
Consider IR such as this: for.body: %iv = phi i64 [ 0, %entry ], [ %iv.next, %for.body ] %accum = phi i32 [ 0, %entry ], [ %add, %for.body ] %gep.a = getelementptr i8, ptr %a, i64 %iv %load.a = load i8, ptr %gep.a, align 1 %ext.a = zext i8 %load.a to i32 %add = add i32 %ext.a, %accum %iv.next = add i64 %iv, 1 %exitcond.not = icmp eq i64 %iv.next, 1025 br i1 %exitcond.not, label %for.exit, label %for.body Conceptually we can vectorise this using partial reductions too, although the current loop vectoriser implementation requires the accumulation of a multiply. For AArch64 this is easily done with a udot or sdot with an identity operand, i.e. a vector of (i16 1). In order to do this I had to teach getScaledReductions that the accumulated value may come from a unary op, hence there is only one extension to consider. Similarly, I updated the vplan and AArch64 TTI cost model to understand the possible unary op. --------- Co-authored-by: Matt Devereau <matthew.devereau@arm.com>
227 lines
9.1 KiB
C++
227 lines
9.1 KiB
C++
//===- VPRecipeBuilder.h - Helper class to build recipes --------*- C++ -*-===//
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//
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// Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions.
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// See https://llvm.org/LICENSE.txt for license information.
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// SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
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//
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//===----------------------------------------------------------------------===//
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#ifndef LLVM_TRANSFORMS_VECTORIZE_VPRECIPEBUILDER_H
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#define LLVM_TRANSFORMS_VECTORIZE_VPRECIPEBUILDER_H
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#include "LoopVectorizationPlanner.h"
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#include "VPlan.h"
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#include "llvm/ADT/DenseMap.h"
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#include "llvm/Analysis/ScalarEvolutionExpressions.h"
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namespace llvm {
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class LoopVectorizationLegality;
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class LoopVectorizationCostModel;
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class TargetLibraryInfo;
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class TargetTransformInfo;
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struct HistogramInfo;
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struct VFRange;
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/// A chain of instructions that form a partial reduction.
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/// Designed to match either:
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/// reduction_bin_op (extend (A), accumulator), or
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/// reduction_bin_op (bin_op (extend (A), (extend (B))), accumulator).
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struct PartialReductionChain {
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PartialReductionChain(Instruction *Reduction, Instruction *ExtendA,
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Instruction *ExtendB, Instruction *ExtendUser)
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: Reduction(Reduction), ExtendA(ExtendA), ExtendB(ExtendB),
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ExtendUser(ExtendUser) {}
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/// The top-level binary operation that forms the reduction to a scalar
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/// after the loop body.
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Instruction *Reduction;
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/// The extension of each of the inner binary operation's operands.
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Instruction *ExtendA;
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Instruction *ExtendB;
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/// The user of the extends that is then reduced.
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Instruction *ExtendUser;
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};
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/// Helper class to create VPRecipies from IR instructions.
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class VPRecipeBuilder {
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/// The VPlan new recipes are added to.
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VPlan &Plan;
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/// The loop that we evaluate.
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Loop *OrigLoop;
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/// Target Library Info.
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const TargetLibraryInfo *TLI;
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// Target Transform Info.
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const TargetTransformInfo *TTI;
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/// The legality analysis.
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LoopVectorizationLegality *Legal;
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/// The profitablity analysis.
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LoopVectorizationCostModel &CM;
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PredicatedScalarEvolution &PSE;
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VPBuilder &Builder;
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/// The mask of each VPBB, generated earlier and used for predicating recipes
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/// in VPBB.
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/// TODO: remove by applying predication when generating the masks.
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DenseMap<VPBasicBlock *, VPValue *> &BlockMaskCache;
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// VPlan construction support: Hold a mapping from ingredients to
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// their recipe.
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DenseMap<Instruction *, VPRecipeBase *> Ingredient2Recipe;
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/// Cross-iteration reduction & first-order recurrence phis for which we need
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/// to add the incoming value from the backedge after all recipes have been
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/// created.
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SmallVector<VPHeaderPHIRecipe *, 4> PhisToFix;
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/// A mapping of partial reduction exit instructions to their scaling factor.
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DenseMap<const Instruction *, unsigned> ScaledReductionMap;
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/// Loop versioning instance for getting noalias metadata guaranteed by
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/// runtime checks.
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LoopVersioning *LVer;
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/// Check if \p I can be widened at the start of \p Range and possibly
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/// decrease the range such that the returned value holds for the entire \p
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/// Range. The function should not be called for memory instructions or calls.
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bool shouldWiden(Instruction *I, VFRange &Range) const;
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/// Check if the load or store instruction \p I should widened for \p
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/// Range.Start and potentially masked. Such instructions are handled by a
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/// recipe that takes an additional VPInstruction for the mask.
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VPWidenMemoryRecipe *tryToWidenMemory(Instruction *I,
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ArrayRef<VPValue *> Operands,
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VFRange &Range);
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/// Check if an induction recipe should be constructed for \p Phi. If so build
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/// and return it. If not, return null.
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VPHeaderPHIRecipe *tryToOptimizeInductionPHI(PHINode *Phi,
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ArrayRef<VPValue *> Operands,
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VFRange &Range);
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/// Optimize the special case where the operand of \p I is a constant integer
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/// induction variable.
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VPWidenIntOrFpInductionRecipe *
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tryToOptimizeInductionTruncate(TruncInst *I, ArrayRef<VPValue *> Operands,
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VFRange &Range);
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/// Handle call instructions. If \p CI can be widened for \p Range.Start,
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/// return a new VPWidenCallRecipe or VPWidenIntrinsicRecipe. Range.End may be
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/// decreased to ensure same decision from \p Range.Start to \p Range.End.
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VPSingleDefRecipe *tryToWidenCall(CallInst *CI, ArrayRef<VPValue *> Operands,
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VFRange &Range);
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/// Check if \p I has an opcode that can be widened and return a VPWidenRecipe
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/// if it can. The function should only be called if the cost-model indicates
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/// that widening should be performed.
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VPWidenRecipe *tryToWiden(Instruction *I, ArrayRef<VPValue *> Operands);
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/// Makes Histogram count operations safe for vectorization, by emitting a
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/// llvm.experimental.vector.histogram.add intrinsic in place of the
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/// Load + Add|Sub + Store operations that perform the histogram in the
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/// original scalar loop.
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VPHistogramRecipe *tryToWidenHistogram(const HistogramInfo *HI,
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ArrayRef<VPValue *> Operands);
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/// Examines reduction operations to see if the target can use a cheaper
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/// operation with a wider per-iteration input VF and narrower PHI VF.
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/// Each element within Chains is a pair with a struct containing reduction
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/// information and the scaling factor between the number of elements in
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/// the input and output.
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/// Recursively calls itself to identify chained scaled reductions.
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/// Returns true if this invocation added an entry to Chains, otherwise false.
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/// i.e. returns false in the case that a subcall adds an entry to Chains,
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/// but the top-level call does not.
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bool getScaledReductions(
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Instruction *PHI, Instruction *RdxExitInstr, VFRange &Range,
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SmallVectorImpl<std::pair<PartialReductionChain, unsigned>> &Chains);
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public:
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VPRecipeBuilder(VPlan &Plan, Loop *OrigLoop, const TargetLibraryInfo *TLI,
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const TargetTransformInfo *TTI,
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LoopVectorizationLegality *Legal,
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LoopVectorizationCostModel &CM,
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PredicatedScalarEvolution &PSE, VPBuilder &Builder,
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DenseMap<VPBasicBlock *, VPValue *> &BlockMaskCache,
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LoopVersioning *LVer)
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: Plan(Plan), OrigLoop(OrigLoop), TLI(TLI), TTI(TTI), Legal(Legal),
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CM(CM), PSE(PSE), Builder(Builder), BlockMaskCache(BlockMaskCache),
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LVer(LVer) {}
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std::optional<unsigned> getScalingForReduction(const Instruction *ExitInst) {
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auto It = ScaledReductionMap.find(ExitInst);
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return It == ScaledReductionMap.end() ? std::nullopt
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: std::make_optional(It->second);
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}
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/// Find all possible partial reductions in the loop and track all of those
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/// that are valid so recipes can be formed later.
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void collectScaledReductions(VFRange &Range);
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/// Create and return a widened recipe for \p R if one can be created within
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/// the given VF \p Range.
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VPRecipeBase *tryToCreateWidenRecipe(VPSingleDefRecipe *R, VFRange &Range);
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/// Create and return a partial reduction recipe for a reduction instruction
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/// along with binary operation and reduction phi operands.
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VPRecipeBase *tryToCreatePartialReduction(Instruction *Reduction,
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ArrayRef<VPValue *> Operands,
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unsigned ScaleFactor);
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/// Set the recipe created for given ingredient.
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void setRecipe(Instruction *I, VPRecipeBase *R) {
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assert(!Ingredient2Recipe.contains(I) &&
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"Cannot reset recipe for instruction.");
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Ingredient2Recipe[I] = R;
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}
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/// Returns the *entry* mask for block \p VPBB or null if the mask is
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/// all-true.
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VPValue *getBlockInMask(VPBasicBlock *VPBB) const {
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return BlockMaskCache.lookup(VPBB);
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}
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/// Return the recipe created for given ingredient.
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VPRecipeBase *getRecipe(Instruction *I) {
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assert(Ingredient2Recipe.count(I) &&
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"Recording this ingredients recipe was not requested");
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assert(Ingredient2Recipe[I] != nullptr &&
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"Ingredient doesn't have a recipe");
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return Ingredient2Recipe[I];
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}
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/// Build a VPReplicationRecipe for \p I using \p Operands. If it is
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/// predicated, add the mask as last operand. Range.End may be decreased to
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/// ensure same recipe behavior from \p Range.Start to \p Range.End.
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VPReplicateRecipe *handleReplication(Instruction *I,
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ArrayRef<VPValue *> Operands,
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VFRange &Range);
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VPValue *getVPValueOrAddLiveIn(Value *V) {
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if (auto *I = dyn_cast<Instruction>(V)) {
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if (auto *R = Ingredient2Recipe.lookup(I))
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return R->getVPSingleValue();
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}
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return Plan.getOrAddLiveIn(V);
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}
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void updateBlockMaskCache(DenseMap<VPValue *, VPValue *> &Old2New) {
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for (auto &[_, V] : BlockMaskCache) {
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if (auto *New = Old2New.lookup(V)) {
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V->replaceAllUsesWith(New);
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V = New;
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}
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}
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}
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};
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} // end namespace llvm
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#endif // LLVM_TRANSFORMS_VECTORIZE_VPRECIPEBUILDER_H
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