diff --git a/src/Conversion/ONNXToKrnl/Tensor/ArgMinMax.cpp b/src/Conversion/ONNXToKrnl/Tensor/ArgMinMax.cpp
index 69fc7136fd..79e9300bf4 100644
--- a/src/Conversion/ONNXToKrnl/Tensor/ArgMinMax.cpp
+++ b/src/Conversion/ONNXToKrnl/Tensor/ArgMinMax.cpp
@@ -38,36 +38,7 @@ inline Value getCondition<ONNXArgMaxOp>(
   return create.math.sgt(next, dstVal);
 }
 
-template <typename ArgOp>
-inline DimsExpr getOutputDims(ArgOp *op, typename ArgOp::Adaptor operandAdaptor,
-    mlir::OpBuilder *rewriter, ArrayValueIndexCapture::GetDenseVal fGetDenseVal,
-    ArrayValueIndexCapture::LoadVal fLoadVal);
-
-template <>
-inline DimsExpr getOutputDims<ONNXArgMinOp>(ONNXArgMinOp *op,
-    typename ONNXArgMinOp::Adaptor operandAdaptor, mlir::OpBuilder *rewriter,
-    ArrayValueIndexCapture::GetDenseVal fGetDenseVal,
-    ArrayValueIndexCapture::LoadVal fLoadVal) {
-  ONNXArgMinOpShapeHelper shapeHelper(op, rewriter, fGetDenseVal, fLoadVal);
-  auto shapecomputed = shapeHelper.computeShape(operandAdaptor);
-  (void)shapecomputed;
-  assert(!failed(shapecomputed) && "expected to succeed");
-  return shapeHelper.dimsForOutput();
-}
-
-template <>
-inline DimsExpr getOutputDims<ONNXArgMaxOp>(ONNXArgMaxOp *op,
-    typename ONNXArgMaxOp::Adaptor operandAdaptor, mlir::OpBuilder *rewriter,
-    ArrayValueIndexCapture::GetDenseVal fGetDenseVal,
-    ArrayValueIndexCapture::LoadVal fLoadVal) {
-  ONNXArgMaxOpShapeHelper shapeHelper(op, rewriter, fGetDenseVal, fLoadVal);
-  auto shapecomputed = shapeHelper.computeShape(operandAdaptor);
-  (void)shapecomputed;
-  assert(!failed(shapecomputed) && "expected to succeed");
-  return shapeHelper.dimsForOutput();
-}
-
-template <typename ArgOp>
+template <typename ArgOp, typename OpShapeHelper>
 struct ONNXArgMinMaxOpLowering : public ConversionPattern {
   ONNXArgMinMaxOpLowering(TypeConverter &typeConverter, MLIRContext *ctx)
       : ConversionPattern(typeConverter, ArgOp::getOperationName(), 1, ctx) {}
@@ -80,9 +51,12 @@ struct ONNXArgMinMaxOpLowering : public ConversionPattern {
     ArgOp argOp = llvm::cast<ArgOp>(op);
 
     typename ArgOp::Adaptor operandAdaptor(operands);
-    DimsExpr OutputDims = getOutputDims<ArgOp>(&argOp, operandAdaptor,
-        &rewriter, krnl::getDenseElementAttributeFromKrnlValue,
+    OpShapeHelper shapeHelper(&argOp, &rewriter,
+        krnl::getDenseElementAttributeFromKrnlValue,
         krnl::loadDenseElementArrayValueAtIndex);
+    auto shapecomputed = shapeHelper.computeShape(operandAdaptor);
+    assert(succeeded(shapecomputed) && "Could not compute output shape");
+    DimsExpr outputDims = shapeHelper.dimsForOutput();
 
     // Convert the reduced output type to MemRefType.
     Type convertedType = typeConverter->convertType(*op->result_type_begin());
@@ -113,7 +87,7 @@ struct ONNXArgMinMaxOpLowering : public ConversionPattern {
 
     // Insert alloc and dealloc
     Value alloc = insertAllocAndDeallocSimple(
-        rewriter, op, reducedMemRefType, loc, OutputDims);
+        rewriter, op, reducedMemRefType, loc, outputDims);
 
     // Constant Value
     MathBuilder createMath(rewriter, loc);
@@ -125,13 +99,13 @@ struct ONNXArgMinMaxOpLowering : public ConversionPattern {
     // 1. Krnl loops to initialize the result.
     ValueRange initLoopDef = createKrnl.defineLoops(reducedRank);
     SmallVector<IndexExpr, 4> initLbs(reducedRank, LiteralIndexExpr(0));
-    createKrnl.iterateIE(initLoopDef, initLoopDef, initLbs, OutputDims,
+    createKrnl.iterateIE(initLoopDef, initLoopDef, initLbs, outputDims,
         [&](KrnlBuilder &createKrnl, ValueRange loopInd) {
           createKrnl.store(minusOne, alloc, loopInd);
         });
 
     // 2. Krnl loop to calculate argmin/argmax.
-    MultiDialectBuilder<KrnlBuilder, MathBuilder> create(rewriter, loc);
+    MultiDialectBuilder<KrnlBuilder, MathBuilder> create(createKrnl);
     ValueRange calcLoopDef = createKrnl.defineLoops(dataRank);
     SmallVector<IndexExpr, 4> lbs(dataRank, LiteralIndexExpr(0));
     MemRefBoundsIndexCapture dataBounds(data);
@@ -184,9 +158,11 @@ struct ONNXArgMinMaxOpLowering : public ConversionPattern {
 
 void populateLoweringONNXArgMinMaxOpPattern(RewritePatternSet &patterns,
     TypeConverter &typeConverter, MLIRContext *ctx) {
-  patterns.insert<ONNXArgMinMaxOpLowering<mlir::ONNXArgMinOp>>(
+  patterns.insert<
+      ONNXArgMinMaxOpLowering<mlir::ONNXArgMinOp, ONNXArgMinOpShapeHelper>>(
       typeConverter, ctx);
-  patterns.insert<ONNXArgMinMaxOpLowering<mlir::ONNXArgMaxOp>>(
+  patterns.insert<
+      ONNXArgMinMaxOpLowering<mlir::ONNXArgMaxOp, ONNXArgMaxOpShapeHelper>>(
       typeConverter, ctx);
 }