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llvm-6502/lib/Transforms/Vectorize/LoopVectorize.cpp
Bill Wendling c7594e48ed Merging r181286: 
------------------------------------------------------------------------
r181286 | arnolds | 2013-05-06 21:37:05 -0700 (Mon, 06 May 2013) | 7 lines

LoopVectorize: getConsecutiveVector must respect signed arithmetic

We were passing an i32 to ConstantInt::get where an i64 was needed and we must
also pass the sign if we pass negatives numbers. The start index passed to
getConsecutiveVector must also be signed.

Should fix PR15882.
------------------------------------------------------------------------


git-svn-id: https://llvm.org/svn/llvm-project/llvm/branches/release_33@181455 91177308-0d34-0410-b5e6-96231b3b80d8
2013-05-08 18:13:06 +00:00

3748 lines
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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/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!
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<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.
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 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());
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());
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() {
assert(TheLoop->getLoopPreheader() && "No preheader!!");
// 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;
}
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)
continue;
// 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 (!AllowedExit.count(it))
//Check that all of the users of the loop are inside the BB.
for (Value::use_iterator I = it->use_begin(), E = it->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 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;
}
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