[MLIR][Vector] Allow Scalable Dim in OneDimMultiReductionToTwoDim (#89978)

To correctly lower multi_reduction of 1-dim scalable vector, e.g., <[4]xf32>

Author: zhaoshiz

mlir/lib/Dialect/Vector/Transforms/LowerVectorMultiReduction.cpp

@@ -437,8 +437,10 @@ struct OneDimMultiReductionToTwoDim
     auto loc = multiReductionOp.getLoc();
     auto srcVectorType = multiReductionOp.getSourceVectorType();
     auto srcShape = srcVectorType.getShape();
-    auto castedType = VectorType::get(ArrayRef<int64_t>{1, srcShape.back()},
-                                      srcVectorType.getElementType());
+    auto castedType = VectorType::get(
+        ArrayRef<int64_t>{1, srcShape.back()}, srcVectorType.getElementType(),
+        ArrayRef<bool>{false, srcVectorType.getScalableDims().back()});
+
     auto accType =
         VectorType::get(ArrayRef<int64_t>{1}, srcVectorType.getElementType());
     assert(!llvm::isa<VectorType>(multiReductionOp.getDestType()) &&
@@ -455,10 +457,11 @@ struct OneDimMultiReductionToTwoDim
         loc, accType, multiReductionOp.getAcc());
     Value castMask;
     if (maskableOp.isMasked()) {
-      auto maskType = llvm::cast<ShapedType>(mask.getType());
-      auto castMaskType =
-          VectorType::get(ArrayRef<int64_t>{1, maskType.getShape().back()},
-                          maskType.getElementType());
+      auto maskType = llvm::cast<VectorType>(mask.getType());
+      auto castMaskType = VectorType::get(
+          ArrayRef<int64_t>{1, maskType.getShape().back()},
+          maskType.getElementType(),
+          ArrayRef<bool>{false, maskType.getScalableDims().back()});
       castMask = rewriter.create<vector::BroadcastOp>(loc, castMaskType, mask);
     }
 

mlir/test/Dialect/Vector/vector-multi-reduction-lowering.mlir

@@ -281,6 +281,23 @@ func.func private @scalable_dims(%A : vector<8x[4]x2xf32>, %B: vector<8x[4]xf32>
 // CHECK:           %[[VAL_163:.*]] = vector.shape_cast %[[VAL_162]] : vector<[32]xf32> to vector<8x[4]xf32>
 // CHECK:           return %[[VAL_163]] : vector<8x[4]xf32>
 
+// Check that OneDimMultiReductionToTwoDim handles scalable dim
+func.func @scalable_dim_1d(%A: vector<[4]xf32>, %B: f32, %C: vector<[4]xi1>) -> f32 {
+    %0 = vector.mask %C { vector.multi_reduction <add>, %A, %B [0] : vector<[4]xf32> to f32 } : vector<[4]xi1> -> f32
+    return %0 : f32
+}
+
+// CHECK-LABEL:  func.func @scalable_dim_1d(
+// CHECK-SAME:                                      %[[ARG_0:.*]]: vector<[4]xf32>,
+// CHECK-SAME:                                      %[[ARG_1:.*]]: f32,
+// CHECK-SAME:                                      %[[ARG_2:.*]]: vector<[4]xi1>) -> f32 {
+// CHECK-DAG:      %[[VAL_0:.*]] = arith.constant 0 : index
+// CHECK-DAG:      %[[VAL_1:.*]] = arith.constant dense<0.000000e+00> : vector<1xf32>
+// CHECK:          %[[VAL_2:.*]] = vector.mask %[[ARG_2]] { vector.reduction <add>, %[[ARG_0]], %[[ARG_1]] : vector<[4]xf32> into f32 } : vector<[4]xi1> -> f32
+// CHECK:          %[[VAL_3:.*]] = vector.insertelement %[[VAL_2]], %[[VAL_1]][%[[VAL_0]] : index] : vector<1xf32>
+// CHECK:          %[[VAL_4:.*]] = vector.extract %[[VAL_3]][0] : f32 from vector<1xf32>
+// CHECK:          return %[[VAL_4]] : f32
+
 module attributes {transform.with_named_sequence} {
   transform.named_sequence @__transform_main(%root : !transform.any_op {transform.readonly}) {
     %func_op = transform.structured.match ops{["func.func"]} in %root : (!transform.any_op) -> !transform.op<"func.func">