[mlir][vector] Use DenseI64ArrayAttr in vector.multi_reduction (llvm#102637)

This prevents some unnecessary conversions to/from int64_t and
IntegerAttr.

Author: MacDue

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

@@ -286,7 +286,7 @@ def Vector_MultiDimReductionOp :
     Arguments<(ins Vector_CombiningKindAttr:$kind,
                    AnyVector:$source,
                    AnyType:$acc,
-                   I64ArrayAttr:$reduction_dims)>,
+                   DenseI64ArrayAttr:$reduction_dims)>,
     Results<(outs AnyType:$dest)> {
   let summary = "Multi-dimensional reduction operation";
   let description = [{
@@ -325,8 +325,8 @@ def Vector_MultiDimReductionOp :
 
     SmallVector<bool> getReductionMask() {
       SmallVector<bool> res(getSourceVectorType().getRank(), false);
-      for (auto ia : getReductionDims().getAsRange<IntegerAttr>())
-        res[ia.getInt()] = true;
+      for (int64_t dim : getReductionDims())
+        res[dim] = true;
       return res;
     }
     static SmallVector<bool> getReductionMask(

mlir/lib/Dialect/Vector/IR/VectorOps.cpp

@@ -445,8 +445,7 @@ void vector::MultiDimReductionOp::build(OpBuilder &builder,
   for (const auto &en : llvm::enumerate(reductionMask))
     if (en.value())
       reductionDims.push_back(en.index());
-  build(builder, result, kind, source, acc,
-        builder.getI64ArrayAttr(reductionDims));
+  build(builder, result, kind, source, acc, reductionDims);
 }
 
 OpFoldResult MultiDimReductionOp::fold(FoldAdaptor adaptor) {
@@ -466,12 +465,14 @@ LogicalResult MultiDimReductionOp::verify() {
   SmallVector<bool> scalableDims;
   Type inferredReturnType;
   auto sourceScalableDims = getSourceVectorType().getScalableDims();
-  for (auto it : llvm::enumerate(getSourceVectorType().getShape()))
-    if (!llvm::any_of(getReductionDims().getValue(), [&](Attribute attr) {
-          return llvm::cast<IntegerAttr>(attr).getValue() == it.index();
-        })) {
-      targetShape.push_back(it.value());
-      scalableDims.push_back(sourceScalableDims[it.index()]);
+  for (auto [dimIdx, dimSize] :
+       llvm::enumerate(getSourceVectorType().getShape()))
+    if (!llvm::any_of(getReductionDims(),
+                      [dimIdx = dimIdx](int64_t reductionDimIdx) {
+                        return reductionDimIdx == static_cast<int64_t>(dimIdx);
+                      })) {
+      targetShape.push_back(dimSize);
+      scalableDims.push_back(sourceScalableDims[dimIdx]);
     }
   // TODO: update to also allow 0-d vectors when available.
   if (targetShape.empty())

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

@@ -67,10 +67,7 @@ class InnerOuterDimReductionConversion
     auto srcRank = multiReductionOp.getSourceVectorType().getRank();
 
     // Separate reduction and parallel dims
-    auto reductionDimsRange =
-        multiReductionOp.getReductionDims().getAsValueRange<IntegerAttr>();
-    auto reductionDims = llvm::to_vector<4>(llvm::map_range(
-        reductionDimsRange, [](const APInt &a) { return a.getZExtValue(); }));
+    ArrayRef<int64_t> reductionDims = multiReductionOp.getReductionDims();
     llvm::SmallDenseSet<int64_t> reductionDimsSet(reductionDims.begin(),
                                                   reductionDims.end());
     int64_t reductionSize = reductionDims.size();