diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
index 050f8ca3f32aeda..6575b39fd45a1f8 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
@@ -132,14 +132,17 @@ bool constantUnaryOpShouldBeFolded(TosaOp unaryOp, DenseElementsAttr values) {
   return inputOp.hasOneUse();
 }
 
-template <typename BaseType>
-DenseElementsAttr transposeType(ElementsAttr attr, ShapedType inputType,
+template <typename RangeType>
+DenseElementsAttr transposeType(const RangeType &data, ShapedType inputType,
                                 ShapedType outputType,
                                 llvm::ArrayRef<int64_t> permValues) {
+  using ElementType = std::decay_t<decltype(*std::begin(data))>;
+
+  assert(inputType.getElementType() == outputType.getElementType());
+
   if (inputType.getNumElements() == 0)
-    return DenseElementsAttr::get(outputType, llvm::ArrayRef<BaseType>{});
+    return DenseElementsAttr::get(outputType, llvm::ArrayRef<ElementType>{});
 
-  auto attrValues = attr.getValues<BaseType>();
   auto inputShape = inputType.getShape();
 
   // The inverted permutation map and strides of the output are used to compute
@@ -148,10 +151,11 @@ DenseElementsAttr transposeType(ElementsAttr attr, ShapedType inputType,
   auto outputStrides = computeStrides(outputType.getShape());
   auto invertedPermValues = invertPermutationVector(permValues);
 
-  auto initialValue = *std::begin(attrValues);
-  SmallVector<BaseType> outputValues(inputType.getNumElements(), initialValue);
+  auto initialValue = *std::begin(data);
+  SmallVector<ElementType> outputValues(inputType.getNumElements(),
+                                        initialValue);
 
-  for (const auto &it : llvm::enumerate(attrValues)) {
+  for (const auto &it : llvm::enumerate(data)) {
     auto srcLinearIndex = it.index();
 
     uint64_t dstLinearIndex = 0;
@@ -170,7 +174,7 @@ DenseElementsAttr transposeType(ElementsAttr attr, ShapedType inputType,
   }
 
   return DenseElementsAttr::get(outputType,
-                                llvm::ArrayRef<BaseType>(outputValues));
+                                llvm::ArrayRef<ElementType>(outputValues));
 }
 
 // A type specialized transposition of an ElementsAttr.
@@ -180,32 +184,28 @@ DenseElementsAttr transposeType(ElementsAttr attr, ShapedType inputType,
 DenseElementsAttr transpose(ElementsAttr attr, ShapedType inputType,
                             ShapedType outputType,
                             llvm::ArrayRef<int64_t> permValues) {
-  auto baseType = inputType.getElementType();
-
-  // Handle possible integer types
-  if (auto intType = dyn_cast<IntegerType>(baseType)) {
-    switch (intType.getWidth()) {
-    case 1:
-      return transposeType<bool>(attr, inputType, outputType, permValues);
-    case 8:
-      return transposeType<int8_t>(attr, inputType, outputType, permValues);
-    case 16:
-      return transposeType<int16_t>(attr, inputType, outputType, permValues);
-    case 32:
-      return transposeType<int32_t>(attr, inputType, outputType, permValues);
-    case 64:
-      return transposeType<int64_t>(attr, inputType, outputType, permValues);
-    default:
-      return transposeType<APInt>(attr, inputType, outputType, permValues);
-    }
-  }
+  if (auto data = attr.tryGetValues<bool>())
+    return transposeType(*data, inputType, outputType, permValues);
 
-  // Handle possible float types
-  if (baseType.isF32()) {
-    return transposeType<float>(attr, inputType, outputType, permValues);
-  }
+  if (auto data = attr.tryGetValues<int8_t>())
+    return transposeType(*data, inputType, outputType, permValues);
+
+  if (auto data = attr.tryGetValues<int16_t>())
+    return transposeType(*data, inputType, outputType, permValues);
+
+  if (auto data = attr.tryGetValues<int32_t>())
+    return transposeType(*data, inputType, outputType, permValues);
 
-  return transposeType<APFloat>(attr, inputType, outputType, permValues);
+  if (auto data = attr.tryGetValues<int64_t>())
+    return transposeType(*data, inputType, outputType, permValues);
+
+  if (auto data = attr.tryGetValues<float>())
+    return transposeType(*data, inputType, outputType, permValues);
+
+  if (auto data = attr.tryGetValues<APFloat>())
+    return transposeType(*data, inputType, outputType, permValues);
+
+  return nullptr;
 }
 
 struct TosaFoldConstantTranspose : public OpRewritePattern<tosa::TransposeOp> {
@@ -228,14 +228,19 @@ struct TosaFoldConstantTranspose : public OpRewritePattern<tosa::TransposeOp> {
     DenseIntElementsAttr permAttr;
     if (!matchPattern(op.getPerms(), m_Constant(&permAttr)))
       return failure();
-    auto permValues = llvm::to_vector<6>(llvm::map_range(
+    auto permValues = llvm::map_to_vector(
         // TOSA allows both 32- and 64-bit integer tensors here.
         permAttr.getValues<APInt>(),
-        [](const APInt &val) { return val.getSExtValue(); }));
+        [](const APInt &val) { return val.getSExtValue(); });
 
     auto inputType = cast<ShapedType>(op.getInput1().getType());
 
     auto resultAttr = transpose(inputValues, inputType, outputType, permValues);
+    if (!resultAttr) {
+      return rewriter.notifyMatchFailure(
+          op, "unsupported attribute or element type");
+    }
+
     rewriter.replaceOpWithNewOp<tosa::ConstOp>(op, outputType, resultAttr);
     return success();
   }
diff --git a/mlir/test/Dialect/Tosa/constant-op-fold.mlir b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
index 27ca3ae3c21be68..de752f31fcbaa1e 100644
--- a/mlir/test/Dialect/Tosa/constant-op-fold.mlir
+++ b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
@@ -112,6 +112,23 @@ func.func @transpose_nofold_quantized_types() -> tensor<1x1x2x2x!quant.uniform<i
   return %0: tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
 }
 
+// CHECK-LABEL: @transpose_nofold_dense_resource
+func.func @transpose_nofold_dense_resource() -> tensor<2x2xf32> {
+  %0 = "tosa.const"() <{value = dense_resource<resource> : tensor<2x2xf32>}> : () -> tensor<2x2xf32>
+  %1 = "tosa.const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<2xi32>
+
+  // CHECK: tosa.transpose
+  %2 = tosa.transpose %0, %1 : (tensor<2x2xf32>, tensor<2xi32>) -> tensor<2x2xf32>
+  return %2 : tensor<2x2xf32>
+}
+{-#
+  dialect_resources: {
+    builtin: {
+      resource: "0x08000000010000000000000002000000000000000300000000000000"
+    }
+  }
+#-}
+
 // -----
 
 // CHECK-LABEL: @fold_add_zero_rhs_f32