| File: | src/gnu/usr.bin/clang/libLLVM/../../../llvm/llvm/lib/Transforms/Vectorize/LoopVectorize.cpp |
| Warning: | line 8981, column 5 Called C++ object pointer is null |
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| 1 | //===- LoopVectorize.cpp - A Loop Vectorizer ------------------------------===// |
| 2 | // |
| 3 | // Part of the LLVM Project, under the Apache License v2.0 with LLVM Exceptions. |
| 4 | // See https://llvm.org/LICENSE.txt for license information. |
| 5 | // SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception |
| 6 | // |
| 7 | //===----------------------------------------------------------------------===// |
| 8 | // |
| 9 | // This is the LLVM loop vectorizer. This pass modifies 'vectorizable' loops |
| 10 | // and generates target-independent LLVM-IR. |
| 11 | // The vectorizer uses the TargetTransformInfo analysis to estimate the costs |
| 12 | // of instructions in order to estimate the profitability of vectorization. |
| 13 | // |
| 14 | // The loop vectorizer combines consecutive loop iterations into a single |
| 15 | // 'wide' iteration. After this transformation the index is incremented |
| 16 | // by the SIMD vector width, and not by one. |
| 17 | // |
| 18 | // This pass has three parts: |
| 19 | // 1. The main loop pass that drives the different parts. |
| 20 | // 2. LoopVectorizationLegality - A unit that checks for the legality |
| 21 | // of the vectorization. |
| 22 | // 3. InnerLoopVectorizer - A unit that performs the actual |
| 23 | // widening of instructions. |
| 24 | // 4. LoopVectorizationCostModel - A unit that checks for the profitability |
| 25 | // of vectorization. It decides on the optimal vector width, which |
| 26 | // can be one, if vectorization is not profitable. |
| 27 | // |
| 28 | // There is a development effort going on to migrate loop vectorizer to the |
| 29 | // VPlan infrastructure and to introduce outer loop vectorization support (see |
| 30 | // docs/Proposal/VectorizationPlan.rst and |
| 31 | // http://lists.llvm.org/pipermail/llvm-dev/2017-December/119523.html). For this |
| 32 | // purpose, we temporarily introduced the VPlan-native vectorization path: an |
| 33 | // alternative vectorization path that is natively implemented on top of the |
| 34 | // VPlan infrastructure. See EnableVPlanNativePath for enabling. |
| 35 | // |
| 36 | //===----------------------------------------------------------------------===// |
| 37 | // |
| 38 | // The reduction-variable vectorization is based on the paper: |
| 39 | // D. Nuzman and R. Henderson. Multi-platform Auto-vectorization. |
| 40 | // |
| 41 | // Variable uniformity checks are inspired by: |
| 42 | // Karrenberg, R. and Hack, S. Whole Function Vectorization. |
| 43 | // |
| 44 | // The interleaved access vectorization is based on the paper: |
| 45 | // Dorit Nuzman, Ira Rosen and Ayal Zaks. Auto-Vectorization of Interleaved |
| 46 | // Data for SIMD |
| 47 | // |
| 48 | // Other ideas/concepts are from: |
| 49 | // A. Zaks and D. Nuzman. Autovectorization in GCC-two years later. |
| 50 | // |
| 51 | // S. Maleki, Y. Gao, M. Garzaran, T. Wong and D. Padua. An Evaluation of |
| 52 | // Vectorizing Compilers. |
| 53 | // |
| 54 | //===----------------------------------------------------------------------===// |
| 55 | |
| 56 | #include "llvm/Transforms/Vectorize/LoopVectorize.h" |
| 57 | #include "LoopVectorizationPlanner.h" |
| 58 | #include "VPRecipeBuilder.h" |
| 59 | #include "VPlan.h" |
| 60 | #include "VPlanHCFGBuilder.h" |
| 61 | #include "VPlanPredicator.h" |
| 62 | #include "VPlanTransforms.h" |
| 63 | #include "llvm/ADT/APInt.h" |
| 64 | #include "llvm/ADT/ArrayRef.h" |
| 65 | #include "llvm/ADT/DenseMap.h" |
| 66 | #include "llvm/ADT/DenseMapInfo.h" |
| 67 | #include "llvm/ADT/Hashing.h" |
| 68 | #include "llvm/ADT/MapVector.h" |
| 69 | #include "llvm/ADT/None.h" |
| 70 | #include "llvm/ADT/Optional.h" |
| 71 | #include "llvm/ADT/STLExtras.h" |
| 72 | #include "llvm/ADT/SmallPtrSet.h" |
| 73 | #include "llvm/ADT/SmallSet.h" |
| 74 | #include "llvm/ADT/SmallVector.h" |
| 75 | #include "llvm/ADT/Statistic.h" |
| 76 | #include "llvm/ADT/StringRef.h" |
| 77 | #include "llvm/ADT/Twine.h" |
| 78 | #include "llvm/ADT/iterator_range.h" |
| 79 | #include "llvm/Analysis/AssumptionCache.h" |
| 80 | #include "llvm/Analysis/BasicAliasAnalysis.h" |
| 81 | #include "llvm/Analysis/BlockFrequencyInfo.h" |
| 82 | #include "llvm/Analysis/CFG.h" |
| 83 | #include "llvm/Analysis/CodeMetrics.h" |
| 84 | #include "llvm/Analysis/DemandedBits.h" |
| 85 | #include "llvm/Analysis/GlobalsModRef.h" |
| 86 | #include "llvm/Analysis/LoopAccessAnalysis.h" |
| 87 | #include "llvm/Analysis/LoopAnalysisManager.h" |
| 88 | #include "llvm/Analysis/LoopInfo.h" |
| 89 | #include "llvm/Analysis/LoopIterator.h" |
| 90 | #include "llvm/Analysis/MemorySSA.h" |
| 91 | #include "llvm/Analysis/OptimizationRemarkEmitter.h" |
| 92 | #include "llvm/Analysis/ProfileSummaryInfo.h" |
| 93 | #include "llvm/Analysis/ScalarEvolution.h" |
| 94 | #include "llvm/Analysis/ScalarEvolutionExpressions.h" |
| 95 | #include "llvm/Analysis/TargetLibraryInfo.h" |
| 96 | #include "llvm/Analysis/TargetTransformInfo.h" |
| 97 | #include "llvm/Analysis/VectorUtils.h" |
| 98 | #include "llvm/IR/Attributes.h" |
| 99 | #include "llvm/IR/BasicBlock.h" |
| 100 | #include "llvm/IR/CFG.h" |
| 101 | #include "llvm/IR/Constant.h" |
| 102 | #include "llvm/IR/Constants.h" |
| 103 | #include "llvm/IR/DataLayout.h" |
| 104 | #include "llvm/IR/DebugInfoMetadata.h" |
| 105 | #include "llvm/IR/DebugLoc.h" |
| 106 | #include "llvm/IR/DerivedTypes.h" |
| 107 | #include "llvm/IR/DiagnosticInfo.h" |
| 108 | #include "llvm/IR/Dominators.h" |
| 109 | #include "llvm/IR/Function.h" |
| 110 | #include "llvm/IR/IRBuilder.h" |
| 111 | #include "llvm/IR/InstrTypes.h" |
| 112 | #include "llvm/IR/Instruction.h" |
| 113 | #include "llvm/IR/Instructions.h" |
| 114 | #include "llvm/IR/IntrinsicInst.h" |
| 115 | #include "llvm/IR/Intrinsics.h" |
| 116 | #include "llvm/IR/LLVMContext.h" |
| 117 | #include "llvm/IR/Metadata.h" |
| 118 | #include "llvm/IR/Module.h" |
| 119 | #include "llvm/IR/Operator.h" |
| 120 | #include "llvm/IR/PatternMatch.h" |
| 121 | #include "llvm/IR/Type.h" |
| 122 | #include "llvm/IR/Use.h" |
| 123 | #include "llvm/IR/User.h" |
| 124 | #include "llvm/IR/Value.h" |
| 125 | #include "llvm/IR/ValueHandle.h" |
| 126 | #include "llvm/IR/Verifier.h" |
| 127 | #include "llvm/InitializePasses.h" |
| 128 | #include "llvm/Pass.h" |
| 129 | #include "llvm/Support/Casting.h" |
| 130 | #include "llvm/Support/CommandLine.h" |
| 131 | #include "llvm/Support/Compiler.h" |
| 132 | #include "llvm/Support/Debug.h" |
| 133 | #include "llvm/Support/ErrorHandling.h" |
| 134 | #include "llvm/Support/InstructionCost.h" |
| 135 | #include "llvm/Support/MathExtras.h" |
| 136 | #include "llvm/Support/raw_ostream.h" |
| 137 | #include "llvm/Transforms/Utils/BasicBlockUtils.h" |
| 138 | #include "llvm/Transforms/Utils/InjectTLIMappings.h" |
| 139 | #include "llvm/Transforms/Utils/LoopSimplify.h" |
| 140 | #include "llvm/Transforms/Utils/LoopUtils.h" |
| 141 | #include "llvm/Transforms/Utils/LoopVersioning.h" |
| 142 | #include "llvm/Transforms/Utils/ScalarEvolutionExpander.h" |
| 143 | #include "llvm/Transforms/Utils/SizeOpts.h" |
| 144 | #include "llvm/Transforms/Vectorize/LoopVectorizationLegality.h" |
| 145 | #include <algorithm> |
| 146 | #include <cassert> |
| 147 | #include <cstdint> |
| 148 | #include <cstdlib> |
| 149 | #include <functional> |
| 150 | #include <iterator> |
| 151 | #include <limits> |
| 152 | #include <memory> |
| 153 | #include <string> |
| 154 | #include <tuple> |
| 155 | #include <utility> |
| 156 | |
| 157 | using namespace llvm; |
| 158 | |
| 159 | #define LV_NAME"loop-vectorize" "loop-vectorize" |
| 160 | #define DEBUG_TYPE"loop-vectorize" LV_NAME"loop-vectorize" |
| 161 | |
| 162 | #ifndef NDEBUG1 |
| 163 | const char VerboseDebug[] = DEBUG_TYPE"loop-vectorize" "-verbose"; |
| 164 | #endif |
| 165 | |
| 166 | /// @{ |
| 167 | /// Metadata attribute names |
| 168 | const char LLVMLoopVectorizeFollowupAll[] = "llvm.loop.vectorize.followup_all"; |
| 169 | const char LLVMLoopVectorizeFollowupVectorized[] = |
| 170 | "llvm.loop.vectorize.followup_vectorized"; |
| 171 | const char LLVMLoopVectorizeFollowupEpilogue[] = |
| 172 | "llvm.loop.vectorize.followup_epilogue"; |
| 173 | /// @} |
| 174 | |
| 175 | STATISTIC(LoopsVectorized, "Number of loops vectorized")static llvm::Statistic LoopsVectorized = {"loop-vectorize", "LoopsVectorized" , "Number of loops vectorized"}; |
| 176 | STATISTIC(LoopsAnalyzed, "Number of loops analyzed for vectorization")static llvm::Statistic LoopsAnalyzed = {"loop-vectorize", "LoopsAnalyzed" , "Number of loops analyzed for vectorization"}; |
| 177 | STATISTIC(LoopsEpilogueVectorized, "Number of epilogues vectorized")static llvm::Statistic LoopsEpilogueVectorized = {"loop-vectorize" , "LoopsEpilogueVectorized", "Number of epilogues vectorized" }; |
| 178 | |
| 179 | static cl::opt<bool> EnableEpilogueVectorization( |
| 180 | "enable-epilogue-vectorization", cl::init(true), cl::Hidden, |
| 181 | cl::desc("Enable vectorization of epilogue loops.")); |
| 182 | |
| 183 | static cl::opt<unsigned> EpilogueVectorizationForceVF( |
| 184 | "epilogue-vectorization-force-VF", cl::init(1), cl::Hidden, |
| 185 | cl::desc("When epilogue vectorization is enabled, and a value greater than " |
| 186 | "1 is specified, forces the given VF for all applicable epilogue " |
| 187 | "loops.")); |
| 188 | |
| 189 | static cl::opt<unsigned> EpilogueVectorizationMinVF( |
| 190 | "epilogue-vectorization-minimum-VF", cl::init(16), cl::Hidden, |
| 191 | cl::desc("Only loops with vectorization factor equal to or larger than " |
| 192 | "the specified value are considered for epilogue vectorization.")); |
| 193 | |
| 194 | /// Loops with a known constant trip count below this number are vectorized only |
| 195 | /// if no scalar iteration overheads are incurred. |
| 196 | static cl::opt<unsigned> TinyTripCountVectorThreshold( |
| 197 | "vectorizer-min-trip-count", cl::init(16), cl::Hidden, |
| 198 | cl::desc("Loops with a constant trip count that is smaller than this " |
| 199 | "value are vectorized only if no scalar iteration overheads " |
| 200 | "are incurred.")); |
| 201 | |
| 202 | static cl::opt<unsigned> PragmaVectorizeMemoryCheckThreshold( |
| 203 | "pragma-vectorize-memory-check-threshold", cl::init(128), cl::Hidden, |
| 204 | cl::desc("The maximum allowed number of runtime memory checks with a " |
| 205 | "vectorize(enable) pragma.")); |
| 206 | |
| 207 | // Option prefer-predicate-over-epilogue indicates that an epilogue is undesired, |
| 208 | // that predication is preferred, and this lists all options. I.e., the |
| 209 | // vectorizer will try to fold the tail-loop (epilogue) into the vector body |
| 210 | // and predicate the instructions accordingly. If tail-folding fails, there are |
| 211 | // different fallback strategies depending on these values: |
| 212 | namespace PreferPredicateTy { |
| 213 | enum Option { |
| 214 | ScalarEpilogue = 0, |
| 215 | PredicateElseScalarEpilogue, |
| 216 | PredicateOrDontVectorize |
| 217 | }; |
| 218 | } // namespace PreferPredicateTy |
| 219 | |
| 220 | static cl::opt<PreferPredicateTy::Option> PreferPredicateOverEpilogue( |
| 221 | "prefer-predicate-over-epilogue", |
| 222 | cl::init(PreferPredicateTy::ScalarEpilogue), |
| 223 | cl::Hidden, |
| 224 | cl::desc("Tail-folding and predication preferences over creating a scalar " |
| 225 | "epilogue loop."), |
| 226 | cl::values(clEnumValN(PreferPredicateTy::ScalarEpilogue,llvm::cl::OptionEnumValue { "scalar-epilogue", int(PreferPredicateTy ::ScalarEpilogue), "Don't tail-predicate loops, create scalar epilogue" } |
| 227 | "scalar-epilogue",llvm::cl::OptionEnumValue { "scalar-epilogue", int(PreferPredicateTy ::ScalarEpilogue), "Don't tail-predicate loops, create scalar epilogue" } |
| 228 | "Don't tail-predicate loops, create scalar epilogue")llvm::cl::OptionEnumValue { "scalar-epilogue", int(PreferPredicateTy ::ScalarEpilogue), "Don't tail-predicate loops, create scalar epilogue" }, |
| 229 | clEnumValN(PreferPredicateTy::PredicateElseScalarEpilogue,llvm::cl::OptionEnumValue { "predicate-else-scalar-epilogue", int(PreferPredicateTy::PredicateElseScalarEpilogue), "prefer tail-folding, create scalar epilogue if tail " "folding fails." } |
| 230 | "predicate-else-scalar-epilogue",llvm::cl::OptionEnumValue { "predicate-else-scalar-epilogue", int(PreferPredicateTy::PredicateElseScalarEpilogue), "prefer tail-folding, create scalar epilogue if tail " "folding fails." } |
| 231 | "prefer tail-folding, create scalar epilogue if tail "llvm::cl::OptionEnumValue { "predicate-else-scalar-epilogue", int(PreferPredicateTy::PredicateElseScalarEpilogue), "prefer tail-folding, create scalar epilogue if tail " "folding fails." } |
| 232 | "folding fails.")llvm::cl::OptionEnumValue { "predicate-else-scalar-epilogue", int(PreferPredicateTy::PredicateElseScalarEpilogue), "prefer tail-folding, create scalar epilogue if tail " "folding fails." }, |
| 233 | clEnumValN(PreferPredicateTy::PredicateOrDontVectorize,llvm::cl::OptionEnumValue { "predicate-dont-vectorize", int(PreferPredicateTy ::PredicateOrDontVectorize), "prefers tail-folding, don't attempt vectorization if " "tail-folding fails." } |
| 234 | "predicate-dont-vectorize",llvm::cl::OptionEnumValue { "predicate-dont-vectorize", int(PreferPredicateTy ::PredicateOrDontVectorize), "prefers tail-folding, don't attempt vectorization if " "tail-folding fails." } |
| 235 | "prefers tail-folding, don't attempt vectorization if "llvm::cl::OptionEnumValue { "predicate-dont-vectorize", int(PreferPredicateTy ::PredicateOrDontVectorize), "prefers tail-folding, don't attempt vectorization if " "tail-folding fails." } |
| 236 | "tail-folding fails.")llvm::cl::OptionEnumValue { "predicate-dont-vectorize", int(PreferPredicateTy ::PredicateOrDontVectorize), "prefers tail-folding, don't attempt vectorization if " "tail-folding fails." })); |
| 237 | |
| 238 | static cl::opt<bool> MaximizeBandwidth( |
| 239 | "vectorizer-maximize-bandwidth", cl::init(false), cl::Hidden, |
| 240 | cl::desc("Maximize bandwidth when selecting vectorization factor which " |
| 241 | "will be determined by the smallest type in loop.")); |
| 242 | |
| 243 | static cl::opt<bool> EnableInterleavedMemAccesses( |
| 244 | "enable-interleaved-mem-accesses", cl::init(false), cl::Hidden, |
| 245 | cl::desc("Enable vectorization on interleaved memory accesses in a loop")); |
| 246 | |
| 247 | /// An interleave-group may need masking if it resides in a block that needs |
| 248 | /// predication, or in order to mask away gaps. |
| 249 | static cl::opt<bool> EnableMaskedInterleavedMemAccesses( |
| 250 | "enable-masked-interleaved-mem-accesses", cl::init(false), cl::Hidden, |
| 251 | cl::desc("Enable vectorization on masked interleaved memory accesses in a loop")); |
| 252 | |
| 253 | static cl::opt<unsigned> TinyTripCountInterleaveThreshold( |
| 254 | "tiny-trip-count-interleave-threshold", cl::init(128), cl::Hidden, |
| 255 | cl::desc("We don't interleave loops with a estimated constant trip count " |
| 256 | "below this number")); |
| 257 | |
| 258 | static cl::opt<unsigned> ForceTargetNumScalarRegs( |
| 259 | "force-target-num-scalar-regs", cl::init(0), cl::Hidden, |
| 260 | cl::desc("A flag that overrides the target's number of scalar registers.")); |
| 261 | |
| 262 | static cl::opt<unsigned> ForceTargetNumVectorRegs( |
| 263 | "force-target-num-vector-regs", cl::init(0), cl::Hidden, |
| 264 | cl::desc("A flag that overrides the target's number of vector registers.")); |
| 265 | |
| 266 | static cl::opt<unsigned> ForceTargetMaxScalarInterleaveFactor( |
| 267 | "force-target-max-scalar-interleave", cl::init(0), cl::Hidden, |
| 268 | cl::desc("A flag that overrides the target's max interleave factor for " |
| 269 | "scalar loops.")); |
| 270 | |
| 271 | static cl::opt<unsigned> ForceTargetMaxVectorInterleaveFactor( |
| 272 | "force-target-max-vector-interleave", cl::init(0), cl::Hidden, |
| 273 | cl::desc("A flag that overrides the target's max interleave factor for " |
| 274 | "vectorized loops.")); |
| 275 | |
| 276 | static cl::opt<unsigned> ForceTargetInstructionCost( |
| 277 | "force-target-instruction-cost", cl::init(0), cl::Hidden, |
| 278 | cl::desc("A flag that overrides the target's expected cost for " |
| 279 | "an instruction to a single constant value. Mostly " |
| 280 | "useful for getting consistent testing.")); |
| 281 | |
| 282 | static cl::opt<bool> ForceTargetSupportsScalableVectors( |
| 283 | "force-target-supports-scalable-vectors", cl::init(false), cl::Hidden, |
| 284 | cl::desc( |
| 285 | "Pretend that scalable vectors are supported, even if the target does " |
| 286 | "not support them. This flag should only be used for testing.")); |
| 287 | |
| 288 | static cl::opt<unsigned> SmallLoopCost( |
| 289 | "small-loop-cost", cl::init(20), cl::Hidden, |
| 290 | cl::desc( |
| 291 | "The cost of a loop that is considered 'small' by the interleaver.")); |
| 292 | |
| 293 | static cl::opt<bool> LoopVectorizeWithBlockFrequency( |
| 294 | "loop-vectorize-with-block-frequency", cl::init(true), cl::Hidden, |
| 295 | cl::desc("Enable the use of the block frequency analysis to access PGO " |
| 296 | "heuristics minimizing code growth in cold regions and being more " |
| 297 | "aggressive in hot regions.")); |
| 298 | |
| 299 | // Runtime interleave loops for load/store throughput. |
| 300 | static cl::opt<bool> EnableLoadStoreRuntimeInterleave( |
| 301 | "enable-loadstore-runtime-interleave", cl::init(true), cl::Hidden, |
| 302 | cl::desc( |
| 303 | "Enable runtime interleaving until load/store ports are saturated")); |
| 304 | |
| 305 | /// Interleave small loops with scalar reductions. |
| 306 | static cl::opt<bool> InterleaveSmallLoopScalarReduction( |
| 307 | "interleave-small-loop-scalar-reduction", cl::init(false), cl::Hidden, |
| 308 | cl::desc("Enable interleaving for loops with small iteration counts that " |
| 309 | "contain scalar reductions to expose ILP.")); |
| 310 | |
| 311 | /// The number of stores in a loop that are allowed to need predication. |
| 312 | static cl::opt<unsigned> NumberOfStoresToPredicate( |
| 313 | "vectorize-num-stores-pred", cl::init(1), cl::Hidden, |
| 314 | cl::desc("Max number of stores to be predicated behind an if.")); |
| 315 | |
| 316 | static cl::opt<bool> EnableIndVarRegisterHeur( |
| 317 | "enable-ind-var-reg-heur", cl::init(true), cl::Hidden, |
| 318 | cl::desc("Count the induction variable only once when interleaving")); |
| 319 | |
| 320 | static cl::opt<bool> EnableCondStoresVectorization( |
| 321 | "enable-cond-stores-vec", cl::init(true), cl::Hidden, |
| 322 | cl::desc("Enable if predication of stores during vectorization.")); |
| 323 | |
| 324 | static cl::opt<unsigned> MaxNestedScalarReductionIC( |
| 325 | "max-nested-scalar-reduction-interleave", cl::init(2), cl::Hidden, |
| 326 | cl::desc("The maximum interleave count to use when interleaving a scalar " |
| 327 | "reduction in a nested loop.")); |
| 328 | |
| 329 | static cl::opt<bool> |
| 330 | PreferInLoopReductions("prefer-inloop-reductions", cl::init(false), |
| 331 | cl::Hidden, |
| 332 | cl::desc("Prefer in-loop vector reductions, " |
| 333 | "overriding the targets preference.")); |
| 334 | |
| 335 | cl::opt<bool> EnableStrictReductions( |
| 336 | "enable-strict-reductions", cl::init(false), cl::Hidden, |
| 337 | cl::desc("Enable the vectorisation of loops with in-order (strict) " |
| 338 | "FP reductions")); |
| 339 | |
| 340 | static cl::opt<bool> PreferPredicatedReductionSelect( |
| 341 | "prefer-predicated-reduction-select", cl::init(false), cl::Hidden, |
| 342 | cl::desc( |
| 343 | "Prefer predicating a reduction operation over an after loop select.")); |
| 344 | |
| 345 | cl::opt<bool> EnableVPlanNativePath( |
| 346 | "enable-vplan-native-path", cl::init(false), cl::Hidden, |
| 347 | cl::desc("Enable VPlan-native vectorization path with " |
| 348 | "support for outer loop vectorization.")); |
| 349 | |
| 350 | // FIXME: Remove this switch once we have divergence analysis. Currently we |
| 351 | // assume divergent non-backedge branches when this switch is true. |
| 352 | cl::opt<bool> EnableVPlanPredication( |
| 353 | "enable-vplan-predication", cl::init(false), cl::Hidden, |
| 354 | cl::desc("Enable VPlan-native vectorization path predicator with " |
| 355 | "support for outer loop vectorization.")); |
| 356 | |
| 357 | // This flag enables the stress testing of the VPlan H-CFG construction in the |
| 358 | // VPlan-native vectorization path. It must be used in conjuction with |
| 359 | // -enable-vplan-native-path. -vplan-verify-hcfg can also be used to enable the |
| 360 | // verification of the H-CFGs built. |
| 361 | static cl::opt<bool> VPlanBuildStressTest( |
| 362 | "vplan-build-stress-test", cl::init(false), cl::Hidden, |
| 363 | cl::desc( |
| 364 | "Build VPlan for every supported loop nest in the function and bail " |
| 365 | "out right after the build (stress test the VPlan H-CFG construction " |
| 366 | "in the VPlan-native vectorization path).")); |
| 367 | |
| 368 | cl::opt<bool> llvm::EnableLoopInterleaving( |
| 369 | "interleave-loops", cl::init(true), cl::Hidden, |
| 370 | cl::desc("Enable loop interleaving in Loop vectorization passes")); |
| 371 | cl::opt<bool> llvm::EnableLoopVectorization( |
| 372 | "vectorize-loops", cl::init(true), cl::Hidden, |
| 373 | cl::desc("Run the Loop vectorization passes")); |
| 374 | |
| 375 | cl::opt<bool> PrintVPlansInDotFormat( |
| 376 | "vplan-print-in-dot-format", cl::init(false), cl::Hidden, |
| 377 | cl::desc("Use dot format instead of plain text when dumping VPlans")); |
| 378 | |
| 379 | /// A helper function that returns true if the given type is irregular. The |
| 380 | /// type is irregular if its allocated size doesn't equal the store size of an |
| 381 | /// element of the corresponding vector type. |
| 382 | static bool hasIrregularType(Type *Ty, const DataLayout &DL) { |
| 383 | // Determine if an array of N elements of type Ty is "bitcast compatible" |
| 384 | // with a <N x Ty> vector. |
| 385 | // This is only true if there is no padding between the array elements. |
| 386 | return DL.getTypeAllocSizeInBits(Ty) != DL.getTypeSizeInBits(Ty); |
| 387 | } |
| 388 | |
| 389 | /// A helper function that returns the reciprocal of the block probability of |
| 390 | /// predicated blocks. If we return X, we are assuming the predicated block |
| 391 | /// will execute once for every X iterations of the loop header. |
| 392 | /// |
| 393 | /// TODO: We should use actual block probability here, if available. Currently, |
| 394 | /// we always assume predicated blocks have a 50% chance of executing. |
| 395 | static unsigned getReciprocalPredBlockProb() { return 2; } |
| 396 | |
| 397 | /// A helper function that returns an integer or floating-point constant with |
| 398 | /// value C. |
| 399 | static Constant *getSignedIntOrFpConstant(Type *Ty, int64_t C) { |
| 400 | return Ty->isIntegerTy() ? ConstantInt::getSigned(Ty, C) |
| 401 | : ConstantFP::get(Ty, C); |
| 402 | } |
| 403 | |
| 404 | /// Returns "best known" trip count for the specified loop \p L as defined by |
| 405 | /// the following procedure: |
| 406 | /// 1) Returns exact trip count if it is known. |
| 407 | /// 2) Returns expected trip count according to profile data if any. |
| 408 | /// 3) Returns upper bound estimate if it is known. |
| 409 | /// 4) Returns None if all of the above failed. |
| 410 | static Optional<unsigned> getSmallBestKnownTC(ScalarEvolution &SE, Loop *L) { |
| 411 | // Check if exact trip count is known. |
| 412 | if (unsigned ExpectedTC = SE.getSmallConstantTripCount(L)) |
| 413 | return ExpectedTC; |
| 414 | |
| 415 | // Check if there is an expected trip count available from profile data. |
| 416 | if (LoopVectorizeWithBlockFrequency) |
| 417 | if (auto EstimatedTC = getLoopEstimatedTripCount(L)) |
| 418 | return EstimatedTC; |
| 419 | |
| 420 | // Check if upper bound estimate is known. |
| 421 | if (unsigned ExpectedTC = SE.getSmallConstantMaxTripCount(L)) |
| 422 | return ExpectedTC; |
| 423 | |
| 424 | return None; |
| 425 | } |
| 426 | |
| 427 | // Forward declare GeneratedRTChecks. |
| 428 | class GeneratedRTChecks; |
| 429 | |
| 430 | namespace llvm { |
| 431 | |
| 432 | /// InnerLoopVectorizer vectorizes loops which contain only one basic |
| 433 | /// block to a specified vectorization factor (VF). |
| 434 | /// This class performs the widening of scalars into vectors, or multiple |
| 435 | /// scalars. This class also implements the following features: |
| 436 | /// * It inserts an epilogue loop for handling loops that don't have iteration |
| 437 | /// counts that are known to be a multiple of the vectorization factor. |
| 438 | /// * It handles the code generation for reduction variables. |
| 439 | /// * Scalarization (implementation using scalars) of un-vectorizable |
| 440 | /// instructions. |
| 441 | /// InnerLoopVectorizer does not perform any vectorization-legality |
| 442 | /// checks, and relies on the caller to check for the different legality |
| 443 | /// aspects. The InnerLoopVectorizer relies on the |
| 444 | /// LoopVectorizationLegality class to provide information about the induction |
| 445 | /// and reduction variables that were found to a given vectorization factor. |
| 446 | class InnerLoopVectorizer { |
| 447 | public: |
| 448 | InnerLoopVectorizer(Loop *OrigLoop, PredicatedScalarEvolution &PSE, |
| 449 | LoopInfo *LI, DominatorTree *DT, |
| 450 | const TargetLibraryInfo *TLI, |
| 451 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 452 | OptimizationRemarkEmitter *ORE, ElementCount VecWidth, |
| 453 | unsigned UnrollFactor, LoopVectorizationLegality *LVL, |
| 454 | LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, |
| 455 | ProfileSummaryInfo *PSI, GeneratedRTChecks &RTChecks) |
| 456 | : OrigLoop(OrigLoop), PSE(PSE), LI(LI), DT(DT), TLI(TLI), TTI(TTI), |
| 457 | AC(AC), ORE(ORE), VF(VecWidth), UF(UnrollFactor), |
| 458 | Builder(PSE.getSE()->getContext()), Legal(LVL), Cost(CM), BFI(BFI), |
| 459 | PSI(PSI), RTChecks(RTChecks) { |
| 460 | // Query this against the original loop and save it here because the profile |
| 461 | // of the original loop header may change as the transformation happens. |
| 462 | OptForSizeBasedOnProfile = llvm::shouldOptimizeForSize( |
| 463 | OrigLoop->getHeader(), PSI, BFI, PGSOQueryType::IRPass); |
| 464 | } |
| 465 | |
| 466 | virtual ~InnerLoopVectorizer() = default; |
| 467 | |
| 468 | /// Create a new empty loop that will contain vectorized instructions later |
| 469 | /// on, while the old loop will be used as the scalar remainder. Control flow |
| 470 | /// is generated around the vectorized (and scalar epilogue) loops consisting |
| 471 | /// of various checks and bypasses. Return the pre-header block of the new |
| 472 | /// loop. |
| 473 | /// In the case of epilogue vectorization, this function is overriden to |
| 474 | /// handle the more complex control flow around the loops. |
| 475 | virtual BasicBlock *createVectorizedLoopSkeleton(); |
| 476 | |
| 477 | /// Widen a single instruction within the innermost loop. |
| 478 | void widenInstruction(Instruction &I, VPValue *Def, VPUser &Operands, |
| 479 | VPTransformState &State); |
| 480 | |
| 481 | /// Widen a single call instruction within the innermost loop. |
| 482 | void widenCallInstruction(CallInst &I, VPValue *Def, VPUser &ArgOperands, |
| 483 | VPTransformState &State); |
| 484 | |
| 485 | /// Widen a single select instruction within the innermost loop. |
| 486 | void widenSelectInstruction(SelectInst &I, VPValue *VPDef, VPUser &Operands, |
| 487 | bool InvariantCond, VPTransformState &State); |
| 488 | |
| 489 | /// Fix the vectorized code, taking care of header phi's, live-outs, and more. |
| 490 | void fixVectorizedLoop(VPTransformState &State); |
| 491 | |
| 492 | // Return true if any runtime check is added. |
| 493 | bool areSafetyChecksAdded() { return AddedSafetyChecks; } |
| 494 | |
| 495 | /// A type for vectorized values in the new loop. Each value from the |
| 496 | /// original loop, when vectorized, is represented by UF vector values in the |
| 497 | /// new unrolled loop, where UF is the unroll factor. |
| 498 | using VectorParts = SmallVector<Value *, 2>; |
| 499 | |
| 500 | /// Vectorize a single GetElementPtrInst based on information gathered and |
| 501 | /// decisions taken during planning. |
| 502 | void widenGEP(GetElementPtrInst *GEP, VPValue *VPDef, VPUser &Indices, |
| 503 | unsigned UF, ElementCount VF, bool IsPtrLoopInvariant, |
| 504 | SmallBitVector &IsIndexLoopInvariant, VPTransformState &State); |
| 505 | |
| 506 | /// Vectorize a single first-order recurrence or pointer induction PHINode in |
| 507 | /// a block. This method handles the induction variable canonicalization. It |
| 508 | /// supports both VF = 1 for unrolled loops and arbitrary length vectors. |
| 509 | void widenPHIInstruction(Instruction *PN, VPWidenPHIRecipe *PhiR, |
| 510 | VPTransformState &State); |
| 511 | |
| 512 | /// A helper function to scalarize a single Instruction in the innermost loop. |
| 513 | /// Generates a sequence of scalar instances for each lane between \p MinLane |
| 514 | /// and \p MaxLane, times each part between \p MinPart and \p MaxPart, |
| 515 | /// inclusive. Uses the VPValue operands from \p Operands instead of \p |
| 516 | /// Instr's operands. |
| 517 | void scalarizeInstruction(Instruction *Instr, VPValue *Def, VPUser &Operands, |
| 518 | const VPIteration &Instance, bool IfPredicateInstr, |
| 519 | VPTransformState &State); |
| 520 | |
| 521 | /// Widen an integer or floating-point induction variable \p IV. If \p Trunc |
| 522 | /// is provided, the integer induction variable will first be truncated to |
| 523 | /// the corresponding type. |
| 524 | void widenIntOrFpInduction(PHINode *IV, Value *Start, TruncInst *Trunc, |
| 525 | VPValue *Def, VPValue *CastDef, |
| 526 | VPTransformState &State); |
| 527 | |
| 528 | /// Construct the vector value of a scalarized value \p V one lane at a time. |
| 529 | void packScalarIntoVectorValue(VPValue *Def, const VPIteration &Instance, |
| 530 | VPTransformState &State); |
| 531 | |
| 532 | /// Try to vectorize interleaved access group \p Group with the base address |
| 533 | /// given in \p Addr, optionally masking the vector operations if \p |
| 534 | /// BlockInMask is non-null. Use \p State to translate given VPValues to IR |
| 535 | /// values in the vectorized loop. |
| 536 | void vectorizeInterleaveGroup(const InterleaveGroup<Instruction> *Group, |
| 537 | ArrayRef<VPValue *> VPDefs, |
| 538 | VPTransformState &State, VPValue *Addr, |
| 539 | ArrayRef<VPValue *> StoredValues, |
| 540 | VPValue *BlockInMask = nullptr); |
| 541 | |
| 542 | /// Vectorize Load and Store instructions with the base address given in \p |
| 543 | /// Addr, optionally masking the vector operations if \p BlockInMask is |
| 544 | /// non-null. Use \p State to translate given VPValues to IR values in the |
| 545 | /// vectorized loop. |
| 546 | void vectorizeMemoryInstruction(Instruction *Instr, VPTransformState &State, |
| 547 | VPValue *Def, VPValue *Addr, |
| 548 | VPValue *StoredValue, VPValue *BlockInMask); |
| 549 | |
| 550 | /// Set the debug location in the builder \p Ptr using the debug location in |
| 551 | /// \p V. If \p Ptr is None then it uses the class member's Builder. |
| 552 | void setDebugLocFromInst(const Value *V, |
| 553 | Optional<IRBuilder<> *> CustomBuilder = None); |
| 554 | |
| 555 | /// Fix the non-induction PHIs in the OrigPHIsToFix vector. |
| 556 | void fixNonInductionPHIs(VPTransformState &State); |
| 557 | |
| 558 | /// Returns true if the reordering of FP operations is not allowed, but we are |
| 559 | /// able to vectorize with strict in-order reductions for the given RdxDesc. |
| 560 | bool useOrderedReductions(RecurrenceDescriptor &RdxDesc); |
| 561 | |
| 562 | /// Create a broadcast instruction. This method generates a broadcast |
| 563 | /// instruction (shuffle) for loop invariant values and for the induction |
| 564 | /// value. If this is the induction variable then we extend it to N, N+1, ... |
| 565 | /// this is needed because each iteration in the loop corresponds to a SIMD |
| 566 | /// element. |
| 567 | virtual Value *getBroadcastInstrs(Value *V); |
| 568 | |
| 569 | protected: |
| 570 | friend class LoopVectorizationPlanner; |
| 571 | |
| 572 | /// A small list of PHINodes. |
| 573 | using PhiVector = SmallVector<PHINode *, 4>; |
| 574 | |
| 575 | /// A type for scalarized values in the new loop. Each value from the |
| 576 | /// original loop, when scalarized, is represented by UF x VF scalar values |
| 577 | /// in the new unrolled loop, where UF is the unroll factor and VF is the |
| 578 | /// vectorization factor. |
| 579 | using ScalarParts = SmallVector<SmallVector<Value *, 4>, 2>; |
| 580 | |
| 581 | /// Set up the values of the IVs correctly when exiting the vector loop. |
| 582 | void fixupIVUsers(PHINode *OrigPhi, const InductionDescriptor &II, |
| 583 | Value *CountRoundDown, Value *EndValue, |
| 584 | BasicBlock *MiddleBlock); |
| 585 | |
| 586 | /// Create a new induction variable inside L. |
| 587 | PHINode *createInductionVariable(Loop *L, Value *Start, Value *End, |
| 588 | Value *Step, Instruction *DL); |
| 589 | |
| 590 | /// Handle all cross-iteration phis in the header. |
| 591 | void fixCrossIterationPHIs(VPTransformState &State); |
| 592 | |
| 593 | /// Fix a first-order recurrence. This is the second phase of vectorizing |
| 594 | /// this phi node. |
| 595 | void fixFirstOrderRecurrence(VPWidenPHIRecipe *PhiR, VPTransformState &State); |
| 596 | |
| 597 | /// Fix a reduction cross-iteration phi. This is the second phase of |
| 598 | /// vectorizing this phi node. |
| 599 | void fixReduction(VPReductionPHIRecipe *Phi, VPTransformState &State); |
| 600 | |
| 601 | /// Clear NSW/NUW flags from reduction instructions if necessary. |
| 602 | void clearReductionWrapFlags(const RecurrenceDescriptor &RdxDesc, |
| 603 | VPTransformState &State); |
| 604 | |
| 605 | /// Fixup the LCSSA phi nodes in the unique exit block. This simply |
| 606 | /// means we need to add the appropriate incoming value from the middle |
| 607 | /// block as exiting edges from the scalar epilogue loop (if present) are |
| 608 | /// already in place, and we exit the vector loop exclusively to the middle |
| 609 | /// block. |
| 610 | void fixLCSSAPHIs(VPTransformState &State); |
| 611 | |
| 612 | /// Iteratively sink the scalarized operands of a predicated instruction into |
| 613 | /// the block that was created for it. |
| 614 | void sinkScalarOperands(Instruction *PredInst); |
| 615 | |
| 616 | /// Shrinks vector element sizes to the smallest bitwidth they can be legally |
| 617 | /// represented as. |
| 618 | void truncateToMinimalBitwidths(VPTransformState &State); |
| 619 | |
| 620 | /// This function adds |
| 621 | /// (StartIdx * Step, (StartIdx + 1) * Step, (StartIdx + 2) * Step, ...) |
| 622 | /// to each vector element of Val. The sequence starts at StartIndex. |
| 623 | /// \p Opcode is relevant for FP induction variable. |
| 624 | virtual Value *getStepVector(Value *Val, int StartIdx, Value *Step, |
| 625 | Instruction::BinaryOps Opcode = |
| 626 | Instruction::BinaryOpsEnd); |
| 627 | |
| 628 | /// Compute scalar induction steps. \p ScalarIV is the scalar induction |
| 629 | /// variable on which to base the steps, \p Step is the size of the step, and |
| 630 | /// \p EntryVal is the value from the original loop that maps to the steps. |
| 631 | /// Note that \p EntryVal doesn't have to be an induction variable - it |
| 632 | /// can also be a truncate instruction. |
| 633 | void buildScalarSteps(Value *ScalarIV, Value *Step, Instruction *EntryVal, |
| 634 | const InductionDescriptor &ID, VPValue *Def, |
| 635 | VPValue *CastDef, VPTransformState &State); |
| 636 | |
| 637 | /// Create a vector induction phi node based on an existing scalar one. \p |
| 638 | /// EntryVal is the value from the original loop that maps to the vector phi |
| 639 | /// node, and \p Step is the loop-invariant step. If \p EntryVal is a |
| 640 | /// truncate instruction, instead of widening the original IV, we widen a |
| 641 | /// version of the IV truncated to \p EntryVal's type. |
| 642 | void createVectorIntOrFpInductionPHI(const InductionDescriptor &II, |
| 643 | Value *Step, Value *Start, |
| 644 | Instruction *EntryVal, VPValue *Def, |
| 645 | VPValue *CastDef, |
| 646 | VPTransformState &State); |
| 647 | |
| 648 | /// Returns true if an instruction \p I should be scalarized instead of |
| 649 | /// vectorized for the chosen vectorization factor. |
| 650 | bool shouldScalarizeInstruction(Instruction *I) const; |
| 651 | |
| 652 | /// Returns true if we should generate a scalar version of \p IV. |
| 653 | bool needsScalarInduction(Instruction *IV) const; |
| 654 | |
| 655 | /// If there is a cast involved in the induction variable \p ID, which should |
| 656 | /// be ignored in the vectorized loop body, this function records the |
| 657 | /// VectorLoopValue of the respective Phi also as the VectorLoopValue of the |
| 658 | /// cast. We had already proved that the casted Phi is equal to the uncasted |
| 659 | /// Phi in the vectorized loop (under a runtime guard), and therefore |
| 660 | /// there is no need to vectorize the cast - the same value can be used in the |
| 661 | /// vector loop for both the Phi and the cast. |
| 662 | /// If \p VectorLoopValue is a scalarized value, \p Lane is also specified, |
| 663 | /// Otherwise, \p VectorLoopValue is a widened/vectorized value. |
| 664 | /// |
| 665 | /// \p EntryVal is the value from the original loop that maps to the vector |
| 666 | /// phi node and is used to distinguish what is the IV currently being |
| 667 | /// processed - original one (if \p EntryVal is a phi corresponding to the |
| 668 | /// original IV) or the "newly-created" one based on the proof mentioned above |
| 669 | /// (see also buildScalarSteps() and createVectorIntOrFPInductionPHI()). In the |
| 670 | /// latter case \p EntryVal is a TruncInst and we must not record anything for |
| 671 | /// that IV, but it's error-prone to expect callers of this routine to care |
| 672 | /// about that, hence this explicit parameter. |
| 673 | void recordVectorLoopValueForInductionCast( |
| 674 | const InductionDescriptor &ID, const Instruction *EntryVal, |
| 675 | Value *VectorLoopValue, VPValue *CastDef, VPTransformState &State, |
| 676 | unsigned Part, unsigned Lane = UINT_MAX(2147483647 *2U +1U)); |
| 677 | |
| 678 | /// Generate a shuffle sequence that will reverse the vector Vec. |
| 679 | virtual Value *reverseVector(Value *Vec); |
| 680 | |
| 681 | /// Returns (and creates if needed) the original loop trip count. |
| 682 | Value *getOrCreateTripCount(Loop *NewLoop); |
| 683 | |
| 684 | /// Returns (and creates if needed) the trip count of the widened loop. |
| 685 | Value *getOrCreateVectorTripCount(Loop *NewLoop); |
| 686 | |
| 687 | /// Returns a bitcasted value to the requested vector type. |
| 688 | /// Also handles bitcasts of vector<float> <-> vector<pointer> types. |
| 689 | Value *createBitOrPointerCast(Value *V, VectorType *DstVTy, |
| 690 | const DataLayout &DL); |
| 691 | |
| 692 | /// Emit a bypass check to see if the vector trip count is zero, including if |
| 693 | /// it overflows. |
| 694 | void emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass); |
| 695 | |
| 696 | /// Emit a bypass check to see if all of the SCEV assumptions we've |
| 697 | /// had to make are correct. Returns the block containing the checks or |
| 698 | /// nullptr if no checks have been added. |
| 699 | BasicBlock *emitSCEVChecks(Loop *L, BasicBlock *Bypass); |
| 700 | |
| 701 | /// Emit bypass checks to check any memory assumptions we may have made. |
| 702 | /// Returns the block containing the checks or nullptr if no checks have been |
| 703 | /// added. |
| 704 | BasicBlock *emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass); |
| 705 | |
| 706 | /// Compute the transformed value of Index at offset StartValue using step |
| 707 | /// StepValue. |
| 708 | /// For integer induction, returns StartValue + Index * StepValue. |
| 709 | /// For pointer induction, returns StartValue[Index * StepValue]. |
| 710 | /// FIXME: The newly created binary instructions should contain nsw/nuw |
| 711 | /// flags, which can be found from the original scalar operations. |
| 712 | Value *emitTransformedIndex(IRBuilder<> &B, Value *Index, ScalarEvolution *SE, |
| 713 | const DataLayout &DL, |
| 714 | const InductionDescriptor &ID) const; |
| 715 | |
| 716 | /// Emit basic blocks (prefixed with \p Prefix) for the iteration check, |
| 717 | /// vector loop preheader, middle block and scalar preheader. Also |
| 718 | /// allocate a loop object for the new vector loop and return it. |
| 719 | Loop *createVectorLoopSkeleton(StringRef Prefix); |
| 720 | |
| 721 | /// Create new phi nodes for the induction variables to resume iteration count |
| 722 | /// in the scalar epilogue, from where the vectorized loop left off (given by |
| 723 | /// \p VectorTripCount). |
| 724 | /// In cases where the loop skeleton is more complicated (eg. epilogue |
| 725 | /// vectorization) and the resume values can come from an additional bypass |
| 726 | /// block, the \p AdditionalBypass pair provides information about the bypass |
| 727 | /// block and the end value on the edge from bypass to this loop. |
| 728 | void createInductionResumeValues( |
| 729 | Loop *L, Value *VectorTripCount, |
| 730 | std::pair<BasicBlock *, Value *> AdditionalBypass = {nullptr, nullptr}); |
| 731 | |
| 732 | /// Complete the loop skeleton by adding debug MDs, creating appropriate |
| 733 | /// conditional branches in the middle block, preparing the builder and |
| 734 | /// running the verifier. Take in the vector loop \p L as argument, and return |
| 735 | /// the preheader of the completed vector loop. |
| 736 | BasicBlock *completeLoopSkeleton(Loop *L, MDNode *OrigLoopID); |
| 737 | |
| 738 | /// Add additional metadata to \p To that was not present on \p Orig. |
| 739 | /// |
| 740 | /// Currently this is used to add the noalias annotations based on the |
| 741 | /// inserted memchecks. Use this for instructions that are *cloned* into the |
| 742 | /// vector loop. |
| 743 | void addNewMetadata(Instruction *To, const Instruction *Orig); |
| 744 | |
| 745 | /// Add metadata from one instruction to another. |
| 746 | /// |
| 747 | /// This includes both the original MDs from \p From and additional ones (\see |
| 748 | /// addNewMetadata). Use this for *newly created* instructions in the vector |
| 749 | /// loop. |
| 750 | void addMetadata(Instruction *To, Instruction *From); |
| 751 | |
| 752 | /// Similar to the previous function but it adds the metadata to a |
| 753 | /// vector of instructions. |
| 754 | void addMetadata(ArrayRef<Value *> To, Instruction *From); |
| 755 | |
| 756 | /// Allow subclasses to override and print debug traces before/after vplan |
| 757 | /// execution, when trace information is requested. |
| 758 | virtual void printDebugTracesAtStart(){}; |
| 759 | virtual void printDebugTracesAtEnd(){}; |
| 760 | |
| 761 | /// The original loop. |
| 762 | Loop *OrigLoop; |
| 763 | |
| 764 | /// A wrapper around ScalarEvolution used to add runtime SCEV checks. Applies |
| 765 | /// dynamic knowledge to simplify SCEV expressions and converts them to a |
| 766 | /// more usable form. |
| 767 | PredicatedScalarEvolution &PSE; |
| 768 | |
| 769 | /// Loop Info. |
| 770 | LoopInfo *LI; |
| 771 | |
| 772 | /// Dominator Tree. |
| 773 | DominatorTree *DT; |
| 774 | |
| 775 | /// Alias Analysis. |
| 776 | AAResults *AA; |
| 777 | |
| 778 | /// Target Library Info. |
| 779 | const TargetLibraryInfo *TLI; |
| 780 | |
| 781 | /// Target Transform Info. |
| 782 | const TargetTransformInfo *TTI; |
| 783 | |
| 784 | /// Assumption Cache. |
| 785 | AssumptionCache *AC; |
| 786 | |
| 787 | /// Interface to emit optimization remarks. |
| 788 | OptimizationRemarkEmitter *ORE; |
| 789 | |
| 790 | /// LoopVersioning. It's only set up (non-null) if memchecks were |
| 791 | /// used. |
| 792 | /// |
| 793 | /// This is currently only used to add no-alias metadata based on the |
| 794 | /// memchecks. The actually versioning is performed manually. |
| 795 | std::unique_ptr<LoopVersioning> LVer; |
| 796 | |
| 797 | /// The vectorization SIMD factor to use. Each vector will have this many |
| 798 | /// vector elements. |
| 799 | ElementCount VF; |
| 800 | |
| 801 | /// The vectorization unroll factor to use. Each scalar is vectorized to this |
| 802 | /// many different vector instructions. |
| 803 | unsigned UF; |
| 804 | |
| 805 | /// The builder that we use |
| 806 | IRBuilder<> Builder; |
| 807 | |
| 808 | // --- Vectorization state --- |
| 809 | |
| 810 | /// The vector-loop preheader. |
| 811 | BasicBlock *LoopVectorPreHeader; |
| 812 | |
| 813 | /// The scalar-loop preheader. |
| 814 | BasicBlock *LoopScalarPreHeader; |
| 815 | |
| 816 | /// Middle Block between the vector and the scalar. |
| 817 | BasicBlock *LoopMiddleBlock; |
| 818 | |
| 819 | /// The unique ExitBlock of the scalar loop if one exists. Note that |
| 820 | /// there can be multiple exiting edges reaching this block. |
| 821 | BasicBlock *LoopExitBlock; |
| 822 | |
| 823 | /// The vector loop body. |
| 824 | BasicBlock *LoopVectorBody; |
| 825 | |
| 826 | /// The scalar loop body. |
| 827 | BasicBlock *LoopScalarBody; |
| 828 | |
| 829 | /// A list of all bypass blocks. The first block is the entry of the loop. |
| 830 | SmallVector<BasicBlock *, 4> LoopBypassBlocks; |
| 831 | |
| 832 | /// The new Induction variable which was added to the new block. |
| 833 | PHINode *Induction = nullptr; |
| 834 | |
| 835 | /// The induction variable of the old basic block. |
| 836 | PHINode *OldInduction = nullptr; |
| 837 | |
| 838 | /// Store instructions that were predicated. |
| 839 | SmallVector<Instruction *, 4> PredicatedInstructions; |
| 840 | |
| 841 | /// Trip count of the original loop. |
| 842 | Value *TripCount = nullptr; |
| 843 | |
| 844 | /// Trip count of the widened loop (TripCount - TripCount % (VF*UF)) |
| 845 | Value *VectorTripCount = nullptr; |
| 846 | |
| 847 | /// The legality analysis. |
| 848 | LoopVectorizationLegality *Legal; |
| 849 | |
| 850 | /// The profitablity analysis. |
| 851 | LoopVectorizationCostModel *Cost; |
| 852 | |
| 853 | // Record whether runtime checks are added. |
| 854 | bool AddedSafetyChecks = false; |
| 855 | |
| 856 | // Holds the end values for each induction variable. We save the end values |
| 857 | // so we can later fix-up the external users of the induction variables. |
| 858 | DenseMap<PHINode *, Value *> IVEndValues; |
| 859 | |
| 860 | // Vector of original scalar PHIs whose corresponding widened PHIs need to be |
| 861 | // fixed up at the end of vector code generation. |
| 862 | SmallVector<PHINode *, 8> OrigPHIsToFix; |
| 863 | |
| 864 | /// BFI and PSI are used to check for profile guided size optimizations. |
| 865 | BlockFrequencyInfo *BFI; |
| 866 | ProfileSummaryInfo *PSI; |
| 867 | |
| 868 | // Whether this loop should be optimized for size based on profile guided size |
| 869 | // optimizatios. |
| 870 | bool OptForSizeBasedOnProfile; |
| 871 | |
| 872 | /// Structure to hold information about generated runtime checks, responsible |
| 873 | /// for cleaning the checks, if vectorization turns out unprofitable. |
| 874 | GeneratedRTChecks &RTChecks; |
| 875 | }; |
| 876 | |
| 877 | class InnerLoopUnroller : public InnerLoopVectorizer { |
| 878 | public: |
| 879 | InnerLoopUnroller(Loop *OrigLoop, PredicatedScalarEvolution &PSE, |
| 880 | LoopInfo *LI, DominatorTree *DT, |
| 881 | const TargetLibraryInfo *TLI, |
| 882 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 883 | OptimizationRemarkEmitter *ORE, unsigned UnrollFactor, |
| 884 | LoopVectorizationLegality *LVL, |
| 885 | LoopVectorizationCostModel *CM, BlockFrequencyInfo *BFI, |
| 886 | ProfileSummaryInfo *PSI, GeneratedRTChecks &Check) |
| 887 | : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 888 | ElementCount::getFixed(1), UnrollFactor, LVL, CM, |
| 889 | BFI, PSI, Check) {} |
| 890 | |
| 891 | private: |
| 892 | Value *getBroadcastInstrs(Value *V) override; |
| 893 | Value *getStepVector(Value *Val, int StartIdx, Value *Step, |
| 894 | Instruction::BinaryOps Opcode = |
| 895 | Instruction::BinaryOpsEnd) override; |
| 896 | Value *reverseVector(Value *Vec) override; |
| 897 | }; |
| 898 | |
| 899 | /// Encapsulate information regarding vectorization of a loop and its epilogue. |
| 900 | /// This information is meant to be updated and used across two stages of |
| 901 | /// epilogue vectorization. |
| 902 | struct EpilogueLoopVectorizationInfo { |
| 903 | ElementCount MainLoopVF = ElementCount::getFixed(0); |
| 904 | unsigned MainLoopUF = 0; |
| 905 | ElementCount EpilogueVF = ElementCount::getFixed(0); |
| 906 | unsigned EpilogueUF = 0; |
| 907 | BasicBlock *MainLoopIterationCountCheck = nullptr; |
| 908 | BasicBlock *EpilogueIterationCountCheck = nullptr; |
| 909 | BasicBlock *SCEVSafetyCheck = nullptr; |
| 910 | BasicBlock *MemSafetyCheck = nullptr; |
| 911 | Value *TripCount = nullptr; |
| 912 | Value *VectorTripCount = nullptr; |
| 913 | |
| 914 | EpilogueLoopVectorizationInfo(unsigned MVF, unsigned MUF, unsigned EVF, |
| 915 | unsigned EUF) |
| 916 | : MainLoopVF(ElementCount::getFixed(MVF)), MainLoopUF(MUF), |
| 917 | EpilogueVF(ElementCount::getFixed(EVF)), EpilogueUF(EUF) { |
| 918 | assert(EUF == 1 &&((void)0) |
| 919 | "A high UF for the epilogue loop is likely not beneficial.")((void)0); |
| 920 | } |
| 921 | }; |
| 922 | |
| 923 | /// An extension of the inner loop vectorizer that creates a skeleton for a |
| 924 | /// vectorized loop that has its epilogue (residual) also vectorized. |
| 925 | /// The idea is to run the vplan on a given loop twice, firstly to setup the |
| 926 | /// skeleton and vectorize the main loop, and secondly to complete the skeleton |
| 927 | /// from the first step and vectorize the epilogue. This is achieved by |
| 928 | /// deriving two concrete strategy classes from this base class and invoking |
| 929 | /// them in succession from the loop vectorizer planner. |
| 930 | class InnerLoopAndEpilogueVectorizer : public InnerLoopVectorizer { |
| 931 | public: |
| 932 | InnerLoopAndEpilogueVectorizer( |
| 933 | Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 934 | DominatorTree *DT, const TargetLibraryInfo *TLI, |
| 935 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 936 | OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI, |
| 937 | LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM, |
| 938 | BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI, |
| 939 | GeneratedRTChecks &Checks) |
| 940 | : InnerLoopVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 941 | EPI.MainLoopVF, EPI.MainLoopUF, LVL, CM, BFI, PSI, |
| 942 | Checks), |
| 943 | EPI(EPI) {} |
| 944 | |
| 945 | // Override this function to handle the more complex control flow around the |
| 946 | // three loops. |
| 947 | BasicBlock *createVectorizedLoopSkeleton() final override { |
| 948 | return createEpilogueVectorizedLoopSkeleton(); |
| 949 | } |
| 950 | |
| 951 | /// The interface for creating a vectorized skeleton using one of two |
| 952 | /// different strategies, each corresponding to one execution of the vplan |
| 953 | /// as described above. |
| 954 | virtual BasicBlock *createEpilogueVectorizedLoopSkeleton() = 0; |
| 955 | |
| 956 | /// Holds and updates state information required to vectorize the main loop |
| 957 | /// and its epilogue in two separate passes. This setup helps us avoid |
| 958 | /// regenerating and recomputing runtime safety checks. It also helps us to |
| 959 | /// shorten the iteration-count-check path length for the cases where the |
| 960 | /// iteration count of the loop is so small that the main vector loop is |
| 961 | /// completely skipped. |
| 962 | EpilogueLoopVectorizationInfo &EPI; |
| 963 | }; |
| 964 | |
| 965 | /// A specialized derived class of inner loop vectorizer that performs |
| 966 | /// vectorization of *main* loops in the process of vectorizing loops and their |
| 967 | /// epilogues. |
| 968 | class EpilogueVectorizerMainLoop : public InnerLoopAndEpilogueVectorizer { |
| 969 | public: |
| 970 | EpilogueVectorizerMainLoop( |
| 971 | Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 972 | DominatorTree *DT, const TargetLibraryInfo *TLI, |
| 973 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 974 | OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI, |
| 975 | LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM, |
| 976 | BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI, |
| 977 | GeneratedRTChecks &Check) |
| 978 | : InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 979 | EPI, LVL, CM, BFI, PSI, Check) {} |
| 980 | /// Implements the interface for creating a vectorized skeleton using the |
| 981 | /// *main loop* strategy (ie the first pass of vplan execution). |
| 982 | BasicBlock *createEpilogueVectorizedLoopSkeleton() final override; |
| 983 | |
| 984 | protected: |
| 985 | /// Emits an iteration count bypass check once for the main loop (when \p |
| 986 | /// ForEpilogue is false) and once for the epilogue loop (when \p |
| 987 | /// ForEpilogue is true). |
| 988 | BasicBlock *emitMinimumIterationCountCheck(Loop *L, BasicBlock *Bypass, |
| 989 | bool ForEpilogue); |
| 990 | void printDebugTracesAtStart() override; |
| 991 | void printDebugTracesAtEnd() override; |
| 992 | }; |
| 993 | |
| 994 | // A specialized derived class of inner loop vectorizer that performs |
| 995 | // vectorization of *epilogue* loops in the process of vectorizing loops and |
| 996 | // their epilogues. |
| 997 | class EpilogueVectorizerEpilogueLoop : public InnerLoopAndEpilogueVectorizer { |
| 998 | public: |
| 999 | EpilogueVectorizerEpilogueLoop( |
| 1000 | Loop *OrigLoop, PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 1001 | DominatorTree *DT, const TargetLibraryInfo *TLI, |
| 1002 | const TargetTransformInfo *TTI, AssumptionCache *AC, |
| 1003 | OptimizationRemarkEmitter *ORE, EpilogueLoopVectorizationInfo &EPI, |
| 1004 | LoopVectorizationLegality *LVL, llvm::LoopVectorizationCostModel *CM, |
| 1005 | BlockFrequencyInfo *BFI, ProfileSummaryInfo *PSI, |
| 1006 | GeneratedRTChecks &Checks) |
| 1007 | : InnerLoopAndEpilogueVectorizer(OrigLoop, PSE, LI, DT, TLI, TTI, AC, ORE, |
| 1008 | EPI, LVL, CM, BFI, PSI, Checks) {} |
| 1009 | /// Implements the interface for creating a vectorized skeleton using the |
| 1010 | /// *epilogue loop* strategy (ie the second pass of vplan execution). |
| 1011 | BasicBlock *createEpilogueVectorizedLoopSkeleton() final override; |
| 1012 | |
| 1013 | protected: |
| 1014 | /// Emits an iteration count bypass check after the main vector loop has |
| 1015 | /// finished to see if there are any iterations left to execute by either |
| 1016 | /// the vector epilogue or the scalar epilogue. |
| 1017 | BasicBlock *emitMinimumVectorEpilogueIterCountCheck(Loop *L, |
| 1018 | BasicBlock *Bypass, |
| 1019 | BasicBlock *Insert); |
| 1020 | void printDebugTracesAtStart() override; |
| 1021 | void printDebugTracesAtEnd() override; |
| 1022 | }; |
| 1023 | } // end namespace llvm |
| 1024 | |
| 1025 | /// Look for a meaningful debug location on the instruction or it's |
| 1026 | /// operands. |
| 1027 | static Instruction *getDebugLocFromInstOrOperands(Instruction *I) { |
| 1028 | if (!I) |
| 1029 | return I; |
| 1030 | |
| 1031 | DebugLoc Empty; |
| 1032 | if (I->getDebugLoc() != Empty) |
| 1033 | return I; |
| 1034 | |
| 1035 | for (Use &Op : I->operands()) { |
| 1036 | if (Instruction *OpInst = dyn_cast<Instruction>(Op)) |
| 1037 | if (OpInst->getDebugLoc() != Empty) |
| 1038 | return OpInst; |
| 1039 | } |
| 1040 | |
| 1041 | return I; |
| 1042 | } |
| 1043 | |
| 1044 | void InnerLoopVectorizer::setDebugLocFromInst( |
| 1045 | const Value *V, Optional<IRBuilder<> *> CustomBuilder) { |
| 1046 | IRBuilder<> *B = (CustomBuilder == None) ? &Builder : *CustomBuilder; |
| 1047 | if (const Instruction *Inst = dyn_cast_or_null<Instruction>(V)) { |
| 1048 | const DILocation *DIL = Inst->getDebugLoc(); |
| 1049 | |
| 1050 | // When a FSDiscriminator is enabled, we don't need to add the multiply |
| 1051 | // factors to the discriminators. |
| 1052 | if (DIL && Inst->getFunction()->isDebugInfoForProfiling() && |
| 1053 | !isa<DbgInfoIntrinsic>(Inst) && !EnableFSDiscriminator) { |
| 1054 | // FIXME: For scalable vectors, assume vscale=1. |
| 1055 | auto NewDIL = |
| 1056 | DIL->cloneByMultiplyingDuplicationFactor(UF * VF.getKnownMinValue()); |
| 1057 | if (NewDIL) |
| 1058 | B->SetCurrentDebugLocation(NewDIL.getValue()); |
| 1059 | else |
| 1060 | LLVM_DEBUG(dbgs()do { } while (false) |
| 1061 | << "Failed to create new discriminator: "do { } while (false) |
| 1062 | << DIL->getFilename() << " Line: " << DIL->getLine())do { } while (false); |
| 1063 | } else |
| 1064 | B->SetCurrentDebugLocation(DIL); |
| 1065 | } else |
| 1066 | B->SetCurrentDebugLocation(DebugLoc()); |
| 1067 | } |
| 1068 | |
| 1069 | /// Write a \p DebugMsg about vectorization to the debug output stream. If \p I |
| 1070 | /// is passed, the message relates to that particular instruction. |
| 1071 | #ifndef NDEBUG1 |
| 1072 | static void debugVectorizationMessage(const StringRef Prefix, |
| 1073 | const StringRef DebugMsg, |
| 1074 | Instruction *I) { |
| 1075 | dbgs() << "LV: " << Prefix << DebugMsg; |
| 1076 | if (I != nullptr) |
| 1077 | dbgs() << " " << *I; |
| 1078 | else |
| 1079 | dbgs() << '.'; |
| 1080 | dbgs() << '\n'; |
| 1081 | } |
| 1082 | #endif |
| 1083 | |
| 1084 | /// Create an analysis remark that explains why vectorization failed |
| 1085 | /// |
| 1086 | /// \p PassName is the name of the pass (e.g. can be AlwaysPrint). \p |
| 1087 | /// RemarkName is the identifier for the remark. If \p I is passed it is an |
| 1088 | /// instruction that prevents vectorization. Otherwise \p TheLoop is used for |
| 1089 | /// the location of the remark. \return the remark object that can be |
| 1090 | /// streamed to. |
| 1091 | static OptimizationRemarkAnalysis createLVAnalysis(const char *PassName, |
| 1092 | StringRef RemarkName, Loop *TheLoop, Instruction *I) { |
| 1093 | Value *CodeRegion = TheLoop->getHeader(); |
| 1094 | DebugLoc DL = TheLoop->getStartLoc(); |
| 1095 | |
| 1096 | if (I) { |
| 1097 | CodeRegion = I->getParent(); |
| 1098 | // If there is no debug location attached to the instruction, revert back to |
| 1099 | // using the loop's. |
| 1100 | if (I->getDebugLoc()) |
| 1101 | DL = I->getDebugLoc(); |
| 1102 | } |
| 1103 | |
| 1104 | return OptimizationRemarkAnalysis(PassName, RemarkName, DL, CodeRegion); |
| 1105 | } |
| 1106 | |
| 1107 | /// Return a value for Step multiplied by VF. |
| 1108 | static Value *createStepForVF(IRBuilder<> &B, Constant *Step, ElementCount VF) { |
| 1109 | assert(isa<ConstantInt>(Step) && "Expected an integer step")((void)0); |
| 1110 | Constant *StepVal = ConstantInt::get( |
| 1111 | Step->getType(), |
| 1112 | cast<ConstantInt>(Step)->getSExtValue() * VF.getKnownMinValue()); |
| 1113 | return VF.isScalable() ? B.CreateVScale(StepVal) : StepVal; |
| 1114 | } |
| 1115 | |
| 1116 | namespace llvm { |
| 1117 | |
| 1118 | /// Return the runtime value for VF. |
| 1119 | Value *getRuntimeVF(IRBuilder<> &B, Type *Ty, ElementCount VF) { |
| 1120 | Constant *EC = ConstantInt::get(Ty, VF.getKnownMinValue()); |
| 1121 | return VF.isScalable() ? B.CreateVScale(EC) : EC; |
| 1122 | } |
| 1123 | |
| 1124 | void reportVectorizationFailure(const StringRef DebugMsg, |
| 1125 | const StringRef OREMsg, const StringRef ORETag, |
| 1126 | OptimizationRemarkEmitter *ORE, Loop *TheLoop, |
| 1127 | Instruction *I) { |
| 1128 | LLVM_DEBUG(debugVectorizationMessage("Not vectorizing: ", DebugMsg, I))do { } while (false); |
| 1129 | LoopVectorizeHints Hints(TheLoop, true /* doesn't matter */, *ORE); |
| 1130 | ORE->emit( |
| 1131 | createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop, I) |
| 1132 | << "loop not vectorized: " << OREMsg); |
| 1133 | } |
| 1134 | |
| 1135 | void reportVectorizationInfo(const StringRef Msg, const StringRef ORETag, |
| 1136 | OptimizationRemarkEmitter *ORE, Loop *TheLoop, |
| 1137 | Instruction *I) { |
| 1138 | LLVM_DEBUG(debugVectorizationMessage("", Msg, I))do { } while (false); |
| 1139 | LoopVectorizeHints Hints(TheLoop, true /* doesn't matter */, *ORE); |
| 1140 | ORE->emit( |
| 1141 | createLVAnalysis(Hints.vectorizeAnalysisPassName(), ORETag, TheLoop, I) |
| 1142 | << Msg); |
| 1143 | } |
| 1144 | |
| 1145 | } // end namespace llvm |
| 1146 | |
| 1147 | #ifndef NDEBUG1 |
| 1148 | /// \return string containing a file name and a line # for the given loop. |
| 1149 | static std::string getDebugLocString(const Loop *L) { |
| 1150 | std::string Result; |
| 1151 | if (L) { |
| 1152 | raw_string_ostream OS(Result); |
| 1153 | if (const DebugLoc LoopDbgLoc = L->getStartLoc()) |
| 1154 | LoopDbgLoc.print(OS); |
| 1155 | else |
| 1156 | // Just print the module name. |
| 1157 | OS << L->getHeader()->getParent()->getParent()->getModuleIdentifier(); |
| 1158 | OS.flush(); |
| 1159 | } |
| 1160 | return Result; |
| 1161 | } |
| 1162 | #endif |
| 1163 | |
| 1164 | void InnerLoopVectorizer::addNewMetadata(Instruction *To, |
| 1165 | const Instruction *Orig) { |
| 1166 | // If the loop was versioned with memchecks, add the corresponding no-alias |
| 1167 | // metadata. |
| 1168 | if (LVer && (isa<LoadInst>(Orig) || isa<StoreInst>(Orig))) |
| 1169 | LVer->annotateInstWithNoAlias(To, Orig); |
| 1170 | } |
| 1171 | |
| 1172 | void InnerLoopVectorizer::addMetadata(Instruction *To, |
| 1173 | Instruction *From) { |
| 1174 | propagateMetadata(To, From); |
| 1175 | addNewMetadata(To, From); |
| 1176 | } |
| 1177 | |
| 1178 | void InnerLoopVectorizer::addMetadata(ArrayRef<Value *> To, |
| 1179 | Instruction *From) { |
| 1180 | for (Value *V : To) { |
| 1181 | if (Instruction *I = dyn_cast<Instruction>(V)) |
| 1182 | addMetadata(I, From); |
| 1183 | } |
| 1184 | } |
| 1185 | |
| 1186 | namespace llvm { |
| 1187 | |
| 1188 | // Loop vectorization cost-model hints how the scalar epilogue loop should be |
| 1189 | // lowered. |
| 1190 | enum ScalarEpilogueLowering { |
| 1191 | |
| 1192 | // The default: allowing scalar epilogues. |
| 1193 | CM_ScalarEpilogueAllowed, |
| 1194 | |
| 1195 | // Vectorization with OptForSize: don't allow epilogues. |
| 1196 | CM_ScalarEpilogueNotAllowedOptSize, |
| 1197 | |
| 1198 | // A special case of vectorisation with OptForSize: loops with a very small |
| 1199 | // trip count are considered for vectorization under OptForSize, thereby |
| 1200 | // making sure the cost of their loop body is dominant, free of runtime |
| 1201 | // guards and scalar iteration overheads. |
| 1202 | CM_ScalarEpilogueNotAllowedLowTripLoop, |
| 1203 | |
| 1204 | // Loop hint predicate indicating an epilogue is undesired. |
| 1205 | CM_ScalarEpilogueNotNeededUsePredicate, |
| 1206 | |
| 1207 | // Directive indicating we must either tail fold or not vectorize |
| 1208 | CM_ScalarEpilogueNotAllowedUsePredicate |
| 1209 | }; |
| 1210 | |
| 1211 | /// ElementCountComparator creates a total ordering for ElementCount |
| 1212 | /// for the purposes of using it in a set structure. |
| 1213 | struct ElementCountComparator { |
| 1214 | bool operator()(const ElementCount &LHS, const ElementCount &RHS) const { |
| 1215 | return std::make_tuple(LHS.isScalable(), LHS.getKnownMinValue()) < |
| 1216 | std::make_tuple(RHS.isScalable(), RHS.getKnownMinValue()); |
| 1217 | } |
| 1218 | }; |
| 1219 | using ElementCountSet = SmallSet<ElementCount, 16, ElementCountComparator>; |
| 1220 | |
| 1221 | /// LoopVectorizationCostModel - estimates the expected speedups due to |
| 1222 | /// vectorization. |
| 1223 | /// In many cases vectorization is not profitable. This can happen because of |
| 1224 | /// a number of reasons. In this class we mainly attempt to predict the |
| 1225 | /// expected speedup/slowdowns due to the supported instruction set. We use the |
| 1226 | /// TargetTransformInfo to query the different backends for the cost of |
| 1227 | /// different operations. |
| 1228 | class LoopVectorizationCostModel { |
| 1229 | public: |
| 1230 | LoopVectorizationCostModel(ScalarEpilogueLowering SEL, Loop *L, |
| 1231 | PredicatedScalarEvolution &PSE, LoopInfo *LI, |
| 1232 | LoopVectorizationLegality *Legal, |
| 1233 | const TargetTransformInfo &TTI, |
| 1234 | const TargetLibraryInfo *TLI, DemandedBits *DB, |
| 1235 | AssumptionCache *AC, |
| 1236 | OptimizationRemarkEmitter *ORE, const Function *F, |
| 1237 | const LoopVectorizeHints *Hints, |
| 1238 | InterleavedAccessInfo &IAI) |
| 1239 | : ScalarEpilogueStatus(SEL), TheLoop(L), PSE(PSE), LI(LI), Legal(Legal), |
| 1240 | TTI(TTI), TLI(TLI), DB(DB), AC(AC), ORE(ORE), TheFunction(F), |
| 1241 | Hints(Hints), InterleaveInfo(IAI) {} |
| 1242 | |
| 1243 | /// \return An upper bound for the vectorization factors (both fixed and |
| 1244 | /// scalable). If the factors are 0, vectorization and interleaving should be |
| 1245 | /// avoided up front. |
| 1246 | FixedScalableVFPair computeMaxVF(ElementCount UserVF, unsigned UserIC); |
| 1247 | |
| 1248 | /// \return True if runtime checks are required for vectorization, and false |
| 1249 | /// otherwise. |
| 1250 | bool runtimeChecksRequired(); |
| 1251 | |
| 1252 | /// \return The most profitable vectorization factor and the cost of that VF. |
| 1253 | /// This method checks every VF in \p CandidateVFs. If UserVF is not ZERO |
| 1254 | /// then this vectorization factor will be selected if vectorization is |
| 1255 | /// possible. |
| 1256 | VectorizationFactor |
| 1257 | selectVectorizationFactor(const ElementCountSet &CandidateVFs); |
| 1258 | |
| 1259 | VectorizationFactor |
| 1260 | selectEpilogueVectorizationFactor(const ElementCount MaxVF, |
| 1261 | const LoopVectorizationPlanner &LVP); |
| 1262 | |
| 1263 | /// Setup cost-based decisions for user vectorization factor. |
| 1264 | /// \return true if the UserVF is a feasible VF to be chosen. |
| 1265 | bool selectUserVectorizationFactor(ElementCount UserVF) { |
| 1266 | collectUniformsAndScalars(UserVF); |
| 1267 | collectInstsToScalarize(UserVF); |
| 1268 | return expectedCost(UserVF).first.isValid(); |
| 1269 | } |
| 1270 | |
| 1271 | /// \return The size (in bits) of the smallest and widest types in the code |
| 1272 | /// that needs to be vectorized. We ignore values that remain scalar such as |
| 1273 | /// 64 bit loop indices. |
| 1274 | std::pair<unsigned, unsigned> getSmallestAndWidestTypes(); |
| 1275 | |
| 1276 | /// \return The desired interleave count. |
| 1277 | /// If interleave count has been specified by metadata it will be returned. |
| 1278 | /// Otherwise, the interleave count is computed and returned. VF and LoopCost |
| 1279 | /// are the selected vectorization factor and the cost of the selected VF. |
| 1280 | unsigned selectInterleaveCount(ElementCount VF, unsigned LoopCost); |
| 1281 | |
| 1282 | /// Memory access instruction may be vectorized in more than one way. |
| 1283 | /// Form of instruction after vectorization depends on cost. |
| 1284 | /// This function takes cost-based decisions for Load/Store instructions |
| 1285 | /// and collects them in a map. This decisions map is used for building |
| 1286 | /// the lists of loop-uniform and loop-scalar instructions. |
| 1287 | /// The calculated cost is saved with widening decision in order to |
| 1288 | /// avoid redundant calculations. |
| 1289 | void setCostBasedWideningDecision(ElementCount VF); |
| 1290 | |
| 1291 | /// A struct that represents some properties of the register usage |
| 1292 | /// of a loop. |
| 1293 | struct RegisterUsage { |
| 1294 | /// Holds the number of loop invariant values that are used in the loop. |
| 1295 | /// The key is ClassID of target-provided register class. |
| 1296 | SmallMapVector<unsigned, unsigned, 4> LoopInvariantRegs; |
| 1297 | /// Holds the maximum number of concurrent live intervals in the loop. |
| 1298 | /// The key is ClassID of target-provided register class. |
| 1299 | SmallMapVector<unsigned, unsigned, 4> MaxLocalUsers; |
| 1300 | }; |
| 1301 | |
| 1302 | /// \return Returns information about the register usages of the loop for the |
| 1303 | /// given vectorization factors. |
| 1304 | SmallVector<RegisterUsage, 8> |
| 1305 | calculateRegisterUsage(ArrayRef<ElementCount> VFs); |
| 1306 | |
| 1307 | /// Collect values we want to ignore in the cost model. |
| 1308 | void collectValuesToIgnore(); |
| 1309 | |
| 1310 | /// Collect all element types in the loop for which widening is needed. |
| 1311 | void collectElementTypesForWidening(); |
| 1312 | |
| 1313 | /// Split reductions into those that happen in the loop, and those that happen |
| 1314 | /// outside. In loop reductions are collected into InLoopReductionChains. |
| 1315 | void collectInLoopReductions(); |
| 1316 | |
| 1317 | /// Returns true if we should use strict in-order reductions for the given |
| 1318 | /// RdxDesc. This is true if the -enable-strict-reductions flag is passed, |
| 1319 | /// the IsOrdered flag of RdxDesc is set and we do not allow reordering |
| 1320 | /// of FP operations. |
| 1321 | bool useOrderedReductions(const RecurrenceDescriptor &RdxDesc) { |
| 1322 | return EnableStrictReductions && !Hints->allowReordering() && |
| 1323 | RdxDesc.isOrdered(); |
| 1324 | } |
| 1325 | |
| 1326 | /// \returns The smallest bitwidth each instruction can be represented with. |
| 1327 | /// The vector equivalents of these instructions should be truncated to this |
| 1328 | /// type. |
| 1329 | const MapVector<Instruction *, uint64_t> &getMinimalBitwidths() const { |
| 1330 | return MinBWs; |
| 1331 | } |
| 1332 | |
| 1333 | /// \returns True if it is more profitable to scalarize instruction \p I for |
| 1334 | /// vectorization factor \p VF. |
| 1335 | bool isProfitableToScalarize(Instruction *I, ElementCount VF) const { |
| 1336 | assert(VF.isVector() &&((void)0) |
| 1337 | "Profitable to scalarize relevant only for VF > 1.")((void)0); |
| 1338 | |
| 1339 | // Cost model is not run in the VPlan-native path - return conservative |
| 1340 | // result until this changes. |
| 1341 | if (EnableVPlanNativePath) |
| 1342 | return false; |
| 1343 | |
| 1344 | auto Scalars = InstsToScalarize.find(VF); |
| 1345 | assert(Scalars != InstsToScalarize.end() &&((void)0) |
| 1346 | "VF not yet analyzed for scalarization profitability")((void)0); |
| 1347 | return Scalars->second.find(I) != Scalars->second.end(); |
| 1348 | } |
| 1349 | |
| 1350 | /// Returns true if \p I is known to be uniform after vectorization. |
| 1351 | bool isUniformAfterVectorization(Instruction *I, ElementCount VF) const { |
| 1352 | if (VF.isScalar()) |
| 1353 | return true; |
| 1354 | |
| 1355 | // Cost model is not run in the VPlan-native path - return conservative |
| 1356 | // result until this changes. |
| 1357 | if (EnableVPlanNativePath) |
| 1358 | return false; |
| 1359 | |
| 1360 | auto UniformsPerVF = Uniforms.find(VF); |
| 1361 | assert(UniformsPerVF != Uniforms.end() &&((void)0) |
| 1362 | "VF not yet analyzed for uniformity")((void)0); |
| 1363 | return UniformsPerVF->second.count(I); |
| 1364 | } |
| 1365 | |
| 1366 | /// Returns true if \p I is known to be scalar after vectorization. |
| 1367 | bool isScalarAfterVectorization(Instruction *I, ElementCount VF) const { |
| 1368 | if (VF.isScalar()) |
| 1369 | return true; |
| 1370 | |
| 1371 | // Cost model is not run in the VPlan-native path - return conservative |
| 1372 | // result until this changes. |
| 1373 | if (EnableVPlanNativePath) |
| 1374 | return false; |
| 1375 | |
| 1376 | auto ScalarsPerVF = Scalars.find(VF); |
| 1377 | assert(ScalarsPerVF != Scalars.end() &&((void)0) |
| 1378 | "Scalar values are not calculated for VF")((void)0); |
| 1379 | return ScalarsPerVF->second.count(I); |
| 1380 | } |
| 1381 | |
| 1382 | /// \returns True if instruction \p I can be truncated to a smaller bitwidth |
| 1383 | /// for vectorization factor \p VF. |
| 1384 | bool canTruncateToMinimalBitwidth(Instruction *I, ElementCount VF) const { |
| 1385 | return VF.isVector() && MinBWs.find(I) != MinBWs.end() && |
| 1386 | !isProfitableToScalarize(I, VF) && |
| 1387 | !isScalarAfterVectorization(I, VF); |
| 1388 | } |
| 1389 | |
| 1390 | /// Decision that was taken during cost calculation for memory instruction. |
| 1391 | enum InstWidening { |
| 1392 | CM_Unknown, |
| 1393 | CM_Widen, // For consecutive accesses with stride +1. |
| 1394 | CM_Widen_Reverse, // For consecutive accesses with stride -1. |
| 1395 | CM_Interleave, |
| 1396 | CM_GatherScatter, |
| 1397 | CM_Scalarize |
| 1398 | }; |
| 1399 | |
| 1400 | /// Save vectorization decision \p W and \p Cost taken by the cost model for |
| 1401 | /// instruction \p I and vector width \p VF. |
| 1402 | void setWideningDecision(Instruction *I, ElementCount VF, InstWidening W, |
| 1403 | InstructionCost Cost) { |
| 1404 | assert(VF.isVector() && "Expected VF >=2")((void)0); |
| 1405 | WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost); |
| 1406 | } |
| 1407 | |
| 1408 | /// Save vectorization decision \p W and \p Cost taken by the cost model for |
| 1409 | /// interleaving group \p Grp and vector width \p VF. |
| 1410 | void setWideningDecision(const InterleaveGroup<Instruction> *Grp, |
| 1411 | ElementCount VF, InstWidening W, |
| 1412 | InstructionCost Cost) { |
| 1413 | assert(VF.isVector() && "Expected VF >=2")((void)0); |
| 1414 | /// Broadcast this decicion to all instructions inside the group. |
| 1415 | /// But the cost will be assigned to one instruction only. |
| 1416 | for (unsigned i = 0; i < Grp->getFactor(); ++i) { |
| 1417 | if (auto *I = Grp->getMember(i)) { |
| 1418 | if (Grp->getInsertPos() == I) |
| 1419 | WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, Cost); |
| 1420 | else |
| 1421 | WideningDecisions[std::make_pair(I, VF)] = std::make_pair(W, 0); |
| 1422 | } |
| 1423 | } |
| 1424 | } |
| 1425 | |
| 1426 | /// Return the cost model decision for the given instruction \p I and vector |
| 1427 | /// width \p VF. Return CM_Unknown if this instruction did not pass |
| 1428 | /// through the cost modeling. |
| 1429 | InstWidening getWideningDecision(Instruction *I, ElementCount VF) const { |
| 1430 | assert(VF.isVector() && "Expected VF to be a vector VF")((void)0); |
| 1431 | // Cost model is not run in the VPlan-native path - return conservative |
| 1432 | // result until this changes. |
| 1433 | if (EnableVPlanNativePath) |
| 1434 | return CM_GatherScatter; |
| 1435 | |
| 1436 | std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF); |
| 1437 | auto Itr = WideningDecisions.find(InstOnVF); |
| 1438 | if (Itr == WideningDecisions.end()) |
| 1439 | return CM_Unknown; |
| 1440 | return Itr->second.first; |
| 1441 | } |
| 1442 | |
| 1443 | /// Return the vectorization cost for the given instruction \p I and vector |
| 1444 | /// width \p VF. |
| 1445 | InstructionCost getWideningCost(Instruction *I, ElementCount VF) { |
| 1446 | assert(VF.isVector() && "Expected VF >=2")((void)0); |
| 1447 | std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF); |
| 1448 | assert(WideningDecisions.find(InstOnVF) != WideningDecisions.end() &&((void)0) |
| 1449 | "The cost is not calculated")((void)0); |
| 1450 | return WideningDecisions[InstOnVF].second; |
| 1451 | } |
| 1452 | |
| 1453 | /// Return True if instruction \p I is an optimizable truncate whose operand |
| 1454 | /// is an induction variable. Such a truncate will be removed by adding a new |
| 1455 | /// induction variable with the destination type. |
| 1456 | bool isOptimizableIVTruncate(Instruction *I, ElementCount VF) { |
| 1457 | // If the instruction is not a truncate, return false. |
| 1458 | auto *Trunc = dyn_cast<TruncInst>(I); |
| 1459 | if (!Trunc) |
| 1460 | return false; |
| 1461 | |
| 1462 | // Get the source and destination types of the truncate. |
| 1463 | Type *SrcTy = ToVectorTy(cast<CastInst>(I)->getSrcTy(), VF); |
| 1464 | Type *DestTy = ToVectorTy(cast<CastInst>(I)->getDestTy(), VF); |
| 1465 | |
| 1466 | // If the truncate is free for the given types, return false. Replacing a |
| 1467 | // free truncate with an induction variable would add an induction variable |
| 1468 | // update instruction to each iteration of the loop. We exclude from this |
| 1469 | // check the primary induction variable since it will need an update |
| 1470 | // instruction regardless. |
| 1471 | Value *Op = Trunc->getOperand(0); |
| 1472 | if (Op != Legal->getPrimaryInduction() && TTI.isTruncateFree(SrcTy, DestTy)) |
| 1473 | return false; |
| 1474 | |
| 1475 | // If the truncated value is not an induction variable, return false. |
| 1476 | return Legal->isInductionPhi(Op); |
| 1477 | } |
| 1478 | |
| 1479 | /// Collects the instructions to scalarize for each predicated instruction in |
| 1480 | /// the loop. |
| 1481 | void collectInstsToScalarize(ElementCount VF); |
| 1482 | |
| 1483 | /// Collect Uniform and Scalar values for the given \p VF. |
| 1484 | /// The sets depend on CM decision for Load/Store instructions |
| 1485 | /// that may be vectorized as interleave, gather-scatter or scalarized. |
| 1486 | void collectUniformsAndScalars(ElementCount VF) { |
| 1487 | // Do the analysis once. |
| 1488 | if (VF.isScalar() || Uniforms.find(VF) != Uniforms.end()) |
| 1489 | return; |
| 1490 | setCostBasedWideningDecision(VF); |
| 1491 | collectLoopUniforms(VF); |
| 1492 | collectLoopScalars(VF); |
| 1493 | } |
| 1494 | |
| 1495 | /// Returns true if the target machine supports masked store operation |
| 1496 | /// for the given \p DataType and kind of access to \p Ptr. |
| 1497 | bool isLegalMaskedStore(Type *DataType, Value *Ptr, Align Alignment) const { |
| 1498 | return Legal->isConsecutivePtr(Ptr) && |
| 1499 | TTI.isLegalMaskedStore(DataType, Alignment); |
| 1500 | } |
| 1501 | |
| 1502 | /// Returns true if the target machine supports masked load operation |
| 1503 | /// for the given \p DataType and kind of access to \p Ptr. |
| 1504 | bool isLegalMaskedLoad(Type *DataType, Value *Ptr, Align Alignment) const { |
| 1505 | return Legal->isConsecutivePtr(Ptr) && |
| 1506 | TTI.isLegalMaskedLoad(DataType, Alignment); |
| 1507 | } |
| 1508 | |
| 1509 | /// Returns true if the target machine can represent \p V as a masked gather |
| 1510 | /// or scatter operation. |
| 1511 | bool isLegalGatherOrScatter(Value *V) { |
| 1512 | bool LI = isa<LoadInst>(V); |
| 1513 | bool SI = isa<StoreInst>(V); |
| 1514 | if (!LI && !SI) |
| 1515 | return false; |
| 1516 | auto *Ty = getLoadStoreType(V); |
| 1517 | Align Align = getLoadStoreAlignment(V); |
| 1518 | return (LI && TTI.isLegalMaskedGather(Ty, Align)) || |
| 1519 | (SI && TTI.isLegalMaskedScatter(Ty, Align)); |
| 1520 | } |
| 1521 | |
| 1522 | /// Returns true if the target machine supports all of the reduction |
| 1523 | /// variables found for the given VF. |
| 1524 | bool canVectorizeReductions(ElementCount VF) const { |
| 1525 | return (all_of(Legal->getReductionVars(), [&](auto &Reduction) -> bool { |
| 1526 | const RecurrenceDescriptor &RdxDesc = Reduction.second; |
| 1527 | return TTI.isLegalToVectorizeReduction(RdxDesc, VF); |
| 1528 | })); |
| 1529 | } |
| 1530 | |
| 1531 | /// Returns true if \p I is an instruction that will be scalarized with |
| 1532 | /// predication. Such instructions include conditional stores and |
| 1533 | /// instructions that may divide by zero. |
| 1534 | /// If a non-zero VF has been calculated, we check if I will be scalarized |
| 1535 | /// predication for that VF. |
| 1536 | bool isScalarWithPredication(Instruction *I) const; |
| 1537 | |
| 1538 | // Returns true if \p I is an instruction that will be predicated either |
| 1539 | // through scalar predication or masked load/store or masked gather/scatter. |
| 1540 | // Superset of instructions that return true for isScalarWithPredication. |
| 1541 | bool isPredicatedInst(Instruction *I) { |
| 1542 | if (!blockNeedsPredication(I->getParent())) |
| 1543 | return false; |
| 1544 | // Loads and stores that need some form of masked operation are predicated |
| 1545 | // instructions. |
| 1546 | if (isa<LoadInst>(I) || isa<StoreInst>(I)) |
| 1547 | return Legal->isMaskRequired(I); |
| 1548 | return isScalarWithPredication(I); |
| 1549 | } |
| 1550 | |
| 1551 | /// Returns true if \p I is a memory instruction with consecutive memory |
| 1552 | /// access that can be widened. |
| 1553 | bool |
| 1554 | memoryInstructionCanBeWidened(Instruction *I, |
| 1555 | ElementCount VF = ElementCount::getFixed(1)); |
| 1556 | |
| 1557 | /// Returns true if \p I is a memory instruction in an interleaved-group |
| 1558 | /// of memory accesses that can be vectorized with wide vector loads/stores |
| 1559 | /// and shuffles. |
| 1560 | bool |
| 1561 | interleavedAccessCanBeWidened(Instruction *I, |
| 1562 | ElementCount VF = ElementCount::getFixed(1)); |
| 1563 | |
| 1564 | /// Check if \p Instr belongs to any interleaved access group. |
| 1565 | bool isAccessInterleaved(Instruction *Instr) { |
| 1566 | return InterleaveInfo.isInterleaved(Instr); |
| 1567 | } |
| 1568 | |
| 1569 | /// Get the interleaved access group that \p Instr belongs to. |
| 1570 | const InterleaveGroup<Instruction> * |
| 1571 | getInterleavedAccessGroup(Instruction *Instr) { |
| 1572 | return InterleaveInfo.getInterleaveGroup(Instr); |
| 1573 | } |
| 1574 | |
| 1575 | /// Returns true if we're required to use a scalar epilogue for at least |
| 1576 | /// the final iteration of the original loop. |
| 1577 | bool requiresScalarEpilogue(ElementCount VF) const { |
| 1578 | if (!isScalarEpilogueAllowed()) |
| 1579 | return false; |
| 1580 | // If we might exit from anywhere but the latch, must run the exiting |
| 1581 | // iteration in scalar form. |
| 1582 | if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) |
| 1583 | return true; |
| 1584 | return VF.isVector() && InterleaveInfo.requiresScalarEpilogue(); |
| 1585 | } |
| 1586 | |
| 1587 | /// Returns true if a scalar epilogue is not allowed due to optsize or a |
| 1588 | /// loop hint annotation. |
| 1589 | bool isScalarEpilogueAllowed() const { |
| 1590 | return ScalarEpilogueStatus == CM_ScalarEpilogueAllowed; |
| 1591 | } |
| 1592 | |
| 1593 | /// Returns true if all loop blocks should be masked to fold tail loop. |
| 1594 | bool foldTailByMasking() const { return FoldTailByMasking; } |
| 1595 | |
| 1596 | bool blockNeedsPredication(BasicBlock *BB) const { |
| 1597 | return foldTailByMasking() || Legal->blockNeedsPredication(BB); |
| 1598 | } |
| 1599 | |
| 1600 | /// A SmallMapVector to store the InLoop reduction op chains, mapping phi |
| 1601 | /// nodes to the chain of instructions representing the reductions. Uses a |
| 1602 | /// MapVector to ensure deterministic iteration order. |
| 1603 | using ReductionChainMap = |
| 1604 | SmallMapVector<PHINode *, SmallVector<Instruction *, 4>, 4>; |
| 1605 | |
| 1606 | /// Return the chain of instructions representing an inloop reduction. |
| 1607 | const ReductionChainMap &getInLoopReductionChains() const { |
| 1608 | return InLoopReductionChains; |
| 1609 | } |
| 1610 | |
| 1611 | /// Returns true if the Phi is part of an inloop reduction. |
| 1612 | bool isInLoopReduction(PHINode *Phi) const { |
| 1613 | return InLoopReductionChains.count(Phi); |
| 1614 | } |
| 1615 | |
| 1616 | /// Estimate cost of an intrinsic call instruction CI if it were vectorized |
| 1617 | /// with factor VF. Return the cost of the instruction, including |
| 1618 | /// scalarization overhead if it's needed. |
| 1619 | InstructionCost getVectorIntrinsicCost(CallInst *CI, ElementCount VF) const; |
| 1620 | |
| 1621 | /// Estimate cost of a call instruction CI if it were vectorized with factor |
| 1622 | /// VF. Return the cost of the instruction, including scalarization overhead |
| 1623 | /// if it's needed. The flag NeedToScalarize shows if the call needs to be |
| 1624 | /// scalarized - |
| 1625 | /// i.e. either vector version isn't available, or is too expensive. |
| 1626 | InstructionCost getVectorCallCost(CallInst *CI, ElementCount VF, |
| 1627 | bool &NeedToScalarize) const; |
| 1628 | |
| 1629 | /// Returns true if the per-lane cost of VectorizationFactor A is lower than |
| 1630 | /// that of B. |
| 1631 | bool isMoreProfitable(const VectorizationFactor &A, |
| 1632 | const VectorizationFactor &B) const; |
| 1633 | |
| 1634 | /// Invalidates decisions already taken by the cost model. |
| 1635 | void invalidateCostModelingDecisions() { |
| 1636 | WideningDecisions.clear(); |
| 1637 | Uniforms.clear(); |
| 1638 | Scalars.clear(); |
| 1639 | } |
| 1640 | |
| 1641 | private: |
| 1642 | unsigned NumPredStores = 0; |
| 1643 | |
| 1644 | /// \return An upper bound for the vectorization factors for both |
| 1645 | /// fixed and scalable vectorization, where the minimum-known number of |
| 1646 | /// elements is a power-of-2 larger than zero. If scalable vectorization is |
| 1647 | /// disabled or unsupported, then the scalable part will be equal to |
| 1648 | /// ElementCount::getScalable(0). |
| 1649 | FixedScalableVFPair computeFeasibleMaxVF(unsigned ConstTripCount, |
| 1650 | ElementCount UserVF); |
| 1651 | |
| 1652 | /// \return the maximized element count based on the targets vector |
| 1653 | /// registers and the loop trip-count, but limited to a maximum safe VF. |
| 1654 | /// This is a helper function of computeFeasibleMaxVF. |
| 1655 | /// FIXME: MaxSafeVF is currently passed by reference to avoid some obscure |
| 1656 | /// issue that occurred on one of the buildbots which cannot be reproduced |
| 1657 | /// without having access to the properietary compiler (see comments on |
| 1658 | /// D98509). The issue is currently under investigation and this workaround |
| 1659 | /// will be removed as soon as possible. |
| 1660 | ElementCount getMaximizedVFForTarget(unsigned ConstTripCount, |
| 1661 | unsigned SmallestType, |
| 1662 | unsigned WidestType, |
| 1663 | const ElementCount &MaxSafeVF); |
| 1664 | |
| 1665 | /// \return the maximum legal scalable VF, based on the safe max number |
| 1666 | /// of elements. |
| 1667 | ElementCount getMaxLegalScalableVF(unsigned MaxSafeElements); |
| 1668 | |
| 1669 | /// The vectorization cost is a combination of the cost itself and a boolean |
| 1670 | /// indicating whether any of the contributing operations will actually |
| 1671 | /// operate on vector values after type legalization in the backend. If this |
| 1672 | /// latter value is false, then all operations will be scalarized (i.e. no |
| 1673 | /// vectorization has actually taken place). |
| 1674 | using VectorizationCostTy = std::pair<InstructionCost, bool>; |
| 1675 | |
| 1676 | /// Returns the expected execution cost. The unit of the cost does |
| 1677 | /// not matter because we use the 'cost' units to compare different |
| 1678 | /// vector widths. The cost that is returned is *not* normalized by |
| 1679 | /// the factor width. If \p Invalid is not nullptr, this function |
| 1680 | /// will add a pair(Instruction*, ElementCount) to \p Invalid for |
| 1681 | /// each instruction that has an Invalid cost for the given VF. |
| 1682 | using InstructionVFPair = std::pair<Instruction *, ElementCount>; |
| 1683 | VectorizationCostTy |
| 1684 | expectedCost(ElementCount VF, |
| 1685 | SmallVectorImpl<InstructionVFPair> *Invalid = nullptr); |
| 1686 | |
| 1687 | /// Returns the execution time cost of an instruction for a given vector |
| 1688 | /// width. Vector width of one means scalar. |
| 1689 | VectorizationCostTy getInstructionCost(Instruction *I, ElementCount VF); |
| 1690 | |
| 1691 | /// The cost-computation logic from getInstructionCost which provides |
| 1692 | /// the vector type as an output parameter. |
| 1693 | InstructionCost getInstructionCost(Instruction *I, ElementCount VF, |
| 1694 | Type *&VectorTy); |
| 1695 | |
| 1696 | /// Return the cost of instructions in an inloop reduction pattern, if I is |
| 1697 | /// part of that pattern. |
| 1698 | Optional<InstructionCost> |
| 1699 | getReductionPatternCost(Instruction *I, ElementCount VF, Type *VectorTy, |
| 1700 | TTI::TargetCostKind CostKind); |
| 1701 | |
| 1702 | /// Calculate vectorization cost of memory instruction \p I. |
| 1703 | InstructionCost getMemoryInstructionCost(Instruction *I, ElementCount VF); |
| 1704 | |
| 1705 | /// The cost computation for scalarized memory instruction. |
| 1706 | InstructionCost getMemInstScalarizationCost(Instruction *I, ElementCount VF); |
| 1707 | |
| 1708 | /// The cost computation for interleaving group of memory instructions. |
| 1709 | InstructionCost getInterleaveGroupCost(Instruction *I, ElementCount VF); |
| 1710 | |
| 1711 | /// The cost computation for Gather/Scatter instruction. |
| 1712 | InstructionCost getGatherScatterCost(Instruction *I, ElementCount VF); |
| 1713 | |
| 1714 | /// The cost computation for widening instruction \p I with consecutive |
| 1715 | /// memory access. |
| 1716 | InstructionCost getConsecutiveMemOpCost(Instruction *I, ElementCount VF); |
| 1717 | |
| 1718 | /// The cost calculation for Load/Store instruction \p I with uniform pointer - |
| 1719 | /// Load: scalar load + broadcast. |
| 1720 | /// Store: scalar store + (loop invariant value stored? 0 : extract of last |
| 1721 | /// element) |
| 1722 | InstructionCost getUniformMemOpCost(Instruction *I, ElementCount VF); |
| 1723 | |
| 1724 | /// Estimate the overhead of scalarizing an instruction. This is a |
| 1725 | /// convenience wrapper for the type-based getScalarizationOverhead API. |
| 1726 | InstructionCost getScalarizationOverhead(Instruction *I, |
| 1727 | ElementCount VF) const; |
| 1728 | |
| 1729 | /// Returns whether the instruction is a load or store and will be a emitted |
| 1730 | /// as a vector operation. |
| 1731 | bool isConsecutiveLoadOrStore(Instruction *I); |
| 1732 | |
| 1733 | /// Returns true if an artificially high cost for emulated masked memrefs |
| 1734 | /// should be used. |
| 1735 | bool useEmulatedMaskMemRefHack(Instruction *I); |
| 1736 | |
| 1737 | /// Map of scalar integer values to the smallest bitwidth they can be legally |
| 1738 | /// represented as. The vector equivalents of these values should be truncated |
| 1739 | /// to this type. |
| 1740 | MapVector<Instruction *, uint64_t> MinBWs; |
| 1741 | |
| 1742 | /// A type representing the costs for instructions if they were to be |
| 1743 | /// scalarized rather than vectorized. The entries are Instruction-Cost |
| 1744 | /// pairs. |
| 1745 | using ScalarCostsTy = DenseMap<Instruction *, InstructionCost>; |
| 1746 | |
| 1747 | /// A set containing all BasicBlocks that are known to present after |
| 1748 | /// vectorization as a predicated block. |
| 1749 | SmallPtrSet<BasicBlock *, 4> PredicatedBBsAfterVectorization; |
| 1750 | |
| 1751 | /// Records whether it is allowed to have the original scalar loop execute at |
| 1752 | /// least once. This may be needed as a fallback loop in case runtime |
| 1753 | /// aliasing/dependence checks fail, or to handle the tail/remainder |
| 1754 | /// iterations when the trip count is unknown or doesn't divide by the VF, |
| 1755 | /// or as a peel-loop to handle gaps in interleave-groups. |
| 1756 | /// Under optsize and when the trip count is very small we don't allow any |
| 1757 | /// iterations to execute in the scalar loop. |
| 1758 | ScalarEpilogueLowering ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| 1759 | |
| 1760 | /// All blocks of loop are to be masked to fold tail of scalar iterations. |
| 1761 | bool FoldTailByMasking = false; |
| 1762 | |
| 1763 | /// A map holding scalar costs for different vectorization factors. The |
| 1764 | /// presence of a cost for an instruction in the mapping indicates that the |
| 1765 | /// instruction will be scalarized when vectorizing with the associated |
| 1766 | /// vectorization factor. The entries are VF-ScalarCostTy pairs. |
| 1767 | DenseMap<ElementCount, ScalarCostsTy> InstsToScalarize; |
| 1768 | |
| 1769 | /// Holds the instructions known to be uniform after vectorization. |
| 1770 | /// The data is collected per VF. |
| 1771 | DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Uniforms; |
| 1772 | |
| 1773 | /// Holds the instructions known to be scalar after vectorization. |
| 1774 | /// The data is collected per VF. |
| 1775 | DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> Scalars; |
| 1776 | |
| 1777 | /// Holds the instructions (address computations) that are forced to be |
| 1778 | /// scalarized. |
| 1779 | DenseMap<ElementCount, SmallPtrSet<Instruction *, 4>> ForcedScalars; |
| 1780 | |
| 1781 | /// PHINodes of the reductions that should be expanded in-loop along with |
| 1782 | /// their associated chains of reduction operations, in program order from top |
| 1783 | /// (PHI) to bottom |
| 1784 | ReductionChainMap InLoopReductionChains; |
| 1785 | |
| 1786 | /// A Map of inloop reduction operations and their immediate chain operand. |
| 1787 | /// FIXME: This can be removed once reductions can be costed correctly in |
| 1788 | /// vplan. This was added to allow quick lookup to the inloop operations, |
| 1789 | /// without having to loop through InLoopReductionChains. |
| 1790 | DenseMap<Instruction *, Instruction *> InLoopReductionImmediateChains; |
| 1791 | |
| 1792 | /// Returns the expected difference in cost from scalarizing the expression |
| 1793 | /// feeding a predicated instruction \p PredInst. The instructions to |
| 1794 | /// scalarize and their scalar costs are collected in \p ScalarCosts. A |
| 1795 | /// non-negative return value implies the expression will be scalarized. |
| 1796 | /// Currently, only single-use chains are considered for scalarization. |
| 1797 | int computePredInstDiscount(Instruction *PredInst, ScalarCostsTy &ScalarCosts, |
| 1798 | ElementCount VF); |
| 1799 | |
| 1800 | /// Collect the instructions that are uniform after vectorization. An |
| 1801 | /// instruction is uniform if we represent it with a single scalar value in |
| 1802 | /// the vectorized loop corresponding to each vector iteration. Examples of |
| 1803 | /// uniform instructions include pointer operands of consecutive or |
| 1804 | /// interleaved memory accesses. Note that although uniformity implies an |
| 1805 | /// instruction will be scalar, the reverse is not true. In general, a |
| 1806 | /// scalarized instruction will be represented by VF scalar values in the |
| 1807 | /// vectorized loop, each corresponding to an iteration of the original |
| 1808 | /// scalar loop. |
| 1809 | void collectLoopUniforms(ElementCount VF); |
| 1810 | |
| 1811 | /// Collect the instructions that are scalar after vectorization. An |
| 1812 | /// instruction is scalar if it is known to be uniform or will be scalarized |
| 1813 | /// during vectorization. Non-uniform scalarized instructions will be |
| 1814 | /// represented by VF values in the vectorized loop, each corresponding to an |
| 1815 | /// iteration of the original scalar loop. |
| 1816 | void collectLoopScalars(ElementCount VF); |
| 1817 | |
| 1818 | /// Keeps cost model vectorization decision and cost for instructions. |
| 1819 | /// Right now it is used for memory instructions only. |
| 1820 | using DecisionList = DenseMap<std::pair<Instruction *, ElementCount>, |
| 1821 | std::pair<InstWidening, InstructionCost>>; |
| 1822 | |
| 1823 | DecisionList WideningDecisions; |
| 1824 | |
| 1825 | /// Returns true if \p V is expected to be vectorized and it needs to be |
| 1826 | /// extracted. |
| 1827 | bool needsExtract(Value *V, ElementCount VF) const { |
| 1828 | Instruction *I = dyn_cast<Instruction>(V); |
| 1829 | if (VF.isScalar() || !I || !TheLoop->contains(I) || |
| 1830 | TheLoop->isLoopInvariant(I)) |
| 1831 | return false; |
| 1832 | |
| 1833 | // Assume we can vectorize V (and hence we need extraction) if the |
| 1834 | // scalars are not computed yet. This can happen, because it is called |
| 1835 | // via getScalarizationOverhead from setCostBasedWideningDecision, before |
| 1836 | // the scalars are collected. That should be a safe assumption in most |
| 1837 | // cases, because we check if the operands have vectorizable types |
| 1838 | // beforehand in LoopVectorizationLegality. |
| 1839 | return Scalars.find(VF) == Scalars.end() || |
| 1840 | !isScalarAfterVectorization(I, VF); |
| 1841 | }; |
| 1842 | |
| 1843 | /// Returns a range containing only operands needing to be extracted. |
| 1844 | SmallVector<Value *, 4> filterExtractingOperands(Instruction::op_range Ops, |
| 1845 | ElementCount VF) const { |
| 1846 | return SmallVector<Value *, 4>(make_filter_range( |
| 1847 | Ops, [this, VF](Value *V) { return this->needsExtract(V, VF); })); |
| 1848 | } |
| 1849 | |
| 1850 | /// Determines if we have the infrastructure to vectorize loop \p L and its |
| 1851 | /// epilogue, assuming the main loop is vectorized by \p VF. |
| 1852 | bool isCandidateForEpilogueVectorization(const Loop &L, |
| 1853 | const ElementCount VF) const; |
| 1854 | |
| 1855 | /// Returns true if epilogue vectorization is considered profitable, and |
| 1856 | /// false otherwise. |
| 1857 | /// \p VF is the vectorization factor chosen for the original loop. |
| 1858 | bool isEpilogueVectorizationProfitable(const ElementCount VF) const; |
| 1859 | |
| 1860 | public: |
| 1861 | /// The loop that we evaluate. |
| 1862 | Loop *TheLoop; |
| 1863 | |
| 1864 | /// Predicated scalar evolution analysis. |
| 1865 | PredicatedScalarEvolution &PSE; |
| 1866 | |
| 1867 | /// Loop Info analysis. |
| 1868 | LoopInfo *LI; |
| 1869 | |
| 1870 | /// Vectorization legality. |
| 1871 | LoopVectorizationLegality *Legal; |
| 1872 | |
| 1873 | /// Vector target information. |
| 1874 | const TargetTransformInfo &TTI; |
| 1875 | |
| 1876 | /// Target Library Info. |
| 1877 | const TargetLibraryInfo *TLI; |
| 1878 | |
| 1879 | /// Demanded bits analysis. |
| 1880 | DemandedBits *DB; |
| 1881 | |
| 1882 | /// Assumption cache. |
| 1883 | AssumptionCache *AC; |
| 1884 | |
| 1885 | /// Interface to emit optimization remarks. |
| 1886 | OptimizationRemarkEmitter *ORE; |
| 1887 | |
| 1888 | const Function *TheFunction; |
| 1889 | |
| 1890 | /// Loop Vectorize Hint. |
| 1891 | const LoopVectorizeHints *Hints; |
| 1892 | |
| 1893 | /// The interleave access information contains groups of interleaved accesses |
| 1894 | /// with the same stride and close to each other. |
| 1895 | InterleavedAccessInfo &InterleaveInfo; |
| 1896 | |
| 1897 | /// Values to ignore in the cost model. |
| 1898 | SmallPtrSet<const Value *, 16> ValuesToIgnore; |
| 1899 | |
| 1900 | /// Values to ignore in the cost model when VF > 1. |
| 1901 | SmallPtrSet<const Value *, 16> VecValuesToIgnore; |
| 1902 | |
| 1903 | /// All element types found in the loop. |
| 1904 | SmallPtrSet<Type *, 16> ElementTypesInLoop; |
| 1905 | |
| 1906 | /// Profitable vector factors. |
| 1907 | SmallVector<VectorizationFactor, 8> ProfitableVFs; |
| 1908 | }; |
| 1909 | } // end namespace llvm |
| 1910 | |
| 1911 | /// Helper struct to manage generating runtime checks for vectorization. |
| 1912 | /// |
| 1913 | /// The runtime checks are created up-front in temporary blocks to allow better |
| 1914 | /// estimating the cost and un-linked from the existing IR. After deciding to |
| 1915 | /// vectorize, the checks are moved back. If deciding not to vectorize, the |
| 1916 | /// temporary blocks are completely removed. |
| 1917 | class GeneratedRTChecks { |
| 1918 | /// Basic block which contains the generated SCEV checks, if any. |
| 1919 | BasicBlock *SCEVCheckBlock = nullptr; |
| 1920 | |
| 1921 | /// The value representing the result of the generated SCEV checks. If it is |
| 1922 | /// nullptr, either no SCEV checks have been generated or they have been used. |
| 1923 | Value *SCEVCheckCond = nullptr; |
| 1924 | |
| 1925 | /// Basic block which contains the generated memory runtime checks, if any. |
| 1926 | BasicBlock *MemCheckBlock = nullptr; |
| 1927 | |
| 1928 | /// The value representing the result of the generated memory runtime checks. |
| 1929 | /// If it is nullptr, either no memory runtime checks have been generated or |
| 1930 | /// they have been used. |
| 1931 | Instruction *MemRuntimeCheckCond = nullptr; |
| 1932 | |
| 1933 | DominatorTree *DT; |
| 1934 | LoopInfo *LI; |
| 1935 | |
| 1936 | SCEVExpander SCEVExp; |
| 1937 | SCEVExpander MemCheckExp; |
| 1938 | |
| 1939 | public: |
| 1940 | GeneratedRTChecks(ScalarEvolution &SE, DominatorTree *DT, LoopInfo *LI, |
| 1941 | const DataLayout &DL) |
| 1942 | : DT(DT), LI(LI), SCEVExp(SE, DL, "scev.check"), |
| 1943 | MemCheckExp(SE, DL, "scev.check") {} |
| 1944 | |
| 1945 | /// Generate runtime checks in SCEVCheckBlock and MemCheckBlock, so we can |
| 1946 | /// accurately estimate the cost of the runtime checks. The blocks are |
| 1947 | /// un-linked from the IR and is added back during vector code generation. If |
| 1948 | /// there is no vector code generation, the check blocks are removed |
| 1949 | /// completely. |
| 1950 | void Create(Loop *L, const LoopAccessInfo &LAI, |
| 1951 | const SCEVUnionPredicate &UnionPred) { |
| 1952 | |
| 1953 | BasicBlock *LoopHeader = L->getHeader(); |
| 1954 | BasicBlock *Preheader = L->getLoopPreheader(); |
| 1955 | |
| 1956 | // Use SplitBlock to create blocks for SCEV & memory runtime checks to |
| 1957 | // ensure the blocks are properly added to LoopInfo & DominatorTree. Those |
| 1958 | // may be used by SCEVExpander. The blocks will be un-linked from their |
| 1959 | // predecessors and removed from LI & DT at the end of the function. |
| 1960 | if (!UnionPred.isAlwaysTrue()) { |
| 1961 | SCEVCheckBlock = SplitBlock(Preheader, Preheader->getTerminator(), DT, LI, |
| 1962 | nullptr, "vector.scevcheck"); |
| 1963 | |
| 1964 | SCEVCheckCond = SCEVExp.expandCodeForPredicate( |
| 1965 | &UnionPred, SCEVCheckBlock->getTerminator()); |
| 1966 | } |
| 1967 | |
| 1968 | const auto &RtPtrChecking = *LAI.getRuntimePointerChecking(); |
| 1969 | if (RtPtrChecking.Need) { |
| 1970 | auto *Pred = SCEVCheckBlock ? SCEVCheckBlock : Preheader; |
| 1971 | MemCheckBlock = SplitBlock(Pred, Pred->getTerminator(), DT, LI, nullptr, |
| 1972 | "vector.memcheck"); |
| 1973 | |
| 1974 | std::tie(std::ignore, MemRuntimeCheckCond) = |
| 1975 | addRuntimeChecks(MemCheckBlock->getTerminator(), L, |
| 1976 | RtPtrChecking.getChecks(), MemCheckExp); |
| 1977 | assert(MemRuntimeCheckCond &&((void)0) |
| 1978 | "no RT checks generated although RtPtrChecking "((void)0) |
| 1979 | "claimed checks are required")((void)0); |
| 1980 | } |
| 1981 | |
| 1982 | if (!MemCheckBlock && !SCEVCheckBlock) |
| 1983 | return; |
| 1984 | |
| 1985 | // Unhook the temporary block with the checks, update various places |
| 1986 | // accordingly. |
| 1987 | if (SCEVCheckBlock) |
| 1988 | SCEVCheckBlock->replaceAllUsesWith(Preheader); |
| 1989 | if (MemCheckBlock) |
| 1990 | MemCheckBlock->replaceAllUsesWith(Preheader); |
| 1991 | |
| 1992 | if (SCEVCheckBlock) { |
| 1993 | SCEVCheckBlock->getTerminator()->moveBefore(Preheader->getTerminator()); |
| 1994 | new UnreachableInst(Preheader->getContext(), SCEVCheckBlock); |
| 1995 | Preheader->getTerminator()->eraseFromParent(); |
| 1996 | } |
| 1997 | if (MemCheckBlock) { |
| 1998 | MemCheckBlock->getTerminator()->moveBefore(Preheader->getTerminator()); |
| 1999 | new UnreachableInst(Preheader->getContext(), MemCheckBlock); |
| 2000 | Preheader->getTerminator()->eraseFromParent(); |
| 2001 | } |
| 2002 | |
| 2003 | DT->changeImmediateDominator(LoopHeader, Preheader); |
| 2004 | if (MemCheckBlock) { |
| 2005 | DT->eraseNode(MemCheckBlock); |
| 2006 | LI->removeBlock(MemCheckBlock); |
| 2007 | } |
| 2008 | if (SCEVCheckBlock) { |
| 2009 | DT->eraseNode(SCEVCheckBlock); |
| 2010 | LI->removeBlock(SCEVCheckBlock); |
| 2011 | } |
| 2012 | } |
| 2013 | |
| 2014 | /// Remove the created SCEV & memory runtime check blocks & instructions, if |
| 2015 | /// unused. |
| 2016 | ~GeneratedRTChecks() { |
| 2017 | SCEVExpanderCleaner SCEVCleaner(SCEVExp, *DT); |
| 2018 | SCEVExpanderCleaner MemCheckCleaner(MemCheckExp, *DT); |
| 2019 | if (!SCEVCheckCond) |
| 2020 | SCEVCleaner.markResultUsed(); |
| 2021 | |
| 2022 | if (!MemRuntimeCheckCond) |
| 2023 | MemCheckCleaner.markResultUsed(); |
| 2024 | |
| 2025 | if (MemRuntimeCheckCond) { |
| 2026 | auto &SE = *MemCheckExp.getSE(); |
| 2027 | // Memory runtime check generation creates compares that use expanded |
| 2028 | // values. Remove them before running the SCEVExpanderCleaners. |
| 2029 | for (auto &I : make_early_inc_range(reverse(*MemCheckBlock))) { |
| 2030 | if (MemCheckExp.isInsertedInstruction(&I)) |
| 2031 | continue; |
| 2032 | SE.forgetValue(&I); |
| 2033 | SE.eraseValueFromMap(&I); |
| 2034 | I.eraseFromParent(); |
| 2035 | } |
| 2036 | } |
| 2037 | MemCheckCleaner.cleanup(); |
| 2038 | SCEVCleaner.cleanup(); |
| 2039 | |
| 2040 | if (SCEVCheckCond) |
| 2041 | SCEVCheckBlock->eraseFromParent(); |
| 2042 | if (MemRuntimeCheckCond) |
| 2043 | MemCheckBlock->eraseFromParent(); |
| 2044 | } |
| 2045 | |
| 2046 | /// Adds the generated SCEVCheckBlock before \p LoopVectorPreHeader and |
| 2047 | /// adjusts the branches to branch to the vector preheader or \p Bypass, |
| 2048 | /// depending on the generated condition. |
| 2049 | BasicBlock *emitSCEVChecks(Loop *L, BasicBlock *Bypass, |
| 2050 | BasicBlock *LoopVectorPreHeader, |
| 2051 | BasicBlock *LoopExitBlock) { |
| 2052 | if (!SCEVCheckCond) |
| 2053 | return nullptr; |
| 2054 | if (auto *C = dyn_cast<ConstantInt>(SCEVCheckCond)) |
| 2055 | if (C->isZero()) |
| 2056 | return nullptr; |
| 2057 | |
| 2058 | auto *Pred = LoopVectorPreHeader->getSinglePredecessor(); |
| 2059 | |
| 2060 | BranchInst::Create(LoopVectorPreHeader, SCEVCheckBlock); |
| 2061 | // Create new preheader for vector loop. |
| 2062 | if (auto *PL = LI->getLoopFor(LoopVectorPreHeader)) |
| 2063 | PL->addBasicBlockToLoop(SCEVCheckBlock, *LI); |
| 2064 | |
| 2065 | SCEVCheckBlock->getTerminator()->eraseFromParent(); |
| 2066 | SCEVCheckBlock->moveBefore(LoopVectorPreHeader); |
| 2067 | Pred->getTerminator()->replaceSuccessorWith(LoopVectorPreHeader, |
| 2068 | SCEVCheckBlock); |
| 2069 | |
| 2070 | DT->addNewBlock(SCEVCheckBlock, Pred); |
| 2071 | DT->changeImmediateDominator(LoopVectorPreHeader, SCEVCheckBlock); |
| 2072 | |
| 2073 | ReplaceInstWithInst( |
| 2074 | SCEVCheckBlock->getTerminator(), |
| 2075 | BranchInst::Create(Bypass, LoopVectorPreHeader, SCEVCheckCond)); |
| 2076 | // Mark the check as used, to prevent it from being removed during cleanup. |
| 2077 | SCEVCheckCond = nullptr; |
| 2078 | return SCEVCheckBlock; |
| 2079 | } |
| 2080 | |
| 2081 | /// Adds the generated MemCheckBlock before \p LoopVectorPreHeader and adjusts |
| 2082 | /// the branches to branch to the vector preheader or \p Bypass, depending on |
| 2083 | /// the generated condition. |
| 2084 | BasicBlock *emitMemRuntimeChecks(Loop *L, BasicBlock *Bypass, |
| 2085 | BasicBlock *LoopVectorPreHeader) { |
| 2086 | // Check if we generated code that checks in runtime if arrays overlap. |
| 2087 | if (!MemRuntimeCheckCond) |
| 2088 | return nullptr; |
| 2089 | |
| 2090 | auto *Pred = LoopVectorPreHeader->getSinglePredecessor(); |
| 2091 | Pred->getTerminator()->replaceSuccessorWith(LoopVectorPreHeader, |
| 2092 | MemCheckBlock); |
| 2093 | |
| 2094 | DT->addNewBlock(MemCheckBlock, Pred); |
| 2095 | DT->changeImmediateDominator(LoopVectorPreHeader, MemCheckBlock); |
| 2096 | MemCheckBlock->moveBefore(LoopVectorPreHeader); |
| 2097 | |
| 2098 | if (auto *PL = LI->getLoopFor(LoopVectorPreHeader)) |
| 2099 | PL->addBasicBlockToLoop(MemCheckBlock, *LI); |
| 2100 | |
| 2101 | ReplaceInstWithInst( |
| 2102 | MemCheckBlock->getTerminator(), |
| 2103 | BranchInst::Create(Bypass, LoopVectorPreHeader, MemRuntimeCheckCond)); |
| 2104 | MemCheckBlock->getTerminator()->setDebugLoc( |
| 2105 | Pred->getTerminator()->getDebugLoc()); |
| 2106 | |
| 2107 | // Mark the check as used, to prevent it from being removed during cleanup. |
| 2108 | MemRuntimeCheckCond = nullptr; |
| 2109 | return MemCheckBlock; |
| 2110 | } |
| 2111 | }; |
| 2112 | |
| 2113 | // Return true if \p OuterLp is an outer loop annotated with hints for explicit |
| 2114 | // vectorization. The loop needs to be annotated with #pragma omp simd |
| 2115 | // simdlen(#) or #pragma clang vectorize(enable) vectorize_width(#). If the |
| 2116 | // vector length information is not provided, vectorization is not considered |
| 2117 | // explicit. Interleave hints are not allowed either. These limitations will be |
| 2118 | // relaxed in the future. |
| 2119 | // Please, note that we are currently forced to abuse the pragma 'clang |
| 2120 | // vectorize' semantics. This pragma provides *auto-vectorization hints* |
| 2121 | // (i.e., LV must check that vectorization is legal) whereas pragma 'omp simd' |
| 2122 | // provides *explicit vectorization hints* (LV can bypass legal checks and |
| 2123 | // assume that vectorization is legal). However, both hints are implemented |
| 2124 | // using the same metadata (llvm.loop.vectorize, processed by |
| 2125 | // LoopVectorizeHints). This will be fixed in the future when the native IR |
| 2126 | // representation for pragma 'omp simd' is introduced. |
| 2127 | static bool isExplicitVecOuterLoop(Loop *OuterLp, |
| 2128 | OptimizationRemarkEmitter *ORE) { |
| 2129 | assert(!OuterLp->isInnermost() && "This is not an outer loop")((void)0); |
| 2130 | LoopVectorizeHints Hints(OuterLp, true /*DisableInterleaving*/, *ORE); |
| 2131 | |
| 2132 | // Only outer loops with an explicit vectorization hint are supported. |
| 2133 | // Unannotated outer loops are ignored. |
| 2134 | if (Hints.getForce() == LoopVectorizeHints::FK_Undefined) |
| 2135 | return false; |
| 2136 | |
| 2137 | Function *Fn = OuterLp->getHeader()->getParent(); |
| 2138 | if (!Hints.allowVectorization(Fn, OuterLp, |
| 2139 | true /*VectorizeOnlyWhenForced*/)) { |
| 2140 | LLVM_DEBUG(dbgs() << "LV: Loop hints prevent outer loop vectorization.\n")do { } while (false); |
| 2141 | return false; |
| 2142 | } |
| 2143 | |
| 2144 | if (Hints.getInterleave() > 1) { |
| 2145 | // TODO: Interleave support is future work. |
| 2146 | LLVM_DEBUG(dbgs() << "LV: Not vectorizing: Interleave is not supported for "do { } while (false) |
| 2147 | "outer loops.\n")do { } while (false); |
| 2148 | Hints.emitRemarkWithHints(); |
| 2149 | return false; |
| 2150 | } |
| 2151 | |
| 2152 | return true; |
| 2153 | } |
| 2154 | |
| 2155 | static void collectSupportedLoops(Loop &L, LoopInfo *LI, |
| 2156 | OptimizationRemarkEmitter *ORE, |
| 2157 | SmallVectorImpl<Loop *> &V) { |
| 2158 | // Collect inner loops and outer loops without irreducible control flow. For |
| 2159 | // now, only collect outer loops that have explicit vectorization hints. If we |
| 2160 | // are stress testing the VPlan H-CFG construction, we collect the outermost |
| 2161 | // loop of every loop nest. |
| 2162 | if (L.isInnermost() || VPlanBuildStressTest || |
| 2163 | (EnableVPlanNativePath && isExplicitVecOuterLoop(&L, ORE))) { |
| 2164 | LoopBlocksRPO RPOT(&L); |
| 2165 | RPOT.perform(LI); |
| 2166 | if (!containsIrreducibleCFG<const BasicBlock *>(RPOT, *LI)) { |
| 2167 | V.push_back(&L); |
| 2168 | // TODO: Collect inner loops inside marked outer loops in case |
| 2169 | // vectorization fails for the outer loop. Do not invoke |
| 2170 | // 'containsIrreducibleCFG' again for inner loops when the outer loop is |
| 2171 | // already known to be reducible. We can use an inherited attribute for |
| 2172 | // that. |
| 2173 | return; |
| 2174 | } |
| 2175 | } |
| 2176 | for (Loop *InnerL : L) |
| 2177 | collectSupportedLoops(*InnerL, LI, ORE, V); |
| 2178 | } |
| 2179 | |
| 2180 | namespace { |
| 2181 | |
| 2182 | /// The LoopVectorize Pass. |
| 2183 | struct LoopVectorize : public FunctionPass { |
| 2184 | /// Pass identification, replacement for typeid |
| 2185 | static char ID; |
| 2186 | |
| 2187 | LoopVectorizePass Impl; |
| 2188 | |
| 2189 | explicit LoopVectorize(bool InterleaveOnlyWhenForced = false, |
| 2190 | bool VectorizeOnlyWhenForced = false) |
| 2191 | : FunctionPass(ID), |
| 2192 | Impl({InterleaveOnlyWhenForced, VectorizeOnlyWhenForced}) { |
| 2193 | initializeLoopVectorizePass(*PassRegistry::getPassRegistry()); |
| 2194 | } |
| 2195 | |
| 2196 | bool runOnFunction(Function &F) override { |
| 2197 | if (skipFunction(F)) |
| 2198 | return false; |
| 2199 | |
| 2200 | auto *SE = &getAnalysis<ScalarEvolutionWrapperPass>().getSE(); |
| 2201 | auto *LI = &getAnalysis<LoopInfoWrapperPass>().getLoopInfo(); |
| 2202 | auto *TTI = &getAnalysis<TargetTransformInfoWrapperPass>().getTTI(F); |
| 2203 | auto *DT = &getAnalysis<DominatorTreeWrapperPass>().getDomTree(); |
| 2204 | auto *BFI = &getAnalysis<BlockFrequencyInfoWrapperPass>().getBFI(); |
| 2205 | auto *TLIP = getAnalysisIfAvailable<TargetLibraryInfoWrapperPass>(); |
| 2206 | auto *TLI = TLIP ? &TLIP->getTLI(F) : nullptr; |
| 2207 | auto *AA = &getAnalysis<AAResultsWrapperPass>().getAAResults(); |
| 2208 | auto *AC = &getAnalysis<AssumptionCacheTracker>().getAssumptionCache(F); |
| 2209 | auto *LAA = &getAnalysis<LoopAccessLegacyAnalysis>(); |
| 2210 | auto *DB = &getAnalysis<DemandedBitsWrapperPass>().getDemandedBits(); |
| 2211 | auto *ORE = &getAnalysis<OptimizationRemarkEmitterWrapperPass>().getORE(); |
| 2212 | auto *PSI = &getAnalysis<ProfileSummaryInfoWrapperPass>().getPSI(); |
| 2213 | |
| 2214 | std::function<const LoopAccessInfo &(Loop &)> GetLAA = |
| 2215 | [&](Loop &L) -> const LoopAccessInfo & { return LAA->getInfo(&L); }; |
| 2216 | |
| 2217 | return Impl.runImpl(F, *SE, *LI, *TTI, *DT, *BFI, TLI, *DB, *AA, *AC, |
| 2218 | GetLAA, *ORE, PSI).MadeAnyChange; |
| 2219 | } |
| 2220 | |
| 2221 | void getAnalysisUsage(AnalysisUsage &AU) const override { |
| 2222 | AU.addRequired<AssumptionCacheTracker>(); |
| 2223 | AU.addRequired<BlockFrequencyInfoWrapperPass>(); |
| 2224 | AU.addRequired<DominatorTreeWrapperPass>(); |
| 2225 | AU.addRequired<LoopInfoWrapperPass>(); |
| 2226 | AU.addRequired<ScalarEvolutionWrapperPass>(); |
| 2227 | AU.addRequired<TargetTransformInfoWrapperPass>(); |
| 2228 | AU.addRequired<AAResultsWrapperPass>(); |
| 2229 | AU.addRequired<LoopAccessLegacyAnalysis>(); |
| 2230 | AU.addRequired<DemandedBitsWrapperPass>(); |
| 2231 | AU.addRequired<OptimizationRemarkEmitterWrapperPass>(); |
| 2232 | AU.addRequired<InjectTLIMappingsLegacy>(); |
| 2233 | |
| 2234 | // We currently do not preserve loopinfo/dominator analyses with outer loop |
| 2235 | // vectorization. Until this is addressed, mark these analyses as preserved |
| 2236 | // only for non-VPlan-native path. |
| 2237 | // TODO: Preserve Loop and Dominator analyses for VPlan-native path. |
| 2238 | if (!EnableVPlanNativePath) { |
| 2239 | AU.addPreserved<LoopInfoWrapperPass>(); |
| 2240 | AU.addPreserved<DominatorTreeWrapperPass>(); |
| 2241 | } |
| 2242 | |
| 2243 | AU.addPreserved<BasicAAWrapperPass>(); |
| 2244 | AU.addPreserved<GlobalsAAWrapperPass>(); |
| 2245 | AU.addRequired<ProfileSummaryInfoWrapperPass>(); |
| 2246 | } |
| 2247 | }; |
| 2248 | |
| 2249 | } // end anonymous namespace |
| 2250 | |
| 2251 | //===----------------------------------------------------------------------===// |
| 2252 | // Implementation of LoopVectorizationLegality, InnerLoopVectorizer and |
| 2253 | // LoopVectorizationCostModel and LoopVectorizationPlanner. |
| 2254 | //===----------------------------------------------------------------------===// |
| 2255 | |
| 2256 | Value *InnerLoopVectorizer::getBroadcastInstrs(Value *V) { |
| 2257 | // We need to place the broadcast of invariant variables outside the loop, |
| 2258 | // but only if it's proven safe to do so. Else, broadcast will be inside |
| 2259 | // vector loop body. |
| 2260 | Instruction *Instr = dyn_cast<Instruction>(V); |
| 2261 | bool SafeToHoist = OrigLoop->isLoopInvariant(V) && |
| 2262 | (!Instr || |
| 2263 | DT->dominates(Instr->getParent(), LoopVectorPreHeader)); |
| 2264 | // Place the code for broadcasting invariant variables in the new preheader. |
| 2265 | IRBuilder<>::InsertPointGuard Guard(Builder); |
| 2266 | if (SafeToHoist) |
| 2267 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 2268 | |
| 2269 | // Broadcast the scalar into all locations in the vector. |
| 2270 | Value *Shuf = Builder.CreateVectorSplat(VF, V, "broadcast"); |
| 2271 | |
| 2272 | return Shuf; |
| 2273 | } |
| 2274 | |
| 2275 | void InnerLoopVectorizer::createVectorIntOrFpInductionPHI( |
| 2276 | const InductionDescriptor &II, Value *Step, Value *Start, |
| 2277 | Instruction *EntryVal, VPValue *Def, VPValue *CastDef, |
| 2278 | VPTransformState &State) { |
| 2279 | assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) &&((void)0) |
| 2280 | "Expected either an induction phi-node or a truncate of it!")((void)0); |
| 2281 | |
| 2282 | // Construct the initial value of the vector IV in the vector loop preheader |
| 2283 | auto CurrIP = Builder.saveIP(); |
| 2284 | Builder.SetInsertPoint(LoopVectorPreHeader->getTerminator()); |
| 2285 | if (isa<TruncInst>(EntryVal)) { |
| 2286 | assert(Start->getType()->isIntegerTy() &&((void)0) |
| 2287 | "Truncation requires an integer type")((void)0); |
| 2288 | auto *TruncType = cast<IntegerType>(EntryVal->getType()); |
| 2289 | Step = Builder.CreateTrunc(Step, TruncType); |
| 2290 | Start = Builder.CreateCast(Instruction::Trunc, Start, TruncType); |
| 2291 | } |
| 2292 | Value *SplatStart = Builder.CreateVectorSplat(VF, Start); |
| 2293 | Value *SteppedStart = |
| 2294 | getStepVector(SplatStart, 0, Step, II.getInductionOpcode()); |
| 2295 | |
| 2296 | // We create vector phi nodes for both integer and floating-point induction |
| 2297 | // variables. Here, we determine the kind of arithmetic we will perform. |
| 2298 | Instruction::BinaryOps AddOp; |
| 2299 | Instruction::BinaryOps MulOp; |
| 2300 | if (Step->getType()->isIntegerTy()) { |
| 2301 | AddOp = Instruction::Add; |
| 2302 | MulOp = Instruction::Mul; |
| 2303 | } else { |
| 2304 | AddOp = II.getInductionOpcode(); |
| 2305 | MulOp = Instruction::FMul; |
| 2306 | } |
| 2307 | |
| 2308 | // Multiply the vectorization factor by the step using integer or |
| 2309 | // floating-point arithmetic as appropriate. |
| 2310 | Type *StepType = Step->getType(); |
| 2311 | if (Step->getType()->isFloatingPointTy()) |
| 2312 | StepType = IntegerType::get(StepType->getContext(), |
| 2313 | StepType->getScalarSizeInBits()); |
| 2314 | Value *RuntimeVF = getRuntimeVF(Builder, StepType, VF); |
| 2315 | if (Step->getType()->isFloatingPointTy()) |
| 2316 | RuntimeVF = Builder.CreateSIToFP(RuntimeVF, Step->getType()); |
| 2317 | Value *Mul = Builder.CreateBinOp(MulOp, Step, RuntimeVF); |
| 2318 | |
| 2319 | // Create a vector splat to use in the induction update. |
| 2320 | // |
| 2321 | // FIXME: If the step is non-constant, we create the vector splat with |
| 2322 | // IRBuilder. IRBuilder can constant-fold the multiply, but it doesn't |
| 2323 | // handle a constant vector splat. |
| 2324 | Value *SplatVF = isa<Constant>(Mul) |
| 2325 | ? ConstantVector::getSplat(VF, cast<Constant>(Mul)) |
| 2326 | : Builder.CreateVectorSplat(VF, Mul); |
| 2327 | Builder.restoreIP(CurrIP); |
| 2328 | |
| 2329 | // We may need to add the step a number of times, depending on the unroll |
| 2330 | // factor. The last of those goes into the PHI. |
| 2331 | PHINode *VecInd = PHINode::Create(SteppedStart->getType(), 2, "vec.ind", |
| 2332 | &*LoopVectorBody->getFirstInsertionPt()); |
| 2333 | VecInd->setDebugLoc(EntryVal->getDebugLoc()); |
| 2334 | Instruction *LastInduction = VecInd; |
| 2335 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2336 | State.set(Def, LastInduction, Part); |
| 2337 | |
| 2338 | if (isa<TruncInst>(EntryVal)) |
| 2339 | addMetadata(LastInduction, EntryVal); |
| 2340 | recordVectorLoopValueForInductionCast(II, EntryVal, LastInduction, CastDef, |
| 2341 | State, Part); |
| 2342 | |
| 2343 | LastInduction = cast<Instruction>( |
| 2344 | Builder.CreateBinOp(AddOp, LastInduction, SplatVF, "step.add")); |
| 2345 | LastInduction->setDebugLoc(EntryVal->getDebugLoc()); |
| 2346 | } |
| 2347 | |
| 2348 | // Move the last step to the end of the latch block. This ensures consistent |
| 2349 | // placement of all induction updates. |
| 2350 | auto *LoopVectorLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| 2351 | auto *Br = cast<BranchInst>(LoopVectorLatch->getTerminator()); |
| 2352 | auto *ICmp = cast<Instruction>(Br->getCondition()); |
| 2353 | LastInduction->moveBefore(ICmp); |
| 2354 | LastInduction->setName("vec.ind.next"); |
| 2355 | |
| 2356 | VecInd->addIncoming(SteppedStart, LoopVectorPreHeader); |
| 2357 | VecInd->addIncoming(LastInduction, LoopVectorLatch); |
| 2358 | } |
| 2359 | |
| 2360 | bool InnerLoopVectorizer::shouldScalarizeInstruction(Instruction *I) const { |
| 2361 | return Cost->isScalarAfterVectorization(I, VF) || |
| 2362 | Cost->isProfitableToScalarize(I, VF); |
| 2363 | } |
| 2364 | |
| 2365 | bool InnerLoopVectorizer::needsScalarInduction(Instruction *IV) const { |
| 2366 | if (shouldScalarizeInstruction(IV)) |
| 2367 | return true; |
| 2368 | auto isScalarInst = [&](User *U) -> bool { |
| 2369 | auto *I = cast<Instruction>(U); |
| 2370 | return (OrigLoop->contains(I) && shouldScalarizeInstruction(I)); |
| 2371 | }; |
| 2372 | return llvm::any_of(IV->users(), isScalarInst); |
| 2373 | } |
| 2374 | |
| 2375 | void InnerLoopVectorizer::recordVectorLoopValueForInductionCast( |
| 2376 | const InductionDescriptor &ID, const Instruction *EntryVal, |
| 2377 | Value *VectorLoopVal, VPValue *CastDef, VPTransformState &State, |
| 2378 | unsigned Part, unsigned Lane) { |
| 2379 | assert((isa<PHINode>(EntryVal) || isa<TruncInst>(EntryVal)) &&((void)0) |
| 2380 | "Expected either an induction phi-node or a truncate of it!")((void)0); |
| 2381 | |
| 2382 | // This induction variable is not the phi from the original loop but the |
| 2383 | // newly-created IV based on the proof that casted Phi is equal to the |
| 2384 | // uncasted Phi in the vectorized loop (under a runtime guard possibly). It |
| 2385 | // re-uses the same InductionDescriptor that original IV uses but we don't |
| 2386 | // have to do any recording in this case - that is done when original IV is |
| 2387 | // processed. |
| 2388 | if (isa<TruncInst>(EntryVal)) |
| 2389 | return; |
| 2390 | |
| 2391 | const SmallVectorImpl<Instruction *> &Casts = ID.getCastInsts(); |
| 2392 | if (Casts.empty()) |
| 2393 | return; |
| 2394 | // Only the first Cast instruction in the Casts vector is of interest. |
| 2395 | // The rest of the Casts (if exist) have no uses outside the |
| 2396 | // induction update chain itself. |
| 2397 | if (Lane < UINT_MAX(2147483647 *2U +1U)) |
| 2398 | State.set(CastDef, VectorLoopVal, VPIteration(Part, Lane)); |
| 2399 | else |
| 2400 | State.set(CastDef, VectorLoopVal, Part); |
| 2401 | } |
| 2402 | |
| 2403 | void InnerLoopVectorizer::widenIntOrFpInduction(PHINode *IV, Value *Start, |
| 2404 | TruncInst *Trunc, VPValue *Def, |
| 2405 | VPValue *CastDef, |
| 2406 | VPTransformState &State) { |
| 2407 | assert((IV->getType()->isIntegerTy() || IV != OldInduction) &&((void)0) |
| 2408 | "Primary induction variable must have an integer type")((void)0); |
| 2409 | |
| 2410 | auto II = Legal->getInductionVars().find(IV); |
| 2411 | assert(II != Legal->getInductionVars().end() && "IV is not an induction")((void)0); |
| 2412 | |
| 2413 | auto ID = II->second; |
| 2414 | assert(IV->getType() == ID.getStartValue()->getType() && "Types must match")((void)0); |
| 2415 | |
| 2416 | // The value from the original loop to which we are mapping the new induction |
| 2417 | // variable. |
| 2418 | Instruction *EntryVal = Trunc ? cast<Instruction>(Trunc) : IV; |
| 2419 | |
| 2420 | auto &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 2421 | |
| 2422 | // Generate code for the induction step. Note that induction steps are |
| 2423 | // required to be loop-invariant |
| 2424 | auto CreateStepValue = [&](const SCEV *Step) -> Value * { |
| 2425 | assert(PSE.getSE()->isLoopInvariant(Step, OrigLoop) &&((void)0) |
| 2426 | "Induction step should be loop invariant")((void)0); |
| 2427 | if (PSE.getSE()->isSCEVable(IV->getType())) { |
| 2428 | SCEVExpander Exp(*PSE.getSE(), DL, "induction"); |
| 2429 | return Exp.expandCodeFor(Step, Step->getType(), |
| 2430 | LoopVectorPreHeader->getTerminator()); |
| 2431 | } |
| 2432 | return cast<SCEVUnknown>(Step)->getValue(); |
| 2433 | }; |
| 2434 | |
| 2435 | // The scalar value to broadcast. This is derived from the canonical |
| 2436 | // induction variable. If a truncation type is given, truncate the canonical |
| 2437 | // induction variable and step. Otherwise, derive these values from the |
| 2438 | // induction descriptor. |
| 2439 | auto CreateScalarIV = [&](Value *&Step) -> Value * { |
| 2440 | Value *ScalarIV = Induction; |
| 2441 | if (IV != OldInduction) { |
| 2442 | ScalarIV = IV->getType()->isIntegerTy() |
| 2443 | ? Builder.CreateSExtOrTrunc(Induction, IV->getType()) |
| 2444 | : Builder.CreateCast(Instruction::SIToFP, Induction, |
| 2445 | IV->getType()); |
| 2446 | ScalarIV = emitTransformedIndex(Builder, ScalarIV, PSE.getSE(), DL, ID); |
| 2447 | ScalarIV->setName("offset.idx"); |
| 2448 | } |
| 2449 | if (Trunc) { |
| 2450 | auto *TruncType = cast<IntegerType>(Trunc->getType()); |
| 2451 | assert(Step->getType()->isIntegerTy() &&((void)0) |
| 2452 | "Truncation requires an integer step")((void)0); |
| 2453 | ScalarIV = Builder.CreateTrunc(ScalarIV, TruncType); |
| 2454 | Step = Builder.CreateTrunc(Step, TruncType); |
| 2455 | } |
| 2456 | return ScalarIV; |
| 2457 | }; |
| 2458 | |
| 2459 | // Create the vector values from the scalar IV, in the absence of creating a |
| 2460 | // vector IV. |
| 2461 | auto CreateSplatIV = [&](Value *ScalarIV, Value *Step) { |
| 2462 | Value *Broadcasted = getBroadcastInstrs(ScalarIV); |
| 2463 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2464 | assert(!VF.isScalable() && "scalable vectors not yet supported.")((void)0); |
| 2465 | Value *EntryPart = |
| 2466 | getStepVector(Broadcasted, VF.getKnownMinValue() * Part, Step, |
| 2467 | ID.getInductionOpcode()); |
| 2468 | State.set(Def, EntryPart, Part); |
| 2469 | if (Trunc) |
| 2470 | addMetadata(EntryPart, Trunc); |
| 2471 | recordVectorLoopValueForInductionCast(ID, EntryVal, EntryPart, CastDef, |
| 2472 | State, Part); |
| 2473 | } |
| 2474 | }; |
| 2475 | |
| 2476 | // Fast-math-flags propagate from the original induction instruction. |
| 2477 | IRBuilder<>::FastMathFlagGuard FMFG(Builder); |
| 2478 | if (ID.getInductionBinOp() && isa<FPMathOperator>(ID.getInductionBinOp())) |
| 2479 | Builder.setFastMathFlags(ID.getInductionBinOp()->getFastMathFlags()); |
| 2480 | |
| 2481 | // Now do the actual transformations, and start with creating the step value. |
| 2482 | Value *Step = CreateStepValue(ID.getStep()); |
| 2483 | if (VF.isZero() || VF.isScalar()) { |
| 2484 | Value *ScalarIV = CreateScalarIV(Step); |
| 2485 | CreateSplatIV(ScalarIV, Step); |
| 2486 | return; |
| 2487 | } |
| 2488 | |
| 2489 | // Determine if we want a scalar version of the induction variable. This is |
| 2490 | // true if the induction variable itself is not widened, or if it has at |
| 2491 | // least one user in the loop that is not widened. |
| 2492 | auto NeedsScalarIV = needsScalarInduction(EntryVal); |
| 2493 | if (!NeedsScalarIV) { |
| 2494 | createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal, Def, CastDef, |
| 2495 | State); |
| 2496 | return; |
| 2497 | } |
| 2498 | |
| 2499 | // Try to create a new independent vector induction variable. If we can't |
| 2500 | // create the phi node, we will splat the scalar induction variable in each |
| 2501 | // loop iteration. |
| 2502 | if (!shouldScalarizeInstruction(EntryVal)) { |
| 2503 | createVectorIntOrFpInductionPHI(ID, Step, Start, EntryVal, Def, CastDef, |
| 2504 | State); |
| 2505 | Value *ScalarIV = CreateScalarIV(Step); |
| 2506 | // Create scalar steps that can be used by instructions we will later |
| 2507 | // scalarize. Note that the addition of the scalar steps will not increase |
| 2508 | // the number of instructions in the loop in the common case prior to |
| 2509 | // InstCombine. We will be trading one vector extract for each scalar step. |
| 2510 | buildScalarSteps(ScalarIV, Step, EntryVal, ID, Def, CastDef, State); |
| 2511 | return; |
| 2512 | } |
| 2513 | |
| 2514 | // All IV users are scalar instructions, so only emit a scalar IV, not a |
| 2515 | // vectorised IV. Except when we tail-fold, then the splat IV feeds the |
| 2516 | // predicate used by the masked loads/stores. |
| 2517 | Value *ScalarIV = CreateScalarIV(Step); |
| 2518 | if (!Cost->isScalarEpilogueAllowed()) |
| 2519 | CreateSplatIV(ScalarIV, Step); |
| 2520 | buildScalarSteps(ScalarIV, Step, EntryVal, ID, Def, CastDef, State); |
| 2521 | } |
| 2522 | |
| 2523 | Value *InnerLoopVectorizer::getStepVector(Value *Val, int StartIdx, Value *Step, |
| 2524 | Instruction::BinaryOps BinOp) { |
| 2525 | // Create and check the types. |
| 2526 | auto *ValVTy = cast<VectorType>(Val->getType()); |
| 2527 | ElementCount VLen = ValVTy->getElementCount(); |
| 2528 | |
| 2529 | Type *STy = Val->getType()->getScalarType(); |
| 2530 | assert((STy->isIntegerTy() || STy->isFloatingPointTy()) &&((void)0) |
| 2531 | "Induction Step must be an integer or FP")((void)0); |
| 2532 | assert(Step->getType() == STy && "Step has wrong type")((void)0); |
| 2533 | |
| 2534 | SmallVector<Constant *, 8> Indices; |
| 2535 | |
| 2536 | // Create a vector of consecutive numbers from zero to VF. |
| 2537 | VectorType *InitVecValVTy = ValVTy; |
| 2538 | Type *InitVecValSTy = STy; |
| 2539 | if (STy->isFloatingPointTy()) { |
| 2540 | InitVecValSTy = |
| 2541 | IntegerType::get(STy->getContext(), STy->getScalarSizeInBits()); |
| 2542 | InitVecValVTy = VectorType::get(InitVecValSTy, VLen); |
| 2543 | } |
| 2544 | Value *InitVec = Builder.CreateStepVector(InitVecValVTy); |
| 2545 | |
| 2546 | // Add on StartIdx |
| 2547 | Value *StartIdxSplat = Builder.CreateVectorSplat( |
| 2548 | VLen, ConstantInt::get(InitVecValSTy, StartIdx)); |
| 2549 | InitVec = Builder.CreateAdd(InitVec, StartIdxSplat); |
| 2550 | |
| 2551 | if (STy->isIntegerTy()) { |
| 2552 | Step = Builder.CreateVectorSplat(VLen, Step); |
| 2553 | assert(Step->getType() == Val->getType() && "Invalid step vec")((void)0); |
| 2554 | // FIXME: The newly created binary instructions should contain nsw/nuw flags, |
| 2555 | // which can be found from the original scalar operations. |
| 2556 | Step = Builder.CreateMul(InitVec, Step); |
| 2557 | return Builder.CreateAdd(Val, Step, "induction"); |
| 2558 | } |
| 2559 | |
| 2560 | // Floating point induction. |
| 2561 | assert((BinOp == Instruction::FAdd || BinOp == Instruction::FSub) &&((void)0) |
| 2562 | "Binary Opcode should be specified for FP induction")((void)0); |
| 2563 | InitVec = Builder.CreateUIToFP(InitVec, ValVTy); |
| 2564 | Step = Builder.CreateVectorSplat(VLen, Step); |
| 2565 | Value *MulOp = Builder.CreateFMul(InitVec, Step); |
| 2566 | return Builder.CreateBinOp(BinOp, Val, MulOp, "induction"); |
| 2567 | } |
| 2568 | |
| 2569 | void InnerLoopVectorizer::buildScalarSteps(Value *ScalarIV, Value *Step, |
| 2570 | Instruction *EntryVal, |
| 2571 | const InductionDescriptor &ID, |
| 2572 | VPValue *Def, VPValue *CastDef, |
| 2573 | VPTransformState &State) { |
| 2574 | // We shouldn't have to build scalar steps if we aren't vectorizing. |
| 2575 | assert(VF.isVector() && "VF should be greater than one")((void)0); |
| 2576 | // Get the value type and ensure it and the step have the same integer type. |
| 2577 | Type *ScalarIVTy = ScalarIV->getType()->getScalarType(); |
| 2578 | assert(ScalarIVTy == Step->getType() &&((void)0) |
| 2579 | "Val and Step should have the same type")((void)0); |
| 2580 | |
| 2581 | // We build scalar steps for both integer and floating-point induction |
| 2582 | // variables. Here, we determine the kind of arithmetic we will perform. |
| 2583 | Instruction::BinaryOps AddOp; |
| 2584 | Instruction::BinaryOps MulOp; |
| 2585 | if (ScalarIVTy->isIntegerTy()) { |
| 2586 | AddOp = Instruction::Add; |
| 2587 | MulOp = Instruction::Mul; |
| 2588 | } else { |
| 2589 | AddOp = ID.getInductionOpcode(); |
| 2590 | MulOp = Instruction::FMul; |
| 2591 | } |
| 2592 | |
| 2593 | // Determine the number of scalars we need to generate for each unroll |
| 2594 | // iteration. If EntryVal is uniform, we only need to generate the first |
| 2595 | // lane. Otherwise, we generate all VF values. |
| 2596 | bool IsUniform = |
| 2597 | Cost->isUniformAfterVectorization(cast<Instruction>(EntryVal), VF); |
| 2598 | unsigned Lanes = IsUniform ? 1 : VF.getKnownMinValue(); |
| 2599 | // Compute the scalar steps and save the results in State. |
| 2600 | Type *IntStepTy = IntegerType::get(ScalarIVTy->getContext(), |
| 2601 | ScalarIVTy->getScalarSizeInBits()); |
| 2602 | Type *VecIVTy = nullptr; |
| 2603 | Value *UnitStepVec = nullptr, *SplatStep = nullptr, *SplatIV = nullptr; |
| 2604 | if (!IsUniform && VF.isScalable()) { |
| 2605 | VecIVTy = VectorType::get(ScalarIVTy, VF); |
| 2606 | UnitStepVec = Builder.CreateStepVector(VectorType::get(IntStepTy, VF)); |
| 2607 | SplatStep = Builder.CreateVectorSplat(VF, Step); |
| 2608 | SplatIV = Builder.CreateVectorSplat(VF, ScalarIV); |
| 2609 | } |
| 2610 | |
| 2611 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2612 | Value *StartIdx0 = |
| 2613 | createStepForVF(Builder, ConstantInt::get(IntStepTy, Part), VF); |
| 2614 | |
| 2615 | if (!IsUniform && VF.isScalable()) { |
| 2616 | auto *SplatStartIdx = Builder.CreateVectorSplat(VF, StartIdx0); |
| 2617 | auto *InitVec = Builder.CreateAdd(SplatStartIdx, UnitStepVec); |
| 2618 | if (ScalarIVTy->isFloatingPointTy()) |
| 2619 | InitVec = Builder.CreateSIToFP(InitVec, VecIVTy); |
| 2620 | auto *Mul = Builder.CreateBinOp(MulOp, InitVec, SplatStep); |
| 2621 | auto *Add = Builder.CreateBinOp(AddOp, SplatIV, Mul); |
| 2622 | State.set(Def, Add, Part); |
| 2623 | recordVectorLoopValueForInductionCast(ID, EntryVal, Add, CastDef, State, |
| 2624 | Part); |
| 2625 | // It's useful to record the lane values too for the known minimum number |
| 2626 | // of elements so we do those below. This improves the code quality when |
| 2627 | // trying to extract the first element, for example. |
| 2628 | } |
| 2629 | |
| 2630 | if (ScalarIVTy->isFloatingPointTy()) |
| 2631 | StartIdx0 = Builder.CreateSIToFP(StartIdx0, ScalarIVTy); |
| 2632 | |
| 2633 | for (unsigned Lane = 0; Lane < Lanes; ++Lane) { |
| 2634 | Value *StartIdx = Builder.CreateBinOp( |
| 2635 | AddOp, StartIdx0, getSignedIntOrFpConstant(ScalarIVTy, Lane)); |
| 2636 | // The step returned by `createStepForVF` is a runtime-evaluated value |
| 2637 | // when VF is scalable. Otherwise, it should be folded into a Constant. |
| 2638 | assert((VF.isScalable() || isa<Constant>(StartIdx)) &&((void)0) |
| 2639 | "Expected StartIdx to be folded to a constant when VF is not "((void)0) |
| 2640 | "scalable")((void)0); |
| 2641 | auto *Mul = Builder.CreateBinOp(MulOp, StartIdx, Step); |
| 2642 | auto *Add = Builder.CreateBinOp(AddOp, ScalarIV, Mul); |
| 2643 | State.set(Def, Add, VPIteration(Part, Lane)); |
| 2644 | recordVectorLoopValueForInductionCast(ID, EntryVal, Add, CastDef, State, |
| 2645 | Part, Lane); |
| 2646 | } |
| 2647 | } |
| 2648 | } |
| 2649 | |
| 2650 | void InnerLoopVectorizer::packScalarIntoVectorValue(VPValue *Def, |
| 2651 | const VPIteration &Instance, |
| 2652 | VPTransformState &State) { |
| 2653 | Value *ScalarInst = State.get(Def, Instance); |
| 2654 | Value *VectorValue = State.get(Def, Instance.Part); |
| 2655 | VectorValue = Builder.CreateInsertElement( |
| 2656 | VectorValue, ScalarInst, |
| 2657 | Instance.Lane.getAsRuntimeExpr(State.Builder, VF)); |
| 2658 | State.set(Def, VectorValue, Instance.Part); |
| 2659 | } |
| 2660 | |
| 2661 | Value *InnerLoopVectorizer::reverseVector(Value *Vec) { |
| 2662 | assert(Vec->getType()->isVectorTy() && "Invalid type")((void)0); |
| 2663 | return Builder.CreateVectorReverse(Vec, "reverse"); |
| 2664 | } |
| 2665 | |
| 2666 | // Return whether we allow using masked interleave-groups (for dealing with |
| 2667 | // strided loads/stores that reside in predicated blocks, or for dealing |
| 2668 | // with gaps). |
| 2669 | static bool useMaskedInterleavedAccesses(const TargetTransformInfo &TTI) { |
| 2670 | // If an override option has been passed in for interleaved accesses, use it. |
| 2671 | if (EnableMaskedInterleavedMemAccesses.getNumOccurrences() > 0) |
| 2672 | return EnableMaskedInterleavedMemAccesses; |
| 2673 | |
| 2674 | return TTI.enableMaskedInterleavedAccessVectorization(); |
| 2675 | } |
| 2676 | |
| 2677 | // Try to vectorize the interleave group that \p Instr belongs to. |
| 2678 | // |
| 2679 | // E.g. Translate following interleaved load group (factor = 3): |
| 2680 | // for (i = 0; i < N; i+=3) { |
| 2681 | // R = Pic[i]; // Member of index 0 |
| 2682 | // G = Pic[i+1]; // Member of index 1 |
| 2683 | // B = Pic[i+2]; // Member of index 2 |
| 2684 | // ... // do something to R, G, B |
| 2685 | // } |
| 2686 | // To: |
| 2687 | // %wide.vec = load <12 x i32> ; Read 4 tuples of R,G,B |
| 2688 | // %R.vec = shuffle %wide.vec, poison, <0, 3, 6, 9> ; R elements |
| 2689 | // %G.vec = shuffle %wide.vec, poison, <1, 4, 7, 10> ; G elements |
| 2690 | // %B.vec = shuffle %wide.vec, poison, <2, 5, 8, 11> ; B elements |
| 2691 | // |
| 2692 | // Or translate following interleaved store group (factor = 3): |
| 2693 | // for (i = 0; i < N; i+=3) { |
| 2694 | // ... do something to R, G, B |
| 2695 | // Pic[i] = R; // Member of index 0 |
| 2696 | // Pic[i+1] = G; // Member of index 1 |
| 2697 | // Pic[i+2] = B; // Member of index 2 |
| 2698 | // } |
| 2699 | // To: |
| 2700 | // %R_G.vec = shuffle %R.vec, %G.vec, <0, 1, 2, ..., 7> |
| 2701 | // %B_U.vec = shuffle %B.vec, poison, <0, 1, 2, 3, u, u, u, u> |
| 2702 | // %interleaved.vec = shuffle %R_G.vec, %B_U.vec, |
| 2703 | // <0, 4, 8, 1, 5, 9, 2, 6, 10, 3, 7, 11> ; Interleave R,G,B elements |
| 2704 | // store <12 x i32> %interleaved.vec ; Write 4 tuples of R,G,B |
| 2705 | void InnerLoopVectorizer::vectorizeInterleaveGroup( |
| 2706 | const InterleaveGroup<Instruction> *Group, ArrayRef<VPValue *> VPDefs, |
| 2707 | VPTransformState &State, VPValue *Addr, ArrayRef<VPValue *> StoredValues, |
| 2708 | VPValue *BlockInMask) { |
| 2709 | Instruction *Instr = Group->getInsertPos(); |
| 2710 | const DataLayout &DL = Instr->getModule()->getDataLayout(); |
| 2711 | |
| 2712 | // Prepare for the vector type of the interleaved load/store. |
| 2713 | Type *ScalarTy = getLoadStoreType(Instr); |
| 2714 | unsigned InterleaveFactor = Group->getFactor(); |
| 2715 | assert(!VF.isScalable() && "scalable vectors not yet supported.")((void)0); |
| 2716 | auto *VecTy = VectorType::get(ScalarTy, VF * InterleaveFactor); |
| 2717 | |
| 2718 | // Prepare for the new pointers. |
| 2719 | SmallVector<Value *, 2> AddrParts; |
| 2720 | unsigned Index = Group->getIndex(Instr); |
| 2721 | |
| 2722 | // TODO: extend the masked interleaved-group support to reversed access. |
| 2723 | assert((!BlockInMask || !Group->isReverse()) &&((void)0) |
| 2724 | "Reversed masked interleave-group not supported.")((void)0); |
| 2725 | |
| 2726 | // If the group is reverse, adjust the index to refer to the last vector lane |
| 2727 | // instead of the first. We adjust the index from the first vector lane, |
| 2728 | // rather than directly getting the pointer for lane VF - 1, because the |
| 2729 | // pointer operand of the interleaved access is supposed to be uniform. For |
| 2730 | // uniform instructions, we're only required to generate a value for the |
| 2731 | // first vector lane in each unroll iteration. |
| 2732 | if (Group->isReverse()) |
| 2733 | Index += (VF.getKnownMinValue() - 1) * Group->getFactor(); |
| 2734 | |
| 2735 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2736 | Value *AddrPart = State.get(Addr, VPIteration(Part, 0)); |
| 2737 | setDebugLocFromInst(AddrPart); |
| 2738 | |
| 2739 | // Notice current instruction could be any index. Need to adjust the address |
| 2740 | // to the member of index 0. |
| 2741 | // |
| 2742 | // E.g. a = A[i+1]; // Member of index 1 (Current instruction) |
| 2743 | // b = A[i]; // Member of index 0 |
| 2744 | // Current pointer is pointed to A[i+1], adjust it to A[i]. |
| 2745 | // |
| 2746 | // E.g. A[i+1] = a; // Member of index 1 |
| 2747 | // A[i] = b; // Member of index 0 |
| 2748 | // A[i+2] = c; // Member of index 2 (Current instruction) |
| 2749 | // Current pointer is pointed to A[i+2], adjust it to A[i]. |
| 2750 | |
| 2751 | bool InBounds = false; |
| 2752 | if (auto *gep = dyn_cast<GetElementPtrInst>(AddrPart->stripPointerCasts())) |
| 2753 | InBounds = gep->isInBounds(); |
| 2754 | AddrPart = Builder.CreateGEP(ScalarTy, AddrPart, Builder.getInt32(-Index)); |
| 2755 | cast<GetElementPtrInst>(AddrPart)->setIsInBounds(InBounds); |
| 2756 | |
| 2757 | // Cast to the vector pointer type. |
| 2758 | unsigned AddressSpace = AddrPart->getType()->getPointerAddressSpace(); |
| 2759 | Type *PtrTy = VecTy->getPointerTo(AddressSpace); |
| 2760 | AddrParts.push_back(Builder.CreateBitCast(AddrPart, PtrTy)); |
| 2761 | } |
| 2762 | |
| 2763 | setDebugLocFromInst(Instr); |
| 2764 | Value *PoisonVec = PoisonValue::get(VecTy); |
| 2765 | |
| 2766 | Value *MaskForGaps = nullptr; |
| 2767 | if (Group->requiresScalarEpilogue() && !Cost->isScalarEpilogueAllowed()) { |
| 2768 | MaskForGaps = createBitMaskForGaps(Builder, VF.getKnownMinValue(), *Group); |
| 2769 | assert(MaskForGaps && "Mask for Gaps is required but it is null")((void)0); |
| 2770 | } |
| 2771 | |
| 2772 | // Vectorize the interleaved load group. |
| 2773 | if (isa<LoadInst>(Instr)) { |
| 2774 | // For each unroll part, create a wide load for the group. |
| 2775 | SmallVector<Value *, 2> NewLoads; |
| 2776 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2777 | Instruction *NewLoad; |
| 2778 | if (BlockInMask || MaskForGaps) { |
| 2779 | assert(useMaskedInterleavedAccesses(*TTI) &&((void)0) |
| 2780 | "masked interleaved groups are not allowed.")((void)0); |
| 2781 | Value *GroupMask = MaskForGaps; |
| 2782 | if (BlockInMask) { |
| 2783 | Value *BlockInMaskPart = State.get(BlockInMask, Part); |
| 2784 | Value *ShuffledMask = Builder.CreateShuffleVector( |
| 2785 | BlockInMaskPart, |
| 2786 | createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), |
| 2787 | "interleaved.mask"); |
| 2788 | GroupMask = MaskForGaps |
| 2789 | ? Builder.CreateBinOp(Instruction::And, ShuffledMask, |
| 2790 | MaskForGaps) |
| 2791 | : ShuffledMask; |
| 2792 | } |
| 2793 | NewLoad = |
| 2794 | Builder.CreateMaskedLoad(VecTy, AddrParts[Part], Group->getAlign(), |
| 2795 | GroupMask, PoisonVec, "wide.masked.vec"); |
| 2796 | } |
| 2797 | else |
| 2798 | NewLoad = Builder.CreateAlignedLoad(VecTy, AddrParts[Part], |
| 2799 | Group->getAlign(), "wide.vec"); |
| 2800 | Group->addMetadata(NewLoad); |
| 2801 | NewLoads.push_back(NewLoad); |
| 2802 | } |
| 2803 | |
| 2804 | // For each member in the group, shuffle out the appropriate data from the |
| 2805 | // wide loads. |
| 2806 | unsigned J = 0; |
| 2807 | for (unsigned I = 0; I < InterleaveFactor; ++I) { |
| 2808 | Instruction *Member = Group->getMember(I); |
| 2809 | |
| 2810 | // Skip the gaps in the group. |
| 2811 | if (!Member) |
| 2812 | continue; |
| 2813 | |
| 2814 | auto StrideMask = |
| 2815 | createStrideMask(I, InterleaveFactor, VF.getKnownMinValue()); |
| 2816 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2817 | Value *StridedVec = Builder.CreateShuffleVector( |
| 2818 | NewLoads[Part], StrideMask, "strided.vec"); |
| 2819 | |
| 2820 | // If this member has different type, cast the result type. |
| 2821 | if (Member->getType() != ScalarTy) { |
| 2822 | assert(!VF.isScalable() && "VF is assumed to be non scalable.")((void)0); |
| 2823 | VectorType *OtherVTy = VectorType::get(Member->getType(), VF); |
| 2824 | StridedVec = createBitOrPointerCast(StridedVec, OtherVTy, DL); |
| 2825 | } |
| 2826 | |
| 2827 | if (Group->isReverse()) |
| 2828 | StridedVec = reverseVector(StridedVec); |
| 2829 | |
| 2830 | State.set(VPDefs[J], StridedVec, Part); |
| 2831 | } |
| 2832 | ++J; |
| 2833 | } |
| 2834 | return; |
| 2835 | } |
| 2836 | |
| 2837 | // The sub vector type for current instruction. |
| 2838 | auto *SubVT = VectorType::get(ScalarTy, VF); |
| 2839 | |
| 2840 | // Vectorize the interleaved store group. |
| 2841 | for (unsigned Part = 0; Part < UF; Part++) { |
| 2842 | // Collect the stored vector from each member. |
| 2843 | SmallVector<Value *, 4> StoredVecs; |
| 2844 | for (unsigned i = 0; i < InterleaveFactor; i++) { |
| 2845 | // Interleaved store group doesn't allow a gap, so each index has a member |
| 2846 | assert(Group->getMember(i) && "Fail to get a member from an interleaved store group")((void)0); |
| 2847 | |
| 2848 | Value *StoredVec = State.get(StoredValues[i], Part); |
| 2849 | |
| 2850 | if (Group->isReverse()) |
| 2851 | StoredVec = reverseVector(StoredVec); |
| 2852 | |
| 2853 | // If this member has different type, cast it to a unified type. |
| 2854 | |
| 2855 | if (StoredVec->getType() != SubVT) |
| 2856 | StoredVec = createBitOrPointerCast(StoredVec, SubVT, DL); |
| 2857 | |
| 2858 | StoredVecs.push_back(StoredVec); |
| 2859 | } |
| 2860 | |
| 2861 | // Concatenate all vectors into a wide vector. |
| 2862 | Value *WideVec = concatenateVectors(Builder, StoredVecs); |
| 2863 | |
| 2864 | // Interleave the elements in the wide vector. |
| 2865 | Value *IVec = Builder.CreateShuffleVector( |
| 2866 | WideVec, createInterleaveMask(VF.getKnownMinValue(), InterleaveFactor), |
| 2867 | "interleaved.vec"); |
| 2868 | |
| 2869 | Instruction *NewStoreInstr; |
| 2870 | if (BlockInMask) { |
| 2871 | Value *BlockInMaskPart = State.get(BlockInMask, Part); |
| 2872 | Value *ShuffledMask = Builder.CreateShuffleVector( |
| 2873 | BlockInMaskPart, |
| 2874 | createReplicatedMask(InterleaveFactor, VF.getKnownMinValue()), |
| 2875 | "interleaved.mask"); |
| 2876 | NewStoreInstr = Builder.CreateMaskedStore( |
| 2877 | IVec, AddrParts[Part], Group->getAlign(), ShuffledMask); |
| 2878 | } |
| 2879 | else |
| 2880 | NewStoreInstr = |
| 2881 | Builder.CreateAlignedStore(IVec, AddrParts[Part], Group->getAlign()); |
| 2882 | |
| 2883 | Group->addMetadata(NewStoreInstr); |
| 2884 | } |
| 2885 | } |
| 2886 | |
| 2887 | void InnerLoopVectorizer::vectorizeMemoryInstruction( |
| 2888 | Instruction *Instr, VPTransformState &State, VPValue *Def, VPValue *Addr, |
| 2889 | VPValue *StoredValue, VPValue *BlockInMask) { |
| 2890 | // Attempt to issue a wide load. |
| 2891 | LoadInst *LI = dyn_cast<LoadInst>(Instr); |
| 2892 | StoreInst *SI = dyn_cast<StoreInst>(Instr); |
| 2893 | |
| 2894 | assert((LI || SI) && "Invalid Load/Store instruction")((void)0); |
| 2895 | assert((!SI || StoredValue) && "No stored value provided for widened store")((void)0); |
| 2896 | assert((!LI || !StoredValue) && "Stored value provided for widened load")((void)0); |
| 2897 | |
| 2898 | LoopVectorizationCostModel::InstWidening Decision = |
| 2899 | Cost->getWideningDecision(Instr, VF); |
| 2900 | assert((Decision == LoopVectorizationCostModel::CM_Widen ||((void)0) |
| 2901 | Decision == LoopVectorizationCostModel::CM_Widen_Reverse ||((void)0) |
| 2902 | Decision == LoopVectorizationCostModel::CM_GatherScatter) &&((void)0) |
| 2903 | "CM decision is not to widen the memory instruction")((void)0); |
| 2904 | |
| 2905 | Type *ScalarDataTy = getLoadStoreType(Instr); |
| 2906 | |
| 2907 | auto *DataTy = VectorType::get(ScalarDataTy, VF); |
| 2908 | const Align Alignment = getLoadStoreAlignment(Instr); |
| 2909 | |
| 2910 | // Determine if the pointer operand of the access is either consecutive or |
| 2911 | // reverse consecutive. |
| 2912 | bool Reverse = (Decision == LoopVectorizationCostModel::CM_Widen_Reverse); |
| 2913 | bool ConsecutiveStride = |
| 2914 | Reverse || (Decision == LoopVectorizationCostModel::CM_Widen); |
| 2915 | bool CreateGatherScatter = |
| 2916 | (Decision == LoopVectorizationCostModel::CM_GatherScatter); |
| 2917 | |
| 2918 | // Either Ptr feeds a vector load/store, or a vector GEP should feed a vector |
| 2919 | // gather/scatter. Otherwise Decision should have been to Scalarize. |
| 2920 | assert((ConsecutiveStride || CreateGatherScatter) &&((void)0) |
| 2921 | "The instruction should be scalarized")((void)0); |
| 2922 | (void)ConsecutiveStride; |
| 2923 | |
| 2924 | VectorParts BlockInMaskParts(UF); |
| 2925 | bool isMaskRequired = BlockInMask; |
| 2926 | if (isMaskRequired) |
| 2927 | for (unsigned Part = 0; Part < UF; ++Part) |
| 2928 | BlockInMaskParts[Part] = State.get(BlockInMask, Part); |
| 2929 | |
| 2930 | const auto CreateVecPtr = [&](unsigned Part, Value *Ptr) -> Value * { |
| 2931 | // Calculate the pointer for the specific unroll-part. |
| 2932 | GetElementPtrInst *PartPtr = nullptr; |
| 2933 | |
| 2934 | bool InBounds = false; |
| 2935 | if (auto *gep = dyn_cast<GetElementPtrInst>(Ptr->stripPointerCasts())) |
| 2936 | InBounds = gep->isInBounds(); |
| 2937 | if (Reverse) { |
| 2938 | // If the address is consecutive but reversed, then the |
| 2939 | // wide store needs to start at the last vector element. |
| 2940 | // RunTimeVF = VScale * VF.getKnownMinValue() |
| 2941 | // For fixed-width VScale is 1, then RunTimeVF = VF.getKnownMinValue() |
| 2942 | Value *RunTimeVF = getRuntimeVF(Builder, Builder.getInt32Ty(), VF); |
| 2943 | // NumElt = -Part * RunTimeVF |
| 2944 | Value *NumElt = Builder.CreateMul(Builder.getInt32(-Part), RunTimeVF); |
| 2945 | // LastLane = 1 - RunTimeVF |
| 2946 | Value *LastLane = Builder.CreateSub(Builder.getInt32(1), RunTimeVF); |
| 2947 | PartPtr = |
| 2948 | cast<GetElementPtrInst>(Builder.CreateGEP(ScalarDataTy, Ptr, NumElt)); |
| 2949 | PartPtr->setIsInBounds(InBounds); |
| 2950 | PartPtr = cast<GetElementPtrInst>( |
| 2951 | Builder.CreateGEP(ScalarDataTy, PartPtr, LastLane)); |
| 2952 | PartPtr->setIsInBounds(InBounds); |
| 2953 | if (isMaskRequired) // Reverse of a null all-one mask is a null mask. |
| 2954 | BlockInMaskParts[Part] = reverseVector(BlockInMaskParts[Part]); |
| 2955 | } else { |
| 2956 | Value *Increment = createStepForVF(Builder, Builder.getInt32(Part), VF); |
| 2957 | PartPtr = cast<GetElementPtrInst>( |
| 2958 | Builder.CreateGEP(ScalarDataTy, Ptr, Increment)); |
| 2959 | PartPtr->setIsInBounds(InBounds); |
| 2960 | } |
| 2961 | |
| 2962 | unsigned AddressSpace = Ptr->getType()->getPointerAddressSpace(); |
| 2963 | return Builder.CreateBitCast(PartPtr, DataTy->getPointerTo(AddressSpace)); |
| 2964 | }; |
| 2965 | |
| 2966 | // Handle Stores: |
| 2967 | if (SI) { |
| 2968 | setDebugLocFromInst(SI); |
| 2969 | |
| 2970 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 2971 | Instruction *NewSI = nullptr; |
| 2972 | Value *StoredVal = State.get(StoredValue, Part); |
| 2973 | if (CreateGatherScatter) { |
| 2974 | Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; |
| 2975 | Value *VectorGep = State.get(Addr, Part); |
| 2976 | NewSI = Builder.CreateMaskedScatter(StoredVal, VectorGep, Alignment, |
| 2977 | MaskPart); |
| 2978 | } else { |
| 2979 | if (Reverse) { |
| 2980 | // If we store to reverse consecutive memory locations, then we need |
| 2981 | // to reverse the order of elements in the stored value. |
| 2982 | StoredVal = reverseVector(StoredVal); |
| 2983 | // We don't want to update the value in the map as it might be used in |
| 2984 | // another expression. So don't call resetVectorValue(StoredVal). |
| 2985 | } |
| 2986 | auto *VecPtr = CreateVecPtr(Part, State.get(Addr, VPIteration(0, 0))); |
| 2987 | if (isMaskRequired) |
| 2988 | NewSI = Builder.CreateMaskedStore(StoredVal, VecPtr, Alignment, |
| 2989 | BlockInMaskParts[Part]); |
| 2990 | else |
| 2991 | NewSI = Builder.CreateAlignedStore(StoredVal, VecPtr, Alignment); |
| 2992 | } |
| 2993 | addMetadata(NewSI, SI); |
| 2994 | } |
| 2995 | return; |
| 2996 | } |
| 2997 | |
| 2998 | // Handle loads. |
| 2999 | assert(LI && "Must have a load instruction")((void)0); |
| 3000 | setDebugLocFromInst(LI); |
| 3001 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 3002 | Value *NewLI; |
| 3003 | if (CreateGatherScatter) { |
| 3004 | Value *MaskPart = isMaskRequired ? BlockInMaskParts[Part] : nullptr; |
| 3005 | Value *VectorGep = State.get(Addr, Part); |
| 3006 | NewLI = Builder.CreateMaskedGather(DataTy, VectorGep, Alignment, MaskPart, |
| 3007 | nullptr, "wide.masked.gather"); |
| 3008 | addMetadata(NewLI, LI); |
| 3009 | } else { |
| 3010 | auto *VecPtr = CreateVecPtr(Part, State.get(Addr, VPIteration(0, 0))); |
| 3011 | if (isMaskRequired) |
| 3012 | NewLI = Builder.CreateMaskedLoad( |
| 3013 | DataTy, VecPtr, Alignment, BlockInMaskParts[Part], |
| 3014 | PoisonValue::get(DataTy), "wide.masked.load"); |
| 3015 | else |
| 3016 | NewLI = |
| 3017 | Builder.CreateAlignedLoad(DataTy, VecPtr, Alignment, "wide.load"); |
| 3018 | |
| 3019 | // Add metadata to the load, but setVectorValue to the reverse shuffle. |
| 3020 | addMetadata(NewLI, LI); |
| 3021 | if (Reverse) |
| 3022 | NewLI = reverseVector(NewLI); |
| 3023 | } |
| 3024 | |
| 3025 | State.set(Def, NewLI, Part); |
| 3026 | } |
| 3027 | } |
| 3028 | |
| 3029 | void InnerLoopVectorizer::scalarizeInstruction(Instruction *Instr, VPValue *Def, |
| 3030 | VPUser &User, |
| 3031 | const VPIteration &Instance, |
| 3032 | bool IfPredicateInstr, |
| 3033 | VPTransformState &State) { |
| 3034 | assert(!Instr->getType()->isAggregateType() && "Can't handle vectors")((void)0); |
| 3035 | |
| 3036 | // llvm.experimental.noalias.scope.decl intrinsics must only be duplicated for |
| 3037 | // the first lane and part. |
| 3038 | if (isa<NoAliasScopeDeclInst>(Instr)) |
| 3039 | if (!Instance.isFirstIteration()) |
| 3040 | return; |
| 3041 | |
| 3042 | setDebugLocFromInst(Instr); |
| 3043 | |
| 3044 | // Does this instruction return a value ? |
| 3045 | bool IsVoidRetTy = Instr->getType()->isVoidTy(); |
| 3046 | |
| 3047 | Instruction *Cloned = Instr->clone(); |
| 3048 | if (!IsVoidRetTy) |
| 3049 | Cloned->setName(Instr->getName() + ".cloned"); |
| 3050 | |
| 3051 | State.Builder.SetInsertPoint(Builder.GetInsertBlock(), |
| 3052 | Builder.GetInsertPoint()); |
| 3053 | // Replace the operands of the cloned instructions with their scalar |
| 3054 | // equivalents in the new loop. |
| 3055 | for (unsigned op = 0, e = User.getNumOperands(); op != e; ++op) { |
| 3056 | auto *Operand = dyn_cast<Instruction>(Instr->getOperand(op)); |
| 3057 | auto InputInstance = Instance; |
| 3058 | if (!Operand || !OrigLoop->contains(Operand) || |
| 3059 | (Cost->isUniformAfterVectorization(Operand, State.VF))) |
| 3060 | InputInstance.Lane = VPLane::getFirstLane(); |
| 3061 | auto *NewOp = State.get(User.getOperand(op), InputInstance); |
| 3062 | Cloned->setOperand(op, NewOp); |
| 3063 | } |
| 3064 | addNewMetadata(Cloned, Instr); |
| 3065 | |
| 3066 | // Place the cloned scalar in the new loop. |
| 3067 | Builder.Insert(Cloned); |
| 3068 | |
| 3069 | State.set(Def, Cloned, Instance); |
| 3070 | |
| 3071 | // If we just cloned a new assumption, add it the assumption cache. |
| 3072 | if (auto *II = dyn_cast<AssumeInst>(Cloned)) |
| 3073 | AC->registerAssumption(II); |
| 3074 | |
| 3075 | // End if-block. |
| 3076 | if (IfPredicateInstr) |
| 3077 | PredicatedInstructions.push_back(Cloned); |
| 3078 | } |
| 3079 | |
| 3080 | PHINode *InnerLoopVectorizer::createInductionVariable(Loop *L, Value *Start, |
| 3081 | Value *End, Value *Step, |
| 3082 | Instruction *DL) { |
| 3083 | BasicBlock *Header = L->getHeader(); |
| 3084 | BasicBlock *Latch = L->getLoopLatch(); |
| 3085 | // As we're just creating this loop, it's possible no latch exists |
| 3086 | // yet. If so, use the header as this will be a single block loop. |
| 3087 | if (!Latch) |
| 3088 | Latch = Header; |
| 3089 | |
| 3090 | IRBuilder<> B(&*Header->getFirstInsertionPt()); |
| 3091 | Instruction *OldInst = getDebugLocFromInstOrOperands(OldInduction); |
| 3092 | setDebugLocFromInst(OldInst, &B); |
| 3093 | auto *Induction = B.CreatePHI(Start->getType(), 2, "index"); |
| 3094 | |
| 3095 | B.SetInsertPoint(Latch->getTerminator()); |
| 3096 | setDebugLocFromInst(OldInst, &B); |
| 3097 | |
| 3098 | // Create i+1 and fill the PHINode. |
| 3099 | // |
| 3100 | // If the tail is not folded, we know that End - Start >= Step (either |
| 3101 | // statically or through the minimum iteration checks). We also know that both |
| 3102 | // Start % Step == 0 and End % Step == 0. We exit the vector loop if %IV + |
| 3103 | // %Step == %End. Hence we must exit the loop before %IV + %Step unsigned |
| 3104 | // overflows and we can mark the induction increment as NUW. |
| 3105 | Value *Next = B.CreateAdd(Induction, Step, "index.next", |
| 3106 | /*NUW=*/!Cost->foldTailByMasking(), /*NSW=*/false); |
| 3107 | Induction->addIncoming(Start, L->getLoopPreheader()); |
| 3108 | Induction->addIncoming(Next, Latch); |
| 3109 | // Create the compare. |
| 3110 | Value *ICmp = B.CreateICmpEQ(Next, End); |
| 3111 | B.CreateCondBr(ICmp, L->getUniqueExitBlock(), Header); |
| 3112 | |
| 3113 | // Now we have two terminators. Remove the old one from the block. |
| 3114 | Latch->getTerminator()->eraseFromParent(); |
| 3115 | |
| 3116 | return Induction; |
| 3117 | } |
| 3118 | |
| 3119 | Value *InnerLoopVectorizer::getOrCreateTripCount(Loop *L) { |
| 3120 | if (TripCount) |
| 3121 | return TripCount; |
| 3122 | |
| 3123 | assert(L && "Create Trip Count for null loop.")((void)0); |
| 3124 | IRBuilder<> Builder(L->getLoopPreheader()->getTerminator()); |
| 3125 | // Find the loop boundaries. |
| 3126 | ScalarEvolution *SE = PSE.getSE(); |
| 3127 | const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount(); |
| 3128 | assert(!isa<SCEVCouldNotCompute>(BackedgeTakenCount) &&((void)0) |
| 3129 | "Invalid loop count")((void)0); |
| 3130 | |
| 3131 | Type *IdxTy = Legal->getWidestInductionType(); |
| 3132 | assert(IdxTy && "No type for induction")((void)0); |
| 3133 | |
| 3134 | // The exit count might have the type of i64 while the phi is i32. This can |
| 3135 | // happen if we have an induction variable that is sign extended before the |
| 3136 | // compare. The only way that we get a backedge taken count is that the |
| 3137 | // induction variable was signed and as such will not overflow. In such a case |
| 3138 | // truncation is legal. |
| 3139 | if (SE->getTypeSizeInBits(BackedgeTakenCount->getType()) > |
| 3140 | IdxTy->getPrimitiveSizeInBits()) |
| 3141 | BackedgeTakenCount = SE->getTruncateOrNoop(BackedgeTakenCount, IdxTy); |
| 3142 | BackedgeTakenCount = SE->getNoopOrZeroExtend(BackedgeTakenCount, IdxTy); |
| 3143 | |
| 3144 | // Get the total trip count from the count by adding 1. |
| 3145 | const SCEV *ExitCount = SE->getAddExpr( |
| 3146 | BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType())); |
| 3147 | |
| 3148 | const DataLayout &DL = L->getHeader()->getModule()->getDataLayout(); |
| 3149 | |
| 3150 | // Expand the trip count and place the new instructions in the preheader. |
| 3151 | // Notice that the pre-header does not change, only the loop body. |
| 3152 | SCEVExpander Exp(*SE, DL, "induction"); |
| 3153 | |
| 3154 | // Count holds the overall loop count (N). |
| 3155 | TripCount = Exp.expandCodeFor(ExitCount, ExitCount->getType(), |
| 3156 | L->getLoopPreheader()->getTerminator()); |
| 3157 | |
| 3158 | if (TripCount->getType()->isPointerTy()) |
| 3159 | TripCount = |
| 3160 | CastInst::CreatePointerCast(TripCount, IdxTy, "exitcount.ptrcnt.to.int", |
| 3161 | L->getLoopPreheader()->getTerminator()); |
| 3162 | |
| 3163 | return TripCount; |
| 3164 | } |
| 3165 | |
| 3166 | Value *InnerLoopVectorizer::getOrCreateVectorTripCount(Loop *L) { |
| 3167 | if (VectorTripCount) |
| 3168 | return VectorTripCount; |
| 3169 | |
| 3170 | Value *TC = getOrCreateTripCount(L); |
| 3171 | IRBuilder<> Builder(L->getLoopPreheader()->getTerminator()); |
| 3172 | |
| 3173 | Type *Ty = TC->getType(); |
| 3174 | // This is where we can make the step a runtime constant. |
| 3175 | Value *Step = createStepForVF(Builder, ConstantInt::get(Ty, UF), VF); |
| 3176 | |
| 3177 | // If the tail is to be folded by masking, round the number of iterations N |
| 3178 | // up to a multiple of Step instead of rounding down. This is done by first |
| 3179 | // adding Step-1 and then rounding down. Note that it's ok if this addition |
| 3180 | // overflows: the vector induction variable will eventually wrap to zero given |
| 3181 | // that it starts at zero and its Step is a power of two; the loop will then |
| 3182 | // exit, with the last early-exit vector comparison also producing all-true. |
| 3183 | if (Cost->foldTailByMasking()) { |
| 3184 | assert(isPowerOf2_32(VF.getKnownMinValue() * UF) &&((void)0) |
| 3185 | "VF*UF must be a power of 2 when folding tail by masking")((void)0); |
| 3186 | assert(!VF.isScalable() &&((void)0) |
| 3187 | "Tail folding not yet supported for scalable vectors")((void)0); |
| 3188 | TC = Builder.CreateAdd( |
| 3189 | TC, ConstantInt::get(Ty, VF.getKnownMinValue() * UF - 1), "n.rnd.up"); |
| 3190 | } |
| 3191 | |
| 3192 | // Now we need to generate the expression for the part of the loop that the |
| 3193 | // vectorized body will execute. This is equal to N - (N % Step) if scalar |
| 3194 | // iterations are not required for correctness, or N - Step, otherwise. Step |
| 3195 | // is equal to the vectorization factor (number of SIMD elements) times the |
| 3196 | // unroll factor (number of SIMD instructions). |
| 3197 | Value *R = Builder.CreateURem(TC, Step, "n.mod.vf"); |
| 3198 | |
| 3199 | // There are cases where we *must* run at least one iteration in the remainder |
| 3200 | // loop. See the cost model for when this can happen. If the step evenly |
| 3201 | // divides the trip count, we set the remainder to be equal to the step. If |
| 3202 | // the step does not evenly divide the trip count, no adjustment is necessary |
| 3203 | // since there will already be scalar iterations. Note that the minimum |
| 3204 | // iterations check ensures that N >= Step. |
| 3205 | if (Cost->requiresScalarEpilogue(VF)) { |
| 3206 | auto *IsZero = Builder.CreateICmpEQ(R, ConstantInt::get(R->getType(), 0)); |
| 3207 | R = Builder.CreateSelect(IsZero, Step, R); |
| 3208 | } |
| 3209 | |
| 3210 | VectorTripCount = Builder.CreateSub(TC, R, "n.vec"); |
| 3211 | |
| 3212 | return VectorTripCount; |
| 3213 | } |
| 3214 | |
| 3215 | Value *InnerLoopVectorizer::createBitOrPointerCast(Value *V, VectorType *DstVTy, |
| 3216 | const DataLayout &DL) { |
| 3217 | // Verify that V is a vector type with same number of elements as DstVTy. |
| 3218 | auto *DstFVTy = cast<FixedVectorType>(DstVTy); |
| 3219 | unsigned VF = DstFVTy->getNumElements(); |
| 3220 | auto *SrcVecTy = cast<FixedVectorType>(V->getType()); |
| 3221 | assert((VF == SrcVecTy->getNumElements()) && "Vector dimensions do not match")((void)0); |
| 3222 | Type *SrcElemTy = SrcVecTy->getElementType(); |
| 3223 | Type *DstElemTy = DstFVTy->getElementType(); |
| 3224 | assert((DL.getTypeSizeInBits(SrcElemTy) == DL.getTypeSizeInBits(DstElemTy)) &&((void)0) |
| 3225 | "Vector elements must have same size")((void)0); |
| 3226 | |
| 3227 | // Do a direct cast if element types are castable. |
| 3228 | if (CastInst::isBitOrNoopPointerCastable(SrcElemTy, DstElemTy, DL)) { |
| 3229 | return Builder.CreateBitOrPointerCast(V, DstFVTy); |
| 3230 | } |
| 3231 | // V cannot be directly casted to desired vector type. |
| 3232 | // May happen when V is a floating point vector but DstVTy is a vector of |
| 3233 | // pointers or vice-versa. Handle this using a two-step bitcast using an |
| 3234 | // intermediate Integer type for the bitcast i.e. Ptr <-> Int <-> Float. |
| 3235 | assert((DstElemTy->isPointerTy() != SrcElemTy->isPointerTy()) &&((void)0) |
| 3236 | "Only one type should be a pointer type")((void)0); |
| 3237 | assert((DstElemTy->isFloatingPointTy() != SrcElemTy->isFloatingPointTy()) &&((void)0) |
| 3238 | "Only one type should be a floating point type")((void)0); |
| 3239 | Type *IntTy = |
| 3240 | IntegerType::getIntNTy(V->getContext(), DL.getTypeSizeInBits(SrcElemTy)); |
| 3241 | auto *VecIntTy = FixedVectorType::get(IntTy, VF); |
| 3242 | Value *CastVal = Builder.CreateBitOrPointerCast(V, VecIntTy); |
| 3243 | return Builder.CreateBitOrPointerCast(CastVal, DstFVTy); |
| 3244 | } |
| 3245 | |
| 3246 | void InnerLoopVectorizer::emitMinimumIterationCountCheck(Loop *L, |
| 3247 | BasicBlock *Bypass) { |
| 3248 | Value *Count = getOrCreateTripCount(L); |
| 3249 | // Reuse existing vector loop preheader for TC checks. |
| 3250 | // Note that new preheader block is generated for vector loop. |
| 3251 | BasicBlock *const TCCheckBlock = LoopVectorPreHeader; |
| 3252 | IRBuilder<> Builder(TCCheckBlock->getTerminator()); |
| 3253 | |
| 3254 | // Generate code to check if the loop's trip count is less than VF * UF, or |
| 3255 | // equal to it in case a scalar epilogue is required; this implies that the |
| 3256 | // vector trip count is zero. This check also covers the case where adding one |
| 3257 | // to the backedge-taken count overflowed leading to an incorrect trip count |
| 3258 | // of zero. In this case we will also jump to the scalar loop. |
| 3259 | auto P = Cost->requiresScalarEpilogue(VF) ? ICmpInst::ICMP_ULE |
| 3260 | : ICmpInst::ICMP_ULT; |
| 3261 | |
| 3262 | // If tail is to be folded, vector loop takes care of all iterations. |
| 3263 | Value *CheckMinIters = Builder.getFalse(); |
| 3264 | if (!Cost->foldTailByMasking()) { |
| 3265 | Value *Step = |
| 3266 | createStepForVF(Builder, ConstantInt::get(Count->getType(), UF), VF); |
| 3267 | CheckMinIters = Builder.CreateICmp(P, Count, Step, "min.iters.check"); |
| 3268 | } |
| 3269 | // Create new preheader for vector loop. |
| 3270 | LoopVectorPreHeader = |
| 3271 | SplitBlock(TCCheckBlock, TCCheckBlock->getTerminator(), DT, LI, nullptr, |
| 3272 | "vector.ph"); |
| 3273 | |
| 3274 | assert(DT->properlyDominates(DT->getNode(TCCheckBlock),((void)0) |
| 3275 | DT->getNode(Bypass)->getIDom()) &&((void)0) |
| 3276 | "TC check is expected to dominate Bypass")((void)0); |
| 3277 | |
| 3278 | // Update dominator for Bypass & LoopExit (if needed). |
| 3279 | DT->changeImmediateDominator(Bypass, TCCheckBlock); |
| 3280 | if (!Cost->requiresScalarEpilogue(VF)) |
| 3281 | // If there is an epilogue which must run, there's no edge from the |
| 3282 | // middle block to exit blocks and thus no need to update the immediate |
| 3283 | // dominator of the exit blocks. |
| 3284 | DT->changeImmediateDominator(LoopExitBlock, TCCheckBlock); |
| 3285 | |
| 3286 | ReplaceInstWithInst( |
| 3287 | TCCheckBlock->getTerminator(), |
| 3288 | BranchInst::Create(Bypass, LoopVectorPreHeader, CheckMinIters)); |
| 3289 | LoopBypassBlocks.push_back(TCCheckBlock); |
| 3290 | } |
| 3291 | |
| 3292 | BasicBlock *InnerLoopVectorizer::emitSCEVChecks(Loop *L, BasicBlock *Bypass) { |
| 3293 | |
| 3294 | BasicBlock *const SCEVCheckBlock = |
| 3295 | RTChecks.emitSCEVChecks(L, Bypass, LoopVectorPreHeader, LoopExitBlock); |
| 3296 | if (!SCEVCheckBlock) |
| 3297 | return nullptr; |
| 3298 | |
| 3299 | assert(!(SCEVCheckBlock->getParent()->hasOptSize() ||((void)0) |
| 3300 | (OptForSizeBasedOnProfile &&((void)0) |
| 3301 | Cost->Hints->getForce() != LoopVectorizeHints::FK_Enabled)) &&((void)0) |
| 3302 | "Cannot SCEV check stride or overflow when optimizing for size")((void)0); |
| 3303 | |
| 3304 | |
| 3305 | // Update dominator only if this is first RT check. |
| 3306 | if (LoopBypassBlocks.empty()) { |
| 3307 | DT->changeImmediateDominator(Bypass, SCEVCheckBlock); |
| 3308 | if (!Cost->requiresScalarEpilogue(VF)) |
| 3309 | // If there is an epilogue which must run, there's no edge from the |
| 3310 | // middle block to exit blocks and thus no need to update the immediate |
| 3311 | // dominator of the exit blocks. |
| 3312 | DT->changeImmediateDominator(LoopExitBlock, SCEVCheckBlock); |
| 3313 | } |
| 3314 | |
| 3315 | LoopBypassBlocks.push_back(SCEVCheckBlock); |
| 3316 | AddedSafetyChecks = true; |
| 3317 | return SCEVCheckBlock; |
| 3318 | } |
| 3319 | |
| 3320 | BasicBlock *InnerLoopVectorizer::emitMemRuntimeChecks(Loop *L, |
| 3321 | BasicBlock *Bypass) { |
| 3322 | // VPlan-native path does not do any analysis for runtime checks currently. |
| 3323 | if (EnableVPlanNativePath) |
| 3324 | return nullptr; |
| 3325 | |
| 3326 | BasicBlock *const MemCheckBlock = |
| 3327 | RTChecks.emitMemRuntimeChecks(L, Bypass, LoopVectorPreHeader); |
| 3328 | |
| 3329 | // Check if we generated code that checks in runtime if arrays overlap. We put |
| 3330 | // the checks into a separate block to make the more common case of few |
| 3331 | // elements faster. |
| 3332 | if (!MemCheckBlock) |
| 3333 | return nullptr; |
| 3334 | |
| 3335 | if (MemCheckBlock->getParent()->hasOptSize() || OptForSizeBasedOnProfile) { |
| 3336 | assert(Cost->Hints->getForce() == LoopVectorizeHints::FK_Enabled &&((void)0) |
| 3337 | "Cannot emit memory checks when optimizing for size, unless forced "((void)0) |
| 3338 | "to vectorize.")((void)0); |
| 3339 | ORE->emit([&]() { |
| 3340 | return OptimizationRemarkAnalysis(DEBUG_TYPE"loop-vectorize", "VectorizationCodeSize", |
| 3341 | L->getStartLoc(), L->getHeader()) |
| 3342 | << "Code-size may be reduced by not forcing " |
| 3343 | "vectorization, or by source-code modifications " |
| 3344 | "eliminating the need for runtime checks " |
| 3345 | "(e.g., adding 'restrict')."; |
| 3346 | }); |
| 3347 | } |
| 3348 | |
| 3349 | LoopBypassBlocks.push_back(MemCheckBlock); |
| 3350 | |
| 3351 | AddedSafetyChecks = true; |
| 3352 | |
| 3353 | // We currently don't use LoopVersioning for the actual loop cloning but we |
| 3354 | // still use it to add the noalias metadata. |
| 3355 | LVer = std::make_unique<LoopVersioning>( |
| 3356 | *Legal->getLAI(), |
| 3357 | Legal->getLAI()->getRuntimePointerChecking()->getChecks(), OrigLoop, LI, |
| 3358 | DT, PSE.getSE()); |
| 3359 | LVer->prepareNoAliasMetadata(); |
| 3360 | return MemCheckBlock; |
| 3361 | } |
| 3362 | |
| 3363 | Value *InnerLoopVectorizer::emitTransformedIndex( |
| 3364 | IRBuilder<> &B, Value *Index, ScalarEvolution *SE, const DataLayout &DL, |
| 3365 | const InductionDescriptor &ID) const { |
| 3366 | |
| 3367 | SCEVExpander Exp(*SE, DL, "induction"); |
| 3368 | auto Step = ID.getStep(); |
| 3369 | auto StartValue = ID.getStartValue(); |
| 3370 | assert(Index->getType()->getScalarType() == Step->getType() &&((void)0) |
| 3371 | "Index scalar type does not match StepValue type")((void)0); |
| 3372 | |
| 3373 | // Note: the IR at this point is broken. We cannot use SE to create any new |
| 3374 | // SCEV and then expand it, hoping that SCEV's simplification will give us |
| 3375 | // a more optimal code. Unfortunately, attempt of doing so on invalid IR may |
| 3376 | // lead to various SCEV crashes. So all we can do is to use builder and rely |
| 3377 | // on InstCombine for future simplifications. Here we handle some trivial |
| 3378 | // cases only. |
| 3379 | auto CreateAdd = [&B](Value *X, Value *Y) { |
| 3380 | assert(X->getType() == Y->getType() && "Types don't match!")((void)0); |
| 3381 | if (auto *CX = dyn_cast<ConstantInt>(X)) |
| 3382 | if (CX->isZero()) |
| 3383 | return Y; |
| 3384 | if (auto *CY = dyn_cast<ConstantInt>(Y)) |
| 3385 | if (CY->isZero()) |
| 3386 | return X; |
| 3387 | return B.CreateAdd(X, Y); |
| 3388 | }; |
| 3389 | |
| 3390 | // We allow X to be a vector type, in which case Y will potentially be |
| 3391 | // splatted into a vector with the same element count. |
| 3392 | auto CreateMul = [&B](Value *X, Value *Y) { |
| 3393 | assert(X->getType()->getScalarType() == Y->getType() &&((void)0) |
| 3394 | "Types don't match!")((void)0); |
| 3395 | if (auto *CX = dyn_cast<ConstantInt>(X)) |
| 3396 | if (CX->isOne()) |
| 3397 | return Y; |
| 3398 | if (auto *CY = dyn_cast<ConstantInt>(Y)) |
| 3399 | if (CY->isOne()) |
| 3400 | return X; |
| 3401 | VectorType *XVTy = dyn_cast<VectorType>(X->getType()); |
| 3402 | if (XVTy && !isa<VectorType>(Y->getType())) |
| 3403 | Y = B.CreateVectorSplat(XVTy->getElementCount(), Y); |
| 3404 | return B.CreateMul(X, Y); |
| 3405 | }; |
| 3406 | |
| 3407 | // Get a suitable insert point for SCEV expansion. For blocks in the vector |
| 3408 | // loop, choose the end of the vector loop header (=LoopVectorBody), because |
| 3409 | // the DomTree is not kept up-to-date for additional blocks generated in the |
| 3410 | // vector loop. By using the header as insertion point, we guarantee that the |
| 3411 | // expanded instructions dominate all their uses. |
| 3412 | auto GetInsertPoint = [this, &B]() { |
| 3413 | BasicBlock *InsertBB = B.GetInsertPoint()->getParent(); |
| 3414 | if (InsertBB != LoopVectorBody && |
| 3415 | LI->getLoopFor(LoopVectorBody) == LI->getLoopFor(InsertBB)) |
| 3416 | return LoopVectorBody->getTerminator(); |
| 3417 | return &*B.GetInsertPoint(); |
| 3418 | }; |
| 3419 | |
| 3420 | switch (ID.getKind()) { |
| 3421 | case InductionDescriptor::IK_IntInduction: { |
| 3422 | assert(!isa<VectorType>(Index->getType()) &&((void)0) |
| 3423 | "Vector indices not supported for integer inductions yet")((void)0); |
| 3424 | assert(Index->getType() == StartValue->getType() &&((void)0) |
| 3425 | "Index type does not match StartValue type")((void)0); |
| 3426 | if (ID.getConstIntStepValue() && ID.getConstIntStepValue()->isMinusOne()) |
| 3427 | return B.CreateSub(StartValue, Index); |
| 3428 | auto *Offset = CreateMul( |
| 3429 | Index, Exp.expandCodeFor(Step, Index->getType(), GetInsertPoint())); |
| 3430 | return CreateAdd(StartValue, Offset); |
| 3431 | } |
| 3432 | case InductionDescriptor::IK_PtrInduction: { |
| 3433 | assert(isa<SCEVConstant>(Step) &&((void)0) |
| 3434 | "Expected constant step for pointer induction")((void)0); |
| 3435 | return B.CreateGEP( |
| 3436 | StartValue->getType()->getPointerElementType(), StartValue, |
| 3437 | CreateMul(Index, |
| 3438 | Exp.expandCodeFor(Step, Index->getType()->getScalarType(), |
| 3439 | GetInsertPoint()))); |
| 3440 | } |
| 3441 | case InductionDescriptor::IK_FpInduction: { |
| 3442 | assert(!isa<VectorType>(Index->getType()) &&((void)0) |
| 3443 | "Vector indices not supported for FP inductions yet")((void)0); |
| 3444 | assert(Step->getType()->isFloatingPointTy() && "Expected FP Step value")((void)0); |
| 3445 | auto InductionBinOp = ID.getInductionBinOp(); |
| 3446 | assert(InductionBinOp &&((void)0) |
| 3447 | (InductionBinOp->getOpcode() == Instruction::FAdd ||((void)0) |
| 3448 | InductionBinOp->getOpcode() == Instruction::FSub) &&((void)0) |
| 3449 | "Original bin op should be defined for FP induction")((void)0); |
| 3450 | |
| 3451 | Value *StepValue = cast<SCEVUnknown>(Step)->getValue(); |
| 3452 | Value *MulExp = B.CreateFMul(StepValue, Index); |
| 3453 | return B.CreateBinOp(InductionBinOp->getOpcode(), StartValue, MulExp, |
| 3454 | "induction"); |
| 3455 | } |
| 3456 | case InductionDescriptor::IK_NoInduction: |
| 3457 | return nullptr; |
| 3458 | } |
| 3459 | llvm_unreachable("invalid enum")__builtin_unreachable(); |
| 3460 | } |
| 3461 | |
| 3462 | Loop *InnerLoopVectorizer::createVectorLoopSkeleton(StringRef Prefix) { |
| 3463 | LoopScalarBody = OrigLoop->getHeader(); |
| 3464 | LoopVectorPreHeader = OrigLoop->getLoopPreheader(); |
| 3465 | assert(LoopVectorPreHeader && "Invalid loop structure")((void)0); |
| 3466 | LoopExitBlock = OrigLoop->getUniqueExitBlock(); // may be nullptr |
| 3467 | assert((LoopExitBlock || Cost->requiresScalarEpilogue(VF)) &&((void)0) |
| 3468 | "multiple exit loop without required epilogue?")((void)0); |
| 3469 | |
| 3470 | LoopMiddleBlock = |
| 3471 | SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| 3472 | LI, nullptr, Twine(Prefix) + "middle.block"); |
| 3473 | LoopScalarPreHeader = |
| 3474 | SplitBlock(LoopMiddleBlock, LoopMiddleBlock->getTerminator(), DT, LI, |
| 3475 | nullptr, Twine(Prefix) + "scalar.ph"); |
| 3476 | |
| 3477 | auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); |
| 3478 | |
| 3479 | // Set up the middle block terminator. Two cases: |
| 3480 | // 1) If we know that we must execute the scalar epilogue, emit an |
| 3481 | // unconditional branch. |
| 3482 | // 2) Otherwise, we must have a single unique exit block (due to how we |
| 3483 | // implement the multiple exit case). In this case, set up a conditonal |
| 3484 | // branch from the middle block to the loop scalar preheader, and the |
| 3485 | // exit block. completeLoopSkeleton will update the condition to use an |
| 3486 | // iteration check, if required to decide whether to execute the remainder. |
| 3487 | BranchInst *BrInst = Cost->requiresScalarEpilogue(VF) ? |
| 3488 | BranchInst::Create(LoopScalarPreHeader) : |
| 3489 | BranchInst::Create(LoopExitBlock, LoopScalarPreHeader, |
| 3490 | Builder.getTrue()); |
| 3491 | BrInst->setDebugLoc(ScalarLatchTerm->getDebugLoc()); |
| 3492 | ReplaceInstWithInst(LoopMiddleBlock->getTerminator(), BrInst); |
| 3493 | |
| 3494 | // We intentionally don't let SplitBlock to update LoopInfo since |
| 3495 | // LoopVectorBody should belong to another loop than LoopVectorPreHeader. |
| 3496 | // LoopVectorBody is explicitly added to the correct place few lines later. |
| 3497 | LoopVectorBody = |
| 3498 | SplitBlock(LoopVectorPreHeader, LoopVectorPreHeader->getTerminator(), DT, |
| 3499 | nullptr, nullptr, Twine(Prefix) + "vector.body"); |
| 3500 | |
| 3501 | // Update dominator for loop exit. |
| 3502 | if (!Cost->requiresScalarEpilogue(VF)) |
| 3503 | // If there is an epilogue which must run, there's no edge from the |
| 3504 | // middle block to exit blocks and thus no need to update the immediate |
| 3505 | // dominator of the exit blocks. |
| 3506 | DT->changeImmediateDominator(LoopExitBlock, LoopMiddleBlock); |
| 3507 | |
| 3508 | // Create and register the new vector loop. |
| 3509 | Loop *Lp = LI->AllocateLoop(); |
| 3510 | Loop *ParentLoop = OrigLoop->getParentLoop(); |
| 3511 | |
| 3512 | // Insert the new loop into the loop nest and register the new basic blocks |
| 3513 | // before calling any utilities such as SCEV that require valid LoopInfo. |
| 3514 | if (ParentLoop) { |
| 3515 | ParentLoop->addChildLoop(Lp); |
| 3516 | } else { |
| 3517 | LI->addTopLevelLoop(Lp); |
| 3518 | } |
| 3519 | Lp->addBasicBlockToLoop(LoopVectorBody, *LI); |
| 3520 | return Lp; |
| 3521 | } |
| 3522 | |
| 3523 | void InnerLoopVectorizer::createInductionResumeValues( |
| 3524 | Loop *L, Value *VectorTripCount, |
| 3525 | std::pair<BasicBlock *, Value *> AdditionalBypass) { |
| 3526 | assert(VectorTripCount && L && "Expected valid arguments")((void)0); |
| 3527 | assert(((AdditionalBypass.first && AdditionalBypass.second) ||((void)0) |
| 3528 | (!AdditionalBypass.first && !AdditionalBypass.second)) &&((void)0) |
| 3529 | "Inconsistent information about additional bypass.")((void)0); |
| 3530 | // We are going to resume the execution of the scalar loop. |
| 3531 | // Go over all of the induction variables that we found and fix the |
| 3532 | // PHIs that are left in the scalar version of the loop. |
| 3533 | // The starting values of PHI nodes depend on the counter of the last |
| 3534 | // iteration in the vectorized loop. |
| 3535 | // If we come from a bypass edge then we need to start from the original |
| 3536 | // start value. |
| 3537 | for (auto &InductionEntry : Legal->getInductionVars()) { |
| 3538 | PHINode *OrigPhi = InductionEntry.first; |
| 3539 | InductionDescriptor II = InductionEntry.second; |
| 3540 | |
| 3541 | // Create phi nodes to merge from the backedge-taken check block. |
| 3542 | PHINode *BCResumeVal = |
| 3543 | PHINode::Create(OrigPhi->getType(), 3, "bc.resume.val", |
| 3544 | LoopScalarPreHeader->getTerminator()); |
| 3545 | // Copy original phi DL over to the new one. |
| 3546 | BCResumeVal->setDebugLoc(OrigPhi->getDebugLoc()); |
| 3547 | Value *&EndValue = IVEndValues[OrigPhi]; |
| 3548 | Value *EndValueFromAdditionalBypass = AdditionalBypass.second; |
| 3549 | if (OrigPhi == OldInduction) { |
| 3550 | // We know what the end value is. |
| 3551 | EndValue = VectorTripCount; |
| 3552 | } else { |
| 3553 | IRBuilder<> B(L->getLoopPreheader()->getTerminator()); |
| 3554 | |
| 3555 | // Fast-math-flags propagate from the original induction instruction. |
| 3556 | if (II.getInductionBinOp() && isa<FPMathOperator>(II.getInductionBinOp())) |
| 3557 | B.setFastMathFlags(II.getInductionBinOp()->getFastMathFlags()); |
| 3558 | |
| 3559 | Type *StepType = II.getStep()->getType(); |
| 3560 | Instruction::CastOps CastOp = |
| 3561 | CastInst::getCastOpcode(VectorTripCount, true, StepType, true); |
| 3562 | Value *CRD = B.CreateCast(CastOp, VectorTripCount, StepType, "cast.crd"); |
| 3563 | const DataLayout &DL = LoopScalarBody->getModule()->getDataLayout(); |
| 3564 | EndValue = emitTransformedIndex(B, CRD, PSE.getSE(), DL, II); |
| 3565 | EndValue->setName("ind.end"); |
| 3566 | |
| 3567 | // Compute the end value for the additional bypass (if applicable). |
| 3568 | if (AdditionalBypass.first) { |
| 3569 | B.SetInsertPoint(&(*AdditionalBypass.first->getFirstInsertionPt())); |
| 3570 | CastOp = CastInst::getCastOpcode(AdditionalBypass.second, true, |
| 3571 | StepType, true); |
| 3572 | CRD = |
| 3573 | B.CreateCast(CastOp, AdditionalBypass.second, StepType, "cast.crd"); |
| 3574 | EndValueFromAdditionalBypass = |
| 3575 | emitTransformedIndex(B, CRD, PSE.getSE(), DL, II); |
| 3576 | EndValueFromAdditionalBypass->setName("ind.end"); |
| 3577 | } |
| 3578 | } |
| 3579 | // The new PHI merges the original incoming value, in case of a bypass, |
| 3580 | // or the value at the end of the vectorized loop. |
| 3581 | BCResumeVal->addIncoming(EndValue, LoopMiddleBlock); |
| 3582 | |
| 3583 | // Fix the scalar body counter (PHI node). |
| 3584 | // The old induction's phi node in the scalar body needs the truncated |
| 3585 | // value. |
| 3586 | for (BasicBlock *BB : LoopBypassBlocks) |
| 3587 | BCResumeVal->addIncoming(II.getStartValue(), BB); |
| 3588 | |
| 3589 | if (AdditionalBypass.first) |
| 3590 | BCResumeVal->setIncomingValueForBlock(AdditionalBypass.first, |
| 3591 | EndValueFromAdditionalBypass); |
| 3592 | |
| 3593 | OrigPhi->setIncomingValueForBlock(LoopScalarPreHeader, BCResumeVal); |
| 3594 | } |
| 3595 | } |
| 3596 | |
| 3597 | BasicBlock *InnerLoopVectorizer::completeLoopSkeleton(Loop *L, |
| 3598 | MDNode *OrigLoopID) { |
| 3599 | assert(L && "Expected valid loop.")((void)0); |
| 3600 | |
| 3601 | // The trip counts should be cached by now. |
| 3602 | Value *Count = getOrCreateTripCount(L); |
| 3603 | Value *VectorTripCount = getOrCreateVectorTripCount(L); |
| 3604 | |
| 3605 | auto *ScalarLatchTerm = OrigLoop->getLoopLatch()->getTerminator(); |
| 3606 | |
| 3607 | // Add a check in the middle block to see if we have completed |
| 3608 | // all of the iterations in the first vector loop. Three cases: |
| 3609 | // 1) If we require a scalar epilogue, there is no conditional branch as |
| 3610 | // we unconditionally branch to the scalar preheader. Do nothing. |
| 3611 | // 2) If (N - N%VF) == N, then we *don't* need to run the remainder. |
| 3612 | // Thus if tail is to be folded, we know we don't need to run the |
| 3613 | // remainder and we can use the previous value for the condition (true). |
| 3614 | // 3) Otherwise, construct a runtime check. |
| 3615 | if (!Cost->requiresScalarEpilogue(VF) && !Cost->foldTailByMasking()) { |
| 3616 | Instruction *CmpN = CmpInst::Create(Instruction::ICmp, CmpInst::ICMP_EQ, |
| 3617 | Count, VectorTripCount, "cmp.n", |
| 3618 | LoopMiddleBlock->getTerminator()); |
| 3619 | |
| 3620 | // Here we use the same DebugLoc as the scalar loop latch terminator instead |
| 3621 | // of the corresponding compare because they may have ended up with |
| 3622 | // different line numbers and we want to avoid awkward line stepping while |
| 3623 | // debugging. Eg. if the compare has got a line number inside the loop. |
| 3624 | CmpN->setDebugLoc(ScalarLatchTerm->getDebugLoc()); |
| 3625 | cast<BranchInst>(LoopMiddleBlock->getTerminator())->setCondition(CmpN); |
| 3626 | } |
| 3627 | |
| 3628 | // Get ready to start creating new instructions into the vectorized body. |
| 3629 | assert(LoopVectorPreHeader == L->getLoopPreheader() &&((void)0) |
| 3630 | "Inconsistent vector loop preheader")((void)0); |
| 3631 | Builder.SetInsertPoint(&*LoopVectorBody->getFirstInsertionPt()); |
| 3632 | |
| 3633 | Optional<MDNode *> VectorizedLoopID = |
| 3634 | makeFollowupLoopID(OrigLoopID, {LLVMLoopVectorizeFollowupAll, |
| 3635 | LLVMLoopVectorizeFollowupVectorized}); |
| 3636 | if (VectorizedLoopID.hasValue()) { |
| 3637 | L->setLoopID(VectorizedLoopID.getValue()); |
| 3638 | |
| 3639 | // Do not setAlreadyVectorized if loop attributes have been defined |
| 3640 | // explicitly. |
| 3641 | return LoopVectorPreHeader; |
| 3642 | } |
| 3643 | |
| 3644 | // Keep all loop hints from the original loop on the vector loop (we'll |
| 3645 | // replace the vectorizer-specific hints below). |
| 3646 | if (MDNode *LID = OrigLoop->getLoopID()) |
| 3647 | L->setLoopID(LID); |
| 3648 | |
| 3649 | LoopVectorizeHints Hints(L, true, *ORE); |
| 3650 | Hints.setAlreadyVectorized(); |
| 3651 | |
| 3652 | #ifdef EXPENSIVE_CHECKS |
| 3653 | assert(DT->verify(DominatorTree::VerificationLevel::Fast))((void)0); |
| 3654 | LI->verify(*DT); |
| 3655 | #endif |
| 3656 | |
| 3657 | return LoopVectorPreHeader; |
| 3658 | } |
| 3659 | |
| 3660 | BasicBlock *InnerLoopVectorizer::createVectorizedLoopSkeleton() { |
| 3661 | /* |
| 3662 | In this function we generate a new loop. The new loop will contain |
| 3663 | the vectorized instructions while the old loop will continue to run the |
| 3664 | scalar remainder. |
| 3665 | |
| 3666 | [ ] <-- loop iteration number check. |
| 3667 | / | |
| 3668 | / v |
| 3669 | | [ ] <-- vector loop bypass (may consist of multiple blocks). |
| 3670 | | / | |
| 3671 | | / v |
| 3672 | || [ ] <-- vector pre header. |
| 3673 | |/ | |
| 3674 | | v |
| 3675 | | [ ] \ |
| 3676 | | [ ]_| <-- vector loop. |
| 3677 | | | |
| 3678 | | v |
| 3679 | \ -[ ] <--- middle-block. |
| 3680 | \/ | |
| 3681 | /\ v |
| 3682 | | ->[ ] <--- new preheader. |
| 3683 | | | |
| 3684 | (opt) v <-- edge from middle to exit iff epilogue is not required. |
| 3685 | | [ ] \ |
| 3686 | | [ ]_| <-- old scalar loop to handle remainder (scalar epilogue). |
| 3687 | \ | |
| 3688 | \ v |
| 3689 | >[ ] <-- exit block(s). |
| 3690 | ... |
| 3691 | */ |
| 3692 | |
| 3693 | // Get the metadata of the original loop before it gets modified. |
| 3694 | MDNode *OrigLoopID = OrigLoop->getLoopID(); |
| 3695 | |
| 3696 | // Workaround! Compute the trip count of the original loop and cache it |
| 3697 | // before we start modifying the CFG. This code has a systemic problem |
| 3698 | // wherein it tries to run analysis over partially constructed IR; this is |
| 3699 | // wrong, and not simply for SCEV. The trip count of the original loop |
| 3700 | // simply happens to be prone to hitting this in practice. In theory, we |
| 3701 | // can hit the same issue for any SCEV, or ValueTracking query done during |
| 3702 | // mutation. See PR49900. |
| 3703 | getOrCreateTripCount(OrigLoop); |
| 3704 | |
| 3705 | // Create an empty vector loop, and prepare basic blocks for the runtime |
| 3706 | // checks. |
| 3707 | Loop *Lp = createVectorLoopSkeleton(""); |
| 3708 | |
| 3709 | // Now, compare the new count to zero. If it is zero skip the vector loop and |
| 3710 | // jump to the scalar loop. This check also covers the case where the |
| 3711 | // backedge-taken count is uint##_max: adding one to it will overflow leading |
| 3712 | // to an incorrect trip count of zero. In this (rare) case we will also jump |
| 3713 | // to the scalar loop. |
| 3714 | emitMinimumIterationCountCheck(Lp, LoopScalarPreHeader); |
| 3715 | |
| 3716 | // Generate the code to check any assumptions that we've made for SCEV |
| 3717 | // expressions. |
| 3718 | emitSCEVChecks(Lp, LoopScalarPreHeader); |
| 3719 | |
| 3720 | // Generate the code that checks in runtime if arrays overlap. We put the |
| 3721 | // checks into a separate block to make the more common case of few elements |
| 3722 | // faster. |
| 3723 | emitMemRuntimeChecks(Lp, LoopScalarPreHeader); |
| 3724 | |
| 3725 | // Some loops have a single integer induction variable, while other loops |
| 3726 | // don't. One example is c++ iterators that often have multiple pointer |
| 3727 | // induction variables. In the code below we also support a case where we |
| 3728 | // don't have a single induction variable. |
| 3729 | // |
| 3730 | // We try to obtain an induction variable from the original loop as hard |
| 3731 | // as possible. However if we don't find one that: |
| 3732 | // - is an integer |
| 3733 | // - counts from zero, stepping by one |
| 3734 | // - is the size of the widest induction variable type |
| 3735 | // then we create a new one. |
| 3736 | OldInduction = Legal->getPrimaryInduction(); |
| 3737 | Type *IdxTy = Legal->getWidestInductionType(); |
| 3738 | Value *StartIdx = ConstantInt::get(IdxTy, 0); |
| 3739 | // The loop step is equal to the vectorization factor (num of SIMD elements) |
| 3740 | // times the unroll factor (num of SIMD instructions). |
| 3741 | Builder.SetInsertPoint(&*Lp->getHeader()->getFirstInsertionPt()); |
| 3742 | Value *Step = createStepForVF(Builder, ConstantInt::get(IdxTy, UF), VF); |
| 3743 | Value *CountRoundDown = getOrCreateVectorTripCount(Lp); |
| 3744 | Induction = |
| 3745 | createInductionVariable(Lp, StartIdx, CountRoundDown, Step, |
| 3746 | getDebugLocFromInstOrOperands(OldInduction)); |
| 3747 | |
| 3748 | // Emit phis for the new starting index of the scalar loop. |
| 3749 | createInductionResumeValues(Lp, CountRoundDown); |
| 3750 | |
| 3751 | return completeLoopSkeleton(Lp, OrigLoopID); |
| 3752 | } |
| 3753 | |
| 3754 | // Fix up external users of the induction variable. At this point, we are |
| 3755 | // in LCSSA form, with all external PHIs that use the IV having one input value, |
| 3756 | // coming from the remainder loop. We need those PHIs to also have a correct |
| 3757 | // value for the IV when arriving directly from the middle block. |
| 3758 | void InnerLoopVectorizer::fixupIVUsers(PHINode *OrigPhi, |
| 3759 | const InductionDescriptor &II, |
| 3760 | Value *CountRoundDown, Value *EndValue, |
| 3761 | BasicBlock *MiddleBlock) { |
| 3762 | // There are two kinds of external IV usages - those that use the value |
| 3763 | // computed in the last iteration (the PHI) and those that use the penultimate |
| 3764 | // value (the value that feeds into the phi from the loop latch). |
| 3765 | // We allow both, but they, obviously, have different values. |
| 3766 | |
| 3767 | assert(OrigLoop->getUniqueExitBlock() && "Expected a single exit block")((void)0); |
| 3768 | |
| 3769 | DenseMap<Value *, Value *> MissingVals; |
| 3770 | |
| 3771 | // An external user of the last iteration's value should see the value that |
| 3772 | // the remainder loop uses to initialize its own IV. |
| 3773 | Value *PostInc = OrigPhi->getIncomingValueForBlock(OrigLoop->getLoopLatch()); |
| 3774 | for (User *U : PostInc->users()) { |
| 3775 | Instruction *UI = cast<Instruction>(U); |
| 3776 | if (!OrigLoop->contains(UI)) { |
| 3777 | assert(isa<PHINode>(UI) && "Expected LCSSA form")((void)0); |
| 3778 | MissingVals[UI] = EndValue; |
| 3779 | } |
| 3780 | } |
| 3781 | |
| 3782 | // An external user of the penultimate value need to see EndValue - Step. |
| 3783 | // The simplest way to get this is to recompute it from the constituent SCEVs, |
| 3784 | // that is Start + (Step * (CRD - 1)). |
| 3785 | for (User *U : OrigPhi->users()) { |
| 3786 | auto *UI = cast<Instruction>(U); |
| 3787 | if (!OrigLoop->contains(UI)) { |
| 3788 | const DataLayout &DL = |
| 3789 | OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 3790 | assert(isa<PHINode>(UI) && "Expected LCSSA form")((void)0); |
| 3791 | |
| 3792 | IRBuilder<> B(MiddleBlock->getTerminator()); |
| 3793 | |
| 3794 | // Fast-math-flags propagate from the original induction instruction. |
| 3795 | if (II.getInductionBinOp() && isa<FPMathOperator>(II.getInductionBinOp())) |
| 3796 | B.setFastMathFlags(II.getInductionBinOp()->getFastMathFlags()); |
| 3797 | |
| 3798 | Value *CountMinusOne = B.CreateSub( |
| 3799 | CountRoundDown, ConstantInt::get(CountRoundDown->getType(), 1)); |
| 3800 | Value *CMO = |
| 3801 | !II.getStep()->getType()->isIntegerTy() |
| 3802 | ? B.CreateCast(Instruction::SIToFP, CountMinusOne, |
| 3803 | II.getStep()->getType()) |
| 3804 | : B.CreateSExtOrTrunc(CountMinusOne, II.getStep()->getType()); |
| 3805 | CMO->setName("cast.cmo"); |
| 3806 | Value *Escape = emitTransformedIndex(B, CMO, PSE.getSE(), DL, II); |
| 3807 | Escape->setName("ind.escape"); |
| 3808 | MissingVals[UI] = Escape; |
| 3809 | } |
| 3810 | } |
| 3811 | |
| 3812 | for (auto &I : MissingVals) { |
| 3813 | PHINode *PHI = cast<PHINode>(I.first); |
| 3814 | // One corner case we have to handle is two IVs "chasing" each-other, |
| 3815 | // that is %IV2 = phi [...], [ %IV1, %latch ] |
| 3816 | // In this case, if IV1 has an external use, we need to avoid adding both |
| 3817 | // "last value of IV1" and "penultimate value of IV2". So, verify that we |
| 3818 | // don't already have an incoming value for the middle block. |
| 3819 | if (PHI->getBasicBlockIndex(MiddleBlock) == -1) |
| 3820 | PHI->addIncoming(I.second, MiddleBlock); |
| 3821 | } |
| 3822 | } |
| 3823 | |
| 3824 | namespace { |
| 3825 | |
| 3826 | struct CSEDenseMapInfo { |
| 3827 | static bool canHandle(const Instruction *I) { |
| 3828 | return isa<InsertElementInst>(I) || isa<ExtractElementInst>(I) || |
| 3829 | isa<ShuffleVectorInst>(I) || isa<GetElementPtrInst>(I); |
| 3830 | } |
| 3831 | |
| 3832 | static inline Instruction *getEmptyKey() { |
| 3833 | return DenseMapInfo<Instruction *>::getEmptyKey(); |
| 3834 | } |
| 3835 | |
| 3836 | static inline Instruction *getTombstoneKey() { |
| 3837 | return DenseMapInfo<Instruction *>::getTombstoneKey(); |
| 3838 | } |
| 3839 | |
| 3840 | static unsigned getHashValue(const Instruction *I) { |
| 3841 | assert(canHandle(I) && "Unknown instruction!")((void)0); |
| 3842 | return hash_combine(I->getOpcode(), hash_combine_range(I->value_op_begin(), |
| 3843 | I->value_op_end())); |
| 3844 | } |
| 3845 | |
| 3846 | static bool isEqual(const Instruction *LHS, const Instruction *RHS) { |
| 3847 | if (LHS == getEmptyKey() || RHS == getEmptyKey() || |
| 3848 | LHS == getTombstoneKey() || RHS == getTombstoneKey()) |
| 3849 | return LHS == RHS; |
| 3850 | return LHS->isIdenticalTo(RHS); |
| 3851 | } |
| 3852 | }; |
| 3853 | |
| 3854 | } // end anonymous namespace |
| 3855 | |
| 3856 | ///Perform cse of induction variable instructions. |
| 3857 | static void cse(BasicBlock *BB) { |
| 3858 | // Perform simple cse. |
| 3859 | SmallDenseMap<Instruction *, Instruction *, 4, CSEDenseMapInfo> CSEMap; |
| 3860 | for (BasicBlock::iterator I = BB->begin(), E = BB->end(); I != E;) { |
| 3861 | Instruction *In = &*I++; |
| 3862 | |
| 3863 | if (!CSEDenseMapInfo::canHandle(In)) |
| 3864 | continue; |
| 3865 | |
| 3866 | // Check if we can replace this instruction with any of the |
| 3867 | // visited instructions. |
| 3868 | if (Instruction *V = CSEMap.lookup(In)) { |
| 3869 | In->replaceAllUsesWith(V); |
| 3870 | In->eraseFromParent(); |
| 3871 | continue; |
| 3872 | } |
| 3873 | |
| 3874 | CSEMap[In] = In; |
| 3875 | } |
| 3876 | } |
| 3877 | |
| 3878 | InstructionCost |
| 3879 | LoopVectorizationCostModel::getVectorCallCost(CallInst *CI, ElementCount VF, |
| 3880 | bool &NeedToScalarize) const { |
| 3881 | Function *F = CI->getCalledFunction(); |
| 3882 | Type *ScalarRetTy = CI->getType(); |
| 3883 | SmallVector<Type *, 4> Tys, ScalarTys; |
| 3884 | for (auto &ArgOp : CI->arg_operands()) |
| 3885 | ScalarTys.push_back(ArgOp->getType()); |
| 3886 | |
| 3887 | // Estimate cost of scalarized vector call. The source operands are assumed |
| 3888 | // to be vectors, so we need to extract individual elements from there, |
| 3889 | // execute VF scalar calls, and then gather the result into the vector return |
| 3890 | // value. |
| 3891 | InstructionCost ScalarCallCost = |
| 3892 | TTI.getCallInstrCost(F, ScalarRetTy, ScalarTys, TTI::TCK_RecipThroughput); |
| 3893 | if (VF.isScalar()) |
| 3894 | return ScalarCallCost; |
| 3895 | |
| 3896 | // Compute corresponding vector type for return value and arguments. |
| 3897 | Type *RetTy = ToVectorTy(ScalarRetTy, VF); |
| 3898 | for (Type *ScalarTy : ScalarTys) |
| 3899 | Tys.push_back(ToVectorTy(ScalarTy, VF)); |
| 3900 | |
| 3901 | // Compute costs of unpacking argument values for the scalar calls and |
| 3902 | // packing the return values to a vector. |
| 3903 | InstructionCost ScalarizationCost = getScalarizationOverhead(CI, VF); |
| 3904 | |
| 3905 | InstructionCost Cost = |
| 3906 | ScalarCallCost * VF.getKnownMinValue() + ScalarizationCost; |
| 3907 | |
| 3908 | // If we can't emit a vector call for this function, then the currently found |
| 3909 | // cost is the cost we need to return. |
| 3910 | NeedToScalarize = true; |
| 3911 | VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); |
| 3912 | Function *VecFunc = VFDatabase(*CI).getVectorizedFunction(Shape); |
| 3913 | |
| 3914 | if (!TLI || CI->isNoBuiltin() || !VecFunc) |
| 3915 | return Cost; |
| 3916 | |
| 3917 | // If the corresponding vector cost is cheaper, return its cost. |
| 3918 | InstructionCost VectorCallCost = |
| 3919 | TTI.getCallInstrCost(nullptr, RetTy, Tys, TTI::TCK_RecipThroughput); |
| 3920 | if (VectorCallCost < Cost) { |
| 3921 | NeedToScalarize = false; |
| 3922 | Cost = VectorCallCost; |
| 3923 | } |
| 3924 | return Cost; |
| 3925 | } |
| 3926 | |
| 3927 | static Type *MaybeVectorizeType(Type *Elt, ElementCount VF) { |
| 3928 | if (VF.isScalar() || (!Elt->isIntOrPtrTy() && !Elt->isFloatingPointTy())) |
| 3929 | return Elt; |
| 3930 | return VectorType::get(Elt, VF); |
| 3931 | } |
| 3932 | |
| 3933 | InstructionCost |
| 3934 | LoopVectorizationCostModel::getVectorIntrinsicCost(CallInst *CI, |
| 3935 | ElementCount VF) const { |
| 3936 | Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| 3937 | assert(ID && "Expected intrinsic call!")((void)0); |
| 3938 | Type *RetTy = MaybeVectorizeType(CI->getType(), VF); |
| 3939 | FastMathFlags FMF; |
| 3940 | if (auto *FPMO = dyn_cast<FPMathOperator>(CI)) |
| 3941 | FMF = FPMO->getFastMathFlags(); |
| 3942 | |
| 3943 | SmallVector<const Value *> Arguments(CI->arg_begin(), CI->arg_end()); |
| 3944 | FunctionType *FTy = CI->getCalledFunction()->getFunctionType(); |
| 3945 | SmallVector<Type *> ParamTys; |
| 3946 | std::transform(FTy->param_begin(), FTy->param_end(), |
| 3947 | std::back_inserter(ParamTys), |
| 3948 | [&](Type *Ty) { return MaybeVectorizeType(Ty, VF); }); |
| 3949 | |
| 3950 | IntrinsicCostAttributes CostAttrs(ID, RetTy, Arguments, ParamTys, FMF, |
| 3951 | dyn_cast<IntrinsicInst>(CI)); |
| 3952 | return TTI.getIntrinsicInstrCost(CostAttrs, |
| 3953 | TargetTransformInfo::TCK_RecipThroughput); |
| 3954 | } |
| 3955 | |
| 3956 | static Type *smallestIntegerVectorType(Type *T1, Type *T2) { |
| 3957 | auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType()); |
| 3958 | auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType()); |
| 3959 | return I1->getBitWidth() < I2->getBitWidth() ? T1 : T2; |
| 3960 | } |
| 3961 | |
| 3962 | static Type *largestIntegerVectorType(Type *T1, Type *T2) { |
| 3963 | auto *I1 = cast<IntegerType>(cast<VectorType>(T1)->getElementType()); |
| 3964 | auto *I2 = cast<IntegerType>(cast<VectorType>(T2)->getElementType()); |
| 3965 | return I1->getBitWidth() > I2->getBitWidth() ? T1 : T2; |
| 3966 | } |
| 3967 | |
| 3968 | void InnerLoopVectorizer::truncateToMinimalBitwidths(VPTransformState &State) { |
| 3969 | // For every instruction `I` in MinBWs, truncate the operands, create a |
| 3970 | // truncated version of `I` and reextend its result. InstCombine runs |
| 3971 | // later and will remove any ext/trunc pairs. |
| 3972 | SmallPtrSet<Value *, 4> Erased; |
| 3973 | for (const auto &KV : Cost->getMinimalBitwidths()) { |
| 3974 | // If the value wasn't vectorized, we must maintain the original scalar |
| 3975 | // type. The absence of the value from State indicates that it |
| 3976 | // wasn't vectorized. |
| 3977 | VPValue *Def = State.Plan->getVPValue(KV.first); |
| 3978 | if (!State.hasAnyVectorValue(Def)) |
| 3979 | continue; |
| 3980 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 3981 | Value *I = State.get(Def, Part); |
| 3982 | if (Erased.count(I) || I->use_empty() || !isa<Instruction>(I)) |
| 3983 | continue; |
| 3984 | Type *OriginalTy = I->getType(); |
| 3985 | Type *ScalarTruncatedTy = |
| 3986 | IntegerType::get(OriginalTy->getContext(), KV.second); |
| 3987 | auto *TruncatedTy = VectorType::get( |
| 3988 | ScalarTruncatedTy, cast<VectorType>(OriginalTy)->getElementCount()); |
| 3989 | if (TruncatedTy == OriginalTy) |
| 3990 | continue; |
| 3991 | |
| 3992 | IRBuilder<> B(cast<Instruction>(I)); |
| 3993 | auto ShrinkOperand = [&](Value *V) -> Value * { |
| 3994 | if (auto *ZI = dyn_cast<ZExtInst>(V)) |
| 3995 | if (ZI->getSrcTy() == TruncatedTy) |
| 3996 | return ZI->getOperand(0); |
| 3997 | return B.CreateZExtOrTrunc(V, TruncatedTy); |
| 3998 | }; |
| 3999 | |
| 4000 | // The actual instruction modification depends on the instruction type, |
| 4001 | // unfortunately. |
| 4002 | Value *NewI = nullptr; |
| 4003 | if (auto *BO = dyn_cast<BinaryOperator>(I)) { |
| 4004 | NewI = B.CreateBinOp(BO->getOpcode(), ShrinkOperand(BO->getOperand(0)), |
| 4005 | ShrinkOperand(BO->getOperand(1))); |
| 4006 | |
| 4007 | // Any wrapping introduced by shrinking this operation shouldn't be |
| 4008 | // considered undefined behavior. So, we can't unconditionally copy |
| 4009 | // arithmetic wrapping flags to NewI. |
| 4010 | cast<BinaryOperator>(NewI)->copyIRFlags(I, /*IncludeWrapFlags=*/false); |
| 4011 | } else if (auto *CI = dyn_cast<ICmpInst>(I)) { |
| 4012 | NewI = |
| 4013 | B.CreateICmp(CI->getPredicate(), ShrinkOperand(CI->getOperand(0)), |
| 4014 | ShrinkOperand(CI->getOperand(1))); |
| 4015 | } else if (auto *SI = dyn_cast<SelectInst>(I)) { |
| 4016 | NewI = B.CreateSelect(SI->getCondition(), |
| 4017 | ShrinkOperand(SI->getTrueValue()), |
| 4018 | ShrinkOperand(SI->getFalseValue())); |
| 4019 | } else if (auto *CI = dyn_cast<CastInst>(I)) { |
| 4020 | switch (CI->getOpcode()) { |
| 4021 | default: |
| 4022 | llvm_unreachable("Unhandled cast!")__builtin_unreachable(); |
| 4023 | case Instruction::Trunc: |
| 4024 | NewI = ShrinkOperand(CI->getOperand(0)); |
| 4025 | break; |
| 4026 | case Instruction::SExt: |
| 4027 | NewI = B.CreateSExtOrTrunc( |
| 4028 | CI->getOperand(0), |
| 4029 | smallestIntegerVectorType(OriginalTy, TruncatedTy)); |
| 4030 | break; |
| 4031 | case Instruction::ZExt: |
| 4032 | NewI = B.CreateZExtOrTrunc( |
| 4033 | CI->getOperand(0), |
| 4034 | smallestIntegerVectorType(OriginalTy, TruncatedTy)); |
| 4035 | break; |
| 4036 | } |
| 4037 | } else if (auto *SI = dyn_cast<ShuffleVectorInst>(I)) { |
| 4038 | auto Elements0 = |
| 4039 | cast<VectorType>(SI->getOperand(0)->getType())->getElementCount(); |
| 4040 | auto *O0 = B.CreateZExtOrTrunc( |
| 4041 | SI->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements0)); |
| 4042 | auto Elements1 = |
| 4043 | cast<VectorType>(SI->getOperand(1)->getType())->getElementCount(); |
| 4044 | auto *O1 = B.CreateZExtOrTrunc( |
| 4045 | SI->getOperand(1), VectorType::get(ScalarTruncatedTy, Elements1)); |
| 4046 | |
| 4047 | NewI = B.CreateShuffleVector(O0, O1, SI->getShuffleMask()); |
| 4048 | } else if (isa<LoadInst>(I) || isa<PHINode>(I)) { |
| 4049 | // Don't do anything with the operands, just extend the result. |
| 4050 | continue; |
| 4051 | } else if (auto *IE = dyn_cast<InsertElementInst>(I)) { |
| 4052 | auto Elements = |
| 4053 | cast<VectorType>(IE->getOperand(0)->getType())->getElementCount(); |
| 4054 | auto *O0 = B.CreateZExtOrTrunc( |
| 4055 | IE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements)); |
| 4056 | auto *O1 = B.CreateZExtOrTrunc(IE->getOperand(1), ScalarTruncatedTy); |
| 4057 | NewI = B.CreateInsertElement(O0, O1, IE->getOperand(2)); |
| 4058 | } else if (auto *EE = dyn_cast<ExtractElementInst>(I)) { |
| 4059 | auto Elements = |
| 4060 | cast<VectorType>(EE->getOperand(0)->getType())->getElementCount(); |
| 4061 | auto *O0 = B.CreateZExtOrTrunc( |
| 4062 | EE->getOperand(0), VectorType::get(ScalarTruncatedTy, Elements)); |
| 4063 | NewI = B.CreateExtractElement(O0, EE->getOperand(2)); |
| 4064 | } else { |
| 4065 | // If we don't know what to do, be conservative and don't do anything. |
| 4066 | continue; |
| 4067 | } |
| 4068 | |
| 4069 | // Lastly, extend the result. |
| 4070 | NewI->takeName(cast<Instruction>(I)); |
| 4071 | Value *Res = B.CreateZExtOrTrunc(NewI, OriginalTy); |
| 4072 | I->replaceAllUsesWith(Res); |
| 4073 | cast<Instruction>(I)->eraseFromParent(); |
| 4074 | Erased.insert(I); |
| 4075 | State.reset(Def, Res, Part); |
| 4076 | } |
| 4077 | } |
| 4078 | |
| 4079 | // We'll have created a bunch of ZExts that are now parentless. Clean up. |
| 4080 | for (const auto &KV : Cost->getMinimalBitwidths()) { |
| 4081 | // If the value wasn't vectorized, we must maintain the original scalar |
| 4082 | // type. The absence of the value from State indicates that it |
| 4083 | // wasn't vectorized. |
| 4084 | VPValue *Def = State.Plan->getVPValue(KV.first); |
| 4085 | if (!State.hasAnyVectorValue(Def)) |
| 4086 | continue; |
| 4087 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4088 | Value *I = State.get(Def, Part); |
| 4089 | ZExtInst *Inst = dyn_cast<ZExtInst>(I); |
| 4090 | if (Inst && Inst->use_empty()) { |
| 4091 | Value *NewI = Inst->getOperand(0); |
| 4092 | Inst->eraseFromParent(); |
| 4093 | State.reset(Def, NewI, Part); |
| 4094 | } |
| 4095 | } |
| 4096 | } |
| 4097 | } |
| 4098 | |
| 4099 | void InnerLoopVectorizer::fixVectorizedLoop(VPTransformState &State) { |
| 4100 | // Insert truncates and extends for any truncated instructions as hints to |
| 4101 | // InstCombine. |
| 4102 | if (VF.isVector()) |
| 4103 | truncateToMinimalBitwidths(State); |
| 4104 | |
| 4105 | // Fix widened non-induction PHIs by setting up the PHI operands. |
| 4106 | if (OrigPHIsToFix.size()) { |
| 4107 | assert(EnableVPlanNativePath &&((void)0) |
| 4108 | "Unexpected non-induction PHIs for fixup in non VPlan-native path")((void)0); |
| 4109 | fixNonInductionPHIs(State); |
| 4110 | } |
| 4111 | |
| 4112 | // At this point every instruction in the original loop is widened to a |
| 4113 | // vector form. Now we need to fix the recurrences in the loop. These PHI |
| 4114 | // nodes are currently empty because we did not want to introduce cycles. |
| 4115 | // This is the second stage of vectorizing recurrences. |
| 4116 | fixCrossIterationPHIs(State); |
| 4117 | |
| 4118 | // Forget the original basic block. |
| 4119 | PSE.getSE()->forgetLoop(OrigLoop); |
| 4120 | |
| 4121 | // If we inserted an edge from the middle block to the unique exit block, |
| 4122 | // update uses outside the loop (phis) to account for the newly inserted |
| 4123 | // edge. |
| 4124 | if (!Cost->requiresScalarEpilogue(VF)) { |
| 4125 | // Fix-up external users of the induction variables. |
| 4126 | for (auto &Entry : Legal->getInductionVars()) |
| 4127 | fixupIVUsers(Entry.first, Entry.second, |
| 4128 | getOrCreateVectorTripCount(LI->getLoopFor(LoopVectorBody)), |
| 4129 | IVEndValues[Entry.first], LoopMiddleBlock); |
| 4130 | |
| 4131 | fixLCSSAPHIs(State); |
| 4132 | } |
| 4133 | |
| 4134 | for (Instruction *PI : PredicatedInstructions) |
| 4135 | sinkScalarOperands(&*PI); |
| 4136 | |
| 4137 | // Remove redundant induction instructions. |
| 4138 | cse(LoopVectorBody); |
| 4139 | |
| 4140 | // Set/update profile weights for the vector and remainder loops as original |
| 4141 | // loop iterations are now distributed among them. Note that original loop |
| 4142 | // represented by LoopScalarBody becomes remainder loop after vectorization. |
| 4143 | // |
| 4144 | // For cases like foldTailByMasking() and requiresScalarEpiloque() we may |
| 4145 | // end up getting slightly roughened result but that should be OK since |
| 4146 | // profile is not inherently precise anyway. Note also possible bypass of |
| 4147 | // vector code caused by legality checks is ignored, assigning all the weight |
| 4148 | // to the vector loop, optimistically. |
| 4149 | // |
| 4150 | // For scalable vectorization we can't know at compile time how many iterations |
| 4151 | // of the loop are handled in one vector iteration, so instead assume a pessimistic |
| 4152 | // vscale of '1'. |
| 4153 | setProfileInfoAfterUnrolling( |
| 4154 | LI->getLoopFor(LoopScalarBody), LI->getLoopFor(LoopVectorBody), |
| 4155 | LI->getLoopFor(LoopScalarBody), VF.getKnownMinValue() * UF); |
| 4156 | } |
| 4157 | |
| 4158 | void InnerLoopVectorizer::fixCrossIterationPHIs(VPTransformState &State) { |
| 4159 | // In order to support recurrences we need to be able to vectorize Phi nodes. |
| 4160 | // Phi nodes have cycles, so we need to vectorize them in two stages. This is |
| 4161 | // stage #2: We now need to fix the recurrences by adding incoming edges to |
| 4162 | // the currently empty PHI nodes. At this point every instruction in the |
| 4163 | // original loop is widened to a vector form so we can use them to construct |
| 4164 | // the incoming edges. |
| 4165 | VPBasicBlock *Header = State.Plan->getEntry()->getEntryBasicBlock(); |
| 4166 | for (VPRecipeBase &R : Header->phis()) { |
| 4167 | if (auto *ReductionPhi = dyn_cast<VPReductionPHIRecipe>(&R)) |
| 4168 | fixReduction(ReductionPhi, State); |
| 4169 | else if (auto *FOR = dyn_cast<VPFirstOrderRecurrencePHIRecipe>(&R)) |
| 4170 | fixFirstOrderRecurrence(FOR, State); |
| 4171 | } |
| 4172 | } |
| 4173 | |
| 4174 | void InnerLoopVectorizer::fixFirstOrderRecurrence(VPWidenPHIRecipe *PhiR, |
| 4175 | VPTransformState &State) { |
| 4176 | // This is the second phase of vectorizing first-order recurrences. An |
| 4177 | // overview of the transformation is described below. Suppose we have the |
| 4178 | // following loop. |
| 4179 | // |
| 4180 | // for (int i = 0; i < n; ++i) |
| 4181 | // b[i] = a[i] - a[i - 1]; |
| 4182 | // |
| 4183 | // There is a first-order recurrence on "a". For this loop, the shorthand |
| 4184 | // scalar IR looks like: |
| 4185 | // |
| 4186 | // scalar.ph: |
| 4187 | // s_init = a[-1] |
| 4188 | // br scalar.body |
| 4189 | // |
| 4190 | // scalar.body: |
| 4191 | // i = phi [0, scalar.ph], [i+1, scalar.body] |
| 4192 | // s1 = phi [s_init, scalar.ph], [s2, scalar.body] |
| 4193 | // s2 = a[i] |
| 4194 | // b[i] = s2 - s1 |
| 4195 | // br cond, scalar.body, ... |
| 4196 | // |
| 4197 | // In this example, s1 is a recurrence because it's value depends on the |
| 4198 | // previous iteration. In the first phase of vectorization, we created a |
| 4199 | // vector phi v1 for s1. We now complete the vectorization and produce the |
| 4200 | // shorthand vector IR shown below (for VF = 4, UF = 1). |
| 4201 | // |
| 4202 | // vector.ph: |
| 4203 | // v_init = vector(..., ..., ..., a[-1]) |
| 4204 | // br vector.body |
| 4205 | // |
| 4206 | // vector.body |
| 4207 | // i = phi [0, vector.ph], [i+4, vector.body] |
| 4208 | // v1 = phi [v_init, vector.ph], [v2, vector.body] |
| 4209 | // v2 = a[i, i+1, i+2, i+3]; |
| 4210 | // v3 = vector(v1(3), v2(0, 1, 2)) |
| 4211 | // b[i, i+1, i+2, i+3] = v2 - v3 |
| 4212 | // br cond, vector.body, middle.block |
| 4213 | // |
| 4214 | // middle.block: |
| 4215 | // x = v2(3) |
| 4216 | // br scalar.ph |
| 4217 | // |
| 4218 | // scalar.ph: |
| 4219 | // s_init = phi [x, middle.block], [a[-1], otherwise] |
| 4220 | // br scalar.body |
| 4221 | // |
| 4222 | // After execution completes the vector loop, we extract the next value of |
| 4223 | // the recurrence (x) to use as the initial value in the scalar loop. |
| 4224 | |
| 4225 | auto *IdxTy = Builder.getInt32Ty(); |
| 4226 | auto *VecPhi = cast<PHINode>(State.get(PhiR, 0)); |
| 4227 | |
| 4228 | // Fix the latch value of the new recurrence in the vector loop. |
| 4229 | VPValue *PreviousDef = PhiR->getBackedgeValue(); |
| 4230 | Value *Incoming = State.get(PreviousDef, UF - 1); |
| 4231 | VecPhi->addIncoming(Incoming, LI->getLoopFor(LoopVectorBody)->getLoopLatch()); |
| 4232 | |
| 4233 | // Extract the last vector element in the middle block. This will be the |
| 4234 | // initial value for the recurrence when jumping to the scalar loop. |
| 4235 | auto *ExtractForScalar = Incoming; |
| 4236 | if (VF.isVector()) { |
| 4237 | auto *One = ConstantInt::get(IdxTy, 1); |
| 4238 | Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); |
| 4239 | auto *RuntimeVF = getRuntimeVF(Builder, IdxTy, VF); |
| 4240 | auto *LastIdx = Builder.CreateSub(RuntimeVF, One); |
| 4241 | ExtractForScalar = Builder.CreateExtractElement(ExtractForScalar, LastIdx, |
| 4242 | "vector.recur.extract"); |
| 4243 | } |
| 4244 | // Extract the second last element in the middle block if the |
| 4245 | // Phi is used outside the loop. We need to extract the phi itself |
| 4246 | // and not the last element (the phi update in the current iteration). This |
| 4247 | // will be the value when jumping to the exit block from the LoopMiddleBlock, |
| 4248 | // when the scalar loop is not run at all. |
| 4249 | Value *ExtractForPhiUsedOutsideLoop = nullptr; |
| 4250 | if (VF.isVector()) { |
| 4251 | auto *RuntimeVF = getRuntimeVF(Builder, IdxTy, VF); |
| 4252 | auto *Idx = Builder.CreateSub(RuntimeVF, ConstantInt::get(IdxTy, 2)); |
| 4253 | ExtractForPhiUsedOutsideLoop = Builder.CreateExtractElement( |
| 4254 | Incoming, Idx, "vector.recur.extract.for.phi"); |
| 4255 | } else if (UF > 1) |
| 4256 | // When loop is unrolled without vectorizing, initialize |
| 4257 | // ExtractForPhiUsedOutsideLoop with the value just prior to unrolled value |
| 4258 | // of `Incoming`. This is analogous to the vectorized case above: extracting |
| 4259 | // the second last element when VF > 1. |
| 4260 | ExtractForPhiUsedOutsideLoop = State.get(PreviousDef, UF - 2); |
| 4261 | |
| 4262 | // Fix the initial value of the original recurrence in the scalar loop. |
| 4263 | Builder.SetInsertPoint(&*LoopScalarPreHeader->begin()); |
| 4264 | PHINode *Phi = cast<PHINode>(PhiR->getUnderlyingValue()); |
| 4265 | auto *Start = Builder.CreatePHI(Phi->getType(), 2, "scalar.recur.init"); |
| 4266 | auto *ScalarInit = PhiR->getStartValue()->getLiveInIRValue(); |
| 4267 | for (auto *BB : predecessors(LoopScalarPreHeader)) { |
| 4268 | auto *Incoming = BB == LoopMiddleBlock ? ExtractForScalar : ScalarInit; |
| 4269 | Start->addIncoming(Incoming, BB); |
| 4270 | } |
| 4271 | |
| 4272 | Phi->setIncomingValueForBlock(LoopScalarPreHeader, Start); |
| 4273 | Phi->setName("scalar.recur"); |
| 4274 | |
| 4275 | // Finally, fix users of the recurrence outside the loop. The users will need |
| 4276 | // either the last value of the scalar recurrence or the last value of the |
| 4277 | // vector recurrence we extracted in the middle block. Since the loop is in |
| 4278 | // LCSSA form, we just need to find all the phi nodes for the original scalar |
| 4279 | // recurrence in the exit block, and then add an edge for the middle block. |
| 4280 | // Note that LCSSA does not imply single entry when the original scalar loop |
| 4281 | // had multiple exiting edges (as we always run the last iteration in the |
| 4282 | // scalar epilogue); in that case, there is no edge from middle to exit and |
| 4283 | // and thus no phis which needed updated. |
| 4284 | if (!Cost->requiresScalarEpilogue(VF)) |
| 4285 | for (PHINode &LCSSAPhi : LoopExitBlock->phis()) |
| 4286 | if (any_of(LCSSAPhi.incoming_values(), |
| 4287 | [Phi](Value *V) { return V == Phi; })) |
| 4288 | LCSSAPhi.addIncoming(ExtractForPhiUsedOutsideLoop, LoopMiddleBlock); |
| 4289 | } |
| 4290 | |
| 4291 | void InnerLoopVectorizer::fixReduction(VPReductionPHIRecipe *PhiR, |
| 4292 | VPTransformState &State) { |
| 4293 | PHINode *OrigPhi = cast<PHINode>(PhiR->getUnderlyingValue()); |
| 4294 | // Get it's reduction variable descriptor. |
| 4295 | assert(Legal->isReductionVariable(OrigPhi) &&((void)0) |
| 4296 | "Unable to find the reduction variable")((void)0); |
| 4297 | const RecurrenceDescriptor &RdxDesc = PhiR->getRecurrenceDescriptor(); |
| 4298 | |
| 4299 | RecurKind RK = RdxDesc.getRecurrenceKind(); |
| 4300 | TrackingVH<Value> ReductionStartValue = RdxDesc.getRecurrenceStartValue(); |
| 4301 | Instruction *LoopExitInst = RdxDesc.getLoopExitInstr(); |
| 4302 | setDebugLocFromInst(ReductionStartValue); |
| 4303 | |
| 4304 | VPValue *LoopExitInstDef = State.Plan->getVPValue(LoopExitInst); |
| 4305 | // This is the vector-clone of the value that leaves the loop. |
| 4306 | Type *VecTy = State.get(LoopExitInstDef, 0)->getType(); |
| 4307 | |
| 4308 | // Wrap flags are in general invalid after vectorization, clear them. |
| 4309 | clearReductionWrapFlags(RdxDesc, State); |
| 4310 | |
| 4311 | // Fix the vector-loop phi. |
| 4312 | |
| 4313 | // Reductions do not have to start at zero. They can start with |
| 4314 | // any loop invariant values. |
| 4315 | BasicBlock *VectorLoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| 4316 | |
| 4317 | unsigned LastPartForNewPhi = PhiR->isOrdered() ? 1 : UF; |
| 4318 | for (unsigned Part = 0; Part < LastPartForNewPhi; ++Part) { |
| 4319 | Value *VecRdxPhi = State.get(PhiR->getVPSingleValue(), Part); |
| 4320 | Value *Val = State.get(PhiR->getBackedgeValue(), Part); |
| 4321 | if (PhiR->isOrdered()) |
| 4322 | Val = State.get(PhiR->getBackedgeValue(), UF - 1); |
| 4323 | |
| 4324 | cast<PHINode>(VecRdxPhi)->addIncoming(Val, VectorLoopLatch); |
| 4325 | } |
| 4326 | |
| 4327 | // Before each round, move the insertion point right between |
| 4328 | // the PHIs and the values we are going to write. |
| 4329 | // This allows us to write both PHINodes and the extractelement |
| 4330 | // instructions. |
| 4331 | Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); |
| 4332 | |
| 4333 | setDebugLocFromInst(LoopExitInst); |
| 4334 | |
| 4335 | Type *PhiTy = OrigPhi->getType(); |
| 4336 | // If tail is folded by masking, the vector value to leave the loop should be |
| 4337 | // a Select choosing between the vectorized LoopExitInst and vectorized Phi, |
| 4338 | // instead of the former. For an inloop reduction the reduction will already |
| 4339 | // be predicated, and does not need to be handled here. |
| 4340 | if (Cost->foldTailByMasking() && !PhiR->isInLoop()) { |
| 4341 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4342 | Value *VecLoopExitInst = State.get(LoopExitInstDef, Part); |
| 4343 | Value *Sel = nullptr; |
| 4344 | for (User *U : VecLoopExitInst->users()) { |
| 4345 | if (isa<SelectInst>(U)) { |
| 4346 | assert(!Sel && "Reduction exit feeding two selects")((void)0); |
| 4347 | Sel = U; |
| 4348 | } else |
| 4349 | assert(isa<PHINode>(U) && "Reduction exit must feed Phi's or select")((void)0); |
| 4350 | } |
| 4351 | assert(Sel && "Reduction exit feeds no select")((void)0); |
| 4352 | State.reset(LoopExitInstDef, Sel, Part); |
| 4353 | |
| 4354 | // If the target can create a predicated operator for the reduction at no |
| 4355 | // extra cost in the loop (for example a predicated vadd), it can be |
| 4356 | // cheaper for the select to remain in the loop than be sunk out of it, |
| 4357 | // and so use the select value for the phi instead of the old |
| 4358 | // LoopExitValue. |
| 4359 | if (PreferPredicatedReductionSelect || |
| 4360 | TTI->preferPredicatedReductionSelect( |
| 4361 | RdxDesc.getOpcode(), PhiTy, |
| 4362 | TargetTransformInfo::ReductionFlags())) { |
| 4363 | auto *VecRdxPhi = |
| 4364 | cast<PHINode>(State.get(PhiR->getVPSingleValue(), Part)); |
| 4365 | VecRdxPhi->setIncomingValueForBlock( |
| 4366 | LI->getLoopFor(LoopVectorBody)->getLoopLatch(), Sel); |
| 4367 | } |
| 4368 | } |
| 4369 | } |
| 4370 | |
| 4371 | // If the vector reduction can be performed in a smaller type, we truncate |
| 4372 | // then extend the loop exit value to enable InstCombine to evaluate the |
| 4373 | // entire expression in the smaller type. |
| 4374 | if (VF.isVector() && PhiTy != RdxDesc.getRecurrenceType()) { |
| 4375 | assert(!PhiR->isInLoop() && "Unexpected truncated inloop reduction!")((void)0); |
| 4376 | Type *RdxVecTy = VectorType::get(RdxDesc.getRecurrenceType(), VF); |
| 4377 | Builder.SetInsertPoint( |
| 4378 | LI->getLoopFor(LoopVectorBody)->getLoopLatch()->getTerminator()); |
| 4379 | VectorParts RdxParts(UF); |
| 4380 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4381 | RdxParts[Part] = State.get(LoopExitInstDef, Part); |
| 4382 | Value *Trunc = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); |
| 4383 | Value *Extnd = RdxDesc.isSigned() ? Builder.CreateSExt(Trunc, VecTy) |
| 4384 | : Builder.CreateZExt(Trunc, VecTy); |
| 4385 | for (Value::user_iterator UI = RdxParts[Part]->user_begin(); |
| 4386 | UI != RdxParts[Part]->user_end();) |
| 4387 | if (*UI != Trunc) { |
| 4388 | (*UI++)->replaceUsesOfWith(RdxParts[Part], Extnd); |
| 4389 | RdxParts[Part] = Extnd; |
| 4390 | } else { |
| 4391 | ++UI; |
| 4392 | } |
| 4393 | } |
| 4394 | Builder.SetInsertPoint(&*LoopMiddleBlock->getFirstInsertionPt()); |
| 4395 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4396 | RdxParts[Part] = Builder.CreateTrunc(RdxParts[Part], RdxVecTy); |
| 4397 | State.reset(LoopExitInstDef, RdxParts[Part], Part); |
| 4398 | } |
| 4399 | } |
| 4400 | |
| 4401 | // Reduce all of the unrolled parts into a single vector. |
| 4402 | Value *ReducedPartRdx = State.get(LoopExitInstDef, 0); |
| 4403 | unsigned Op = RecurrenceDescriptor::getOpcode(RK); |
| 4404 | |
| 4405 | // The middle block terminator has already been assigned a DebugLoc here (the |
| 4406 | // OrigLoop's single latch terminator). We want the whole middle block to |
| 4407 | // appear to execute on this line because: (a) it is all compiler generated, |
| 4408 | // (b) these instructions are always executed after evaluating the latch |
| 4409 | // conditional branch, and (c) other passes may add new predecessors which |
| 4410 | // terminate on this line. This is the easiest way to ensure we don't |
| 4411 | // accidentally cause an extra step back into the loop while debugging. |
| 4412 | setDebugLocFromInst(LoopMiddleBlock->getTerminator()); |
| 4413 | if (PhiR->isOrdered()) |
| 4414 | ReducedPartRdx = State.get(LoopExitInstDef, UF - 1); |
| 4415 | else { |
| 4416 | // Floating-point operations should have some FMF to enable the reduction. |
| 4417 | IRBuilderBase::FastMathFlagGuard FMFG(Builder); |
| 4418 | Builder.setFastMathFlags(RdxDesc.getFastMathFlags()); |
| 4419 | for (unsigned Part = 1; Part < UF; ++Part) { |
| 4420 | Value *RdxPart = State.get(LoopExitInstDef, Part); |
| 4421 | if (Op != Instruction::ICmp && Op != Instruction::FCmp) { |
| 4422 | ReducedPartRdx = Builder.CreateBinOp( |
| 4423 | (Instruction::BinaryOps)Op, RdxPart, ReducedPartRdx, "bin.rdx"); |
| 4424 | } else { |
| 4425 | ReducedPartRdx = createMinMaxOp(Builder, RK, ReducedPartRdx, RdxPart); |
| 4426 | } |
| 4427 | } |
| 4428 | } |
| 4429 | |
| 4430 | // Create the reduction after the loop. Note that inloop reductions create the |
| 4431 | // target reduction in the loop using a Reduction recipe. |
| 4432 | if (VF.isVector() && !PhiR->isInLoop()) { |
| 4433 | ReducedPartRdx = |
| 4434 | createTargetReduction(Builder, TTI, RdxDesc, ReducedPartRdx); |
| 4435 | // If the reduction can be performed in a smaller type, we need to extend |
| 4436 | // the reduction to the wider type before we branch to the original loop. |
| 4437 | if (PhiTy != RdxDesc.getRecurrenceType()) |
| 4438 | ReducedPartRdx = RdxDesc.isSigned() |
| 4439 | ? Builder.CreateSExt(ReducedPartRdx, PhiTy) |
| 4440 | : Builder.CreateZExt(ReducedPartRdx, PhiTy); |
| 4441 | } |
| 4442 | |
| 4443 | // Create a phi node that merges control-flow from the backedge-taken check |
| 4444 | // block and the middle block. |
| 4445 | PHINode *BCBlockPhi = PHINode::Create(PhiTy, 2, "bc.merge.rdx", |
| 4446 | LoopScalarPreHeader->getTerminator()); |
| 4447 | for (unsigned I = 0, E = LoopBypassBlocks.size(); I != E; ++I) |
| 4448 | BCBlockPhi->addIncoming(ReductionStartValue, LoopBypassBlocks[I]); |
| 4449 | BCBlockPhi->addIncoming(ReducedPartRdx, LoopMiddleBlock); |
| 4450 | |
| 4451 | // Now, we need to fix the users of the reduction variable |
| 4452 | // inside and outside of the scalar remainder loop. |
| 4453 | |
| 4454 | // We know that the loop is in LCSSA form. We need to update the PHI nodes |
| 4455 | // in the exit blocks. See comment on analogous loop in |
| 4456 | // fixFirstOrderRecurrence for a more complete explaination of the logic. |
| 4457 | if (!Cost->requiresScalarEpilogue(VF)) |
| 4458 | for (PHINode &LCSSAPhi : LoopExitBlock->phis()) |
| 4459 | if (any_of(LCSSAPhi.incoming_values(), |
| 4460 | [LoopExitInst](Value *V) { return V == LoopExitInst; })) |
| 4461 | LCSSAPhi.addIncoming(ReducedPartRdx, LoopMiddleBlock); |
| 4462 | |
| 4463 | // Fix the scalar loop reduction variable with the incoming reduction sum |
| 4464 | // from the vector body and from the backedge value. |
| 4465 | int IncomingEdgeBlockIdx = |
| 4466 | OrigPhi->getBasicBlockIndex(OrigLoop->getLoopLatch()); |
| 4467 | assert(IncomingEdgeBlockIdx >= 0 && "Invalid block index")((void)0); |
| 4468 | // Pick the other block. |
| 4469 | int SelfEdgeBlockIdx = (IncomingEdgeBlockIdx ? 0 : 1); |
| 4470 | OrigPhi->setIncomingValue(SelfEdgeBlockIdx, BCBlockPhi); |
| 4471 | OrigPhi->setIncomingValue(IncomingEdgeBlockIdx, LoopExitInst); |
| 4472 | } |
| 4473 | |
| 4474 | void InnerLoopVectorizer::clearReductionWrapFlags(const RecurrenceDescriptor &RdxDesc, |
| 4475 | VPTransformState &State) { |
| 4476 | RecurKind RK = RdxDesc.getRecurrenceKind(); |
| 4477 | if (RK != RecurKind::Add && RK != RecurKind::Mul) |
| 4478 | return; |
| 4479 | |
| 4480 | Instruction *LoopExitInstr = RdxDesc.getLoopExitInstr(); |
| 4481 | assert(LoopExitInstr && "null loop exit instruction")((void)0); |
| 4482 | SmallVector<Instruction *, 8> Worklist; |
| 4483 | SmallPtrSet<Instruction *, 8> Visited; |
| 4484 | Worklist.push_back(LoopExitInstr); |
| 4485 | Visited.insert(LoopExitInstr); |
| 4486 | |
| 4487 | while (!Worklist.empty()) { |
| 4488 | Instruction *Cur = Worklist.pop_back_val(); |
| 4489 | if (isa<OverflowingBinaryOperator>(Cur)) |
| 4490 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4491 | Value *V = State.get(State.Plan->getVPValue(Cur), Part); |
| 4492 | cast<Instruction>(V)->dropPoisonGeneratingFlags(); |
| 4493 | } |
| 4494 | |
| 4495 | for (User *U : Cur->users()) { |
| 4496 | Instruction *UI = cast<Instruction>(U); |
| 4497 | if ((Cur != LoopExitInstr || OrigLoop->contains(UI->getParent())) && |
| 4498 | Visited.insert(UI).second) |
| 4499 | Worklist.push_back(UI); |
| 4500 | } |
| 4501 | } |
| 4502 | } |
| 4503 | |
| 4504 | void InnerLoopVectorizer::fixLCSSAPHIs(VPTransformState &State) { |
| 4505 | for (PHINode &LCSSAPhi : LoopExitBlock->phis()) { |
| 4506 | if (LCSSAPhi.getBasicBlockIndex(LoopMiddleBlock) != -1) |
| 4507 | // Some phis were already hand updated by the reduction and recurrence |
| 4508 | // code above, leave them alone. |
| 4509 | continue; |
| 4510 | |
| 4511 | auto *IncomingValue = LCSSAPhi.getIncomingValue(0); |
| 4512 | // Non-instruction incoming values will have only one value. |
| 4513 | |
| 4514 | VPLane Lane = VPLane::getFirstLane(); |
| 4515 | if (isa<Instruction>(IncomingValue) && |
| 4516 | !Cost->isUniformAfterVectorization(cast<Instruction>(IncomingValue), |
| 4517 | VF)) |
| 4518 | Lane = VPLane::getLastLaneForVF(VF); |
| 4519 | |
| 4520 | // Can be a loop invariant incoming value or the last scalar value to be |
| 4521 | // extracted from the vectorized loop. |
| 4522 | Builder.SetInsertPoint(LoopMiddleBlock->getTerminator()); |
| 4523 | Value *lastIncomingValue = |
| 4524 | OrigLoop->isLoopInvariant(IncomingValue) |
| 4525 | ? IncomingValue |
| 4526 | : State.get(State.Plan->getVPValue(IncomingValue), |
| 4527 | VPIteration(UF - 1, Lane)); |
| 4528 | LCSSAPhi.addIncoming(lastIncomingValue, LoopMiddleBlock); |
| 4529 | } |
| 4530 | } |
| 4531 | |
| 4532 | void InnerLoopVectorizer::sinkScalarOperands(Instruction *PredInst) { |
| 4533 | // The basic block and loop containing the predicated instruction. |
| 4534 | auto *PredBB = PredInst->getParent(); |
| 4535 | auto *VectorLoop = LI->getLoopFor(PredBB); |
| 4536 | |
| 4537 | // Initialize a worklist with the operands of the predicated instruction. |
| 4538 | SetVector<Value *> Worklist(PredInst->op_begin(), PredInst->op_end()); |
| 4539 | |
| 4540 | // Holds instructions that we need to analyze again. An instruction may be |
| 4541 | // reanalyzed if we don't yet know if we can sink it or not. |
| 4542 | SmallVector<Instruction *, 8> InstsToReanalyze; |
| 4543 | |
| 4544 | // Returns true if a given use occurs in the predicated block. Phi nodes use |
| 4545 | // their operands in their corresponding predecessor blocks. |
| 4546 | auto isBlockOfUsePredicated = [&](Use &U) -> bool { |
| 4547 | auto *I = cast<Instruction>(U.getUser()); |
| 4548 | BasicBlock *BB = I->getParent(); |
| 4549 | if (auto *Phi = dyn_cast<PHINode>(I)) |
| 4550 | BB = Phi->getIncomingBlock( |
| 4551 | PHINode::getIncomingValueNumForOperand(U.getOperandNo())); |
| 4552 | return BB == PredBB; |
| 4553 | }; |
| 4554 | |
| 4555 | // Iteratively sink the scalarized operands of the predicated instruction |
| 4556 | // into the block we created for it. When an instruction is sunk, it's |
| 4557 | // operands are then added to the worklist. The algorithm ends after one pass |
| 4558 | // through the worklist doesn't sink a single instruction. |
| 4559 | bool Changed; |
| 4560 | do { |
| 4561 | // Add the instructions that need to be reanalyzed to the worklist, and |
| 4562 | // reset the changed indicator. |
| 4563 | Worklist.insert(InstsToReanalyze.begin(), InstsToReanalyze.end()); |
| 4564 | InstsToReanalyze.clear(); |
| 4565 | Changed = false; |
| 4566 | |
| 4567 | while (!Worklist.empty()) { |
| 4568 | auto *I = dyn_cast<Instruction>(Worklist.pop_back_val()); |
| 4569 | |
| 4570 | // We can't sink an instruction if it is a phi node, is not in the loop, |
| 4571 | // or may have side effects. |
| 4572 | if (!I || isa<PHINode>(I) || !VectorLoop->contains(I) || |
| 4573 | I->mayHaveSideEffects()) |
| 4574 | continue; |
| 4575 | |
| 4576 | // If the instruction is already in PredBB, check if we can sink its |
| 4577 | // operands. In that case, VPlan's sinkScalarOperands() succeeded in |
| 4578 | // sinking the scalar instruction I, hence it appears in PredBB; but it |
| 4579 | // may have failed to sink I's operands (recursively), which we try |
| 4580 | // (again) here. |
| 4581 | if (I->getParent() == PredBB) { |
| 4582 | Worklist.insert(I->op_begin(), I->op_end()); |
| 4583 | continue; |
| 4584 | } |
| 4585 | |
| 4586 | // It's legal to sink the instruction if all its uses occur in the |
| 4587 | // predicated block. Otherwise, there's nothing to do yet, and we may |
| 4588 | // need to reanalyze the instruction. |
| 4589 | if (!llvm::all_of(I->uses(), isBlockOfUsePredicated)) { |
| 4590 | InstsToReanalyze.push_back(I); |
| 4591 | continue; |
| 4592 | } |
| 4593 | |
| 4594 | // Move the instruction to the beginning of the predicated block, and add |
| 4595 | // it's operands to the worklist. |
| 4596 | I->moveBefore(&*PredBB->getFirstInsertionPt()); |
| 4597 | Worklist.insert(I->op_begin(), I->op_end()); |
| 4598 | |
| 4599 | // The sinking may have enabled other instructions to be sunk, so we will |
| 4600 | // need to iterate. |
| 4601 | Changed = true; |
| 4602 | } |
| 4603 | } while (Changed); |
| 4604 | } |
| 4605 | |
| 4606 | void InnerLoopVectorizer::fixNonInductionPHIs(VPTransformState &State) { |
| 4607 | for (PHINode *OrigPhi : OrigPHIsToFix) { |
| 4608 | VPWidenPHIRecipe *VPPhi = |
| 4609 | cast<VPWidenPHIRecipe>(State.Plan->getVPValue(OrigPhi)); |
| 4610 | PHINode *NewPhi = cast<PHINode>(State.get(VPPhi, 0)); |
| 4611 | // Make sure the builder has a valid insert point. |
| 4612 | Builder.SetInsertPoint(NewPhi); |
| 4613 | for (unsigned i = 0; i < VPPhi->getNumOperands(); ++i) { |
| 4614 | VPValue *Inc = VPPhi->getIncomingValue(i); |
| 4615 | VPBasicBlock *VPBB = VPPhi->getIncomingBlock(i); |
| 4616 | NewPhi->addIncoming(State.get(Inc, 0), State.CFG.VPBB2IRBB[VPBB]); |
| 4617 | } |
| 4618 | } |
| 4619 | } |
| 4620 | |
| 4621 | bool InnerLoopVectorizer::useOrderedReductions(RecurrenceDescriptor &RdxDesc) { |
| 4622 | return Cost->useOrderedReductions(RdxDesc); |
| 4623 | } |
| 4624 | |
| 4625 | void InnerLoopVectorizer::widenGEP(GetElementPtrInst *GEP, VPValue *VPDef, |
| 4626 | VPUser &Operands, unsigned UF, |
| 4627 | ElementCount VF, bool IsPtrLoopInvariant, |
| 4628 | SmallBitVector &IsIndexLoopInvariant, |
| 4629 | VPTransformState &State) { |
| 4630 | // Construct a vector GEP by widening the operands of the scalar GEP as |
| 4631 | // necessary. We mark the vector GEP 'inbounds' if appropriate. A GEP |
| 4632 | // results in a vector of pointers when at least one operand of the GEP |
| 4633 | // is vector-typed. Thus, to keep the representation compact, we only use |
| 4634 | // vector-typed operands for loop-varying values. |
| 4635 | |
| 4636 | if (VF.isVector() && IsPtrLoopInvariant && IsIndexLoopInvariant.all()) { |
| 4637 | // If we are vectorizing, but the GEP has only loop-invariant operands, |
| 4638 | // the GEP we build (by only using vector-typed operands for |
| 4639 | // loop-varying values) would be a scalar pointer. Thus, to ensure we |
| 4640 | // produce a vector of pointers, we need to either arbitrarily pick an |
| 4641 | // operand to broadcast, or broadcast a clone of the original GEP. |
| 4642 | // Here, we broadcast a clone of the original. |
| 4643 | // |
| 4644 | // TODO: If at some point we decide to scalarize instructions having |
| 4645 | // loop-invariant operands, this special case will no longer be |
| 4646 | // required. We would add the scalarization decision to |
| 4647 | // collectLoopScalars() and teach getVectorValue() to broadcast |
| 4648 | // the lane-zero scalar value. |
| 4649 | auto *Clone = Builder.Insert(GEP->clone()); |
| 4650 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4651 | Value *EntryPart = Builder.CreateVectorSplat(VF, Clone); |
| 4652 | State.set(VPDef, EntryPart, Part); |
| 4653 | addMetadata(EntryPart, GEP); |
| 4654 | } |
| 4655 | } else { |
| 4656 | // If the GEP has at least one loop-varying operand, we are sure to |
| 4657 | // produce a vector of pointers. But if we are only unrolling, we want |
| 4658 | // to produce a scalar GEP for each unroll part. Thus, the GEP we |
| 4659 | // produce with the code below will be scalar (if VF == 1) or vector |
| 4660 | // (otherwise). Note that for the unroll-only case, we still maintain |
| 4661 | // values in the vector mapping with initVector, as we do for other |
| 4662 | // instructions. |
| 4663 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4664 | // The pointer operand of the new GEP. If it's loop-invariant, we |
| 4665 | // won't broadcast it. |
| 4666 | auto *Ptr = IsPtrLoopInvariant |
| 4667 | ? State.get(Operands.getOperand(0), VPIteration(0, 0)) |
| 4668 | : State.get(Operands.getOperand(0), Part); |
| 4669 | |
| 4670 | // Collect all the indices for the new GEP. If any index is |
| 4671 | // loop-invariant, we won't broadcast it. |
| 4672 | SmallVector<Value *, 4> Indices; |
| 4673 | for (unsigned I = 1, E = Operands.getNumOperands(); I < E; I++) { |
| 4674 | VPValue *Operand = Operands.getOperand(I); |
| 4675 | if (IsIndexLoopInvariant[I - 1]) |
| 4676 | Indices.push_back(State.get(Operand, VPIteration(0, 0))); |
| 4677 | else |
| 4678 | Indices.push_back(State.get(Operand, Part)); |
| 4679 | } |
| 4680 | |
| 4681 | // Create the new GEP. Note that this GEP may be a scalar if VF == 1, |
| 4682 | // but it should be a vector, otherwise. |
| 4683 | auto *NewGEP = |
| 4684 | GEP->isInBounds() |
| 4685 | ? Builder.CreateInBoundsGEP(GEP->getSourceElementType(), Ptr, |
| 4686 | Indices) |
| 4687 | : Builder.CreateGEP(GEP->getSourceElementType(), Ptr, Indices); |
| 4688 | assert((VF.isScalar() || NewGEP->getType()->isVectorTy()) &&((void)0) |
| 4689 | "NewGEP is not a pointer vector")((void)0); |
| 4690 | State.set(VPDef, NewGEP, Part); |
| 4691 | addMetadata(NewGEP, GEP); |
| 4692 | } |
| 4693 | } |
| 4694 | } |
| 4695 | |
| 4696 | void InnerLoopVectorizer::widenPHIInstruction(Instruction *PN, |
| 4697 | VPWidenPHIRecipe *PhiR, |
| 4698 | VPTransformState &State) { |
| 4699 | PHINode *P = cast<PHINode>(PN); |
| 4700 | if (EnableVPlanNativePath) { |
| 4701 | // Currently we enter here in the VPlan-native path for non-induction |
| 4702 | // PHIs where all control flow is uniform. We simply widen these PHIs. |
| 4703 | // Create a vector phi with no operands - the vector phi operands will be |
| 4704 | // set at the end of vector code generation. |
| 4705 | Type *VecTy = (State.VF.isScalar()) |
| 4706 | ? PN->getType() |
| 4707 | : VectorType::get(PN->getType(), State.VF); |
| 4708 | Value *VecPhi = Builder.CreatePHI(VecTy, PN->getNumOperands(), "vec.phi"); |
| 4709 | State.set(PhiR, VecPhi, 0); |
| 4710 | OrigPHIsToFix.push_back(P); |
| 4711 | |
| 4712 | return; |
| 4713 | } |
| 4714 | |
| 4715 | assert(PN->getParent() == OrigLoop->getHeader() &&((void)0) |
| 4716 | "Non-header phis should have been handled elsewhere")((void)0); |
| 4717 | |
| 4718 | // In order to support recurrences we need to be able to vectorize Phi nodes. |
| 4719 | // Phi nodes have cycles, so we need to vectorize them in two stages. This is |
| 4720 | // stage #1: We create a new vector PHI node with no incoming edges. We'll use |
| 4721 | // this value when we vectorize all of the instructions that use the PHI. |
| 4722 | |
| 4723 | assert(!Legal->isReductionVariable(P) &&((void)0) |
| 4724 | "reductions should be handled elsewhere")((void)0); |
| 4725 | |
| 4726 | setDebugLocFromInst(P); |
| 4727 | |
| 4728 | // This PHINode must be an induction variable. |
| 4729 | // Make sure that we know about it. |
| 4730 | assert(Legal->getInductionVars().count(P) && "Not an induction variable")((void)0); |
| 4731 | |
| 4732 | InductionDescriptor II = Legal->getInductionVars().lookup(P); |
| 4733 | const DataLayout &DL = OrigLoop->getHeader()->getModule()->getDataLayout(); |
| 4734 | |
| 4735 | // FIXME: The newly created binary instructions should contain nsw/nuw flags, |
| 4736 | // which can be found from the original scalar operations. |
| 4737 | switch (II.getKind()) { |
| 4738 | case InductionDescriptor::IK_NoInduction: |
| 4739 | llvm_unreachable("Unknown induction")__builtin_unreachable(); |
| 4740 | case InductionDescriptor::IK_IntInduction: |
| 4741 | case InductionDescriptor::IK_FpInduction: |
| 4742 | llvm_unreachable("Integer/fp induction is handled elsewhere.")__builtin_unreachable(); |
| 4743 | case InductionDescriptor::IK_PtrInduction: { |
| 4744 | // Handle the pointer induction variable case. |
| 4745 | assert(P->getType()->isPointerTy() && "Unexpected type.")((void)0); |
| 4746 | |
| 4747 | if (Cost->isScalarAfterVectorization(P, State.VF)) { |
| 4748 | // This is the normalized GEP that starts counting at zero. |
| 4749 | Value *PtrInd = |
| 4750 | Builder.CreateSExtOrTrunc(Induction, II.getStep()->getType()); |
| 4751 | // Determine the number of scalars we need to generate for each unroll |
| 4752 | // iteration. If the instruction is uniform, we only need to generate the |
| 4753 | // first lane. Otherwise, we generate all VF values. |
| 4754 | bool IsUniform = Cost->isUniformAfterVectorization(P, State.VF); |
| 4755 | unsigned Lanes = IsUniform ? 1 : State.VF.getKnownMinValue(); |
| 4756 | |
| 4757 | bool NeedsVectorIndex = !IsUniform && VF.isScalable(); |
| 4758 | Value *UnitStepVec = nullptr, *PtrIndSplat = nullptr; |
| 4759 | if (NeedsVectorIndex) { |
| 4760 | Type *VecIVTy = VectorType::get(PtrInd->getType(), VF); |
| 4761 | UnitStepVec = Builder.CreateStepVector(VecIVTy); |
| 4762 | PtrIndSplat = Builder.CreateVectorSplat(VF, PtrInd); |
| 4763 | } |
| 4764 | |
| 4765 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4766 | Value *PartStart = createStepForVF( |
| 4767 | Builder, ConstantInt::get(PtrInd->getType(), Part), VF); |
| 4768 | |
| 4769 | if (NeedsVectorIndex) { |
| 4770 | Value *PartStartSplat = Builder.CreateVectorSplat(VF, PartStart); |
| 4771 | Value *Indices = Builder.CreateAdd(PartStartSplat, UnitStepVec); |
| 4772 | Value *GlobalIndices = Builder.CreateAdd(PtrIndSplat, Indices); |
| 4773 | Value *SclrGep = |
| 4774 | emitTransformedIndex(Builder, GlobalIndices, PSE.getSE(), DL, II); |
| 4775 | SclrGep->setName("next.gep"); |
| 4776 | State.set(PhiR, SclrGep, Part); |
| 4777 | // We've cached the whole vector, which means we can support the |
| 4778 | // extraction of any lane. |
| 4779 | continue; |
| 4780 | } |
| 4781 | |
| 4782 | for (unsigned Lane = 0; Lane < Lanes; ++Lane) { |
| 4783 | Value *Idx = Builder.CreateAdd( |
| 4784 | PartStart, ConstantInt::get(PtrInd->getType(), Lane)); |
| 4785 | Value *GlobalIdx = Builder.CreateAdd(PtrInd, Idx); |
| 4786 | Value *SclrGep = |
| 4787 | emitTransformedIndex(Builder, GlobalIdx, PSE.getSE(), DL, II); |
| 4788 | SclrGep->setName("next.gep"); |
| 4789 | State.set(PhiR, SclrGep, VPIteration(Part, Lane)); |
| 4790 | } |
| 4791 | } |
| 4792 | return; |
| 4793 | } |
| 4794 | assert(isa<SCEVConstant>(II.getStep()) &&((void)0) |
| 4795 | "Induction step not a SCEV constant!")((void)0); |
| 4796 | Type *PhiType = II.getStep()->getType(); |
| 4797 | |
| 4798 | // Build a pointer phi |
| 4799 | Value *ScalarStartValue = II.getStartValue(); |
| 4800 | Type *ScStValueType = ScalarStartValue->getType(); |
| 4801 | PHINode *NewPointerPhi = |
| 4802 | PHINode::Create(ScStValueType, 2, "pointer.phi", Induction); |
| 4803 | NewPointerPhi->addIncoming(ScalarStartValue, LoopVectorPreHeader); |
| 4804 | |
| 4805 | // A pointer induction, performed by using a gep |
| 4806 | BasicBlock *LoopLatch = LI->getLoopFor(LoopVectorBody)->getLoopLatch(); |
| 4807 | Instruction *InductionLoc = LoopLatch->getTerminator(); |
| 4808 | const SCEV *ScalarStep = II.getStep(); |
| 4809 | SCEVExpander Exp(*PSE.getSE(), DL, "induction"); |
| 4810 | Value *ScalarStepValue = |
| 4811 | Exp.expandCodeFor(ScalarStep, PhiType, InductionLoc); |
| 4812 | Value *RuntimeVF = getRuntimeVF(Builder, PhiType, VF); |
| 4813 | Value *NumUnrolledElems = |
| 4814 | Builder.CreateMul(RuntimeVF, ConstantInt::get(PhiType, State.UF)); |
| 4815 | Value *InductionGEP = GetElementPtrInst::Create( |
| 4816 | ScStValueType->getPointerElementType(), NewPointerPhi, |
| 4817 | Builder.CreateMul(ScalarStepValue, NumUnrolledElems), "ptr.ind", |
| 4818 | InductionLoc); |
| 4819 | NewPointerPhi->addIncoming(InductionGEP, LoopLatch); |
| 4820 | |
| 4821 | // Create UF many actual address geps that use the pointer |
| 4822 | // phi as base and a vectorized version of the step value |
| 4823 | // (<step*0, ..., step*N>) as offset. |
| 4824 | for (unsigned Part = 0; Part < State.UF; ++Part) { |
| 4825 | Type *VecPhiType = VectorType::get(PhiType, State.VF); |
| 4826 | Value *StartOffsetScalar = |
| 4827 | Builder.CreateMul(RuntimeVF, ConstantInt::get(PhiType, Part)); |
| 4828 | Value *StartOffset = |
| 4829 | Builder.CreateVectorSplat(State.VF, StartOffsetScalar); |
| 4830 | // Create a vector of consecutive numbers from zero to VF. |
| 4831 | StartOffset = |
| 4832 | Builder.CreateAdd(StartOffset, Builder.CreateStepVector(VecPhiType)); |
| 4833 | |
| 4834 | Value *GEP = Builder.CreateGEP( |
| 4835 | ScStValueType->getPointerElementType(), NewPointerPhi, |
| 4836 | Builder.CreateMul( |
| 4837 | StartOffset, Builder.CreateVectorSplat(State.VF, ScalarStepValue), |
| 4838 | "vector.gep")); |
| 4839 | State.set(PhiR, GEP, Part); |
| 4840 | } |
| 4841 | } |
| 4842 | } |
| 4843 | } |
| 4844 | |
| 4845 | /// A helper function for checking whether an integer division-related |
| 4846 | /// instruction may divide by zero (in which case it must be predicated if |
| 4847 | /// executed conditionally in the scalar code). |
| 4848 | /// TODO: It may be worthwhile to generalize and check isKnownNonZero(). |
| 4849 | /// Non-zero divisors that are non compile-time constants will not be |
| 4850 | /// converted into multiplication, so we will still end up scalarizing |
| 4851 | /// the division, but can do so w/o predication. |
| 4852 | static bool mayDivideByZero(Instruction &I) { |
| 4853 | assert((I.getOpcode() == Instruction::UDiv ||((void)0) |
| 4854 | I.getOpcode() == Instruction::SDiv ||((void)0) |
| 4855 | I.getOpcode() == Instruction::URem ||((void)0) |
| 4856 | I.getOpcode() == Instruction::SRem) &&((void)0) |
| 4857 | "Unexpected instruction")((void)0); |
| 4858 | Value *Divisor = I.getOperand(1); |
| 4859 | auto *CInt = dyn_cast<ConstantInt>(Divisor); |
| 4860 | return !CInt || CInt->isZero(); |
| 4861 | } |
| 4862 | |
| 4863 | void InnerLoopVectorizer::widenInstruction(Instruction &I, VPValue *Def, |
| 4864 | VPUser &User, |
| 4865 | VPTransformState &State) { |
| 4866 | switch (I.getOpcode()) { |
| 4867 | case Instruction::Call: |
| 4868 | case Instruction::Br: |
| 4869 | case Instruction::PHI: |
| 4870 | case Instruction::GetElementPtr: |
| 4871 | case Instruction::Select: |
| 4872 | llvm_unreachable("This instruction is handled by a different recipe.")__builtin_unreachable(); |
| 4873 | case Instruction::UDiv: |
| 4874 | case Instruction::SDiv: |
| 4875 | case Instruction::SRem: |
| 4876 | case Instruction::URem: |
| 4877 | case Instruction::Add: |
| 4878 | case Instruction::FAdd: |
| 4879 | case Instruction::Sub: |
| 4880 | case Instruction::FSub: |
| 4881 | case Instruction::FNeg: |
| 4882 | case Instruction::Mul: |
| 4883 | case Instruction::FMul: |
| 4884 | case Instruction::FDiv: |
| 4885 | case Instruction::FRem: |
| 4886 | case Instruction::Shl: |
| 4887 | case Instruction::LShr: |
| 4888 | case Instruction::AShr: |
| 4889 | case Instruction::And: |
| 4890 | case Instruction::Or: |
| 4891 | case Instruction::Xor: { |
| 4892 | // Just widen unops and binops. |
| 4893 | setDebugLocFromInst(&I); |
| 4894 | |
| 4895 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4896 | SmallVector<Value *, 2> Ops; |
| 4897 | for (VPValue *VPOp : User.operands()) |
| 4898 | Ops.push_back(State.get(VPOp, Part)); |
| 4899 | |
| 4900 | Value *V = Builder.CreateNAryOp(I.getOpcode(), Ops); |
| 4901 | |
| 4902 | if (auto *VecOp = dyn_cast<Instruction>(V)) |
| 4903 | VecOp->copyIRFlags(&I); |
| 4904 | |
| 4905 | // Use this vector value for all users of the original instruction. |
| 4906 | State.set(Def, V, Part); |
| 4907 | addMetadata(V, &I); |
| 4908 | } |
| 4909 | |
| 4910 | break; |
| 4911 | } |
| 4912 | case Instruction::ICmp: |
| 4913 | case Instruction::FCmp: { |
| 4914 | // Widen compares. Generate vector compares. |
| 4915 | bool FCmp = (I.getOpcode() == Instruction::FCmp); |
| 4916 | auto *Cmp = cast<CmpInst>(&I); |
| 4917 | setDebugLocFromInst(Cmp); |
| 4918 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4919 | Value *A = State.get(User.getOperand(0), Part); |
| 4920 | Value *B = State.get(User.getOperand(1), Part); |
| 4921 | Value *C = nullptr; |
| 4922 | if (FCmp) { |
| 4923 | // Propagate fast math flags. |
| 4924 | IRBuilder<>::FastMathFlagGuard FMFG(Builder); |
| 4925 | Builder.setFastMathFlags(Cmp->getFastMathFlags()); |
| 4926 | C = Builder.CreateFCmp(Cmp->getPredicate(), A, B); |
| 4927 | } else { |
| 4928 | C = Builder.CreateICmp(Cmp->getPredicate(), A, B); |
| 4929 | } |
| 4930 | State.set(Def, C, Part); |
| 4931 | addMetadata(C, &I); |
| 4932 | } |
| 4933 | |
| 4934 | break; |
| 4935 | } |
| 4936 | |
| 4937 | case Instruction::ZExt: |
| 4938 | case Instruction::SExt: |
| 4939 | case Instruction::FPToUI: |
| 4940 | case Instruction::FPToSI: |
| 4941 | case Instruction::FPExt: |
| 4942 | case Instruction::PtrToInt: |
| 4943 | case Instruction::IntToPtr: |
| 4944 | case Instruction::SIToFP: |
| 4945 | case Instruction::UIToFP: |
| 4946 | case Instruction::Trunc: |
| 4947 | case Instruction::FPTrunc: |
| 4948 | case Instruction::BitCast: { |
| 4949 | auto *CI = cast<CastInst>(&I); |
| 4950 | setDebugLocFromInst(CI); |
| 4951 | |
| 4952 | /// Vectorize casts. |
| 4953 | Type *DestTy = |
| 4954 | (VF.isScalar()) ? CI->getType() : VectorType::get(CI->getType(), VF); |
| 4955 | |
| 4956 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 4957 | Value *A = State.get(User.getOperand(0), Part); |
| 4958 | Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy); |
| 4959 | State.set(Def, Cast, Part); |
| 4960 | addMetadata(Cast, &I); |
| 4961 | } |
| 4962 | break; |
| 4963 | } |
| 4964 | default: |
| 4965 | // This instruction is not vectorized by simple widening. |
| 4966 | LLVM_DEBUG(dbgs() << "LV: Found an unhandled instruction: " << I)do { } while (false); |
| 4967 | llvm_unreachable("Unhandled instruction!")__builtin_unreachable(); |
| 4968 | } // end of switch. |
| 4969 | } |
| 4970 | |
| 4971 | void InnerLoopVectorizer::widenCallInstruction(CallInst &I, VPValue *Def, |
| 4972 | VPUser &ArgOperands, |
| 4973 | VPTransformState &State) { |
| 4974 | assert(!isa<DbgInfoIntrinsic>(I) &&((void)0) |
| 4975 | "DbgInfoIntrinsic should have been dropped during VPlan construction")((void)0); |
| 4976 | setDebugLocFromInst(&I); |
| 4977 | |
| 4978 | Module *M = I.getParent()->getParent()->getParent(); |
| 4979 | auto *CI = cast<CallInst>(&I); |
| 4980 | |
| 4981 | SmallVector<Type *, 4> Tys; |
| 4982 | for (Value *ArgOperand : CI->arg_operands()) |
| 4983 | Tys.push_back(ToVectorTy(ArgOperand->getType(), VF.getKnownMinValue())); |
| 4984 | |
| 4985 | Intrinsic::ID ID = getVectorIntrinsicIDForCall(CI, TLI); |
| 4986 | |
| 4987 | // The flag shows whether we use Intrinsic or a usual Call for vectorized |
| 4988 | // version of the instruction. |
| 4989 | // Is it beneficial to perform intrinsic call compared to lib call? |
| 4990 | bool NeedToScalarize = false; |
| 4991 | InstructionCost CallCost = Cost->getVectorCallCost(CI, VF, NeedToScalarize); |
| 4992 | InstructionCost IntrinsicCost = ID ? Cost->getVectorIntrinsicCost(CI, VF) : 0; |
| 4993 | bool UseVectorIntrinsic = ID && IntrinsicCost <= CallCost; |
| 4994 | assert((UseVectorIntrinsic || !NeedToScalarize) &&((void)0) |
| 4995 | "Instruction should be scalarized elsewhere.")((void)0); |
| 4996 | assert((IntrinsicCost.isValid() || CallCost.isValid()) &&((void)0) |
| 4997 | "Either the intrinsic cost or vector call cost must be valid")((void)0); |
| 4998 | |
| 4999 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 5000 | SmallVector<Type *, 2> TysForDecl = {CI->getType()}; |
| 5001 | SmallVector<Value *, 4> Args; |
| 5002 | for (auto &I : enumerate(ArgOperands.operands())) { |
| 5003 | // Some intrinsics have a scalar argument - don't replace it with a |
| 5004 | // vector. |
| 5005 | Value *Arg; |
| 5006 | if (!UseVectorIntrinsic || !hasVectorInstrinsicScalarOpd(ID, I.index())) |
| 5007 | Arg = State.get(I.value(), Part); |
| 5008 | else { |
| 5009 | Arg = State.get(I.value(), VPIteration(0, 0)); |
| 5010 | if (hasVectorInstrinsicOverloadedScalarOpd(ID, I.index())) |
| 5011 | TysForDecl.push_back(Arg->getType()); |
| 5012 | } |
| 5013 | Args.push_back(Arg); |
| 5014 | } |
| 5015 | |
| 5016 | Function *VectorF; |
| 5017 | if (UseVectorIntrinsic) { |
| 5018 | // Use vector version of the intrinsic. |
| 5019 | if (VF.isVector()) |
| 5020 | TysForDecl[0] = VectorType::get(CI->getType()->getScalarType(), VF); |
| 5021 | VectorF = Intrinsic::getDeclaration(M, ID, TysForDecl); |
| 5022 | assert(VectorF && "Can't retrieve vector intrinsic.")((void)0); |
| 5023 | } else { |
| 5024 | // Use vector version of the function call. |
| 5025 | const VFShape Shape = VFShape::get(*CI, VF, false /*HasGlobalPred*/); |
| 5026 | #ifndef NDEBUG1 |
| 5027 | assert(VFDatabase(*CI).getVectorizedFunction(Shape) != nullptr &&((void)0) |
| 5028 | "Can't create vector function.")((void)0); |
| 5029 | #endif |
| 5030 | VectorF = VFDatabase(*CI).getVectorizedFunction(Shape); |
| 5031 | } |
| 5032 | SmallVector<OperandBundleDef, 1> OpBundles; |
| 5033 | CI->getOperandBundlesAsDefs(OpBundles); |
| 5034 | CallInst *V = Builder.CreateCall(VectorF, Args, OpBundles); |
| 5035 | |
| 5036 | if (isa<FPMathOperator>(V)) |
| 5037 | V->copyFastMathFlags(CI); |
| 5038 | |
| 5039 | State.set(Def, V, Part); |
| 5040 | addMetadata(V, &I); |
| 5041 | } |
| 5042 | } |
| 5043 | |
| 5044 | void InnerLoopVectorizer::widenSelectInstruction(SelectInst &I, VPValue *VPDef, |
| 5045 | VPUser &Operands, |
| 5046 | bool InvariantCond, |
| 5047 | VPTransformState &State) { |
| 5048 | setDebugLocFromInst(&I); |
| 5049 | |
| 5050 | // The condition can be loop invariant but still defined inside the |
| 5051 | // loop. This means that we can't just use the original 'cond' value. |
| 5052 | // We have to take the 'vectorized' value and pick the first lane. |
| 5053 | // Instcombine will make this a no-op. |
| 5054 | auto *InvarCond = InvariantCond |
| 5055 | ? State.get(Operands.getOperand(0), VPIteration(0, 0)) |
| 5056 | : nullptr; |
| 5057 | |
| 5058 | for (unsigned Part = 0; Part < UF; ++Part) { |
| 5059 | Value *Cond = |
| 5060 | InvarCond ? InvarCond : State.get(Operands.getOperand(0), Part); |
| 5061 | Value *Op0 = State.get(Operands.getOperand(1), Part); |
| 5062 | Value *Op1 = State.get(Operands.getOperand(2), Part); |
| 5063 | Value *Sel = Builder.CreateSelect(Cond, Op0, Op1); |
| 5064 | State.set(VPDef, Sel, Part); |
| 5065 | addMetadata(Sel, &I); |
| 5066 | } |
| 5067 | } |
| 5068 | |
| 5069 | void LoopVectorizationCostModel::collectLoopScalars(ElementCount VF) { |
| 5070 | // We should not collect Scalars more than once per VF. Right now, this |
| 5071 | // function is called from collectUniformsAndScalars(), which already does |
| 5072 | // this check. Collecting Scalars for VF=1 does not make any sense. |
| 5073 | assert(VF.isVector() && Scalars.find(VF) == Scalars.end() &&((void)0) |
| 5074 | "This function should not be visited twice for the same VF")((void)0); |
| 5075 | |
| 5076 | SmallSetVector<Instruction *, 8> Worklist; |
| 5077 | |
| 5078 | // These sets are used to seed the analysis with pointers used by memory |
| 5079 | // accesses that will remain scalar. |
| 5080 | SmallSetVector<Instruction *, 8> ScalarPtrs; |
| 5081 | SmallPtrSet<Instruction *, 8> PossibleNonScalarPtrs; |
| 5082 | auto *Latch = TheLoop->getLoopLatch(); |
| 5083 | |
| 5084 | // A helper that returns true if the use of Ptr by MemAccess will be scalar. |
| 5085 | // The pointer operands of loads and stores will be scalar as long as the |
| 5086 | // memory access is not a gather or scatter operation. The value operand of a |
| 5087 | // store will remain scalar if the store is scalarized. |
| 5088 | auto isScalarUse = [&](Instruction *MemAccess, Value *Ptr) { |
| 5089 | InstWidening WideningDecision = getWideningDecision(MemAccess, VF); |
| 5090 | assert(WideningDecision != CM_Unknown &&((void)0) |
| 5091 | "Widening decision should be ready at this moment")((void)0); |
| 5092 | if (auto *Store = dyn_cast<StoreInst>(MemAccess)) |
| 5093 | if (Ptr == Store->getValueOperand()) |
| 5094 | return WideningDecision == CM_Scalarize; |
| 5095 | assert(Ptr == getLoadStorePointerOperand(MemAccess) &&((void)0) |
| 5096 | "Ptr is neither a value or pointer operand")((void)0); |
| 5097 | return WideningDecision != CM_GatherScatter; |
| 5098 | }; |
| 5099 | |
| 5100 | // A helper that returns true if the given value is a bitcast or |
| 5101 | // getelementptr instruction contained in the loop. |
| 5102 | auto isLoopVaryingBitCastOrGEP = [&](Value *V) { |
| 5103 | return ((isa<BitCastInst>(V) && V->getType()->isPointerTy()) || |
| 5104 | isa<GetElementPtrInst>(V)) && |
| 5105 | !TheLoop->isLoopInvariant(V); |
| 5106 | }; |
| 5107 | |
| 5108 | auto isScalarPtrInduction = [&](Instruction *MemAccess, Value *Ptr) { |
| 5109 | if (!isa<PHINode>(Ptr) || |
| 5110 | !Legal->getInductionVars().count(cast<PHINode>(Ptr))) |
| 5111 | return false; |
| 5112 | auto &Induction = Legal->getInductionVars()[cast<PHINode>(Ptr)]; |
| 5113 | if (Induction.getKind() != InductionDescriptor::IK_PtrInduction) |
| 5114 | return false; |
| 5115 | return isScalarUse(MemAccess, Ptr); |
| 5116 | }; |
| 5117 | |
| 5118 | // A helper that evaluates a memory access's use of a pointer. If the |
| 5119 | // pointer is actually the pointer induction of a loop, it is being |
| 5120 | // inserted into Worklist. If the use will be a scalar use, and the |
| 5121 | // pointer is only used by memory accesses, we place the pointer in |
| 5122 | // ScalarPtrs. Otherwise, the pointer is placed in PossibleNonScalarPtrs. |
| 5123 | auto evaluatePtrUse = [&](Instruction *MemAccess, Value *Ptr) { |
| 5124 | if (isScalarPtrInduction(MemAccess, Ptr)) { |
| 5125 | Worklist.insert(cast<Instruction>(Ptr)); |
| 5126 | LLVM_DEBUG(dbgs() << "LV: Found new scalar instruction: " << *Ptrdo { } while (false) |
| 5127 | << "\n")do { } while (false); |
| 5128 | |
| 5129 | Instruction *Update = cast<Instruction>( |
| 5130 | cast<PHINode>(Ptr)->getIncomingValueForBlock(Latch)); |
| 5131 | ScalarPtrs.insert(Update); |
| 5132 | return; |
| 5133 | } |
| 5134 | // We only care about bitcast and getelementptr instructions contained in |
| 5135 | // the loop. |
| 5136 | if (!isLoopVaryingBitCastOrGEP(Ptr)) |
| 5137 | return; |
| 5138 | |
| 5139 | // If the pointer has already been identified as scalar (e.g., if it was |
| 5140 | // also identified as uniform), there's nothing to do. |
| 5141 | auto *I = cast<Instruction>(Ptr); |
| 5142 | if (Worklist.count(I)) |
| 5143 | return; |
| 5144 | |
| 5145 | // If all users of the pointer will be memory accesses and scalar, place the |
| 5146 | // pointer in ScalarPtrs. Otherwise, place the pointer in |
| 5147 | // PossibleNonScalarPtrs. |
| 5148 | if (llvm::all_of(I->users(), [&](User *U) { |
| 5149 | return (isa<LoadInst>(U) || isa<StoreInst>(U)) && |
| 5150 | isScalarUse(cast<Instruction>(U), Ptr); |
| 5151 | })) |
| 5152 | ScalarPtrs.insert(I); |
| 5153 | else |
| 5154 | PossibleNonScalarPtrs.insert(I); |
| 5155 | }; |
| 5156 | |
| 5157 | // We seed the scalars analysis with three classes of instructions: (1) |
| 5158 | // instructions marked uniform-after-vectorization and (2) bitcast, |
| 5159 | // getelementptr and (pointer) phi instructions used by memory accesses |
| 5160 | // requiring a scalar use. |
| 5161 | // |
| 5162 | // (1) Add to the worklist all instructions that have been identified as |
| 5163 | // uniform-after-vectorization. |
| 5164 | Worklist.insert(Uniforms[VF].begin(), Uniforms[VF].end()); |
| 5165 | |
| 5166 | // (2) Add to the worklist all bitcast and getelementptr instructions used by |
| 5167 | // memory accesses requiring a scalar use. The pointer operands of loads and |
| 5168 | // stores will be scalar as long as the memory accesses is not a gather or |
| 5169 | // scatter operation. The value operand of a store will remain scalar if the |
| 5170 | // store is scalarized. |
| 5171 | for (auto *BB : TheLoop->blocks()) |
| 5172 | for (auto &I : *BB) { |
| 5173 | if (auto *Load = dyn_cast<LoadInst>(&I)) { |
| 5174 | evaluatePtrUse(Load, Load->getPointerOperand()); |
| 5175 | } else if (auto *Store = dyn_cast<StoreInst>(&I)) { |
| 5176 | evaluatePtrUse(Store, Store->getPointerOperand()); |
| 5177 | evaluatePtrUse(Store, Store->getValueOperand()); |
| 5178 | } |
| 5179 | } |
| 5180 | for (auto *I : ScalarPtrs) |
| 5181 | if (!PossibleNonScalarPtrs.count(I)) { |
| 5182 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *I << "\n")do { } while (false); |
| 5183 | Worklist.insert(I); |
| 5184 | } |
| 5185 | |
| 5186 | // Insert the forced scalars. |
| 5187 | // FIXME: Currently widenPHIInstruction() often creates a dead vector |
| 5188 | // induction variable when the PHI user is scalarized. |
| 5189 | auto ForcedScalar = ForcedScalars.find(VF); |
| 5190 | if (ForcedScalar != ForcedScalars.end()) |
| 5191 | for (auto *I : ForcedScalar->second) |
| 5192 | Worklist.insert(I); |
| 5193 | |
| 5194 | // Expand the worklist by looking through any bitcasts and getelementptr |
| 5195 | // instructions we've already identified as scalar. This is similar to the |
| 5196 | // expansion step in collectLoopUniforms(); however, here we're only |
| 5197 | // expanding to include additional bitcasts and getelementptr instructions. |
| 5198 | unsigned Idx = 0; |
| 5199 | while (Idx != Worklist.size()) { |
| 5200 | Instruction *Dst = Worklist[Idx++]; |
| 5201 | if (!isLoopVaryingBitCastOrGEP(Dst->getOperand(0))) |
| 5202 | continue; |
| 5203 | auto *Src = cast<Instruction>(Dst->getOperand(0)); |
| 5204 | if (llvm::all_of(Src->users(), [&](User *U) -> bool { |
| 5205 | auto *J = cast<Instruction>(U); |
| 5206 | return !TheLoop->contains(J) || Worklist.count(J) || |
| 5207 | ((isa<LoadInst>(J) || isa<StoreInst>(J)) && |
| 5208 | isScalarUse(J, Src)); |
| 5209 | })) { |
| 5210 | Worklist.insert(Src); |
| 5211 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Src << "\n")do { } while (false); |
| 5212 | } |
| 5213 | } |
| 5214 | |
| 5215 | // An induction variable will remain scalar if all users of the induction |
| 5216 | // variable and induction variable update remain scalar. |
| 5217 | for (auto &Induction : Legal->getInductionVars()) { |
| 5218 | auto *Ind = Induction.first; |
| 5219 | auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| 5220 | |
| 5221 | // If tail-folding is applied, the primary induction variable will be used |
| 5222 | // to feed a vector compare. |
| 5223 | if (Ind == Legal->getPrimaryInduction() && foldTailByMasking()) |
| 5224 | continue; |
| 5225 | |
| 5226 | // Determine if all users of the induction variable are scalar after |
| 5227 | // vectorization. |
| 5228 | auto ScalarInd = llvm::all_of(Ind->users(), [&](User *U) -> bool { |
| 5229 | auto *I = cast<Instruction>(U); |
| 5230 | return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I); |
| 5231 | }); |
| 5232 | if (!ScalarInd) |
| 5233 | continue; |
| 5234 | |
| 5235 | // Determine if all users of the induction variable update instruction are |
| 5236 | // scalar after vectorization. |
| 5237 | auto ScalarIndUpdate = |
| 5238 | llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| 5239 | auto *I = cast<Instruction>(U); |
| 5240 | return I == Ind || !TheLoop->contains(I) || Worklist.count(I); |
| 5241 | }); |
| 5242 | if (!ScalarIndUpdate) |
| 5243 | continue; |
| 5244 | |
| 5245 | // The induction variable and its update instruction will remain scalar. |
| 5246 | Worklist.insert(Ind); |
| 5247 | Worklist.insert(IndUpdate); |
| 5248 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *Ind << "\n")do { } while (false); |
| 5249 | LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdatedo { } while (false) |
| 5250 | << "\n")do { } while (false); |
| 5251 | } |
| 5252 | |
| 5253 | Scalars[VF].insert(Worklist.begin(), Worklist.end()); |
| 5254 | } |
| 5255 | |
| 5256 | bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I) const { |
| 5257 | if (!blockNeedsPredication(I->getParent())) |
| 5258 | return false; |
| 5259 | switch(I->getOpcode()) { |
| 5260 | default: |
| 5261 | break; |
| 5262 | case Instruction::Load: |
| 5263 | case Instruction::Store: { |
| 5264 | if (!Legal->isMaskRequired(I)) |
| 5265 | return false; |
| 5266 | auto *Ptr = getLoadStorePointerOperand(I); |
| 5267 | auto *Ty = getLoadStoreType(I); |
| 5268 | const Align Alignment = getLoadStoreAlignment(I); |
| 5269 | return isa<LoadInst>(I) ? !(isLegalMaskedLoad(Ty, Ptr, Alignment) || |
| 5270 | TTI.isLegalMaskedGather(Ty, Alignment)) |
| 5271 | : !(isLegalMaskedStore(Ty, Ptr, Alignment) || |
| 5272 | TTI.isLegalMaskedScatter(Ty, Alignment)); |
| 5273 | } |
| 5274 | case Instruction::UDiv: |
| 5275 | case Instruction::SDiv: |
| 5276 | case Instruction::SRem: |
| 5277 | case Instruction::URem: |
| 5278 | return mayDivideByZero(*I); |
| 5279 | } |
| 5280 | return false; |
| 5281 | } |
| 5282 | |
| 5283 | bool LoopVectorizationCostModel::interleavedAccessCanBeWidened( |
| 5284 | Instruction *I, ElementCount VF) { |
| 5285 | assert(isAccessInterleaved(I) && "Expecting interleaved access.")((void)0); |
| 5286 | assert(getWideningDecision(I, VF) == CM_Unknown &&((void)0) |
| 5287 | "Decision should not be set yet.")((void)0); |
| 5288 | auto *Group = getInterleavedAccessGroup(I); |
| 5289 | assert(Group && "Must have a group.")((void)0); |
| 5290 | |
| 5291 | // If the instruction's allocated size doesn't equal it's type size, it |
| 5292 | // requires padding and will be scalarized. |
| 5293 | auto &DL = I->getModule()->getDataLayout(); |
| 5294 | auto *ScalarTy = getLoadStoreType(I); |
| 5295 | if (hasIrregularType(ScalarTy, DL)) |
| 5296 | return false; |
| 5297 | |
| 5298 | // Check if masking is required. |
| 5299 | // A Group may need masking for one of two reasons: it resides in a block that |
| 5300 | // needs predication, or it was decided to use masking to deal with gaps. |
| 5301 | bool PredicatedAccessRequiresMasking = |
| 5302 | Legal->blockNeedsPredication(I->getParent()) && Legal->isMaskRequired(I); |
| 5303 | bool AccessWithGapsRequiresMasking = |
| 5304 | Group->requiresScalarEpilogue() && !isScalarEpilogueAllowed(); |
| 5305 | if (!PredicatedAccessRequiresMasking && !AccessWithGapsRequiresMasking) |
| 5306 | return true; |
| 5307 | |
| 5308 | // If masked interleaving is required, we expect that the user/target had |
| 5309 | // enabled it, because otherwise it either wouldn't have been created or |
| 5310 | // it should have been invalidated by the CostModel. |
| 5311 | assert(useMaskedInterleavedAccesses(TTI) &&((void)0) |
| 5312 | "Masked interleave-groups for predicated accesses are not enabled.")((void)0); |
| 5313 | |
| 5314 | auto *Ty = getLoadStoreType(I); |
| 5315 | const Align Alignment = getLoadStoreAlignment(I); |
| 5316 | return isa<LoadInst>(I) ? TTI.isLegalMaskedLoad(Ty, Alignment) |
| 5317 | : TTI.isLegalMaskedStore(Ty, Alignment); |
| 5318 | } |
| 5319 | |
| 5320 | bool LoopVectorizationCostModel::memoryInstructionCanBeWidened( |
| 5321 | Instruction *I, ElementCount VF) { |
| 5322 | // Get and ensure we have a valid memory instruction. |
| 5323 | LoadInst *LI = dyn_cast<LoadInst>(I); |
| 5324 | StoreInst *SI = dyn_cast<StoreInst>(I); |
| 5325 | assert((LI || SI) && "Invalid memory instruction")((void)0); |
| 5326 | |
| 5327 | auto *Ptr = getLoadStorePointerOperand(I); |
| 5328 | |
| 5329 | // In order to be widened, the pointer should be consecutive, first of all. |
| 5330 | if (!Legal->isConsecutivePtr(Ptr)) |
| 5331 | return false; |
| 5332 | |
| 5333 | // If the instruction is a store located in a predicated block, it will be |
| 5334 | // scalarized. |
| 5335 | if (isScalarWithPredication(I)) |
| 5336 | return false; |
| 5337 | |
| 5338 | // If the instruction's allocated size doesn't equal it's type size, it |
| 5339 | // requires padding and will be scalarized. |
| 5340 | auto &DL = I->getModule()->getDataLayout(); |
| 5341 | auto *ScalarTy = LI ? LI->getType() : SI->getValueOperand()->getType(); |
| 5342 | if (hasIrregularType(ScalarTy, DL)) |
| 5343 | return false; |
| 5344 | |
| 5345 | return true; |
| 5346 | } |
| 5347 | |
| 5348 | void LoopVectorizationCostModel::collectLoopUniforms(ElementCount VF) { |
| 5349 | // We should not collect Uniforms more than once per VF. Right now, |
| 5350 | // this function is called from collectUniformsAndScalars(), which |
| 5351 | // already does this check. Collecting Uniforms for VF=1 does not make any |
| 5352 | // sense. |
| 5353 | |
| 5354 | assert(VF.isVector() && Uniforms.find(VF) == Uniforms.end() &&((void)0) |
| 5355 | "This function should not be visited twice for the same VF")((void)0); |
| 5356 | |
| 5357 | // Visit the list of Uniforms. If we'll not find any uniform value, we'll |
| 5358 | // not analyze again. Uniforms.count(VF) will return 1. |
| 5359 | Uniforms[VF].clear(); |
| 5360 | |
| 5361 | // We now know that the loop is vectorizable! |
| 5362 | // Collect instructions inside the loop that will remain uniform after |
| 5363 | // vectorization. |
| 5364 | |
| 5365 | // Global values, params and instructions outside of current loop are out of |
| 5366 | // scope. |
| 5367 | auto isOutOfScope = [&](Value *V) -> bool { |
| 5368 | Instruction *I = dyn_cast<Instruction>(V); |
| 5369 | return (!I || !TheLoop->contains(I)); |
| 5370 | }; |
| 5371 | |
| 5372 | SetVector<Instruction *> Worklist; |
| 5373 | BasicBlock *Latch = TheLoop->getLoopLatch(); |
| 5374 | |
| 5375 | // Instructions that are scalar with predication must not be considered |
| 5376 | // uniform after vectorization, because that would create an erroneous |
| 5377 | // replicating region where only a single instance out of VF should be formed. |
| 5378 | // TODO: optimize such seldom cases if found important, see PR40816. |
| 5379 | auto addToWorklistIfAllowed = [&](Instruction *I) -> void { |
| 5380 | if (isOutOfScope(I)) { |
| 5381 | LLVM_DEBUG(dbgs() << "LV: Found not uniform due to scope: "do { } while (false) |
| 5382 | << *I << "\n")do { } while (false); |
| 5383 | return; |
| 5384 | } |
| 5385 | if (isScalarWithPredication(I)) { |
| 5386 | LLVM_DEBUG(dbgs() << "LV: Found not uniform being ScalarWithPredication: "do { } while (false) |
| 5387 | << *I << "\n")do { } while (false); |
| 5388 | return; |
| 5389 | } |
| 5390 | LLVM_DEBUG(dbgs() << "LV: Found uniform instruction: " << *I << "\n")do { } while (false); |
| 5391 | Worklist.insert(I); |
| 5392 | }; |
| 5393 | |
| 5394 | // Start with the conditional branch. If the branch condition is an |
| 5395 | // instruction contained in the loop that is only used by the branch, it is |
| 5396 | // uniform. |
| 5397 | auto *Cmp = dyn_cast<Instruction>(Latch->getTerminator()->getOperand(0)); |
| 5398 | if (Cmp && TheLoop->contains(Cmp) && Cmp->hasOneUse()) |
| 5399 | addToWorklistIfAllowed(Cmp); |
| 5400 | |
| 5401 | auto isUniformDecision = [&](Instruction *I, ElementCount VF) { |
| 5402 | InstWidening WideningDecision = getWideningDecision(I, VF); |
| 5403 | assert(WideningDecision != CM_Unknown &&((void)0) |
| 5404 | "Widening decision should be ready at this moment")((void)0); |
| 5405 | |
| 5406 | // A uniform memory op is itself uniform. We exclude uniform stores |
| 5407 | // here as they demand the last lane, not the first one. |
| 5408 | if (isa<LoadInst>(I) && Legal->isUniformMemOp(*I)) { |
| 5409 | assert(WideningDecision == CM_Scalarize)((void)0); |
| 5410 | return true; |
| 5411 | } |
| 5412 | |
| 5413 | return (WideningDecision == CM_Widen || |
| 5414 | WideningDecision == CM_Widen_Reverse || |
| 5415 | WideningDecision == CM_Interleave); |
| 5416 | }; |
| 5417 | |
| 5418 | |
| 5419 | // Returns true if Ptr is the pointer operand of a memory access instruction |
| 5420 | // I, and I is known to not require scalarization. |
| 5421 | auto isVectorizedMemAccessUse = [&](Instruction *I, Value *Ptr) -> bool { |
| 5422 | return getLoadStorePointerOperand(I) == Ptr && isUniformDecision(I, VF); |
| 5423 | }; |
| 5424 | |
| 5425 | // Holds a list of values which are known to have at least one uniform use. |
| 5426 | // Note that there may be other uses which aren't uniform. A "uniform use" |
| 5427 | // here is something which only demands lane 0 of the unrolled iterations; |
| 5428 | // it does not imply that all lanes produce the same value (e.g. this is not |
| 5429 | // the usual meaning of uniform) |
| 5430 | SetVector<Value *> HasUniformUse; |
| 5431 | |
| 5432 | // Scan the loop for instructions which are either a) known to have only |
| 5433 | // lane 0 demanded or b) are uses which demand only lane 0 of their operand. |
| 5434 | for (auto *BB : TheLoop->blocks()) |
| 5435 | for (auto &I : *BB) { |
| 5436 | if (IntrinsicInst *II = dyn_cast<IntrinsicInst>(&I)) { |
| 5437 | switch (II->getIntrinsicID()) { |
| 5438 | case Intrinsic::sideeffect: |
| 5439 | case Intrinsic::experimental_noalias_scope_decl: |
| 5440 | case Intrinsic::assume: |
| 5441 | case Intrinsic::lifetime_start: |
| 5442 | case Intrinsic::lifetime_end: |
| 5443 | if (TheLoop->hasLoopInvariantOperands(&I)) |
| 5444 | addToWorklistIfAllowed(&I); |
| 5445 | break; |
| 5446 | default: |
| 5447 | break; |
| 5448 | } |
| 5449 | } |
| 5450 | |
| 5451 | // If there's no pointer operand, there's nothing to do. |
| 5452 | auto *Ptr = getLoadStorePointerOperand(&I); |
| 5453 | if (!Ptr) |
| 5454 | continue; |
| 5455 | |
| 5456 | // A uniform memory op is itself uniform. We exclude uniform stores |
| 5457 | // here as they demand the last lane, not the first one. |
| 5458 | if (isa<LoadInst>(I) && Legal->isUniformMemOp(I)) |
| 5459 | addToWorklistIfAllowed(&I); |
| 5460 | |
| 5461 | if (isUniformDecision(&I, VF)) { |
| 5462 | assert(isVectorizedMemAccessUse(&I, Ptr) && "consistency check")((void)0); |
| 5463 | HasUniformUse.insert(Ptr); |
| 5464 | } |
| 5465 | } |
| 5466 | |
| 5467 | // Add to the worklist any operands which have *only* uniform (e.g. lane 0 |
| 5468 | // demanding) users. Since loops are assumed to be in LCSSA form, this |
| 5469 | // disallows uses outside the loop as well. |
| 5470 | for (auto *V : HasUniformUse) { |
| 5471 | if (isOutOfScope(V)) |
| 5472 | continue; |
| 5473 | auto *I = cast<Instruction>(V); |
| 5474 | auto UsersAreMemAccesses = |
| 5475 | llvm::all_of(I->users(), [&](User *U) -> bool { |
| 5476 | return isVectorizedMemAccessUse(cast<Instruction>(U), V); |
| 5477 | }); |
| 5478 | if (UsersAreMemAccesses) |
| 5479 | addToWorklistIfAllowed(I); |
| 5480 | } |
| 5481 | |
| 5482 | // Expand Worklist in topological order: whenever a new instruction |
| 5483 | // is added , its users should be already inside Worklist. It ensures |
| 5484 | // a uniform instruction will only be used by uniform instructions. |
| 5485 | unsigned idx = 0; |
| 5486 | while (idx != Worklist.size()) { |
| 5487 | Instruction *I = Worklist[idx++]; |
| 5488 | |
| 5489 | for (auto OV : I->operand_values()) { |
| 5490 | // isOutOfScope operands cannot be uniform instructions. |
| 5491 | if (isOutOfScope(OV)) |
| 5492 | continue; |
| 5493 | // First order recurrence Phi's should typically be considered |
| 5494 | // non-uniform. |
| 5495 | auto *OP = dyn_cast<PHINode>(OV); |
| 5496 | if (OP && Legal->isFirstOrderRecurrence(OP)) |
| 5497 | continue; |
| 5498 | // If all the users of the operand are uniform, then add the |
| 5499 | // operand into the uniform worklist. |
| 5500 | auto *OI = cast<Instruction>(OV); |
| 5501 | if (llvm::all_of(OI->users(), [&](User *U) -> bool { |
| 5502 | auto *J = cast<Instruction>(U); |
| 5503 | return Worklist.count(J) || isVectorizedMemAccessUse(J, OI); |
| 5504 | })) |
| 5505 | addToWorklistIfAllowed(OI); |
| 5506 | } |
| 5507 | } |
| 5508 | |
| 5509 | // For an instruction to be added into Worklist above, all its users inside |
| 5510 | // the loop should also be in Worklist. However, this condition cannot be |
| 5511 | // true for phi nodes that form a cyclic dependence. We must process phi |
| 5512 | // nodes separately. An induction variable will remain uniform if all users |
| 5513 | // of the induction variable and induction variable update remain uniform. |
| 5514 | // The code below handles both pointer and non-pointer induction variables. |
| 5515 | for (auto &Induction : Legal->getInductionVars()) { |
| 5516 | auto *Ind = Induction.first; |
| 5517 | auto *IndUpdate = cast<Instruction>(Ind->getIncomingValueForBlock(Latch)); |
| 5518 | |
| 5519 | // Determine if all users of the induction variable are uniform after |
| 5520 | // vectorization. |
| 5521 | auto UniformInd = llvm::all_of(Ind->users(), [&](User *U) -> bool { |
| 5522 | auto *I = cast<Instruction>(U); |
| 5523 | return I == IndUpdate || !TheLoop->contains(I) || Worklist.count(I) || |
| 5524 | isVectorizedMemAccessUse(I, Ind); |
| 5525 | }); |
| 5526 | if (!UniformInd) |
| 5527 | continue; |
| 5528 | |
| 5529 | // Determine if all users of the induction variable update instruction are |
| 5530 | // uniform after vectorization. |
| 5531 | auto UniformIndUpdate = |
| 5532 | llvm::all_of(IndUpdate->users(), [&](User *U) -> bool { |
| 5533 | auto *I = cast<Instruction>(U); |
| 5534 | return I == Ind || !TheLoop->contains(I) || Worklist.count(I) || |
| 5535 | isVectorizedMemAccessUse(I, IndUpdate); |
| 5536 | }); |
| 5537 | if (!UniformIndUpdate) |
| 5538 | continue; |
| 5539 | |
| 5540 | // The induction variable and its update instruction will remain uniform. |
| 5541 | addToWorklistIfAllowed(Ind); |
| 5542 | addToWorklistIfAllowed(IndUpdate); |
| 5543 | } |
| 5544 | |
| 5545 | Uniforms[VF].insert(Worklist.begin(), Worklist.end()); |
| 5546 | } |
| 5547 | |
| 5548 | bool LoopVectorizationCostModel::runtimeChecksRequired() { |
| 5549 | LLVM_DEBUG(dbgs() << "LV: Performing code size checks.\n")do { } while (false); |
| 5550 | |
| 5551 | if (Legal->getRuntimePointerChecking()->Need) { |
| 5552 | reportVectorizationFailure("Runtime ptr check is required with -Os/-Oz", |
| 5553 | "runtime pointer checks needed. Enable vectorization of this " |
| 5554 | "loop with '#pragma clang loop vectorize(enable)' when " |
| 5555 | "compiling with -Os/-Oz", |
| 5556 | "CantVersionLoopWithOptForSize", ORE, TheLoop); |
| 5557 | return true; |
| 5558 | } |
| 5559 | |
| 5560 | if (!PSE.getUnionPredicate().getPredicates().empty()) { |
| 5561 | reportVectorizationFailure("Runtime SCEV check is required with -Os/-Oz", |
| 5562 | "runtime SCEV checks needed. Enable vectorization of this " |
| 5563 | "loop with '#pragma clang loop vectorize(enable)' when " |
| 5564 | "compiling with -Os/-Oz", |
| 5565 | "CantVersionLoopWithOptForSize", ORE, TheLoop); |
| 5566 | return true; |
| 5567 | } |
| 5568 | |
| 5569 | // FIXME: Avoid specializing for stride==1 instead of bailing out. |
| 5570 | if (!Legal->getLAI()->getSymbolicStrides().empty()) { |
| 5571 | reportVectorizationFailure("Runtime stride check for small trip count", |
| 5572 | "runtime stride == 1 checks needed. Enable vectorization of " |
| 5573 | "this loop without such check by compiling with -Os/-Oz", |
| 5574 | "CantVersionLoopWithOptForSize", ORE, TheLoop); |
| 5575 | return true; |
| 5576 | } |
| 5577 | |
| 5578 | return false; |
| 5579 | } |
| 5580 | |
| 5581 | ElementCount |
| 5582 | LoopVectorizationCostModel::getMaxLegalScalableVF(unsigned MaxSafeElements) { |
| 5583 | if (!TTI.supportsScalableVectors() && !ForceTargetSupportsScalableVectors) { |
| 5584 | reportVectorizationInfo( |
| 5585 | "Disabling scalable vectorization, because target does not " |
| 5586 | "support scalable vectors.", |
| 5587 | "ScalableVectorsUnsupported", ORE, TheLoop); |
| 5588 | return ElementCount::getScalable(0); |
| 5589 | } |
| 5590 | |
| 5591 | if (Hints->isScalableVectorizationDisabled()) { |
| 5592 | reportVectorizationInfo("Scalable vectorization is explicitly disabled", |
| 5593 | "ScalableVectorizationDisabled", ORE, TheLoop); |
| 5594 | return ElementCount::getScalable(0); |
| 5595 | } |
| 5596 | |
| 5597 | auto MaxScalableVF = ElementCount::getScalable( |
| 5598 | std::numeric_limits<ElementCount::ScalarTy>::max()); |
| 5599 | |
| 5600 | // Test that the loop-vectorizer can legalize all operations for this MaxVF. |
| 5601 | // FIXME: While for scalable vectors this is currently sufficient, this should |
| 5602 | // be replaced by a more detailed mechanism that filters out specific VFs, |
| 5603 | // instead of invalidating vectorization for a whole set of VFs based on the |
| 5604 | // MaxVF. |
| 5605 | |
| 5606 | // Disable scalable vectorization if the loop contains unsupported reductions. |
| 5607 | if (!canVectorizeReductions(MaxScalableVF)) { |
| 5608 | reportVectorizationInfo( |
| 5609 | "Scalable vectorization not supported for the reduction " |
| 5610 | "operations found in this loop.", |
| 5611 | "ScalableVFUnfeasible", ORE, TheLoop); |
| 5612 | return ElementCount::getScalable(0); |
| 5613 | } |
| 5614 | |
| 5615 | // Disable scalable vectorization if the loop contains any instructions |
| 5616 | // with element types not supported for scalable vectors. |
| 5617 | if (any_of(ElementTypesInLoop, [&](Type *Ty) { |
| 5618 | return !Ty->isVoidTy() && |
| 5619 | !this->TTI.isElementTypeLegalForScalableVector(Ty); |
| 5620 | })) { |
| 5621 | reportVectorizationInfo("Scalable vectorization is not supported " |
| 5622 | "for all element types found in this loop.", |
| 5623 | "ScalableVFUnfeasible", ORE, TheLoop); |
| 5624 | return ElementCount::getScalable(0); |
| 5625 | } |
| 5626 | |
| 5627 | if (Legal->isSafeForAnyVectorWidth()) |
| 5628 | return MaxScalableVF; |
| 5629 | |
| 5630 | // Limit MaxScalableVF by the maximum safe dependence distance. |
| 5631 | Optional<unsigned> MaxVScale = TTI.getMaxVScale(); |
| 5632 | MaxScalableVF = ElementCount::getScalable( |
| 5633 | MaxVScale ? (MaxSafeElements / MaxVScale.getValue()) : 0); |
| 5634 | if (!MaxScalableVF) |
| 5635 | reportVectorizationInfo( |
| 5636 | "Max legal vector width too small, scalable vectorization " |
| 5637 | "unfeasible.", |
| 5638 | "ScalableVFUnfeasible", ORE, TheLoop); |
| 5639 | |
| 5640 | return MaxScalableVF; |
| 5641 | } |
| 5642 | |
| 5643 | FixedScalableVFPair |
| 5644 | LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount, |
| 5645 | ElementCount UserVF) { |
| 5646 | MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI); |
| 5647 | unsigned SmallestType, WidestType; |
| 5648 | std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes(); |
| 5649 | |
| 5650 | // Get the maximum safe dependence distance in bits computed by LAA. |
| 5651 | // It is computed by MaxVF * sizeOf(type) * 8, where type is taken from |
| 5652 | // the memory accesses that is most restrictive (involved in the smallest |
| 5653 | // dependence distance). |
| 5654 | unsigned MaxSafeElements = |
| 5655 | PowerOf2Floor(Legal->getMaxSafeVectorWidthInBits() / WidestType); |
| 5656 | |
| 5657 | auto MaxSafeFixedVF = ElementCount::getFixed(MaxSafeElements); |
| 5658 | auto MaxSafeScalableVF = getMaxLegalScalableVF(MaxSafeElements); |
| 5659 | |
| 5660 | LLVM_DEBUG(dbgs() << "LV: The max safe fixed VF is: " << MaxSafeFixedVFdo { } while (false) |
| 5661 | << ".\n")do { } while (false); |
| 5662 | LLVM_DEBUG(dbgs() << "LV: The max safe scalable VF is: " << MaxSafeScalableVFdo { } while (false) |
| 5663 | << ".\n")do { } while (false); |
| 5664 | |
| 5665 | // First analyze the UserVF, fall back if the UserVF should be ignored. |
| 5666 | if (UserVF) { |
| 5667 | auto MaxSafeUserVF = |
| 5668 | UserVF.isScalable() ? MaxSafeScalableVF : MaxSafeFixedVF; |
| 5669 | |
| 5670 | if (ElementCount::isKnownLE(UserVF, MaxSafeUserVF)) { |
| 5671 | // If `VF=vscale x N` is safe, then so is `VF=N` |
| 5672 | if (UserVF.isScalable()) |
| 5673 | return FixedScalableVFPair( |
| 5674 | ElementCount::getFixed(UserVF.getKnownMinValue()), UserVF); |
| 5675 | else |
| 5676 | return UserVF; |
| 5677 | } |
| 5678 | |
| 5679 | assert(ElementCount::isKnownGT(UserVF, MaxSafeUserVF))((void)0); |
| 5680 | |
| 5681 | // Only clamp if the UserVF is not scalable. If the UserVF is scalable, it |
| 5682 | // is better to ignore the hint and let the compiler choose a suitable VF. |
| 5683 | if (!UserVF.isScalable()) { |
| 5684 | LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVFdo { } while (false) |
| 5685 | << " is unsafe, clamping to max safe VF="do { } while (false) |
| 5686 | << MaxSafeFixedVF << ".\n")do { } while (false); |
| 5687 | ORE->emit([&]() { |
| 5688 | return OptimizationRemarkAnalysis(DEBUG_TYPE"loop-vectorize", "VectorizationFactor", |
| 5689 | TheLoop->getStartLoc(), |
| 5690 | TheLoop->getHeader()) |
| 5691 | << "User-specified vectorization factor " |
| 5692 | << ore::NV("UserVectorizationFactor", UserVF) |
| 5693 | << " is unsafe, clamping to maximum safe vectorization factor " |
| 5694 | << ore::NV("VectorizationFactor", MaxSafeFixedVF); |
| 5695 | }); |
| 5696 | return MaxSafeFixedVF; |
| 5697 | } |
| 5698 | |
| 5699 | LLVM_DEBUG(dbgs() << "LV: User VF=" << UserVFdo { } while (false) |
| 5700 | << " is unsafe. Ignoring scalable UserVF.\n")do { } while (false); |
| 5701 | ORE->emit([&]() { |
| 5702 | return OptimizationRemarkAnalysis(DEBUG_TYPE"loop-vectorize", "VectorizationFactor", |
| 5703 | TheLoop->getStartLoc(), |
| 5704 | TheLoop->getHeader()) |
| 5705 | << "User-specified vectorization factor " |
| 5706 | << ore::NV("UserVectorizationFactor", UserVF) |
| 5707 | << " is unsafe. Ignoring the hint to let the compiler pick a " |
| 5708 | "suitable VF."; |
| 5709 | }); |
| 5710 | } |
| 5711 | |
| 5712 | LLVM_DEBUG(dbgs() << "LV: The Smallest and Widest types: " << SmallestTypedo { } while (false) |
| 5713 | << " / " << WidestType << " bits.\n")do { } while (false); |
| 5714 | |
| 5715 | FixedScalableVFPair Result(ElementCount::getFixed(1), |
| 5716 | ElementCount::getScalable(0)); |
| 5717 | if (auto MaxVF = getMaximizedVFForTarget(ConstTripCount, SmallestType, |
| 5718 | WidestType, MaxSafeFixedVF)) |
| 5719 | Result.FixedVF = MaxVF; |
| 5720 | |
| 5721 | if (auto MaxVF = getMaximizedVFForTarget(ConstTripCount, SmallestType, |
| 5722 | WidestType, MaxSafeScalableVF)) |
| 5723 | if (MaxVF.isScalable()) { |
| 5724 | Result.ScalableVF = MaxVF; |
| 5725 | LLVM_DEBUG(dbgs() << "LV: Found feasible scalable VF = " << MaxVFdo { } while (false) |
| 5726 | << "\n")do { } while (false); |
| 5727 | } |
| 5728 | |
| 5729 | return Result; |
| 5730 | } |
| 5731 | |
| 5732 | FixedScalableVFPair |
| 5733 | LoopVectorizationCostModel::computeMaxVF(ElementCount UserVF, unsigned UserIC) { |
| 5734 | if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) { |
| 5735 | // TODO: It may by useful to do since it's still likely to be dynamically |
| 5736 | // uniform if the target can skip. |
| 5737 | reportVectorizationFailure( |
| 5738 | "Not inserting runtime ptr check for divergent target", |
| 5739 | "runtime pointer checks needed. Not enabled for divergent target", |
| 5740 | "CantVersionLoopWithDivergentTarget", ORE, TheLoop); |
| 5741 | return FixedScalableVFPair::getNone(); |
| 5742 | } |
| 5743 | |
| 5744 | unsigned TC = PSE.getSE()->getSmallConstantTripCount(TheLoop); |
| 5745 | LLVM_DEBUG(dbgs() << "LV: Found trip count: " << TC << '\n')do { } while (false); |
| 5746 | if (TC == 1) { |
| 5747 | reportVectorizationFailure("Single iteration (non) loop", |
| 5748 | "loop trip count is one, irrelevant for vectorization", |
| 5749 | "SingleIterationLoop", ORE, TheLoop); |
| 5750 | return FixedScalableVFPair::getNone(); |
| 5751 | } |
| 5752 | |
| 5753 | switch (ScalarEpilogueStatus) { |
| 5754 | case CM_ScalarEpilogueAllowed: |
| 5755 | return computeFeasibleMaxVF(TC, UserVF); |
| 5756 | case CM_ScalarEpilogueNotAllowedUsePredicate: |
| 5757 | LLVM_FALLTHROUGH[[gnu::fallthrough]]; |
| 5758 | case CM_ScalarEpilogueNotNeededUsePredicate: |
| 5759 | LLVM_DEBUG(do { } while (false) |
| 5760 | dbgs() << "LV: vector predicate hint/switch found.\n"do { } while (false) |
| 5761 | << "LV: Not allowing scalar epilogue, creating predicated "do { } while (false) |
| 5762 | << "vector loop.\n")do { } while (false); |
| 5763 | break; |
| 5764 | case CM_ScalarEpilogueNotAllowedLowTripLoop: |
| 5765 | // fallthrough as a special case of OptForSize |
| 5766 | case CM_ScalarEpilogueNotAllowedOptSize: |
| 5767 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedOptSize) |
| 5768 | LLVM_DEBUG(do { } while (false) |
| 5769 | dbgs() << "LV: Not allowing scalar epilogue due to -Os/-Oz.\n")do { } while (false); |
| 5770 | else |
| 5771 | LLVM_DEBUG(dbgs() << "LV: Not allowing scalar epilogue due to low trip "do { } while (false) |
| 5772 | << "count.\n")do { } while (false); |
| 5773 | |
| 5774 | // Bail if runtime checks are required, which are not good when optimising |
| 5775 | // for size. |
| 5776 | if (runtimeChecksRequired()) |
| 5777 | return FixedScalableVFPair::getNone(); |
| 5778 | |
| 5779 | break; |
| 5780 | } |
| 5781 | |
| 5782 | // The only loops we can vectorize without a scalar epilogue, are loops with |
| 5783 | // a bottom-test and a single exiting block. We'd have to handle the fact |
| 5784 | // that not every instruction executes on the last iteration. This will |
| 5785 | // require a lane mask which varies through the vector loop body. (TODO) |
| 5786 | if (TheLoop->getExitingBlock() != TheLoop->getLoopLatch()) { |
| 5787 | // If there was a tail-folding hint/switch, but we can't fold the tail by |
| 5788 | // masking, fallback to a vectorization with a scalar epilogue. |
| 5789 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) { |
| 5790 | LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a "do { } while (false) |
| 5791 | "scalar epilogue instead.\n")do { } while (false); |
| 5792 | ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| 5793 | return computeFeasibleMaxVF(TC, UserVF); |
| 5794 | } |
| 5795 | return FixedScalableVFPair::getNone(); |
| 5796 | } |
| 5797 | |
| 5798 | // Now try the tail folding |
| 5799 | |
| 5800 | // Invalidate interleave groups that require an epilogue if we can't mask |
| 5801 | // the interleave-group. |
| 5802 | if (!useMaskedInterleavedAccesses(TTI)) { |
| 5803 | assert(WideningDecisions.empty() && Uniforms.empty() && Scalars.empty() &&((void)0) |
| 5804 | "No decisions should have been taken at this point")((void)0); |
| 5805 | // Note: There is no need to invalidate any cost modeling decisions here, as |
| 5806 | // non where taken so far. |
| 5807 | InterleaveInfo.invalidateGroupsRequiringScalarEpilogue(); |
| 5808 | } |
| 5809 | |
| 5810 | FixedScalableVFPair MaxFactors = computeFeasibleMaxVF(TC, UserVF); |
| 5811 | // Avoid tail folding if the trip count is known to be a multiple of any VF |
| 5812 | // we chose. |
| 5813 | // FIXME: The condition below pessimises the case for fixed-width vectors, |
| 5814 | // when scalable VFs are also candidates for vectorization. |
| 5815 | if (MaxFactors.FixedVF.isVector() && !MaxFactors.ScalableVF) { |
| 5816 | ElementCount MaxFixedVF = MaxFactors.FixedVF; |
| 5817 | assert((UserVF.isNonZero() || isPowerOf2_32(MaxFixedVF.getFixedValue())) &&((void)0) |
| 5818 | "MaxFixedVF must be a power of 2")((void)0); |
| 5819 | unsigned MaxVFtimesIC = UserIC ? MaxFixedVF.getFixedValue() * UserIC |
| 5820 | : MaxFixedVF.getFixedValue(); |
| 5821 | ScalarEvolution *SE = PSE.getSE(); |
| 5822 | const SCEV *BackedgeTakenCount = PSE.getBackedgeTakenCount(); |
| 5823 | const SCEV *ExitCount = SE->getAddExpr( |
| 5824 | BackedgeTakenCount, SE->getOne(BackedgeTakenCount->getType())); |
| 5825 | const SCEV *Rem = SE->getURemExpr( |
| 5826 | SE->applyLoopGuards(ExitCount, TheLoop), |
| 5827 | SE->getConstant(BackedgeTakenCount->getType(), MaxVFtimesIC)); |
| 5828 | if (Rem->isZero()) { |
| 5829 | // Accept MaxFixedVF if we do not have a tail. |
| 5830 | LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n")do { } while (false); |
| 5831 | return MaxFactors; |
| 5832 | } |
| 5833 | } |
| 5834 | |
| 5835 | // For scalable vectors, don't use tail folding as this is currently not yet |
| 5836 | // supported. The code is likely to have ended up here if the tripcount is |
| 5837 | // low, in which case it makes sense not to use scalable vectors. |
| 5838 | if (MaxFactors.ScalableVF.isVector()) |
| 5839 | MaxFactors.ScalableVF = ElementCount::getScalable(0); |
| 5840 | |
| 5841 | // If we don't know the precise trip count, or if the trip count that we |
| 5842 | // found modulo the vectorization factor is not zero, try to fold the tail |
| 5843 | // by masking. |
| 5844 | // FIXME: look for a smaller MaxVF that does divide TC rather than masking. |
| 5845 | if (Legal->prepareToFoldTailByMasking()) { |
| 5846 | FoldTailByMasking = true; |
| 5847 | return MaxFactors; |
| 5848 | } |
| 5849 | |
| 5850 | // If there was a tail-folding hint/switch, but we can't fold the tail by |
| 5851 | // masking, fallback to a vectorization with a scalar epilogue. |
| 5852 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotNeededUsePredicate) { |
| 5853 | LLVM_DEBUG(dbgs() << "LV: Cannot fold tail by masking: vectorize with a "do { } while (false) |
| 5854 | "scalar epilogue instead.\n")do { } while (false); |
| 5855 | ScalarEpilogueStatus = CM_ScalarEpilogueAllowed; |
| 5856 | return MaxFactors; |
| 5857 | } |
| 5858 | |
| 5859 | if (ScalarEpilogueStatus == CM_ScalarEpilogueNotAllowedUsePredicate) { |
| 5860 | LLVM_DEBUG(dbgs() << "LV: Can't fold tail by masking: don't vectorize\n")do { } while (false); |
| 5861 | return FixedScalableVFPair::getNone(); |
| 5862 | } |
| 5863 | |
| 5864 | if (TC == 0) { |
| 5865 | reportVectorizationFailure( |
| 5866 | "Unable to calculate the loop count due to complex control flow", |
| 5867 | "unable to calculate the loop count due to complex control flow", |
| 5868 | "UnknownLoopCountComplexCFG", ORE, TheLoop); |
| 5869 | return FixedScalableVFPair::getNone(); |
| 5870 | } |
| 5871 |