[LoopVectorizer] NFC: Return ElementCount from compute[Feasible]MaxVF

[sdesmalen]

Summary

Interfaces changed to return ElementCount:

• LoopVectorizationCostModel::computeMaxVF
• LoopVectorizationCostModel::computeFeasibleMaxVF

This is NFC for fixed-width vectors.

[ctetreau]

looks good to me

[dmgreen]

Is expected to make anything work yet for scalable vectors, or is it just updating interfaces? (i.e. will the code be checked somehow to make sure it will work for scalable vectors).

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

5283

Would this TC mod check still be valid for scalable vectors? If not does it need an assert?

[sdesmalen]

Added assert

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

5283

Good spot, you're right that this shouldn't work yet for scalable vectors. I've added an assert here.

[sdesmalen]

Is expected to make anything work yet for scalable vectors, or is it just updating interfaces? (i.e. will the code be checked somehow to make sure it will work for scalable vectors).

For this patch, it is just a matter of updating the interfaces. It will be up to follow-up patches to make the code actually work for scalable vectors (such as D91077).
That's why adding the assert on line 5271 that scalable vectors are not yet supported is good enough for this patch.

[dmgreen]

Makes sense, thanks

[sdesmalen]

Thanks @ctetreau and @dmgreen!

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

LoopVectorizationCostModel(ScalarEpilogueLowering SEL, Loop *L,
                              const LoopVectorizeHints *Hints,
                              InterleavedAccessInfo &IAI)
       : ScalarEpilogueStatus(SEL), TheLoop(L), PSE(PSE), LI(LI), Legal(Legal),
         TTI(TTI), TLI(TLI), DB(DB), AC(AC), ORE(ORE), TheFunction(F),
         Hints(Hints), InterleaveInfo(IAI) {}
 
   /// \return An upper bound for the vectorization factor, or None if
   /// vectorization and interleaving should be avoided up front.
-  Optional<unsigned> computeMaxVF(unsigned UserVF, unsigned UserIC);
+  Optional<ElementCount> computeMaxVF(ElementCount UserVF, unsigned UserIC);
 
   /// \return True if runtime checks are required for vectorization, and false
   /// otherwise.
   bool runtimeChecksRequired();
 
   /// \return The most profitable vectorization factor and the cost of that VF.
   /// This method checks every power of two up to MaxVF. If UserVF is not ZERO
   /// then this vectorization factor will be selected if vectorization is
public:
   }
 
 private:
   unsigned NumPredStores = 0;
 
   /// \return An upper bound for the vectorization factor, a power-of-2 larger
   /// than zero. One is returned if vectorization should best be avoided due
   /// to cost.
-  unsigned computeFeasibleMaxVF(unsigned ConstTripCount);
+  ElementCount computeFeasibleMaxVF(unsigned ConstTripCount);
 
   /// The vectorization cost is a combination of the cost itself and a boolean
   /// indicating whether any of the contributing operations will actually
   /// operate on
   /// vector values after type legalization in the backend. If this latter value
   /// is
   /// false, then all operations will be scalarized (i.e. no vectorization has
   /// actually taken place).
reportVectorizationFailure("Runtime stride check for small trip count",
         "this loop without such check by compiling with -Os/-Oz",
         "CantVersionLoopWithOptForSize", ORE, TheLoop);
     return true;
   }
 
   return false;
 }
 
-Optional<unsigned> LoopVectorizationCostModel::computeMaxVF(unsigned UserVF,
-                                                            unsigned UserIC) {
+Optional<ElementCount>
+LoopVectorizationCostModel::computeMaxVF(ElementCount UserVF, unsigned UserIC) {
   if (Legal->getRuntimePointerChecking()->Need && TTI.hasBranchDivergence()) {
     // TODO: It may by useful to do since it's still likely to be dynamically
     // uniform if the target can skip.
     reportVectorizationFailure(
         "Not inserting runtime ptr check for divergent target",
         "runtime pointer checks needed. Not enabled for divergent target",
         "CantVersionLoopWithDivergentTarget", ORE, TheLoop);
     return None;
LoopVectorizationCostModel::computeMaxVF(ElementCount UserVF, unsigned UserIC) {
   if (!useMaskedInterleavedAccesses(TTI)) {
     assert(WideningDecisions.empty() && Uniforms.empty() && Scalars.empty() &&
            "No decisions should have been taken at this point");
     // Note: There is no need to invalidate any cost modeling decisions here, as
     // non where taken so far.
     InterleaveInfo.invalidateGroupsRequiringScalarEpilogue();
   }
 
-  unsigned MaxVF = UserVF ? UserVF : computeFeasibleMaxVF(TC);
-  assert((UserVF || isPowerOf2_32(MaxVF)) && "MaxVF must be a power of 2");
-  unsigned MaxVFtimesIC = UserIC ? MaxVF * UserIC : MaxVF;
+  ElementCount MaxVF = UserVF ? UserVF : computeFeasibleMaxVF(TC);
+  assert(!MaxVF.isScalable() &&
+         "Scalable vectors do not yet support tail folding");
+  assert((UserVF.isNonZero() || isPowerOf2_32(MaxVF.getFixedValue())) &&
+         "MaxVF must be a power of 2");
+  unsigned MaxVFtimesIC =
+      UserIC ? MaxVF.getFixedValue() * UserIC : MaxVF.getFixedValue();
   if (TC > 0 && TC % MaxVFtimesIC == 0) {
     // Accept MaxVF if we do not have a tail.
     LLVM_DEBUG(dbgs() << "LV: No tail will remain for any chosen VF.\n");
     return MaxVF;
   }
 
   // If we don't know the precise trip count, or if the trip count that we
   // found modulo the vectorization factor is not zero, try to fold the tail
   // by masking.
reportVectorizationFailure(
       "Cannot optimize for size and vectorize at the same time.",
       "cannot optimize for size and vectorize at the same time. "
       "Enable vectorization of this loop with '#pragma clang loop "
       "vectorize(enable)' when compiling with -Os/-Oz",
       "NoTailLoopWithOptForSize", ORE, TheLoop);
   return None;
 }
 
-unsigned
+ElementCount
 LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount) {
   MinBWs = computeMinimumValueSizes(TheLoop->getBlocks(), *DB, &TTI);
   unsigned SmallestType, WidestType;
   std::tie(SmallestType, WidestType) = getSmallestAndWidestTypes();
   unsigned WidestRegister = TTI.getRegisterBitWidth(true);
 
   // Get the maximum safe dependence distance in bits computed by LAA.
   // It is computed by MaxVF * sizeOf(type) * 8, where type is taken from
LoopVectorizationCostModel::computeFeasibleMaxVF(unsigned ConstTripCount) {
   LLVM_DEBUG(dbgs() << "LV: The Widest register safe to use is: "
                     << WidestRegister << " bits.\n");
 
   assert(MaxVectorSize <= 256 && "Did not expect to pack so many elements"
                                  " into one vector!");
   if (MaxVectorSize == 0) {
     LLVM_DEBUG(dbgs() << "LV: The target has no vector registers.\n");
     MaxVectorSize = 1;
-    return MaxVectorSize;
+    return ElementCount::getFixed(MaxVectorSize);
   } else if (ConstTripCount && ConstTripCount < MaxVectorSize &&
              isPowerOf2_32(ConstTripCount)) {
     // We need to clamp the VF to be the ConstTripCount. There is no point in
     // choosing a higher viable VF as done in the loop below.
     LLVM_DEBUG(dbgs() << "LV: Clamping the MaxVF to the constant trip count: "
                       << ConstTripCount << "\n");
     MaxVectorSize = ConstTripCount;
-    return MaxVectorSize;
+    return ElementCount::getFixed(MaxVectorSize);
   }
 
   unsigned MaxVF = MaxVectorSize;
   if (TTI.shouldMaximizeVectorBandwidth(!isScalarEpilogueAllowed()) ||
       (MaximizeBandwidth && isScalarEpilogueAllowed())) {
     // Collect all viable vectorization factors larger than the default MaxVF
     // (i.e. MaxVectorSize).
     SmallVector<ElementCount, 8> VFs;
if (TTI.shouldMaximizeVectorBandwidth(!isScalarEpilogueAllowed()) ||
     if (unsigned MinVF = TTI.getMinimumVF(SmallestType)) {
       if (MaxVF < MinVF) {
         LLVM_DEBUG(dbgs() << "LV: Overriding calculated MaxVF(" << MaxVF
                           << ") with target's minimum: " << MinVF << '\n');
         MaxVF = MinVF;
       }
     }
   }
-  return MaxVF;
+  return ElementCount::getFixed(MaxVF);
 }
 
 VectorizationFactor
 LoopVectorizationCostModel::selectVectorizationFactor(ElementCount MaxVF) {
   assert(!MaxVF.isScalable() && "scalable vectors not yet supported");
 
   float Cost = expectedCost(ElementCount::getFixed(1)).first;
   const float ScalarCost = Cost;
LLVM_DEBUG(
                 "VPlan-native path.\n");
   return VectorizationFactor::Disabled();
 }
 
 Optional<VectorizationFactor>
 LoopVectorizationPlanner::plan(ElementCount UserVF, unsigned UserIC) {
   assert(!UserVF.isScalable() && "scalable vectorization not yet handled");
   assert(OrigLoop->isInnermost() && "Inner loop expected.");
-  Optional<unsigned> MaybeMaxVF =
-      CM.computeMaxVF(UserVF.getKnownMinValue(), UserIC);
+  Optional<ElementCount> MaybeMaxVF = CM.computeMaxVF(UserVF, UserIC);
   if (!MaybeMaxVF) // Cases that should not to be vectorized nor interleaved.
     return None;
 
   // Invalidate interleave groups if all blocks of loop will be predicated.
   if (CM.blockNeedsPredication(OrigLoop->getHeader()) &&
       !useMaskedInterleavedAccesses(*TTI)) {
     LLVM_DEBUG(
         dbgs()
if (!UserVF.isZero()) {
     // profitable to scalarize.
     CM.selectUserVectorizationFactor(UserVF);
     CM.collectInLoopReductions();
     buildVPlansWithVPRecipes(UserVF, UserVF);
     LLVM_DEBUG(printPlans(dbgs()));
     return {{UserVF, 0}};
   }
 
-  ElementCount MaxVF = ElementCount::getFixed(MaybeMaxVF.getValue());
+  ElementCount MaxVF = MaybeMaxVF.getValue();
   assert(MaxVF.isNonZero() && "MaxVF is zero.");
 
   for (ElementCount VF = ElementCount::getFixed(1);
        ElementCount::isKnownLE(VF, MaxVF); VF *= 2) {
     // Collect Uniform and Scalar instructions after vectorization with VF.
     CM.collectUniformsAndScalars(VF);
 
     // Collect the instructions (and their associated costs) that will be more