperand()) != 0;
// Check for a load.
if (LoadInst *LI = dyn_cast<LoadInst>(Inst))
return Legal->isConsecutivePtr(LI->getPointerOperand()) != 0;
return false;
}