[mlir][Linalg] Refactor and improve vectorization to add support for reduction into 0-d tensors.

[nicolasvasilache]

Summary

This revision takes advantage of the recently added support for 0-d transfers and vector.multi_reduction that return a scalar.

[pifon2a]

mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp

256

nit: update this to use the new macro

mlir/test/Dialect/Linalg/vectorization.mlir

877

nit: wrap the lines with CHECK-SAME?

[nicolasvasilache]

Address review.

mlir/include/mlir/Dialect/Vector/VectorOps.td

let description = [{
     ```
   }];
 
   let builders = [
     // Builder that sets padding to zero.
     OpBuilder<(ins "VectorType":$vector, "Value":$source,
       "ValueRange":$indices, "AffineMap":$permutationMap,
       CArg<"ArrayRef<bool>", "{}">:$inBounds)>,
-    // Builder that sets padding to 'getMinorIdentityMap'.
+    // Builder that sets permutation map to 'getMinorIdentityMap'.
     OpBuilder<(ins "VectorType":$vector, "Value":$source,
       "ValueRange":$indices, "Value":$padding,
       CArg<"ArrayRef<bool>", "{}">:$inBounds)>,
     // Builder that sets permutation map (resp. padding) to
     // 'getMinorIdentityMap' (resp. zero).
     OpBuilder<(ins "VectorType":$vector, "Value":$source,
       "ValueRange":$indices, CArg<"ArrayRef<bool>", "{}">:$inBounds)>,
     // Builder that does not set mask.
     OpBuilder<(ins "Type":$vector, "Value":$source,
       "ValueRange":$indices, "AffineMapAttr":$permutationMap, "Value":$padding,
       "ArrayAttr":$inBounds)>,
     // Builder that does not set mask.
     OpBuilder<(ins "Type":$vector, "Value":$source,
       "ValueRange":$indices, "AffineMap":$permutationMap, "Value":$padding,
       "ArrayAttr":$inBounds)>
   ];
 
+  let extraClassDeclaration = [{
+    /// Temporary convenience builders to account for the fact that we do not
+    /// have 0-d vectors atm. These create a constant `vector<1xt>` and
+    /// insert/extract into it.
+    // Builder that sets permutation map (resp. padding) to
+    // 'getMinorIdentityMap' (resp. zero).
+    static Value createScalarOp(OpBuilder &builder, Location loc, Value source,
+                                ValueRange indices,
+                                ArrayRef<bool> inBounds = ArrayRef<bool>{});
+  }];
+
   let hasCanonicalizer = 1;
   let hasFolder = 1;
 }
 
 def Vector_TransferWriteOp :
   Vector_Op<"transfer_write", [
       DeclareOpInterfaceMethods<VectorTransferOpInterface>,
       DeclareOpInterfaceMethods<VectorUnrollOpInterface, ["getShapeForUnroll"]>,
let description = [{
     // Special encoding for 0-d transfer with 0-d tensor/memref, vector shape
     // {1} and permutation_map (d0) -> (0).
     %1 = vector.transfer_write %0, %arg0[] {permutation_map = affine_map<(d0)->(0)>} :
       vector<1xf32>, tensor<f32>
     ```
   }];
 
   let builders = [
+    // Builder that sets an empty mask.
+    OpBuilder<(ins "Value":$vector, "Value":$source, "ValueRange":$indices,
+      "AffineMap":$permutationMap, CArg<"ArrayRef<bool>", "{}">:$inBounds)>,
     // Builder that sets permutation map to 'getMinorIdentityMap'.
     OpBuilder<(ins "Value":$vector, "Value":$source, "ValueRange":$indices,
       CArg<"ArrayRef<bool>", "{}">:$inBounds)>,
     OpBuilder<(ins "Value":$vector, "Value":$source, "ValueRange":$indices,
-      "AffineMap":$permutationMap)>,
-    OpBuilder<(ins "Value":$vector, "Value":$source, "ValueRange":$indices,
       "AffineMapAttr":$permutationMap, "ArrayAttr":$inBounds)>,
     OpBuilder<(ins "Value":$vector, "Value":$source, "ValueRange":$indices,
       "AffineMap":$permutationMap, "Value":$mask, "ArrayAttr":$inBounds)>,
     OpBuilder<(ins "Value":$vector, "Value":$source, "ValueRange":$indices,
       "AffineMap":$permutationMap, "ArrayAttr":$inBounds)>,
   ];
 
+  let extraClassDeclaration = [{
+    /// Temporary convenience builders to account for the fact that we do not
+    /// have 0-d vectors atm. These create a constant `vector<1xt>` and
+    /// insert/extract into it.
+    // Builder that sets permutation map (resp. padding) to
+    // 'getMinorIdentityMap' (resp. zero).
+    static Operation *createScalarOp(
+      OpBuilder &builder, Location loc, Value value,
+      Value dest, ValueRange indices,
+      ArrayRef<bool> inBounds = ArrayRef<bool>{});
+  }];
+
   let hasFolder = 1;
   let hasCanonicalizer = 1;
 }
 
 def Vector_LoadOp : Vector_Op<"load"> {
   let summary = "reads an n-D slice of memory into an n-D vector";
   let description = [{
     The 'vector.load' operation reads an n-D slice of memory into an n-D

mlir/lib/Dialect/Linalg/Transforms/Vectorization.cpp

 
 using namespace mlir;
 using namespace mlir::linalg;
 
 using llvm::dbgs;
 
 #define DEBUG_TYPE "linalg-vectorization"
 
+#define DBGS() (llvm::dbgs() << '[' << DEBUG_TYPE << "] ")
+#define LDBG(X) LLVM_DEBUG(DBGS() << X)
+
 /// Return the unique instance of OpType in `block` if it is indeed unique.
 /// Return null if none or more than 1 instances exist.
 template <typename OpType>
 static OpType getSingleOpOfType(Block &block) {
   OpType res;
   block.walk([&](OpType op) {
     if (res) {
       res = nullptr;
struct VectorizationResult {
   /// Replacement behavior is specified by `status`.
   Operation *newOp;
 };
 
 /// Return a vector type of the same shape and element type as the (assumed)
 /// ShapedType of `v`.
 static VectorType extractVectorTypeFromShapedValue(Value v) {
   auto st = v.getType().cast<ShapedType>();
-  if (st.isa<MemRefType>() && st.getShape().empty())
+  if (st.getShape().empty())
     return VectorType();
   return VectorType::get(st.getShape(), st.getElementType());
 }
 
 static llvm::Optional<vector::CombiningKind>
 getKindForOp(Operation *reductionOp) {
   if (!reductionOp)
     return llvm::None;
static Value broadcastIfNeeded(OpBuilder &b, Value value,
   if (auto vecType = value.getType().dyn_cast<VectorType>()) {
     // If the shape already fits, return `value`.
     if (vecType.getShape() == shape)
       return value;
   }
   return b.create<vector::BroadcastOp>(loc, targetVectorType, value);
 }
 
-/// If value of assumed VectorType has a shape different than `shape`, build and
-/// return a new vector.broadcast to `shape`.
-/// Otherwise, just return value.
-static Value reduceIfNeeded(OpBuilder &b, VectorType targetVectorType,
-                            Value value, OpOperand *outputOperand) {
+/// Assuming `outputOperand` is an output operand of a LinalgOp, determine
+/// whether a reduction is needed to produce a `targetType` and create that
+/// reduction if it is the case.
+static Value reduceIfNeeded(OpBuilder &b, Type targetType, Value value,
+                            OpOperand *outputOperand) {
+  LDBG("Reduce " << value << " to type " << targetType);
+  LDBG("In LinalgOp operand #" << outputOperand->getOperandNumber() << "\n"
+                               << *(outputOperand->getOwner()));
   auto linalgOp = cast<LinalgOp>(outputOperand->getOwner());
   auto vecType = value.getType().dyn_cast<VectorType>();
-  if (!vecType || vecType.getShape() == targetVectorType.getShape())
+  VectorType targetVectorType = targetType.dyn_cast<VectorType>();
+  if (!vecType)
+    return value;
+  if (targetVectorType && vecType.getShape() == targetVectorType.getShape())
     return value;
 
+  // At this point, we know we need to reduce. Detect the reduction operator.
   unsigned pos = 0;
   MLIRContext *ctx = b.getContext();
   SmallVector<AffineExpr> exprs;
   for (auto s : linalgOp.iterator_types())
     if (isParallelIterator(s))
       exprs.push_back(getAffineDimExpr(pos++, ctx));
   auto loc = value.getLoc();
 
-  // At this point, we know we need to reduce. Detect the reduction operator.
   auto maybeKind = matchLinalgReduction(outputOperand);
   assert(maybeKind && "Failed precondition: could not get reduction kind");
   unsigned idx = 0;
   SmallVector<bool> reductionMask(linalgOp.iterator_types().size(), false);
   for (auto attr : linalgOp.iterator_types()) {
     if (isReductionIterator(attr))
       reductionMask[idx] = true;
     ++idx;
   }
   return b.create<vector::MultiDimReductionOp>(loc, value, reductionMask,
                                                *maybeKind);
 }
 
 /// Build a vector.transfer_read from `source` at indices set to all `0`.
-/// If source has rank zero, build an memref.load.
+/// If source has rank zero, build a `vector<1xt> transfer_read + extract`.
 /// Return the produced value.
-static Value buildVectorRead(OpBuilder &b, Value source, VectorType vectorType,
+static Value buildVectorRead(OpBuilder &b, Value source, Type readType,
                              AffineMap map) {
   Location loc = source.getLoc();
   auto shapedType = source.getType().cast<ShapedType>();
   SmallVector<Value> indices(shapedType.getRank(),
                              b.create<ConstantIndexOp>(loc, 0));
+  if (auto vectorType = readType.dyn_cast<VectorType>())
     return b.create<vector::TransferReadOp>(loc, vectorType, source, indices,
                                             map);
+  return vector::TransferReadOp::createScalarOp(b, loc, source, indices);
 }
 
 /// Build a vector.transfer_write of `value` into `outputOperand` at indices set
 /// to all `0`; where `outputOperand` is an output operand of the LinalgOp
 /// currently being vectorized. If `dest` has null rank, build an memref.store.
 /// Return the produced value or null if no value is produced.
 static Value buildVectorWrite(OpBuilder &b, Value value,
                               OpOperand *outputOperand) {
   Operation *write;
   Location loc = value.getLoc();
+  auto linalgOp = cast<LinalgOp>(outputOperand->getOwner());
   if (VectorType vectorType =
           extractVectorTypeFromShapedValue(outputOperand->get())) {
-    auto linalgOp = cast<LinalgOp>(outputOperand->getOwner());
     AffineMap map =
         reindexIndexingMap(linalgOp.getTiedIndexingMap(outputOperand));
     SmallVector<int64_t> transposeShape =
         applyPermutationMap(inversePermutation(map), vectorType.getShape());
+    assert(!transposeShape.empty() && "unexpected empty transpose shape");
     vectorType = VectorType::get(transposeShape, vectorType.getElementType());
     SmallVector<Value> indices(linalgOp.getRank(outputOperand),
                                b.create<ConstantIndexOp>(loc, 0));
     value = broadcastIfNeeded(b, value, vectorType.getShape());
     value = reduceIfNeeded(b, vectorType, value, outputOperand);
     write = b.create<vector::TransferWriteOp>(loc, value, outputOperand->get(),
                                               indices, map);
   } else {
-    write = b.create<memref::StoreOp>(loc, value, outputOperand->get());
+    value =
+        reduceIfNeeded(b, getElementTypeOrSelf(value), value, outputOperand);
+    write = vector::TransferWriteOp::createScalarOp(
+        b, loc, value, outputOperand->get(), ValueRange{});
   }
   LLVM_DEBUG(dbgs() << "\n[" DEBUG_TYPE "]: vectorized op: " << *write);
   if (!write->getResults().empty())
     return write->getResult(0);
   return Value();
 }
 
 // Custom vectorization function type. Produce a vector form of Operation*
 // assuming all its vectorized operands are already in the BlockAndValueMapping.
 // Return nullptr if the Operation cannot be vectorized.
LogicalResult vectorizeAsLinalgGeneric(
   SmallVector<AffineMap> indexings;
   for (OpOperand *opOperand : linalgOp.getInputAndOutputOperands()) {
     BlockArgument bbarg = block.getArgument(opOperand->getOperandNumber());
     if (linalgOp.isScalar(opOperand)) {
       bvm.map(bbarg, opOperand->get());
       continue;
     }
     // TODO: 0-d vectors.
-    if (linalgOp.getShape(opOperand).empty()) {
-      Value loaded =
-          b.create<memref::LoadOp>(linalgOp.getLoc(), opOperand->get());
-      LLVM_DEBUG(dbgs() << "\n[" DEBUG_TYPE "]: new vectorized bbarg("
-                        << bbarg.getArgNumber() << "): " << loaded);
-      bvm.map(bbarg, loaded);
-      bvm.map(opOperand->get(), loaded);
-      continue;
-    }
+    Type readType;
     AffineMap map;
-    VectorType vectorType;
+    if (linalgOp.getShape(opOperand).empty()) {
+      readType = bbarg.getType();
+    } else {
       if (broadcastToMaximalCommonShape) {
         map = inverseAndBroadcastProjectedPermuation(
             linalgOp.getTiedIndexingMap(opOperand));
-      vectorType = VectorType::get(commonVectorShape,
+        readType = VectorType::get(commonVectorShape,
                                    getElementTypeOrSelf(opOperand->get()));
       } else {
         map = inversePermutation(
             reindexIndexingMap(linalgOp.getTiedIndexingMap(opOperand)));
-      vectorType = VectorType::get(map.compose(linalgOp.getShape(opOperand)),
+        readType = VectorType::get(map.compose(linalgOp.getShape(opOperand)),
                                    getElementTypeOrSelf(opOperand->get()));
       }
-    Value vectorRead = buildVectorRead(b, opOperand->get(), vectorType, map);
+    }
+    Value readValue = buildVectorRead(b, opOperand->get(), readType, map);
     LLVM_DEBUG(dbgs() << "\n[" DEBUG_TYPE "]: new vectorized bbarg("
-                      << bbarg.getArgNumber() << "): " << vectorRead);
-    bvm.map(bbarg, vectorRead);
-    bvm.map(opOperand->get(), vectorRead);
+                      << bbarg.getArgNumber() << "): " << readValue);
+    bvm.map(bbarg, readValue);
+    bvm.map(opOperand->get(), readValue);
   }
 
   auto hooks = llvm::to_vector<4>(customVectorizationHooks);
   // 4a. Register CustomVectorizationHook for yieldOp.
   CustomVectorizationHook vectorizeYield =
       [&](Operation *op,
           const BlockAndValueMapping &bvm) -> VectorizationResult {
     return vectorizeLinalgYield(b, op, bvm, linalgOp, newResults);

 static bool allIndexingsAreProjectedPermutation(LinalgOp op) {
   return llvm::all_of(op.getIndexingMaps(),
                       [](AffineMap m) { return m.isProjectedPermutation(); });
 }
 
 // TODO: probably need some extra checks for reduction followed by consumer
 // ops that may not commute (e.g. linear reduction + non-linear instructions).
 static LogicalResult reductionPreconditions(LinalgOp op) {
-  if (llvm::none_of(op.iterator_types(), isReductionIterator))
+  if (llvm::none_of(op.iterator_types(), isReductionIterator)) {
+    LDBG("reduction precondition failed: no reduction iterator");
     return failure();
+  }
   for (OpOperand *opOperand : op.getOutputOperands()) {
-    if (!matchLinalgReduction(opOperand))
+    if (!matchLinalgReduction(opOperand)) {
+      LDBG("reduction precondition failed: reduction detection failed");
       return failure();
     }
+  }
   return success();
 }
 
 LogicalResult mlir::linalg::vectorizeLinalgOpPrecondition(Operation *op) {
   auto linalgOp = cast<linalg::LinalgOp>(op);
   // All types must be static shape to go to vector.
-  if (linalgOp.hasDynamicShape())
+  if (linalgOp.hasDynamicShape()) {
+    LDBG("precondition failed: dynamic shape");
     return failure();
+  }
   if (isElementwise(op))
     return success();
   if (isaContractionOpInterface(linalgOp))
     return success();
   // TODO: the common vector shape is equal to the static loop sizes only when
   // all indexing maps are projected permutations. For convs and stencils the
   // logic will need to evolve.
-  if (allIndexingsAreProjectedPermutation(linalgOp) &&
-      succeeded(reductionPreconditions(linalgOp)))
-    return success();
+  if (!allIndexingsAreProjectedPermutation(linalgOp)) {
+    LDBG("precondition failed: not projected permutations");
+    return failure();
+  }
+  if (failed(reductionPreconditions(linalgOp))) {
+    LDBG("precondition failed: reduction preconditions");
     return failure();
   }
+  return success();
+}
 
 LogicalResult
 mlir::linalg::vectorizeLinalgOp(OpBuilder &b, Operation *op,
                                 SmallVectorImpl<Value> &newResults) {
   if (failed(vectorizeLinalgOpPrecondition(op)))
     return failure();
 
   auto linalgOp = cast<LinalgOp>(op);

mlir/lib/Dialect/Vector/VectorOps.cpp

 void TransferReadOp::build(OpBuilder &builder, OperationState &result,
                            Type vectorType, Value source, ValueRange indices,
                            AffineMapAttr permutationMap, Value padding,
                            ArrayAttr inBounds) {
   build(builder, result, vectorType, source, indices, permutationMap, padding,
         /*mask=*/Value(), inBounds);
 }
 
+Value TransferReadOp::createScalarOp(OpBuilder &builder, Location loc,
+                                     Value source, ValueRange indices,
+                                     ArrayRef<bool> inBounds) {
+  Type elemType = source.getType().cast<ShapedType>().getElementType();
+  auto vectorType = VectorType::get(ArrayRef<int64_t>{1}, elemType);
+  AffineMap map = AffineMap::get(/*numDims=*/1, /*numSymbols=*/0,
+                                 getAffineConstantExpr(0, loc.getContext()));
+  Value read = builder.create<vector::TransferReadOp>(loc, vectorType, source,
+                                                      indices, map, inBounds);
+  return builder.create<vector::ExtractOp>(loc, read, ArrayRef<int64_t>{0});
+}
+
 static void printTransferAttrs(OpAsmPrinter &p, VectorTransferOpInterface op) {
   SmallVector<StringRef, 3> elidedAttrs;
   elidedAttrs.push_back(TransferReadOp::getOperandSegmentSizeAttr());
   if (op.permutation_map().isMinorIdentity())
     elidedAttrs.push_back(op.getPermutationMapAttrName());
   bool elideInBounds = true;
   if (auto inBounds = op.in_bounds()) {
     for (auto attr : *inBounds) {
void TransferReadOp::getCanonicalizationPatterns(RewritePatternSet &results,
                                                  MLIRContext *context) {
   results.add<FoldExtractSliceIntoTransferRead>(context);
 }
 
 //===----------------------------------------------------------------------===//
 // TransferWriteOp
 //===----------------------------------------------------------------------===//
 
+void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
+                            Value vector, Value dest, ValueRange indices,
+                            AffineMap permutationMap, ArrayRef<bool> inBounds) {
+  if (inBounds.empty())
+    return build(builder, result, vector, dest, indices, permutationMap,
+                 /*mask=*/Value(), ArrayAttr());
+  build(builder, result, vector, dest, indices, permutationMap,
+        /*mask=*/Value(), builder.getBoolArrayAttr(inBounds));
+}
+
 /// Builder that sets permutation map to 'getMinorIdentityMap'.
 void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
                             Value vector, Value source, ValueRange indices,
                             ArrayRef<bool> inBounds) {
   auto vectorType = vector.getType().cast<VectorType>();
   auto permMap = getTransferMinorIdentityMap(
       source.getType().cast<ShapedType>(), vectorType);
   if (inBounds.empty())
     return build(builder, result, vector, source, indices, permMap,
                  ArrayAttr());
   ArrayAttr inBoundsArrayAttr = builder.getBoolArrayAttr(inBounds);
   build(builder, result, vector, source, indices, permMap, inBoundsArrayAttr);
 }
 
 void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
                             Value vector, Value source, ValueRange indices,
-                            AffineMap permutationMap) {
-  build(builder, result, vector, source, indices, permutationMap,
-        /*inBounds=*/ArrayAttr());
-}
-
-void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
-                            Value vector, Value source, ValueRange indices,
                             AffineMapAttr permutationMap,
                             /*optional*/ ArrayAttr inBounds) {
   Type resultType = source.getType().dyn_cast<RankedTensorType>();
   build(builder, result, resultType, vector, source, indices, permutationMap,
         /*mask=*/Value(), inBounds);
 }
 
 void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
void TransferWriteOp::build(OpBuilder &builder, OperationState &result,
                             Value vector, Value source, ValueRange indices,
                             AffineMap permutationMap, /*optional*/ Value mask,
                             /*optional*/ ArrayAttr inBounds) {
   Type resultType = source.getType().dyn_cast<RankedTensorType>();
   build(builder, result, resultType, vector, source, indices, permutationMap,
         mask, inBounds);
 }
 
+Operation *TransferWriteOp::createScalarOp(OpBuilder &builder, Location loc,
+                                           Value value, Value dest,
+                                           ValueRange indices,
+                                           ArrayRef<bool> inBounds) {
+  Value vectorOfAScalar = value;
+  if (!value.getType().isa<VectorType>())
+    vectorOfAScalar = builder.create<vector::BroadcastOp>(
+        loc, VectorType::get({1}, value.getType()), value);
+  AffineMap map = AffineMap::get(/*numDims=*/1, /*numSymbols=*/0,
+                                 getAffineConstantExpr(0, loc.getContext()));
+  return builder.create<vector::TransferWriteOp>(loc, vectorOfAScalar, dest,
+                                                 indices, map, inBounds);
+}
+
 static ParseResult parseTransferWriteOp(OpAsmParser &parser,
                                         OperationState &result) {
   auto &builder = parser.getBuilder();
   llvm::SMLoc typesLoc;
   OpAsmParser::OperandType vectorInfo, sourceInfo;
   SmallVector<OpAsmParser::OperandType, 8> indexInfo;
   SmallVector<Type, 2> types;
   OpAsmParser::OperandType maskInfo;

mlir/test/Dialect/Linalg/vectorization.mlir

func @test_vectorize_fill(%A : memref<8x16xf32>, %arg0 : f32) {
   linalg.fill(%arg0, %A) : f32, memref<8x16xf32>
   return
 }
 
 // -----
 
 // CHECK-LABEL: func @test_vectorize_fill
 func @test_vectorize_fill_scalar(%A : memref<f32>, %arg0 : f32) {
-  //  CHECK-SAME: (%[[M:.*]]: memref<f32>, %[[V:.*]]: f32)
-  //       CHECK:   store %[[V]], %[[M]][] : memref<f32>
+  // CHECK-SAME: (%[[M:.*]]: memref<f32>, %[[val:.*]]: f32)
+  //      CHECK:   %[[VEC:.*]] = vector.broadcast %[[val]] : f32 to vector<1xf32>
+  //      CHECK:   vector.transfer_write %[[VEC]], %[[M]][] {{.*}} : vector<1xf32>, memref<f32>
   linalg.fill(%arg0, %A) : f32, memref<f32>
   return
 }
 
 // -----
 
 // CHECK-LABEL: func @test_vectorize_copy
 func @test_vectorize_copy(%A : memref<8x16xf32>, %B : memref<8x16xf32>) {
   //       CHECK: %[[V:.*]] = vector.transfer_read {{.*}} : memref<8x16xf32>, vector<8x16xf32>
   //       CHECK: vector.transfer_write %[[V]], {{.*}} : vector<8x16xf32>, memref<8x16xf32>
   linalg.copy(%A, %B) :  memref<8x16xf32>, memref<8x16xf32>
   return
 }
 
 // -----
 
 // CHECK-LABEL: func @test_vectorize_copy_scalar
 func @test_vectorize_copy_scalar(%A : memref<f32>, %B : memref<f32>) {
-  //       CHECK: %[[V:.*]] = memref.load {{.*}} : memref<f32>
-  //       CHECK: store %[[V]], {{.*}} : memref<f32>
+  //  CHECK-SAME: (%[[A:.*]]: memref<f32>, %[[B:.*]]: memref<f32>)
+  //       CHECK:   %[[V:.*]] = vector.transfer_read %[[A]][]{{.*}} : memref<f32>, vector<1xf32>
+  //       CHECK:   %[[val:.*]] = vector.extract %[[V]][0] : vector<1xf32>
+  //       CHECK:   %[[VV:.*]] = vector.broadcast %[[val]] : f32 to vector<1xf32>
+  //       CHECK:   vector.transfer_write %[[VV]], %[[B]][] {{.*}} : vector<1xf32>, memref<f32>
   linalg.copy(%A, %B) :  memref<f32>, memref<f32>
   return
 }
 
 // -----
 
 // CHECK-LABEL: func @test_vectorize_trailing_index
   //  CHECK-SAME: (%[[ARG0:.*]]: memref<1x2x4x8xindex>)
%red = linalg.generic {indexing_maps = [affine_map<(d0, d1) -> (d0, d1)>,
                          ins(%arg0 : tensor<4x4xf32>) outs(%fill : tensor<4xf32>) {
   ^bb0(%in0: f32, %out0: f32):  // no predecessors
     %min = minf %in0, %out0 : f32
     linalg.yield %min : f32
   } -> tensor<4xf32>
   return %red : tensor<4xf32>
 }
 
+// -----
+
+// CHECK-LABEL: func @reduce_1d(
+//  CHECK-SAME:   %[[A:.*]]: tensor<32xf32>
+func @reduce_1d(%arg0: tensor<32xf32>) -> tensor<f32> {
+  // CHECK-DAG: %[[F0_v1:.*]] = constant dense<0.000000e+00> : vector<1xf32>
+  // CHECK-DAG: %[[F0_v32:.*]] = constant dense<0.000000e+00> : vector<32xf32>
+  // CHECK-DAG: %[[C0:.*]] = constant 0 : index
+  %f0 = constant 0.000000e+00 : f32
+
+  //     CHECK: %[[init:.*]] = linalg.init_tensor [] : tensor<f32>
+  %0 = linalg.init_tensor [] : tensor<f32>
+
+  //     CHECK: %[[filled:.*]] = vector.transfer_write %[[F0_v1]], %[[init]][] {{.*}} : vector<1xf32>, tensor<f32>
+  %1 = linalg.fill(%f0, %0) : f32, tensor<f32> -> tensor<f32>
+
+  //     CHECK: %[[read:.*]] = vector.transfer_read %[[A]][%[[C0]]]{{.*}} : tensor<32xf32>, vector<32xf32>
+  //     CHECK: %[[added:.*]] = addf %[[read]], %[[F0_v32]] : vector<32xf32>
+  //     CHECK: %[[reduced:.*]] = vector.multi_reduction #vector.kind<add>, %[[added]] [0] : vector<32xf32> to f32
+  //     CHECK: %[[reduced_v1:.*]] = vector.broadcast %[[reduced]] : f32 to vector<1xf32>
+  //     CHECK: %[[res:.*]] = vector.transfer_write %[[reduced_v1]], %[[filled]][] {{.*}} : vector<1xf32>, tensor<f32>
+  %2 = linalg.generic {
+         indexing_maps = [affine_map<(d0) -> (d0)>,
+                          affine_map<(d0) -> ()>],
+         iterator_types = ["reduction"]}
+         ins(%arg0 : tensor<32xf32>)
+         outs(%1 : tensor<f32>) {
+    ^bb0(%a: f32, %b: f32):  // no predecessors
+      %3 = addf %a, %b : f32
+      linalg.yield %3 : f32
+    } -> tensor<f32>
+
+  return %2 : tensor<f32>
+}
+