[LoopVectorizer] NFC: Remove unnecessary asserts that VF cannot be scalable.

[sdesmalen]

Summary

This patch removes a number of asserts that VF is not scalable, even though
the code where this assert lives does nothing that prevents VF being scalable.

[dmgreen]

As far as I can tell these do sound OK, except perhaps for the one I put a question on.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

5102

What happens if this function returns true? Do (predicated) scalars work OK for scalable vectors.

[sdesmalen]

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

5102

If this function returns true, the operation would have to be scalarized. For scalable vectors, scalarization is only supported if the value is uniform after vectorization, so it can extract the first or last element. Scalarizing a non-uniform value is not supported, because there is currently no way to expand to a scalarization loop (and even if there would be, we probably wouldn't want to).

From the comment in collectLoopUniforms (above addToWorklistIfAllowed), it seems that when isScalarWithPredication returns true, the value cannot be uniform. This means that the cost-model/legalization should prevent this from happening for scalable vectors. This is one of the reasons we're adding a new cost class in D91174 so that it would never pick a scalable VF when scalarization is required.

There are currently other asserts that guard this case from happening, like in VPReplicateRecipe::execute.

[dmgreen]

LGTM.

llvm/lib/Transforms/Vectorize/LoopVectorize.cpp

if (auto *LI = dyn_cast<LoadInst>(I))
     return LI->getType();
   return cast<StoreInst>(I)->getValueOperand()->getType();
 }
 
 /// A helper function that returns true if the given type is irregular. The
 /// type is irregular if its allocated size doesn't equal the store size of an
 /// element of the corresponding vector type at the given vectorization factor.
 static bool hasIrregularType(Type *Ty, const DataLayout &DL, ElementCount VF) {
-  assert(!VF.isScalable() && "scalable vectors not yet supported.");
   // Determine if an array of VF elements of type Ty is "bitcast compatible"
   // with a <VF x Ty> vector.
   if (VF.isVector()) {
     auto *VectorTy = VectorType::get(Ty, VF);
     return TypeSize::get(VF.getKnownMinValue() *
                              DL.getTypeAllocSize(Ty).getFixedValue(),
                          VF.isScalable()) != DL.getTypeStoreSize(VectorTy);
   }
for (unsigned i = 0; i < Grp->getFactor(); ++i) {
       }
     }
   }
 
   /// Return the cost model decision for the given instruction \p I and vector
   /// width \p VF. Return CM_Unknown if this instruction did not pass
   /// through the cost modeling.
   InstWidening getWideningDecision(Instruction *I, ElementCount VF) {
-    assert(!VF.isScalable() && "scalable vectors not yet supported.");
-    assert(VF.isVector() && "Expected VF >=2");
-
+    assert(VF.isVector() && "Expected VF to be a vector VF");
     // Cost model is not run in the VPlan-native path - return conservative
     // result until this changes.
     if (EnableVPlanNativePath)
       return CM_GatherScatter;
 
     std::pair<Instruction *, ElementCount> InstOnVF = std::make_pair(I, VF);
     auto Itr = WideningDecisions.find(InstOnVF);
     if (Itr == WideningDecisions.end())
void InnerLoopVectorizer::fixVectorizedLoop() {
   // loop iterations are now distributed among them. Note that original loop
   // represented by LoopScalarBody becomes remainder loop after vectorization.
   //
   // For cases like foldTailByMasking() and requiresScalarEpiloque() we may
   // end up getting slightly roughened result but that should be OK since
   // profile is not inherently precise anyway. Note also possible bypass of
   // vector code caused by legality checks is ignored, assigning all the weight
   // to the vector loop, optimistically.
-  assert(!VF.isScalable() &&
-         "cannot use scalable ElementCount to determine unroll factor");
+  //
+  // For scalable vectorization we can't know at compile time how many iterations
+  // of the loop are handled in one vector iteration, so instead assume a pessimistic
+  // vscale of '1'.
   setProfileInfoAfterUnrolling(
       LI->getLoopFor(LoopScalarBody), LI->getLoopFor(LoopVectorBody),
       LI->getLoopFor(LoopScalarBody), VF.getKnownMinValue() * UF);
 }
 
 void InnerLoopVectorizer::fixCrossIterationPHIs() {
   // In order to support recurrences we need to be able to vectorize Phi nodes.
   // Phi nodes have cycles, so we need to vectorize them in two stages. This is
static bool mayDivideByZero(Instruction &I) {
   Value *Divisor = I.getOperand(1);
   auto *CInt = dyn_cast<ConstantInt>(Divisor);
   return !CInt || CInt->isZero();
 }
 
 void InnerLoopVectorizer::widenInstruction(Instruction &I, VPValue *Def,
                                            VPUser &User,
                                            VPTransformState &State) {
-  assert(!VF.isScalable() && "scalable vectors not yet supported.");
   switch (I.getOpcode()) {
   case Instruction::Call:
   case Instruction::Br:
   case Instruction::PHI:
   case Instruction::GetElementPtr:
   case Instruction::Select:
     llvm_unreachable("This instruction is handled by a different recipe.");
   case Instruction::UDiv:
void InnerLoopVectorizer::widenInstruction(Instruction &I, VPValue *Def,
   case Instruction::UIToFP:
   case Instruction::Trunc:
   case Instruction::FPTrunc:
   case Instruction::BitCast: {
     auto *CI = cast<CastInst>(&I);
     setDebugLocFromInst(Builder, CI);
 
     /// Vectorize casts.
-    assert(!VF.isScalable() && "VF is assumed to be non scalable.");
     Type *DestTy =
         (VF.isScalar()) ? CI->getType() : VectorType::get(CI->getType(), VF);
 
     for (unsigned Part = 0; Part < UF; ++Part) {
       Value *A = State.get(User.getOperand(0), Part);
       Value *Cast = Builder.CreateCast(CI->getOpcode(), A, DestTy);
       State.set(Def, &I, Cast, Part);
       addMetadata(Cast, &I);
LLVM_DEBUG(dbgs() << "LV: Found scalar instruction: " << *IndUpdate
                       << "\n");
   }
 
   Scalars[VF].insert(Worklist.begin(), Worklist.end());
 }
 
 bool LoopVectorizationCostModel::isScalarWithPredication(Instruction *I,
                                                          ElementCount VF) {
-  assert(!VF.isScalable() && "scalable vectors not yet supported.");
   if (!blockNeedsPredication(I->getParent()))
     return false;
   switch(I->getOpcode()) {
   default:
     break;
   case Instruction::Load:
   case Instruction::Store: {
     if (!Legal->isMaskRequired(I))
while (!Worklist.empty()) {
     ScalarCosts[I] = ScalarCost;
   }
 
   return Discount;
 }
 
 LoopVectorizationCostModel::VectorizationCostTy
 LoopVectorizationCostModel::expectedCost(ElementCount VF) {
-  assert(!VF.isScalable() && "scalable vectors not yet supported.");
   VectorizationCostTy Cost;
 
   // For each block.
   for (BasicBlock *BB : TheLoop->blocks()) {
     VectorizationCostTy BlockCost;
 
     // For each instruction in the old loop.
     for (Instruction &I : BB->instructionsWithoutDebug()) {

 
 VPWidenMemoryInstructionRecipe *
 VPRecipeBuilder::tryToWidenMemory(Instruction *I, VFRange &Range,
                                   VPlanPtr &Plan) {
   assert((isa<LoadInst>(I) || isa<StoreInst>(I)) &&
          "Must be called with either a load or store");
 
   auto willWiden = [&](ElementCount VF) -> bool {
-    assert(!VF.isScalable() && "unexpected scalable ElementCount");
     if (VF.isScalar())
       return false;
     LoopVectorizationCostModel::InstWidening Decision =
         CM.getWideningDecision(I, VF);
     assert(Decision != LoopVectorizationCostModel::CM_Unknown &&
            "CM decision should be taken at this point.");
     if (Decision == LoopVectorizationCostModel::CM_Interleave)
       return true;