File: | src/gnu/usr.bin/clang/libLLVM/../../../llvm/llvm/lib/Transforms/Vectorize/LoopVectorize.cpp |
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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 |