diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
index 8f822f930e164..55f675dd4892d 100644
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOps.td
@@ -2023,7 +2023,7 @@ def Tosa_TransposeOp : Tosa_InferShapedTypeOp<"transpose",
 
   let arguments = (ins
     Tosa_Tensor:$input1,
-    Tosa_Int32Tensor:$perms
+    DenseI32ArrayAttr:$perms
   );
 
   let results = (
@@ -2035,10 +2035,6 @@ def Tosa_TransposeOp : Tosa_InferShapedTypeOp<"transpose",
     Extension<[Tosa_EXT_FP8E4M3, Tosa_EXT_FP8E5M2, Tosa_EXT_BF16]>,
   ];
 
-  let extraClassDeclaration = [{
-    LogicalResult getConstantPerms(llvm::SmallVector<int32_t> &perms);
-  }];
-
   let hasCanonicalizer = 1;
   let hasFolder = 1;
   let hasVerifier = 1;
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
index a8fd536dd2548..42e88ee9026ac 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -329,13 +329,11 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
         SmallVector<int64_t> newWeightShape;
         for (auto dim : weightPerm)
           newWeightShape.push_back(weightShape[dim]);
-        auto weightPermAttr = rewriter.getI32TensorAttr(weightPerm);
-        Value weightPermValue =
-            rewriter.create<arith::ConstantOp>(loc, weightPermAttr);
+        auto weightPermAttr = rewriter.getDenseI32ArrayAttr(weightPerm);
         Type newWeightTy =
             RankedTensorType::get(newWeightShape, weightTy.getElementType());
         weight = rewriter.create<tosa::TransposeOp>(loc, newWeightTy, weight,
-                                                    weightPermValue);
+                                                    weightPermAttr);
       }
     }
 
@@ -353,13 +351,11 @@ class ConvConverter : public OpConversionPattern<TosaConvOp> {
       SmallVector<int64_t> newWeightShape;
       for (auto dim : weightPerm)
         newWeightShape.push_back(weightShape[dim]);
-      auto weightPermAttr = rewriter.getI32TensorAttr(weightPerm);
-      Value weightPermValue =
-          rewriter.create<arith::ConstantOp>(loc, weightPermAttr);
+      auto weightPermAttr = rewriter.getDenseI32ArrayAttr(weightPerm);
       Type newWeightTy =
           RankedTensorType::get(newWeightShape, weightTy.getElementType());
       weight = rewriter.create<tosa::TransposeOp>(loc, newWeightTy, weight,
-                                                  weightPermValue);
+                                                  weightPermAttr);
     }
 
     // Extract the attributes for convolution.
@@ -970,9 +966,7 @@ class TransposeConverter : public OpRewritePattern<tosa::TransposeOp> {
 
   LogicalResult matchAndRewrite(tosa::TransposeOp op,
                                 PatternRewriter &rewriter) const final {
-    SmallVector<int32_t> constantPerms;
-    if (failed(op.getConstantPerms(constantPerms)))
-      return failure();
+    const llvm::ArrayRef<int32_t> constantPerms = op.getPerms();
 
     Location loc = op.getLoc();
     // The verifier should have made sure we have a valid TOSA permutation
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
index 9bfc2aae1d6a5..8e2d8662ece8d 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaCanonicalizations.cpp
@@ -88,13 +88,10 @@ struct ConsolidateTransposeOptimization
       return rewriter.notifyMatchFailure(transposeOp,
                                          "input must be transpose operation");
 
-    SmallVector<int32_t> transposePerms, innerTransposePerms;
-    if (transposeOp.getConstantPerms(transposePerms).failed())
-      return rewriter.notifyMatchFailure(transposeOp,
-                                         "transpose perms must be constant");
-    if (innerTranspose.getConstantPerms(innerTransposePerms).failed())
-      return rewriter.notifyMatchFailure(
-          transposeOp, "inner transpose perms must be constant");
+    const llvm::ArrayRef<int32_t> transposePerms = transposeOp.getPerms();
+    const llvm::ArrayRef<int32_t> innerTransposePerms =
+        innerTranspose.getPerms();
+
     if (transposePerms.size() != innerTransposePerms.size())
       return rewriter.notifyMatchFailure(
           transposeOp,
@@ -108,15 +105,9 @@ struct ConsolidateTransposeOptimization
     for (int i = 0, s = transposePerms.size(); i < s; ++i)
       perms[i] = innerTransposePerms[transposePerms[i]];
 
-    auto permsTy =
-        RankedTensorType::get(transposePerms.size(), rewriter.getI32Type());
-    auto permsAttr = DenseIntElementsAttr::get(permsTy, perms);
-    Value permsValue = rewriter.create<tosa::ConstOp>(transposeOp.getLoc(),
-                                                      permsTy, permsAttr);
-
     rewriter.replaceOpWithNewOp<tosa::TransposeOp>(
         transposeOp, transposeOp.getResult().getType(),
-        innerTranspose.getInput1(), permsValue);
+        innerTranspose.getInput1(), rewriter.getDenseI32ArrayAttr(perms));
 
     return success();
   }
@@ -128,10 +119,6 @@ struct TransposeIsReshape : public OpRewritePattern<tosa::TransposeOp> {
 
   LogicalResult matchAndRewrite(tosa::TransposeOp op,
                                 PatternRewriter &rewriter) const override {
-    DenseIntElementsAttr permAttr;
-    if (!matchPattern(op.getPerms(), m_Constant(&permAttr)))
-      return rewriter.notifyMatchFailure(op, "Non-constant permutation");
-
     if (op.getInput1().getDefiningOp<tosa::TransposeOp>())
       return rewriter.notifyMatchFailure(
           op, "Src is from transpose, can compose transposes");
@@ -156,9 +143,7 @@ struct TransposeIsReshape : public OpRewritePattern<tosa::TransposeOp> {
     if (numDynDims > 1)
       return rewriter.notifyMatchFailure(op, "Has more than one dynamic dim.");
 
-    SmallVector<int64_t> permValues = llvm::to_vector<6>(
-        llvm::map_range(permAttr.getValues<APInt>(),
-                        [](const APInt &val) { return val.getSExtValue(); }));
+    const llvm::ArrayRef<int32_t> permValues = op.getPerms();
 
     SmallVector<int64_t> nonZeroPerms;
     nonZeroPerms.reserve(permValues.size());
@@ -1175,9 +1160,7 @@ OpFoldResult TransposeOp::fold(FoldAdaptor adaptor) {
   }
 
   // Transpose is not the identity transpose.
-  SmallVector<int32_t> perms;
-  if (getConstantPerms(perms).failed())
-    return {};
+  const llvm::ArrayRef<int32_t> perms = getPerms();
 
   if (!llvm::equal(llvm::seq<int32_t>(0, perms.size()), perms))
     return {};
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
index e9c33e1b1bf10..9401df329b7fd 100644
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -1374,54 +1374,37 @@ llvm::LogicalResult tosa::ReshapeOp::verify() {
   return mlir::success();
 }
 
-LogicalResult tosa::TransposeOp::getConstantPerms(SmallVector<int32_t> &perms) {
-  // Perms must be constants.
-  DenseIntElementsAttr permsAttr;
-  if (!matchPattern(getPerms(), m_Constant(&permsAttr)))
-    return failure();
-
-  perms.clear();
-  for (auto v : permsAttr.getValues<APInt>())
-    perms.push_back(v.getSExtValue());
-
-  return success();
-}
-
 LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
     MLIRContext *context, ::std::optional<Location> location,
     TransposeOp::Adaptor adaptor,
     SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
   ShapeAdaptor inputShape(adaptor.getInput1().getType());
-  ShapeAdaptor permsShape(adaptor.getPerms().getType());
-
-  // We cannot infer anything from a rank-0 "permutation" tensor.
-  if (permsShape.hasRank() && permsShape.getRank() == 0)
-    return failure();
 
   // If input rank and permutation length is unknown, the output rank is
   // unknown.
-  if (!inputShape.hasRank() || !permsShape.hasRank() ||
-      permsShape.isDynamicDim(0)) {
+  if (!inputShape.hasRank()) {
     inferredReturnShapes.push_back(ShapedTypeComponents());
     return success();
   }
 
+  const auto inputRank = inputShape.getRank();
+
   // This would imply the number of permutations does not match the rank of
   // the input which is illegal.
-  if (permsShape.getDimSize(0) != inputShape.getRank()) {
+  if (adaptor.getPerms().size() != static_cast<size_t>(inputRank)) {
     return failure();
   }
 
   SmallVector<int64_t> outputShape;
   // Rank-0 means no permutations matter.
-  if (inputShape.getRank() == 0) {
+  if (inputRank == 0) {
     inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
     return success();
   }
 
   // Check whether the input dimensions are all the same.
   bool allTheSame = true;
-  for (int i = 1, s = inputShape.getRank(); i < s; i++) {
+  for (int i = 1, s = inputRank; i < s; i++) {
     if (inputShape.getDimSize(0) != inputShape.getDimSize(i)) {
       allTheSame = false;
       break;
@@ -1431,34 +1414,21 @@ LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
   // If all of the input dimensions are the same we don't care about the
   // permutation.
   if (allTheSame) {
-    outputShape.resize(inputShape.getRank(), inputShape.getDimSize(0));
+    outputShape.resize(inputRank, inputShape.getDimSize(0));
     inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
     return success();
   }
 
-  outputShape.resize(inputShape.getRank(), ShapedType::kDynamic);
-  // If the permuations are a constant we can directly determine the output
-  // shape.
-  DenseIntElementsAttr attr;
-  if (matchPattern(adaptor.getPerms(), m_Constant(&attr)) &&
-      attr.getType().getRank() == 1) {
-    ShapeAdaptor permShape = attr;
-    // Constant permutation must be the same length as the input rank.
-    if (inputShape.getRank() != permShape.getRank())
-      return emitOptionalError(location,
-                               "constant permutation must be the same length"
-                               " as the input rank");
-
-    // Constant permutation values must be within the input rank.
-    for (int i = 0, e = inputShape.getRank(); i < e; i++) {
-      if (inputShape.getRank() <= permShape.getDimSize(i))
-        return failure();
-    }
+  outputShape.resize(inputRank, ShapedType::kDynamic);
 
-    outputShape.reserve(inputShape.getRank());
-    for (int i = 0, s = inputShape.getRank(); i < s; i++) {
-      outputShape[i] = inputShape.getDimSize(permShape.getDimSize(i));
-    }
+  // Constant permutation values must be within the input rank.
+  if (llvm::any_of(adaptor.getPerms(),
+                   [inputRank](const auto i) { return i >= inputRank; }))
+    return failure();
+
+  outputShape.reserve(inputRank);
+  for (int i = 0, s = inputRank; i < s; i++) {
+    outputShape[i] = inputShape.getDimSize(adaptor.getPerms()[i]);
   }
 
   inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
@@ -1467,75 +1437,60 @@ LogicalResult tosa::TransposeOp::inferReturnTypeComponents(
 
 LogicalResult tosa::TransposeOp::verify() {
   TensorType inputType = getInput1().getType();
-  TensorType permType = getPerms().getType();
   TensorType outputType = getOutput().getType();
+  const llvm::ArrayRef<int32_t> constantPerms = getPerms();
 
-  if (permType.hasRank() && permType.getRank() != 1)
-    return emitOpError()
-           << "expected permutation tensor to be rank 1 but got rank "
-           << permType.getRank();
-  if (inputType.hasRank() && permType.hasRank())
-    if (!permType.isDynamicDim(0) &&
-        permType.getDimSize(0) != inputType.getRank())
-      return emitOpError() << "expected permutation tensor dim 0 to have size "
-                           << inputType.getRank()
-                           << " (input rank) but got size "
-                           << permType.getDimSize(0);
+  if (inputType.hasRank() &&
+      constantPerms.size() != static_cast<size_t>(inputType.getRank()))
+    return emitOpError() << "expected perms attribute to have size "
+                         << inputType.getRank() << " (input rank) but got size "
+                         << constantPerms.size();
   if (inputType.hasRank() && outputType.hasRank() &&
       inputType.getRank() != outputType.getRank())
     return emitOpError()
            << "expected input tensor rank to equal result tensor rank";
-  if (outputType.hasRank() && permType.hasRank())
-    if (!permType.isDynamicDim(0) &&
-        permType.getDimSize(0) != outputType.getRank())
-      return emitOpError() << "expected permutation tensor dim 0 to have size "
-                           << outputType.getRank()
-                           << " (output rank) but got size "
-                           << permType.getDimSize(0);
-
-  SmallVector<int32_t> constantPerms;
-  if (succeeded(getConstantPerms(constantPerms))) {
-    // Assert that the permutation tensor has a rank, which means that the
-    // rank has been verified above.
-    assert(permType.hasRank() &&
-           "Unexpectedly found permutation tensor without rank");
-    if (!llvm::all_of(constantPerms,
-                      [&constantPerms](int32_t s) {
-                        return s >= 0 &&
-                               static_cast<size_t>(s) < constantPerms.size();
-                      }) ||
-        !isPermutationVector(llvm::to_vector(llvm::map_range(
-            constantPerms, [](int32_t v) -> int64_t { return v; }))))
-      return emitOpError() << "expected valid permutation tensor";
-
-    // Verify that the types of the input and output tensors are properly
-    // permuted.
-    if (inputType.hasRank() && outputType.hasRank()) {
-      assert(constantPerms.size() == static_cast<size_t>(inputType.getRank()) &&
-             inputType.getRank() == outputType.getRank());
-
-      for (auto i = 0; i < outputType.getRank(); i++) {
-        if (inputType.isDynamicDim(constantPerms[i]) ||
-            outputType.isDynamicDim(i))
-          continue;
-
-        if (inputType.getDimSize(constantPerms[i]) != outputType.getDimSize(i))
-          return emitOpError()
-                 << "expected output tensor dim " << i << " to match "
-                 << "input dim " << constantPerms[i] << " with value of "
-                 << inputType.getDimSize(constantPerms[i]);
-      }
+  if (outputType.hasRank() &&
+      constantPerms.size() != static_cast<size_t>(outputType.getRank()))
+    return emitOpError() << "expected perms attribute to have size "
+                         << outputType.getRank()
+                         << " (output rank) but got size "
+                         << constantPerms.size();
+
+  if (!llvm::all_of(constantPerms,
+                    [&constantPerms](int32_t s) {
+                      return s >= 0 &&
+                             static_cast<size_t>(s) < constantPerms.size();
+                    }) ||
+      !isPermutationVector(llvm::to_vector(llvm::map_range(
+          constantPerms, [](int32_t v) -> int64_t { return v; }))))
+    return emitOpError() << "expected valid permutation indices";
+
+  // Verify that the types of the input and output tensors are properly
+  // permuted.
+  if (inputType.hasRank() && outputType.hasRank()) {
+    assert(constantPerms.size() == static_cast<size_t>(inputType.getRank()) &&
+           inputType.getRank() == outputType.getRank());
+
+    for (auto i = 0; i < outputType.getRank(); i++) {
+      if (inputType.isDynamicDim(constantPerms[i]) ||
+          outputType.isDynamicDim(i))
+        continue;
+
+      if (inputType.getDimSize(constantPerms[i]) != outputType.getDimSize(i))
+        return emitOpError()
+               << "expected output tensor dim " << i << " to match "
+               << "input dim " << constantPerms[i] << " with value of "
+               << inputType.getDimSize(constantPerms[i]);
     }
   }
+
   return success();
 }
 
 LogicalResult TransposeOp::reifyResultShapes(
     OpBuilder &builder, ReifiedRankedShapedTypeDims &reifiedReturnShapes) {
 
-  SmallVector<int32_t> transposePerms;
-  if (getConstantPerms(transposePerms).failed())
-    return failure();
+  const llvm::ArrayRef<int32_t> transposePerms = getPerms();
 
   Value input = getInput1();
   auto inputType = cast<TensorType>(input.getType());
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
index 26baddcf1dd15..61011b6df4617 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -166,13 +166,9 @@ class TransposeConvStridedConverter
         getTosaConstShape(rewriter, loc, weightReshapeDims0));
 
     // Transpose the factored-out stride to the output channels.
-    Value transposeWeightVal = rewriter.create<tosa::ConstOp>(
-        loc, RankedTensorType::get({6}, rewriter.getI32Type()),
-        rewriter.getI32TensorAttr({2, 4, 0, 1, 3, 5}));
-
     weight = CreateOpAndInferShape<tosa::TransposeOp>(
         rewriter, loc, UnrankedTensorType::get(weightETy), weight,
-        transposeWeightVal);
+        rewriter.getDenseI32ArrayAttr({2, 4, 0, 1, 3, 5}));
 
     // Collapse the strides and output channels into a single dimension.
     llvm::SmallVector<int64_t, 4> weightReshapeDims1 = {
@@ -269,13 +265,9 @@ class TransposeConvStridedConverter
         convReshapeDims0Value);
 
     // Transpose the factored-out stride to the output channels.
-    Value transposeConvVal = rewriter.create<tosa::ConstOp>(
-        loc, RankedTensorType::get({6}, rewriter.getI32Type()),
-        rewriter.getI32TensorAttr({0, 1, 3, 2, 4, 5}));
-
     conv2d = CreateOpAndInferShape<tosa::TransposeOp>(
         rewriter, loc, UnrankedTensorType::get(convETy), conv2d,
-        transposeConvVal);
+        rewriter.getDenseI32ArrayAttr({0, 1, 3, 2, 4, 5}));
 
     // Fuse striding behavior back into width / height.
     llvm::SmallVector<int64_t, 6> convReshapeDims1 = {
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
index 403ac48b91559..43e9507b4d95a 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaFolders.cpp
@@ -224,13 +224,8 @@ struct TosaFoldConstantTranspose : public OpRewritePattern<tosa::TransposeOp> {
     if (!llvm::hasSingleElement(op.getInput1().getDefiningOp()->getUsers()))
       return failure();
 
-    DenseIntElementsAttr permAttr;
-    if (!matchPattern(op.getPerms(), m_Constant(&permAttr)))
-      return failure();
     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(); });
+        op.getPerms(), [](const int32_t v) { return static_cast<int64_t>(v); });
 
     auto inputType = cast<ShapedType>(op.getInput1().getType());
 
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
index 64e5c31793f84..36f511fa616d8 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaReduceTransposes.cpp
@@ -367,9 +367,7 @@ std::optional<Value> TosaReduceTransposes::buildMappedToValue(
 std::optional<Value> TosaReduceTransposes::buildMappedToValue(
     TransposeOp transposeOp, const DenseMap<Value, Value> &valuesMap,
     IRRewriter &rewriter, ArrayRef<int32_t> hoistedPerms) {
-  SmallVector<int32_t> perms;
-  if (failed(transposeOp.getConstantPerms(perms)) ||
-      !areInvolutionTransposes(hoistedPerms, perms))
+  if (!areInvolutionTransposes(hoistedPerms, transposeOp.getPerms()))
     return std::nullopt;
   return transposeOp.getInput1();
 }
@@ -506,14 +504,11 @@ bool TosaReduceTransposes::dependenciesAreValid(
       // replaced.
       Operation *user = use.getOwner();
       if (auto otherTranspose = llvm::dyn_cast<TransposeOp>(user)) {
-        SmallVector<int32_t> otherPerms;
-
         // Can later think about cases where transpose -> transpose
         // or reshape -> transpose, where the transposes are not necessarily
         // the same perms as the hoisted, if implementing a more general
         // transform. These could be permitted.
-        if (failed(otherTranspose.getConstantPerms(otherPerms)) ||
-            !llvm::equal(perms, otherPerms))
+        if (!llvm::equal(perms, otherTranspose.getPerms()))
           return false;
       } else if (userNotContainedInValidTransposeDependencies(
                      user, validTransposes, transposeInfo)) {
@@ -607,9 +602,8 @@ void TosaReduceTransposes::runOnOperation() {
         !llvm::isa<RankedTensorType>(output.getType()))
       return;
 
-    // No transformation when transpose permutation non-constant.
-    if (failed(transposeOp.getConstantPerms(perms)))
-      return;
+    llvm::for_each(transposeOp.getPerms(),
+                   [&perms](const auto i) { perms.emplace_back(i); });
 
     // We let --canonicalize deal with identity transpose.
     if (llvm::equal(llvm::seq<int32_t>(0, perms.size()), perms))
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp
index f74a4b4c58b80..f2abb29b4fe66 100644
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaValidation.cpp
@@ -56,15 +56,6 @@ static LogicalResult checkConstantOperandPad(Operation *op) {
   return success();
 }
 
-static LogicalResult checkConstantOperandTranspose(Operation *op) {
-  if (auto transposeOp = dyn_cast<tosa::TransposeOp>(op)) {
-    DenseElementsAttr perms;
-    if (!matchPattern(transposeOp.getPerms(), m_Constant(&perms)))
-      return op->emitOpError("perms of transpose is not constant");
-  }
-  return success();
-}
-
 struct TosaLevel {
   int32_t MAX_RANK = 0;
   int32_t MAX_KERNEL = 0;
@@ -118,7 +109,6 @@ struct TosaValidation : public tosa::impl::TosaValidationBase<TosaValidation> {
 private:
   void populateConstantOperandChecks() {
     constCheckers.emplace_back(checkConstantOperandPad);
-    constCheckers.emplace_back(checkConstantOperandTranspose);
   }
 
   bool levelCheckKernel(Operation *op, int32_t v,
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
index a524359b49759..e0955513ccfd8 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -463,7 +463,6 @@ func.func @conv2d_scalar_bias_f32(%input: tensor<1x49x42x27xf32>, %weights: tens
 
 // CHECK-LABEL: @conv2d_i8
 func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi8>, %bias: tensor<28xi8>) -> () {
-  // HWCF: %[[TRANSPOSE_DIMS:.+]] = arith.constant dense<[1, 2, 3, 0]> : tensor<4xi32>
   // HWCF: %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x1x1x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<1x1x27x28xi8>) permutation = [1, 2, 3, 0]
   // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x45x40x28xi32>
   // CHECK: %[[BROADCAST:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2 : tensor<28xi8>) outs(%[[INIT]] : tensor<1x45x40x28xi32>) {
@@ -485,7 +484,6 @@ func.func @conv2d_i8(%input: tensor<1x49x42x27xi8>, %weights: tensor<28x1x1x27xi
 
 // CHECK-LABEL: @conv2d_f32
 func.func @conv2d_f32(%input: tensor<1x49x42x27xf32>, %weights: tensor<28x3x3x27xf32>, %bias: tensor<28xf32>) -> () {
-  // HWCF: %[[TRANSPOSE_DIMS:.+]] = arith.constant dense<[1, 2, 3, 0]> : tensor<4xi32>
   // HWCF: %[[TRANSPOSE:.+]] =  linalg.transpose ins(%arg1 : tensor<28x3x3x27xf32>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x3x27x28xf32>) permutation = [1, 2, 3, 0]
 
   // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<1x45x40x28xf32>
@@ -786,7 +784,6 @@ func.func @depthwise_conv2d_dyn_w_h(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<3x
 
 // CHECK-LABEL: @conv3d_f32
 func.func @conv3d_f32(%input: tensor<1x49x48x47x27xf32>, %weights: tensor<28x3x4x5x27xf32>, %bias: tensor<28xf32>) -> () {
-  // CHECK-DAG:  %[[PERMS:.+]] = arith.constant dense<[1, 2, 3, 4, 0]>
   // CHECK-DAG:  %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x3x4x5x27xf32>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x28xf32>) permutation = [1, 2, 3, 4, 0]
   // CHECK-DAG:  %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xf32>
   // CHECK:      %[[BROADCAST:.+]] = linalg.generic
@@ -824,7 +821,6 @@ func.func @conv3d_scalar_bias_f32(%input: tensor<1x49x48x47x27xf32>, %weights: t
 
 // CHECK-LABEL: @conv3d_i8
 func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5x27xi8>, %bias: tensor<28xi32>) -> () {
-  // CHECK-DAG:  %[[PERMS:.+]] = arith.constant dense<[1, 2, 3, 4, 0]>
   // CHECK-DAG:  %[[TRANSPOSE:.+]] = linalg.transpose ins(%arg1 : tensor<28x3x4x5x27xi8>) outs(%[[TRANSPOSEDINIT:.+]] : tensor<3x4x5x27x28xi8>) permutation = [1, 2, 3, 4, 0]
   // CHECK-DAG:  %[[INIT:.+]] = tensor.empty() : tensor<1x47x45x43x28xi32>
   // CHECK:      %[[BROADCAST:.+]] = linalg.generic
@@ -852,10 +848,9 @@ func.func @conv3d_i8(%input: tensor<1x49x48x47x27xi8>, %weights: tensor<28x3x4x5
 // CHECK-LABEL: @test_transpose
 // CHECK-SAME: (%[[ARG0:.+]]: tensor<1x2x3xi32>)
 func.func @test_transpose(%arg0: tensor<1x2x3xi32>) -> () {
-  %0 = arith.constant dense<[1, 2, 0]> : tensor<3xi32>
   // CHECK: %[[INIT:.+]] = tensor.empty() : tensor<2x3x1xi32>
   // CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[ARG0]] : tensor<1x2x3xi32>) outs(%[[INIT]] : tensor<2x3x1xi32>) permutation = [1, 2, 0]
-  %1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>)  -> tensor<2x3x1xi32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 2, 0>}: (tensor<1x2x3xi32>)  -> tensor<2x3x1xi32>
   return
 }
 
@@ -864,12 +859,11 @@ func.func @test_transpose(%arg0: tensor<1x2x3xi32>) -> () {
 // CHECK-LABEL: @test_transpose_dyn
 // CHECK-SAME: (%[[ARG0:.+]]: tensor<1x?x3x4xi32>)
 func.func @test_transpose_dyn(%arg0: tensor<1x?x3x4xi32>) -> () {
-  %0 = arith.constant dense<[1, 3, 0, 2]> : tensor<4xi32>
   // CHECK: %[[C1:.+]] = arith.constant 1
   // CHECK: %[[DIM:.+]] = tensor.dim %[[ARG0]], %[[C1]]
   // CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM]]) : tensor<?x4x1x3xi32>
   // CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[ARG0]] : tensor<1x?x3x4xi32>) outs(%[[INIT]] : tensor<?x4x1x3xi32>) permutation = [1, 3, 0, 2]
-  %1 = tosa.transpose %arg0, %0 : (tensor<1x?x3x4xi32>, tensor<4xi32>)  -> tensor<?x4x1x3xi32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 3, 0, 2>}: (tensor<1x?x3x4xi32>)  -> tensor<?x4x1x3xi32>
   return
 }
 
@@ -878,14 +872,13 @@ func.func @test_transpose_dyn(%arg0: tensor<1x?x3x4xi32>) -> () {
 // CHECK-LABEL: @test_transpose_dyn_multiple_2d
 // CHECK-SAME: (%[[ARG0:.+]]: tensor<?x?xf32>)
 func.func @test_transpose_dyn_multiple_2d(%arg0: tensor<?x?xf32>) -> () {
-  %0 = arith.constant dense<[1, 0]> : tensor<2xi32>
   // CHECK-DAG: %[[C0:.+]] = arith.constant 0
   // CHECK-DAG: %[[DIM0:.+]] = tensor.dim %[[ARG0]], %[[C0]]
   // CHECK-DAG: %[[C1:.+]] = arith.constant 1
   // CHECK-DAG: %[[DIM1:.+]] = tensor.dim %[[ARG0]], %[[C1]]
   // CHECK: %[[INIT:.+]] = tensor.empty(%[[DIM1]], %[[DIM0]])
   // CHECK: %[[TRANSPOSE:.+]] = linalg.transpose ins(%[[ARG0]] : tensor<?x?xf32>) outs(%[[INIT]] : tensor<?x?xf32>) permutation = [1, 0]
-  %1 = tosa.transpose %arg0, %0 : (tensor<?x?xf32>, tensor<2xi32>)  -> tensor<?x?xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<?x?xf32>)  -> tensor<?x?xf32>
   return
 }
 
@@ -894,7 +887,6 @@ func.func @test_transpose_dyn_multiple_2d(%arg0: tensor<?x?xf32>) -> () {
 // CHECK-LABEL: @test_transpose_dyn_multiple_3d
 // CHECK-SAME: (%[[ARG0:.+]]: tensor<?x?x?xf32>)
 func.func @test_transpose_dyn_multiple_3d(%arg0: tensor<?x?x?xf32>) {
-  %0 = arith.constant dense<[2, 0, 1]> : tensor<3xi32>
   // CHECK-DAG: %[[C0:.*]] = arith.constant 0 : index
   // CHECK-DAG: %[[DIM0:.*]] = tensor.dim %[[ARG0]], %[[C0]] : tensor<?x?x?xf32>
   // CHECK-DAG: %[[C1:.*]] = arith.constant 1 : index
@@ -903,6 +895,6 @@ func.func @test_transpose_dyn_multiple_3d(%arg0: tensor<?x?x?xf32>) {
   // CHECK-DAG: %[[DIM2:.*]] = tensor.dim %[[ARG0]], %[[C2]] : tensor<?x?x?xf32>
   // CHECK: %[[INIT:.*]] = tensor.empty(%[[DIM2]], %[[DIM0]], %[[DIM1]]) : tensor<?x?x?xf32>
   // CHECK: %[[TRANSPOSE:.*]] = linalg.transpose ins(%[[ARG0]] : tensor<?x?x?xf32>) outs(%[[INIT]] : tensor<?x?x?xf32>) permutation = [2, 0, 1]
-  %1 = "tosa.transpose"(%arg0, %0) : (tensor<?x?x?xf32>, tensor<3xi32>) -> tensor<?x?x?xf32>
+  %1 = "tosa.transpose"(%arg0) {perms = array<i32: 2, 0, 1>} : (tensor<?x?x?xf32>) -> tensor<?x?x?xf32>
   return
 }
diff --git a/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir b/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir
index ef8b80f6b5c22..e354eb91d7557 100644
--- a/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir
+++ b/mlir/test/Dialect/MemRef/resolve-dim-ops.mlir
@@ -34,8 +34,7 @@ func.func @dim_out_of_bounds_2(%idx1 : index, %idx2 : index) -> index {
 //  CHECK-NEXT:           tensor.dim %[[arg]], %[[c2]]
 //  CHECK-NEXT:           return
 func.func @dynamic_dim_of_transpose_op(%arg0: tensor<1x2x?x8xi8>) -> index {
-  %0 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<1x2x?x8xi8>, tensor<4xi32>) -> tensor<1x8x2x?xi8>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 0, 3, 1, 2> }: (tensor<1x2x?x8xi8>) -> tensor<1x8x2x?xi8>
   %c3 = arith.constant 3 : index
   %dim = tensor.dim %1, %c3 : tensor<1x8x2x?xi8>
   return %dim : index
@@ -47,8 +46,7 @@ func.func @dynamic_dim_of_transpose_op(%arg0: tensor<1x2x?x8xi8>) -> index {
 //  CHECK:           arith.constant 100 : index
 //  CHECK:           return
 func.func @static_dim_of_transpose_op(%arg0: tensor<1x100x?x8xi8>) -> index {
-  %0 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<1x100x?x8xi8>, tensor<4xi32>) -> tensor<1x8x100x?xi8>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 0, 3, 1, 2> }: (tensor<1x100x?x8xi8>) -> tensor<1x8x100x?xi8>
   %c2 = arith.constant 2 : index
   %dim = tensor.dim %1, %c2 : tensor<1x8x100x?xi8>
   return %dim : index
diff --git a/mlir/test/Dialect/Tosa/availability.mlir b/mlir/test/Dialect/Tosa/availability.mlir
index e66ff4cacfd89..879ee450cf88f 100644
--- a/mlir/test/Dialect/Tosa/availability.mlir
+++ b/mlir/test/Dialect/Tosa/availability.mlir
@@ -553,10 +553,9 @@ func.func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> {
 // -----
 // CHECK-LABEL: transpose
 func.func @test_transpose(%arg0: tensor<13x21x3xf32>) -> tensor<3x13x21xf32> {
-  %0 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
   // CHECK: profiles: [ [pro_int, pro_fp] ]
   // CHECK: extensions: [ [fp8e4m3, fp8e5m2, bf16] ]
-  %1 = tosa.transpose %arg0, %0 : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<3x13x21xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>}: (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
   return %1 : tensor<3x13x21xf32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
index 0e177a076ee7a..9f3a6057da844 100644
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -631,12 +631,11 @@ func.func @reshape_canonicalize_quant_nofold() -> (tensor<1x3x!quant.uniform<i8:
 
 // CHECK-LABEL: @transpose_canonicalize_strip_quant
 func.func @transpose_canonicalize_strip_quant() -> (tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>) {
-  // CHECK-DAG: tosa.const_shape {value = dense<[2, 1, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
-  // CHECK-DAG: "tosa.const"() <{value = dense<0> : tensor<1x2x3xi8>}> : () -> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
-  // CHECK: tosa.reshape %0, %1 : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>, !tosa.shape<3>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
-  %perms = "tosa.const"() {value = dense<[1, 0, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
+  // CHECK-DAG: %[[SHAPE:.*]] = tosa.const_shape {value = dense<[2, 1, 3]> : tensor<3xindex>} : () -> !tosa.shape<3>
+  // CHECK-DAG: %[[CONST:.*]] = "tosa.const"() <{value = dense<0> : tensor<1x2x3xi8>}> : () -> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
+  // CHECK: tosa.reshape %[[CONST]], %[[SHAPE]] : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>, !tosa.shape<3>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
   %0 = "tosa.const"() {value = dense<0> : tensor<1x2x3xi8>} : ()-> tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>
-  %1 = tosa.transpose %0, %perms : (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>, tensor<3xi32>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
+  %1 = tosa.transpose %0 { perms = array<i32: 1, 0, 2> }: (tensor<1x2x3x!quant.uniform<i8:f32, 1.000000e+00>>) -> tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
   return %1 :  tensor<2x1x3x!quant.uniform<i8:f32, 1.000000e+00>>
 }
 
@@ -688,8 +687,7 @@ func.func @tile_nofold(%arg0: tensor<3x4xf32>) -> tensor<3x8xf32> {
 func.func @transpose_no_op(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32> {
   // CHECK: return %arg0
   // CHECK-NOT: tosa.transpose
-  %perms = "tosa.const"() {value = dense<[0, 1, 2, 3]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %arg0, %perms : (tensor<3x4x5x6xf32>, tensor<4xi32>) -> tensor<3x4x5x6xf32>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 0, 1, 2, 3> }: (tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32>
   return %1 : tensor<3x4x5x6xf32>
 }
 
@@ -699,13 +697,22 @@ func.func @transpose_no_op(%arg0: tensor<3x4x5x6xf32>) -> tensor<3x4x5x6xf32> {
 func.func @transpose_is_reshape(%arg0: tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32> {
   // CHECK: %[[CONST0:.+]] = tosa.const_shape {value = dense<[1, 4, 1, 5]> : tensor<4xindex>} : () -> !tosa.shape<4>
   // CHECK: tosa.reshape %arg0, %[[CONST0]]
-  %perms = "tosa.const"() <{value = dense<[3, 1, 0, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<1x4x5x1xf32>, tensor<4xi32>) -> tensor<1x4x1x5xf32>
+  %0 = tosa.transpose %arg0 { perms = array<i32: 3, 1, 0, 2> }: (tensor<1x4x5x1xf32>) -> tensor<1x4x1x5xf32>
   return %0 : tensor<1x4x1x5xf32>
 }
 
 // -----
 
+// CHECK-LABEL: @transpose_is_reshape_unknown_dim
+func.func @transpose_is_reshape_unknown_dim(%arg0: tensor<1x4x?x1xf32>) -> tensor<1x4x1x?xf32> {
+  // CHECK: %[[CONST0:.+]] = tosa.const_shape {value = dense<[1, 4, 1, -1]> : tensor<4xindex>} : () -> !tosa.shape<4>
+  // CHECK: tosa.reshape %arg0, %[[CONST0]]
+  %0 = tosa.transpose %arg0 { perms = array<i32: 3, 1, 0, 2> }: (tensor<1x4x?x1xf32>) -> tensor<1x4x1x?xf32>
+  return %0 : tensor<1x4x1x?xf32>
+}
+
+// -----
+
 // CHECK-LABEL: @single_bit_reshape
 // https://github.com/llvm/llvm-project/issues/55440
 func.func @single_bit_reshape() -> tensor<1xi1> {
diff --git a/mlir/test/Dialect/Tosa/constant-op-fold.mlir b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
index e6fb741df9598..190aa777d3470 100644
--- a/mlir/test/Dialect/Tosa/constant-op-fold.mlir
+++ b/mlir/test/Dialect/Tosa/constant-op-fold.mlir
@@ -20,34 +20,30 @@ func.func @argmax_dynamic_shape_no_fold_dim_size_1(%arg0: tensor<?x1x3xf32>) ->
 // CHECK-LABEL: @transpose_fold
 func.func @transpose_fold(%arg0: tensor<3x4xf32>) -> tensor<3x4xf32> {
   // CHECK: return %arg0
-  %0 = arith.constant dense<[0, 1]> : tensor<2xi32>
-  %1 = tosa.transpose %arg0, %0 { perms = [1, 0] }: (tensor<3x4xf32>, tensor<2xi32>) -> tensor<3x4xf32>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 0, 1> }: (tensor<3x4xf32>) -> tensor<3x4xf32>
   return %1 : tensor<3x4xf32>
 }
 
 // CHECK-LABEL: @transpose_nofold
 func.func @transpose_nofold(%arg0: tensor<3x3xf32>) -> tensor<3x3xf32> {
   // CHECK: tosa.transpose
-  %0 = arith.constant dense<[1, 0]> : tensor<2xi32>
-  %1 = tosa.transpose %arg0, %0 { perms = [1, 0] }: (tensor<3x3xf32>, tensor<2xi32>) -> tensor<3x3xf32>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 1, 0> }: (tensor<3x3xf32>) -> tensor<3x3xf32>
   return %1 : tensor<3x3xf32>
 }
 
 // CHECK-LABEL: @transpose_nofold_shape
 func.func @transpose_nofold_shape(%arg0: tensor<3x4xf32>) -> tensor<?x?xf32> {
   // CHECK: tosa.transpose
-  %0 = arith.constant dense<[1, 0]> : tensor<2xi32>
-  %1 = tosa.transpose %arg0, %0 { perms = [1, 0] }: (tensor<3x4xf32>, tensor<2xi32>) -> tensor<?x?xf32>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 1, 0> }: (tensor<3x4xf32>) -> tensor<?x?xf32>
   return %1 : tensor<?x?xf32>
 }
 
 // CHECK-LABEL: @transpose_fold_splat
 func.func @transpose_fold_splat() -> tensor<3x2xf32> {
   %input = "tosa.const"() {value = dense<4.0> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   //               CHECK: %[[CST:.+]] = "tosa.const"() <{
   // CHECK-SAME{LITERAL}: value = dense<4.000000e+00> : tensor<3x2xf32>
-  %1 = tosa.transpose %input, %perms : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
   // CHECK: return %[[CST]]
   return %1 : tensor<3x2xf32>
 }
@@ -55,10 +51,9 @@ func.func @transpose_fold_splat() -> tensor<3x2xf32> {
 // CHECK-LABEL: @transpose_fold_2d_float
 func.func @transpose_fold_2d_float() -> tensor<3x2xf32> {
   %input = "tosa.const"() {value = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   //               CHECK: %[[CST:.+]] = "tosa.const"() <{
   // CHECK-SAME{LITERAL}: value = dense<[[0.000000e+00, 3.000000e+00], [1.000000e+00, 4.000000e+00], [2.000000e+00, 5.000000e+00]]> : tensor<3x2xf32>
-  %1 = tosa.transpose %input, %perms : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
   // CHECK: return %[[CST]]
   return %1 : tensor<3x2xf32>
 }
@@ -66,10 +61,9 @@ func.func @transpose_fold_2d_float() -> tensor<3x2xf32> {
 // CHECK-LABEL: @transpose_fold_2d_bool
 func.func @transpose_fold_2d_bool() -> tensor<3x2xi1> {
   %input = "tosa.const"() {value = dense<[[true, false, false], [false, false, true]]> : tensor<2x3xi1>} : () -> tensor<2x3xi1>
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   //               CHECK: %[[CST:.+]] = "tosa.const"() <{
   // CHECK-SAME{LITERAL}: value = dense<[[true, false], [false, false], [false, true]]> : tensor<3x2xi1>
-  %1 = tosa.transpose %input, %perms : (tensor<2x3xi1>, tensor<2xi32>) -> tensor<3x2xi1>
+  %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xi1>) -> tensor<3x2xi1>
   // CHECK: return %[[CST]]
   return %1 : tensor<3x2xi1>
 }
@@ -80,50 +74,46 @@ func.func @transpose_fold_4d_int() -> tensor<3x1x4x2xi32> {
     [[ 0,  1,  2,  3], [ 4,  5,  6,  7], [ 8,  9, 10, 11]],
     [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]
   ]]> : tensor<1x2x3x4xi32>} : () -> tensor<1x2x3x4xi32>
-  %perms = "tosa.const"() {value = dense<[2, 0, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
   //               CHECK: %[[CST:.+]] = "tosa.const"() <{
   // CHECK-SAME{LITERAL}: value = dense<[
   // CHECK-SAME{LITERAL}:   [[[0, 12], [1, 13], [2, 14], [3, 15]]],
   // CHECK-SAME{LITERAL}:   [[[4, 16], [5, 17], [6, 18], [7, 19]]],
   // CHECK-SAME{LITERAL}:   [[[8, 20], [9, 21], [10, 22], [11, 23]]]
   // CHECK-SAME{LITERAL}: ]>
-  %1 = tosa.transpose %input, %perms : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<3x1x4x2xi32>
+  %1 = tosa.transpose %input { perms = array<i32: 2, 0, 3, 1> }: (tensor<1x2x3x4xi32>) -> tensor<3x1x4x2xi32>
   // CHECK: return %[[CST]]
   return %1 : tensor<3x1x4x2xi32>
 }
 
 // CHECK-LABEL: @transpose_nofold_non_cst_input
 func.func @transpose_nofold_non_cst_input(%input: tensor<2x3xf32>) -> tensor<3x2xf32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   // CHECK: tosa.transpose
-  %1 = tosa.transpose %input, %perms : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
-  return %1 : tensor<3x2xf32>
-}
-
-// CHECK-LABEL: @transpose_nofold_non_cst_perms
-func.func @transpose_nofold_non_cst_perms(%perms: tensor<2xi32>) -> tensor<3x2xf32> {
-  %input = "tosa.const"() {value = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
-  // CHECK: tosa.transpose
-  %1 = tosa.transpose %input, %perms : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
   return %1 : tensor<3x2xf32>
 }
 
 // CHECK-LABEL: @transpose_nofold_multi_users
 func.func @transpose_nofold_multi_users() -> (tensor<3x2xf32>, tensor<2x3xf32>) {
   %input = "tosa.const"() {value = dense<[[0.0, 1.0, 2.0], [3.0, 4.0, 5.0]]> : tensor<2x3xf32>} : () -> tensor<2x3xf32>
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   // CHECK: tosa.transpose
-  %1 = tosa.transpose %input, %perms : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %1 = tosa.transpose %input { perms = array<i32: 1, 0> }: (tensor<2x3xf32>) -> tensor<3x2xf32>
   return %1, %input : tensor<3x2xf32>, tensor<2x3xf32>
 }
 
+// CHECK-LABEL: @transpose_nofold_quantized_types
+func.func @transpose_nofold_quantized_types() -> tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>> {
+  %input = "tosa.const"() {value = dense<-127> : tensor<2x1x1x2xi8>} : () -> tensor<2x1x1x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
+  // CHECK: tosa.transpose
+  %0 = tosa.transpose %input { perms = array<i32: 1, 2, 3, 0> }: (tensor<2x1x1x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>) -> tensor<1x1x2x2x!quant.uniform<i8<-127:127>:f32:3, {1.000000e-01,1.000000e-01}>>
+  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>
+  %2 = tosa.transpose %0 { perms = array<i32: 1, 0> }: (tensor<2x2xf32>) -> tensor<2x2xf32>
   return %2 : tensor<2x2xf32>
 }
 {-#
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
index 1aa8547cb2fdb..44f208aee212d 100644
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -235,97 +235,74 @@ func.func @test_pad_padding_shape_mismatch(%arg0: tensor<13x21x3xf32>) -> tensor
 
 // -----
 
-func.func @test_transpose_non_const(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3xi32>) -> tensor<3x13x21xf32> {
-  // expected-error@+1 {{'tosa.transpose' op perms of transpose is not constant}}
-  %0 = tosa.transpose %arg0, %arg1 : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<3x13x21xf32>
-  return %0 : tensor<3x13x21xf32>
-}
-
-// -----
-
 func.func @test_transpose_io_rank_mismatch(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3xi32>) -> tensor<3x13x21x1xf32> {
   // expected-error@+1 {{'tosa.transpose' op expected input tensor rank to equal result tensor rank}}
-  %0 = tosa.transpose %arg0, %arg1 : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<3x13x21x1xf32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 2, 1, 0>}: (tensor<13x21x3xf32>) -> tensor<3x13x21x1xf32>
   return %0 : tensor<3x13x21x1xf32>
 }
 
 // -----
 
-func.func @test_transpose_invalid_perms_rank(%arg0: tensor<13x21x3xf32>, %arg1: tensor<3x2xi32>) -> tensor<3x13x21xf32> {
-  // expected-error@+1 {{'tosa.transpose' op expected permutation tensor to be rank 1 but got rank 2}}
-  %0 = tosa.transpose %arg0, %arg1 : (tensor<13x21x3xf32>, tensor<3x2xi32>) -> tensor<3x13x21xf32>
-  return %0 : tensor<3x13x21xf32>
-}
-
-// -----
-
 func.func @test_transpose_rank0_perms() {
   %14 = tensor.empty() : tensor<5x27xi64>
-  %cst = tensor.empty() : tensor<i32>
-  // expected-error@+1 {{'tosa.transpose' op expected permutation tensor to be rank 1 but got rank 0}}
-  %72 = tosa.transpose %14, %cst : (tensor<5x27xi64>, tensor<i32>) -> tensor<?x?xi64>
+  // expected-error@+1 {{'tosa.transpose' op expected perms attribute to have size 2 (input rank) but got size 0}}
+  %72 = tosa.transpose %14 {perms = array<i32> }: (tensor<5x27xi64>) -> tensor<?x?xi64>
   return
 }
 
 // -----
 
-func.func @test_transpose_invalid_perms_size(%arg0: tensor<13x21x3xf32>, %arg1: tensor<7xi32>) -> tensor<3x13x21xf32> {
-  // expected-error@+1 {{'tosa.transpose' op expected permutation tensor dim 0 to have size 3 (input rank) but got size 7}}
-  %0 = tosa.transpose %arg0, %arg1 : (tensor<13x21x3xf32>, tensor<7xi32>) -> tensor<3x13x21xf32>
+func.func @test_transpose_invalid_perms_size(%arg0: tensor<13x21x3xf32>) -> tensor<3x13x21xf32> {
+  // expected-error@+1 {{'tosa.transpose' op expected perms attribute to have size 3 (input rank) but got size 7}}
+  %0 = tosa.transpose %arg0 {perms = array<i32: 6, 5, 4, 3, 2, 1, 0> }: (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
   return %0 : tensor<3x13x21xf32>
 }
 
 // -----
 
 func.func @test_transpose_invalid_permutation_tensor(%arg0: tensor<13x21x3xf32>) -> tensor<?x?x?xf32> {
-  %perms = arith.constant dense<[2, 0, 0]> : tensor<3xi32>
-  // expected-error@+1 {{'tosa.transpose' op expected valid permutation tensor}}
-  %0 = tosa.transpose %arg0, %perms : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<?x?x?xf32>
+  // expected-error@+1 {{'tosa.transpose' op expected valid permutation indices}}
+  %0 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 0> }: (tensor<13x21x3xf32>) -> tensor<?x?x?xf32>
   return %0 : tensor<?x?x?xf32>
 }
 
 // -----
 
 func.func @test_transpose_invalid_permutation_negative(%arg0: tensor<3x2xi32>) -> tensor<*xi32> {
-  %perms = "tosa.const"() {value = dense<[-1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  // expected-error@+1 {{'tosa.transpose' op expected valid permutation tensor}}
-  %1 = tosa.transpose %arg0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<*xi32>
+  // expected-error@+1 {{'tosa.transpose' op expected valid permutation indices}}
+  %1 = tosa.transpose %arg0 {perms = array<i32: -1, 0> }: (tensor<3x2xi32>) -> tensor<*xi32>
   return %1 : tensor<*xi32>
 }
 
 // -----
 
 func.func @test_transpose_invalid_permutation_tensor_above_range(%arg0: tensor<3x2xi32>) -> tensor<*xi32> {
-  %perms = "tosa.const"() {value = dense<[2, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  // expected-error@+1 {{'tosa.transpose' op expected valid permutation tensor}}
-  %1 = tosa.transpose %arg0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<*xi32>
+  // expected-error@+1 {{'tosa.transpose' op expected valid permutation indices}}
+  %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0> }: (tensor<3x2xi32>) -> tensor<*xi32>
   return %1 : tensor<*xi32>
 }
 
 // -----
 
 func.func @test_transpose_invalid_permutation_types(%arg0: tensor<3x2xi32>) -> tensor<3x4xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   // expected-error@+1 {{'tosa.transpose' op expected output tensor dim 0 to match input dim 1 with value of 2}}
-  %1 = tosa.transpose %arg0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<3x4xi32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0> }: (tensor<3x2xi32>) -> tensor<3x4xi32>
   return %1 : tensor<3x4xi32>
 }
 
 // -----
 
 func.func @test_transpose_invalid_permutation_types_dynamic_dim_ok(%arg0: tensor<2x?xi32>) -> tensor<3x4xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   // expected-error@+1 {{'tosa.transpose' op expected output tensor dim 1 to match input dim 0 with value of 2}}
-  %1 = tosa.transpose %arg0, %perms : (tensor<2x?xi32>, tensor<2xi32>) -> tensor<3x4xi32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0> }: (tensor<2x?xi32>) -> tensor<3x4xi32>
   return %1 : tensor<3x4xi32>
 }
 
 // -----
 
 func.func @test_transpose_element_type_mismatch(%arg0: tensor<2x3xi32>) -> tensor<3x2xf32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
   // expected-error@+1 {{'tosa.transpose' op failed to verify that all of {input1, output} have same element type}}
-  %1 = tosa.transpose %arg0, %perms : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>} : (tensor<2x3xi32>) -> tensor<3x2xf32>
   return %1 : tensor<3x2xf32>
 }
 
@@ -674,10 +651,9 @@ func.func @test_tile_io_rank_mismatch() {
 
 // CHECK-LABEL: @test_invalid_constant_permutation
 func.func @test_invalid_constant_permutation() {
-  // expected-error@+3 {{'tosa.transpose' op expected valid permutation tensor}}
   %0 = tensor.empty() : tensor<3x4x5xi32>
-  %1 = arith.constant dense<[3, 0, 1]> : tensor<3xi32>
-  %2 = tosa.transpose %0, %1 : (tensor<3x4x5xi32>, tensor<3xi32>) -> tensor<3x4x5xi32>
+  // expected-error@+1 {{'tosa.transpose' op expected valid permutation indices}}
+  %2 = tosa.transpose %0 {perms = array<i32: 3, 0, 1>}: (tensor<3x4x5xi32>) -> tensor<3x4x5xi32>
   return
 }
 
@@ -685,11 +661,10 @@ func.func @test_invalid_constant_permutation() {
 
 // CHECK-LABEL: test_rank_size_constant_permutation
 func.func @test_rank_size_constant_permutation() {
-  // expected-error@+4 {{'tosa.transpose' op expected valid permutation tensor}}
   %0 = arith.constant 6 : index
-  %1 = arith.constant dense<[0, 2]> : tensor<2xi32>
   %2 = tensor.empty(%0) : tensor<?x27xi64>
-  %3 = tosa.transpose %2, %1 : (tensor<?x27xi64>, tensor<2xi32>) -> tensor<?x27xi64>
+  // expected-error@+1 {{'tosa.transpose' op expected valid permutation indices}}
+  %3 = tosa.transpose %2 {perms = array<i32: 0, 2>}: (tensor<?x27xi64>) -> tensor<?x27xi64>
   return
 }
 
@@ -697,11 +672,10 @@ func.func @test_rank_size_constant_permutation() {
 
 // CHECK-LABEL: test_large_constant_permutation
 func.func @test_large_constant_permutation() {
-  // expected-error@+4 {{'tosa.transpose' op expected valid permutation tensor}}
   %0 = arith.constant 6 : index
-  %1 = arith.constant dense<[1185677355, 332462212]> : tensor<2xi32>
   %2 = tensor.empty(%0) : tensor<?x27xi64>
-  %3 = tosa.transpose %2, %1 : (tensor<?x27xi64>, tensor<2xi32>) -> tensor<?x27xi64>
+  // expected-error@+1 {{'tosa.transpose' op expected valid permutation indices}}
+  %3 = tosa.transpose %2 {perms = array<i32: 1185677355, 332462212>}: (tensor<?x27xi64>) -> tensor<?x27xi64>
   return
 }
 
diff --git a/mlir/test/Dialect/Tosa/level_check.mlir b/mlir/test/Dialect/Tosa/level_check.mlir
index 90c4551564d1e..67efab7f1559f 100644
--- a/mlir/test/Dialect/Tosa/level_check.mlir
+++ b/mlir/test/Dialect/Tosa/level_check.mlir
@@ -105,9 +105,8 @@ func.func @test_tile(%arg0: tensor<1x1x1x1x13x21x3xf32>) -> tensor<1x1x1x1x39x21
 // -----
 
 func.func @test_transpose(%arg0: tensor<13x21x3x1x1x1x1xf32>) -> tensor<3x13x21x1x1x1x1xf32> {
-  %0 = "tosa.const"() {value = dense<[2, 0, 1, 3, 4, 5, 6]> : tensor<7xi32>} : () -> tensor<7xi32>
   // expected-error@+1 {{'tosa.transpose' op failed level check: operand rank(shape) <= MAX_RANK}}
-  %1 = "tosa.transpose"(%arg0, %0) : (tensor<13x21x3x1x1x1x1xf32>, tensor<7xi32>) -> tensor<3x13x21x1x1x1x1xf32>
+  %1 = "tosa.transpose"(%arg0) {perms = array<i32: 2, 0, 1, 3, 4, 5, 6>} : (tensor<13x21x3x1x1x1x1xf32>) -> tensor<3x13x21x1x1x1x1xf32>
   return %1 : tensor<3x13x21x1x1x1x1xf32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
index cd73377c7f587..3694c7d472bc3 100644
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -640,24 +640,21 @@ func.func @test_tile(%arg0: tensor<13x21x3xf32>) -> tensor<39x21x6xf32> {
 // -----
 // CHECK-LABEL: transpose
 func.func @test_transpose(%arg0: tensor<13x21x3xf32>) -> tensor<3x13x21xf32> {
-  %0 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<13x21x3xf32>, tensor<3xi32>) -> tensor<3x13x21xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x21x3xf32>) -> tensor<3x13x21xf32>
   return %1 : tensor<3x13x21xf32>
 }
 
 // -----
 // CHECK-LABEL: transpose_dynamic_dim
 func.func @test_transpose_dynamic_dim(%arg0: tensor<13x?x3xf32>) -> tensor<3x13x?xf32> {
-  %0 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<13x?x3xf32>, tensor<3xi32>) -> tensor<3x13x?xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x?x3xf32>) -> tensor<3x13x?xf32>
   return %1 : tensor<3x13x?xf32>
 }
 
 // -----
 // CHECK-LABEL: transpose_half_dynamic_dim
 func.func @test_transpose_half_dynamic_dim(%arg0: tensor<13x3x3xf32>) -> tensor<3x13x?xf32> {
-  %0 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<13x3x3xf32>, tensor<3xi32>) -> tensor<3x13x?xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 2, 0, 1>} : (tensor<13x3x3xf32>) -> tensor<3x13x?xf32>
   return %1 : tensor<3x13x?xf32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
index ae7a8e90b4281..944dd5f2a4d10 100644
--- a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
@@ -54,11 +54,10 @@ func.func @transpose_conv2d_quantized_padded(%arg0: tensor<2x16x14x3xi8>, %arg1:
 func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<5x3x5x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {
   // Manipulate the weight matrix to handle striding.
   // CHECK-DAG: %[[PADV:.+]] = tosa.const_shape {value = dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
-  // CHECK-DAG: %[[TRANSV:.+]]  = "tosa.const"() <{value = dense<[2, 4, 0, 1, 3, 5]> : tensor<6xi32>}
   // CHECK-DAG: %[[PADW:.+]]  = tosa.pad %arg1, %[[PADV]]
   // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {value = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}
   // CHECK-DAG: %[[RESW1:.+]]  = tosa.reshape %[[PADW]], %[[CONST1]]
-  // CHECK-DAG: %[[TRANS:.+]]  = tosa.transpose %[[RESW1]], %[[TRANSV]]
+  // CHECK-DAG: %[[TRANS:.+]]  = tosa.transpose %[[RESW1]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}
   // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {value = dense<[30, 2, 2, 3]> : tensor<4xindex>}
   // CHECK-DAG: %[[RESW2:.+]]  = tosa.reshape %[[TRANS]], %[[CONST3]]
   // CHECK-DAG: %[[REV1:.+]]  = tosa.reverse %[[RESW2]] {axis = 1 : i32}
@@ -68,7 +67,6 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
 
   // Pad out the input matrix to handle the transpose conv.
   // CHECK-DAG: %[[PAD:.+]] = tosa.const_shape {value = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
-  // CHECK-DAG: %[[TRANS2:.+]]  = "tosa.const"() <{value = dense<[0, 1, 3, 2, 4, 5]> : tensor<6xi32>}
   // CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]]
 
   // Manipulate the final shape.
@@ -76,7 +74,7 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
   // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]] {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
   // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {value = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
   // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
-  // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]], %[[TRANS2]]
+  // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}
   // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {value = dense<[2, 36, 48, 5]> : tensor<4xindex>
   // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]], %[[CONST8]]
   // CHECK-DAG: %[[SLICE:.+]] = tosa.slice %[[RESHAPE_OUT_2]], %[[START]], %[[SIZE]]
@@ -95,11 +93,10 @@ func.func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<
 func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1: tensor<5x3x5x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x35x47x5xi32>) {
   // Manipulate the weight matrix to handle striding.
   // CHECK-DAG: %[[PADV:.+]]  = tosa.const_shape {value = dense<[0, 0, 0, 1, 0, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
-  // CHECK-DAG: %[[TRANSV:.+]]  = "tosa.const"() <{value = dense<[2, 4, 0, 1, 3, 5]> : tensor<6xi32>}
   // CHECK-DAG: %[[PADW:.+]]  = tosa.pad %arg1, %[[PADV]] {input_zp = 42 : i32}
   // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {value = dense<[5, 2, 2, 2, 3, 3]> : tensor<6xindex>}
   // CHECK-DAG: %[[RESW1:.+]]  = tosa.reshape %[[PADW]], %[[CONST1]]
-  // CHECK-DAG: %[[TRANS:.+]]  = tosa.transpose %[[RESW1]], %[[TRANSV]]
+  // CHECK-DAG: %[[TRANS:.+]]  = tosa.transpose %[[RESW1]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}
   // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {value = dense<[30, 2, 2, 3]> : tensor<4xindex>}
   // CHECK-DAG: %[[RESW2:.+]]  = tosa.reshape %[[TRANS]], %[[CONST3]]
   // CHECK-DAG: %[[REV1:.+]]  = tosa.reverse %[[RESW2]] {axis = 1 : i32}
@@ -109,7 +106,6 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
 
   // Pad out the input matrix to handle the transpose conv.
   // CHECK-DAG: %[[PAD:.+]]  = tosa.const_shape {value = dense<[0, 0, 1, 1, 1, 1, 0, 0]> : tensor<8xindex>} : () -> !tosa.shape<8>
-  // CHECK-DAG: %[[TRANS2:.+]]  = "tosa.const"() <{value = dense<[0, 1, 3, 2, 4, 5]> : tensor<6xi32>}
   // CHECK-DAG: %[[NEWINPUT:.+]] = tosa.pad %arg0, %[[PAD]] {input_zp = -22 : i32}
 
   // Manipulate the final shape.
@@ -119,7 +115,7 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
   // CHECK-DAG: %[[CONV:.+]] = tosa.conv2d %[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]], %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
   // CHECK-DAG: %[[CONV_NEW_SHAPE:.*]] = tosa.const_shape  {value = dense<[2, 18, 16, 2, 3, 5]> : tensor<6xindex>}
   // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = tosa.reshape %[[CONV]], %[[CONV_NEW_SHAPE]]
-  // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]], %[[TRANS2]]
+  // CHECK-DAG: %[[TRANS_OUT:.+]] = tosa.transpose %[[RESHAPE_OUT_1]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}
   // CHECK-DAG: %[[TRANS_NEW_SHAPE:.+]] = tosa.const_shape {value = dense<[2, 36, 48, 5]> : tensor<4xindex>}
   // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = tosa.reshape %[[TRANS_OUT]], %[[TRANS_NEW_SHAPE]]
   // CHECK-DAG: %[[SLICE:.+]] = tosa.slice %[[RESHAPE_OUT_2]], %[[START]], %[[SIZE]]
@@ -138,12 +134,10 @@ func.func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1
 func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 : tensor<1x2x1x1xi8>, %arg2 : tensor<1xi32>) -> (tensor<1x19x2x1xi32>) {
   // CHECK-DAG: %[[WEIGHT_PAD:.+]] = tosa.const_shape  {value = dense<[0, 0, 0, 0, 0, 1, 0, 0]> : tensor<8xindex>}
   // CHECK-DAG: %[[CONST1:.+]] = tosa.const_shape {value = dense<[1, 2, 1, 1, 2, 1]> : tensor<6xindex>}
-  // CHECK-DAG: %[[WEIGHT_PERMS:.+]] = "tosa.const"() <{value = dense<[2, 4, 0, 1, 3, 5]> : tensor<6xi32>}
   // CHECK-DAG: %[[CONST3:.+]] = tosa.const_shape {value = dense<[2, 2, 1, 1]> : tensor<4xindex>}
   // CHECK-DAG: %[[INPUT_PAD:.+]] = tosa.const_shape  {value = dense<[0, 0, 1, 1, 0, 0, 0, 0]> : tensor<8xindex>}
   // CHECK-DAG: %[[ZERO:.+]] = "tosa.const"() <{value = dense<0> : tensor<2xi32>}
   // CHECK-DAG: %[[CONST6:.+]] = tosa.const_shape {value = dense<[1, 17, 1, 1, 2, 1]> : tensor<6xindex>}
-  // CHECK-DAG: %[[RESULT_PERMS:.+]] = "tosa.const"() <{value = dense<[0, 1, 3, 2, 4, 5]> : tensor<6xi32>}
   // CHECK-DAG: %[[CONST8:.+]] = tosa.const_shape {value = dense<[1, 17, 2, 1]> : tensor<4xindex>}
   // CHECK-DAG: %[[RESULT_PAD:.+]] = tosa.const_shape  {value = dense<[0, 0, 2, 0, 0, 0, 0, 0]> : tensor<8xindex>}
   // CHECK-DAG: %[[CONST10:.+]] = tosa.const_shape {value = dense<1> : tensor<4xindex>}
@@ -151,13 +145,13 @@ func.func @transpose_conv2d_strided_overpad(%arg0 : tensor<1x16x1x1xi8>, %arg1 :
   // CHECK-DAG: %[[WEIGHT_ZP:.*]] = "tosa.const"() <{value = dense<93> : tensor<1xi8>}>
   // CHECK: %[[PAD_WEIGHT:.+]] = tosa.pad %arg1, %[[WEIGHT_PAD]] {input_zp = 93 : i32}
   // CHECK: %[[RESHAPE_WEIGHT_0:.+]] = tosa.reshape %[[PAD_WEIGHT]], %[[CONST1]]
-  // CHECK: %[[TRANSPOSE_WEIGHT:.+]] = tosa.transpose %[[RESHAPE_WEIGHT_0]], %[[WEIGHT_PERMS]]
+  // CHECK: %[[TRANSPOSE_WEIGHT:.+]] = tosa.transpose %[[RESHAPE_WEIGHT_0]] {perms = array<i32: 2, 4, 0, 1, 3, 5>}
   // CHECK: %[[RESHAPE_WEIGHT_1:.+]] = tosa.reshape %[[TRANSPOSE_WEIGHT]], %[[CONST3]]
   // CHECK: %[[REVERSE:.+]] = tosa.reverse %[[RESHAPE_WEIGHT_1]] {axis = 1 : i32}
   // CHECK: %[[PAD_INPUT:.+]] = tosa.pad %arg0, %[[INPUT_PAD]] {input_zp = -103 : i32}
   // CHECK: %[[CONV:.+]] = tosa.conv2d %[[PAD_INPUT]], %[[REVERSE]], %[[ZERO]], %[[INPUT_ZP]], %[[WEIGHT_ZP]] {acc_type = i32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>}
   // CHECK: %[[RESHAPE_RESULT_0:.+]] = tosa.reshape %[[CONV]], %[[CONST6]]
-  // CHECK: %[[TRANSPOSE_RESULT:.+]] = tosa.transpose %[[RESHAPE_RESULT_0]], %[[RESULT_PERMS]]
+  // CHECK: %[[TRANSPOSE_RESULT:.+]] = tosa.transpose %[[RESHAPE_RESULT_0]] {perms = array<i32: 0, 1, 3, 2, 4, 5>}
   // CHECK: %[[RESHAPE_RESULT_1:.+]] = tosa.reshape %[[TRANSPOSE_RESULT]], %[[CONST8]]
   // CHECK: %[[PAD_RESULT:.+]] = tosa.pad %[[RESHAPE_RESULT_1]], %[[RESULT_PAD]]
   // CHECK: %[[RESHAPE_ARG2:.+]] = tosa.reshape %arg2, %[[CONST10]]
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
index b5b0cbff25cfd..1821b78091aad 100644
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -577,29 +577,10 @@ func.func @test_tile(%arg0 : tensor<2x3x?xi32>) -> () {
 
 // -----
 
-// CHECK-LABEL: @test_transpose_same
-func.func @test_transpose_same(%arg0 : tensor<4x4x4xi32>, %arg1 : tensor<3xi32>) -> () {
-  // CHECK: tosa.transpose %arg0, %arg1 : (tensor<4x4x4xi32>, tensor<3xi32>) -> tensor<4x4x4xi32>
-  %0 = tosa.transpose %arg0, %arg1 : (tensor<4x4x4xi32>, tensor<3xi32>) -> tensor<?x?x?xi32>
-  return
-}
-
-// -----
-
-// CHECK-LABEL: @test_transpose_perm_unknown
-func.func @test_transpose_perm_unknown(%arg0 : tensor<4x4x5xi32>, %arg1 : tensor<3xi32>) -> () {
-  // CHECK: tosa.transpose %arg0, %arg1 : (tensor<4x4x5xi32>, tensor<3xi32>) -> tensor<?x?x?xi32>
-  %0 = tosa.transpose %arg0, %arg1 : (tensor<4x4x5xi32>, tensor<3xi32>) -> tensor<?x?x?xi32>
-  return
-}
-
-// -----
-
 // CHECK-LABEL: @test_transpose_static
 func.func @test_transpose_static(%arg0 : tensor<3x4x5xi32>) -> () {
-  %0 = arith.constant dense<[2, 1, 0]> : tensor<3xi32>
-  // CHECK: tosa.transpose %arg0, %cst : (tensor<3x4x5xi32>, tensor<3xi32>) -> tensor<5x4x3xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<3x4x5xi32>, tensor<3xi32>) -> tensor<?x?x?xi32>
+  // CHECK: tosa.transpose %arg0 {perms = array<i32: 2, 1, 0>} : (tensor<3x4x5xi32>) -> tensor<5x4x3xi32>
+  %1 = tosa.transpose %arg0 { perms = array<i32: 2, 1, 0> }: (tensor<3x4x5xi32>) -> tensor<?x?x?xi32>
   return
 }
 
diff --git a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
index 3a293009a5455..ed87a6d7f73db 100644
--- a/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-reduce-transposes.mlir
@@ -4,11 +4,9 @@
 // CHECK-NEXT: %[[RESULT:.*]] = tosa.ceil %arg0
 // CHECK-NEXT: return %[[RESULT]]
 func.func @test_transpose_tracks_to_nullifying_single_step(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
   %ceil = tosa.ceil %0 : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %ceil, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %1 = tosa.transpose %ceil {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %1 : tensor<1x2x3x4xi32>
 }
 
@@ -20,13 +18,11 @@ func.func @test_transpose_tracks_to_nullifying_single_step(%arg0: tensor<1x2x3x4
 // CHECK-NEXT: %[[NOT:.*]] = tosa.bitwise_not %[[ABS]]
 // CHECK-NEXT: return %[[NOT]]
 func.func @test_transpose_tracks_to_nullifying_multi_unary_step(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
   %clamp = tosa.clamp %0 {max_val = 1 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %abs = tosa.abs %clamp : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %bitwise_not = tosa.bitwise_not %abs : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %bitwise_not, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %1 = tosa.transpose %bitwise_not {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %1 : tensor<1x2x3x4xi32>
 }
 
@@ -38,14 +34,12 @@ func.func @test_transpose_tracks_to_nullifying_multi_unary_step(%arg0: tensor<1x
 // CHECK-NEXT: %[[ADD:.*]] = tosa.add %[[CLAMP]], %[[ABS]]
 // CHECK-NEXT: return %[[ADD]]
 func.func @test_transpose_tracks_to_nullifying_diverging_binary(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
-  %transpose1 = tosa.transpose %arg1, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
   %clamp = tosa.clamp %transpose0 {max_val = 1 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %abs = tosa.abs %transpose1 : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %add = tosa.add %clamp, %abs : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %result = tosa.transpose %add, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %result : tensor<1x2x3x4xi32>
 }
 
@@ -58,14 +52,12 @@ func.func @test_transpose_tracks_to_nullifying_diverging_binary(%arg0: tensor<1x
 // CHECK-NEXT: %[[ADD:.*]] = tosa.add %[[CLAMP]], %[[ABS]]
 // CHECK-NEXT: return %[[ADD]]
 func.func @test_transpose_tracks_to_nullifying_diverging_binary_with_broadcasting(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x1x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
-  %transpose1 = tosa.transpose %arg1, %perms0 : (tensor<1x2x1x4xi32>, tensor<4xi32>) -> tensor<1x1x4x2xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x1x4xi32>) -> tensor<1x1x4x2xi32>
   %clamp = tosa.clamp %transpose0 {max_val = 1 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %abs = tosa.abs %transpose1 : (tensor<1x1x4x2xi32>) -> tensor<1x1x4x2xi32>
   %add = tosa.add %clamp, %abs : (tensor<1x3x4x2xi32>, tensor<1x1x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %result = tosa.transpose %add, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %result : tensor<1x2x3x4xi32>
 }
 
@@ -75,11 +67,9 @@ func.func @test_transpose_tracks_to_nullifying_diverging_binary_with_broadcastin
 // CHECK-NEXT: %[[RESULT:.*]] = tosa.add %arg0, %arg0
 // CHECK-NEXT: return %[[RESULT]]
 func.func @test_transpose_tracks_to_nullifying__converging_binary(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
   %clamp = tosa.add %0, %0 : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %1 : tensor<1x2x3x4xi32>
 }
 
@@ -102,20 +92,20 @@ func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10
     %5 = "tosa.const"() <{value = dense_resource<torch_tensor_3_torch.float32> : tensor<3xf32>}> : () -> tensor<3xf32>
     %6 = "tosa.const"() <{value = dense<[0, 2, 3, 1]> : tensor<4xi32>}> : () -> tensor<4xi32>
     %7 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
-    %8 = tosa.transpose %arg0, %6 : (tensor<3x3x10x10xf32>, tensor<4xi32>) -> tensor<3x10x10x3xf32>
-    %9 = tosa.transpose %4, %6 : (tensor<3x3x2x2xf32>, tensor<4xi32>) -> tensor<3x2x2x3xf32>
+    %8 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x10x10xf32>) -> tensor<3x10x10x3xf32>
+    %9 = tosa.transpose %4 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
     %10 = tosa.conv2d %8, %9, %5 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x10x10x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>) -> tensor<3x9x9x3xf32>
-    %11 = tosa.transpose %10, %7 : (tensor<3x9x9x3xf32>, tensor<4xi32>) -> tensor<3x3x9x9xf32>
+    %11 = tosa.transpose %10 {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x9x9x3xf32>) -> tensor<3x3x9x9xf32>
     %12 = tosa.ceil %11 : (tensor<3x3x9x9xf32>) -> tensor<3x3x9x9xf32>
-    %13 = tosa.transpose %12, %6 : (tensor<3x3x9x9xf32>, tensor<4xi32>) -> tensor<3x9x9x3xf32>
-    %14 = tosa.transpose %3, %6 : (tensor<3x3x2x2xf32>, tensor<4xi32>) -> tensor<3x2x2x3xf32>
+    %13 = tosa.transpose %12 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x9x9xf32>) -> tensor<3x9x9x3xf32>
+    %14 = tosa.transpose %3 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
     %15 = tosa.conv2d %13, %14, %2 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x9x9x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>) -> tensor<3x8x8x3xf32>
-    %16 = tosa.transpose %15, %7 : (tensor<3x8x8x3xf32>, tensor<4xi32>) -> tensor<3x3x8x8xf32>
+    %16 = tosa.transpose %15 {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x8x8x3xf32>) -> tensor<3x3x8x8xf32>
     %17 = tosa.floor %16 : (tensor<3x3x8x8xf32>) -> tensor<3x3x8x8xf32>
-    %18 = tosa.transpose %17, %6 : (tensor<3x3x8x8xf32>, tensor<4xi32>) -> tensor<3x8x8x3xf32>
-    %19 = tosa.transpose %1, %6 : (tensor<3x3x2x2xf32>, tensor<4xi32>) -> tensor<3x2x2x3xf32>
+    %18 = tosa.transpose %17 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x8x8xf32>) -> tensor<3x8x8x3xf32>
+    %19 = tosa.transpose %1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x2x2xf32>) -> tensor<3x2x2x3xf32>
     %20 = tosa.conv2d %18, %19, %0 {acc_type = f32, dilation = array<i64: 1, 1>, pad = array<i64: 0, 0, 0, 0>, stride = array<i64: 1, 1>} : (tensor<3x8x8x3xf32>, tensor<3x2x2x3xf32>, tensor<3xf32>) -> tensor<3x7x7x3xf32>
-    %21 = tosa.transpose %20, %7 : (tensor<3x7x7x3xf32>, tensor<4xi32>) -> tensor<3x3x7x7xf32>
+    %21 = tosa.transpose %20 {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x7x7x3xf32>) -> tensor<3x3x7x7xf32>
     return %21 : tensor<3x3x7x7xf32>
 }
 
@@ -126,13 +116,11 @@ func.func @test_torch_conv2d_with_elementwise_in_between(%arg0: tensor<3x3x10x10
 // CHECK-NEXT: %[[RES:.*]] = tosa.mul %arg0, %arg1, %[[SHIFT]]
 // CHECK-NEXT: return %[[RES]]
 func.func @test_mulop_conversion(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
-  %transpose1 = tosa.transpose %arg1, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
   %shift = "tosa.const"() <{value = dense<0> : tensor<1xi8>}> : () -> tensor<1xi8>
   %mul = tosa.mul %transpose0, %transpose1, %shift : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>, tensor<1xi8>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %result = tosa.transpose %mul, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %result = tosa.transpose %mul {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %result : tensor<1x2x3x4xi32>
 }
 
@@ -141,15 +129,14 @@ func.func @test_mulop_conversion(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x3
 // COM: this case is a reshape we don't convert, since can't fold the transpose into it.
 // COM: a transform actually occurs underneath the hood, but it results in identical IR.
 // CHECK-LABEL: @test_basic_non_broadcasting_reshape
-// CHECK-DAG: %[[VAL_1:.*]] = tosa.const_shape  {value = dense<[1, 3, 2]> : tensor<3xindex>}
-// CHECK-DAG: %[[VAL_2:.*]] = "tosa.const"() <{value = dense<[0, 2, 1]> : tensor<3xi32>}>
-// CHECK: %[[VAL_3:.*]] = tosa.reshape %arg0, %[[VAL_1]] : (tensor<2x3xi32>, !tosa.shape<3>) -> tensor<1x3x2xi32>
-// CHECK: %[[VAL_4:.*]] = tosa.transpose %[[VAL_3]], %[[VAL_2]] : (tensor<1x3x2xi32>, tensor<3xi32>) -> tensor<1x2x3xi32>
+// CHECK: %[[SHAPE:.+]] = tosa.const_shape {value = dense<[1, 3, 2]> : tensor<3xindex>}
+// CHECK: %[[RESHAPED:.+]] = tosa.reshape %arg0, %[[SHAPE]] : (tensor<2x3xi32>, !tosa.shape<3>) -> tensor<1x3x2xi32>
+// CHECK: tosa.transpose %[[RESHAPED]] {perms = array<i32: 0, 2, 1>} : (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
 func.func @test_basic_non_broadcasting_reshape(%arg0: tensor<2x3xi32>) -> tensor<1x2x3xi32> {
   %shape = tosa.const_shape {value = dense<[1, 3, 2]> : tensor<3xindex>} : () -> !tosa.shape<3>
   %perms = "tosa.const"() {value = dense<[0, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
   %1 = tosa.reshape %arg0, %shape : (tensor<2x3xi32>, !tosa.shape<3>) -> tensor<1x3x2xi32>
-  %2 = tosa.transpose %1, %perms : (tensor<1x3x2xi32>, tensor<3xi32>) -> tensor<1x2x3xi32>
+  %2 = tosa.transpose %1 {perms = array<i32: 0, 2, 1>}: (tensor<1x3x2xi32>) -> tensor<1x2x3xi32>
   return %2 : tensor<1x2x3xi32>
 }
 
@@ -163,7 +150,7 @@ func.func @test_dynamic_broadcasting_reshape(%arg0: tensor<?xi32>) -> tensor<1x1
   %shape = tosa.const_shape {value = dense<[1, -1, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
   %perms = "tosa.const"() {value = dense<[0, 2, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
   %1 = tosa.reshape %arg0, %shape : (tensor<?xi32>, !tosa.shape<3>) -> tensor<1x?x1xi32>
-  %2 = tosa.transpose %1, %perms : (tensor<1x?x1xi32>, tensor<3xi32>) -> tensor<1x1x?xi32>
+  %2 = tosa.transpose %1 {perms = array<i32: 0, 2, 1>}: (tensor<1x?x1xi32>) -> tensor<1x1x?xi32>
   return %2 : tensor<1x1x?xi32>
 }
 
@@ -179,10 +166,9 @@ func.func @test_reshape_for_broadcast(%arg0: tensor<4x3x2xi32>) -> tensor<4x3x2x
   %0 = "tosa.const"() {value = dense<[1,2,3,4]> : tensor<4xi32>} : () -> tensor<4xi32>
   %1 = tosa.const_shape {value = dense<[1, 1, 4]> : tensor<3xindex>} : () -> !tosa.shape<3>
   %reshape = tosa.reshape %0, %1 : (tensor<4xi32>, !tosa.shape<3>) -> tensor<1x1x4xi32>
-  %perms0 = "tosa.const"() {value = dense<[2, 1, 0]> : tensor<3xi32>} : () -> tensor<3xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<4x3x2xi32>, tensor<3xi32>) -> tensor<2x3x4xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 2, 1, 0>}: (tensor<4x3x2xi32>) -> tensor<2x3x4xi32>
   %add = tosa.add %transpose0, %reshape : (tensor<2x3x4xi32>, tensor<1x1x4xi32>) -> tensor<2x3x4xi32>
-  %transpose1 = tosa.transpose %add, %perms0 : (tensor<2x3x4xi32>, tensor<3xi32>) -> tensor<4x3x2xi32>
+  %transpose1 = tosa.transpose %add {perms = array<i32: 2, 1, 0>}: (tensor<2x3x4xi32>) -> tensor<4x3x2xi32>
   return %transpose1 : tensor<4x3x2xi32>
 }
 
@@ -223,7 +209,7 @@ func.func @test_resnet18_common_case(%arg0: tensor<64xf32>, %arg1: tensor<64xf32
     %64 = "tosa.const"() <{value = dense<[0, 3, 1, 2]> : tensor<4xi32>}> : () -> tensor<4xi32>
     %69 = "tosa.const"() <{value = dense<9.99999974E-6> : tensor<1xf32>}> : () -> tensor<1xf32>
     %70 = "tosa.const"() <{value = dense<5.000000e-01> : tensor<1xf32>}> : () -> tensor<1xf32>
-    %75 = tosa.transpose %74, %64 : (tensor<1x112x112x64xf32>, tensor<4xi32>) -> tensor<1x64x112x112xf32>
+    %75 = tosa.transpose %74 {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x112x112x64xf32>) -> tensor<1x64x112x112xf32>
     %76 = tosa.add %arg1, %69 : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
     %77 = tosa.pow %76, %70 : (tensor<64xf32>, tensor<1xf32>) -> tensor<64xf32>
     %78 = tosa.reciprocal %77 : (tensor<64xf32>) -> tensor<64xf32>
@@ -236,54 +222,45 @@ func.func @test_resnet18_common_case(%arg0: tensor<64xf32>, %arg1: tensor<64xf32
     %85 = tosa.reshape %59, %58 : (tensor<64xf32>, !tosa.shape<4>) -> tensor<1x64x1x1xf32>
     %86 = tosa.add %84, %85 : (tensor<1x64x112x112xf32>, tensor<1x64x1x1xf32>) -> tensor<1x64x112x112xf32>
     %87 = tosa.clamp %86 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x64x112x112xf32>) -> tensor<1x64x112x112xf32>
-    %88 = tosa.transpose %87, %63 : (tensor<1x64x112x112xf32>, tensor<4xi32>) -> tensor<1x112x112x64xf32>
+    %88 = tosa.transpose %87 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x64x112x112xf32>) -> tensor<1x112x112x64xf32>
     return %88 : tensor<1x112x112x64xf32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_back_to_back_nullifiers
-// CHECK: %[[PERMS:.*]] = "tosa.const"
-// CHECK: %[[RES:.*]] = tosa.transpose %arg0, %[[PERMS]]
+// CHECK: %[[RES:.*]] = tosa.transpose %arg0 {perms = array<i32: 1, 0>}
 // CHECK: return %[[RES]]
 func.func @test_back_to_back_nullifiers(%arg0: tensor<2x3xi32>) -> tensor<3x2xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<2x3xi32>
-  %2 = tosa.transpose %1, %perms : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
+  %2 = tosa.transpose %1 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
   return %2 : tensor<3x2xi32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_back_to_back_nullifiers_different_transposes
-// CHECK: %[[PERMS:.*]] = "tosa.const"
-// CHECK: %[[RES:.*]] = tosa.transpose %arg0, %[[PERMS]]
+// CHECK: %[[RES:.*]] = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}
 // CHECK: return %[[RES]]
 func.func @test_back_to_back_nullifiers_different_transposes(%arg0: tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<2x4x5x3xi32>
-  %1 = tosa.transpose %0, %perms1 : (tensor<2x4x5x3xi32>, tensor<4xi32>) -> tensor<2x3x4x5xi32>
-  %2 = tosa.transpose %1, %perms0 : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<2x4x5x3xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 0, 3, 1, 2>}: (tensor<2x4x5x3xi32>) -> tensor<2x3x4x5xi32>
+  %2 = tosa.transpose %1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<2x3x4x5xi32>) -> tensor<2x4x5x3xi32>
   return %2 : tensor<2x4x5x3xi32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_no_transform_if_outside_fan_in_cone
-// CHECK: tosa.const
 // CHECK: %[[CLAMP_IN:.*]] = tosa.transpose
 // CHECK: %[[RES2:.*]] = tosa.clamp %[[CLAMP_IN]]
-// CHECK: tosa.const
 // CHECK: %[[RES1:.*]] = tosa.transpose
 // CHECK: return %[[RES1]], %[[RES2]]
 func.func @test_no_transform_if_outside_fan_in_cone(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   %clamp = tosa.clamp %0  {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   return %1, %clamp : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
 }
 
@@ -298,29 +275,25 @@ func.func @test_two_different_downstream_converge_to_reshape_same_perms(%arg0: t
   %shape = tosa.const_shape {value = dense<[1, 64, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
   %1 = tosa.reshape %arg0, %shape : (tensor<64xf32>, !tosa.shape<3>) -> tensor<1x64x1xf32>
   %2 = tosa.clamp %1 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x64x1xf32>) -> tensor<1x64x1xf32>
-  %3 = tosa.transpose %1, %0 : (tensor<1x64x1xf32>, tensor<3xi32>) -> tensor<1x1x64xf32>
-  %4 = tosa.transpose %2, %0 : (tensor<1x64x1xf32>, tensor<3xi32>) -> tensor<1x1x64xf32>
+  %3 = tosa.transpose %1 {perms = array<i32: 0, 2, 1>}: (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
+  %4 = tosa.transpose %2 {perms = array<i32: 0, 2, 1>}: (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
   return %3, %4 : tensor<1x1x64xf32>, tensor<1x1x64xf32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_two_different_downstream_converge_to_reshape_different_perms
-// CHECK-DAG: tosa.const
-// CHECK-DAG: tosa.const
 // CHECK-DAG: %[[RESHAPE:.*]] = tosa.reshape
 // CHECK-DAG: %[[CLAMP:.*]] = tosa.clamp %[[RESHAPE]]
 // CHECK-DAG: %[[RET1:.*]] = tosa.transpose
 // CHECK-DAG: %[[RET2:.*]] = tosa.transpose
 // CHECK-DAG: return %[[RET1]], %[[RET2]]
 func.func @test_two_different_downstream_converge_to_reshape_different_perms(%arg0: tensor<64xf32>) -> (tensor<1x1x64xf32>, tensor<64x1x1xf32>) {
-  %0 = "tosa.const"() <{value = dense<[1, 2, 0]> : tensor<3xi32>}> : () -> tensor<3xi32>
-  %1 = "tosa.const"() <{value = dense<[0, 2, 1]> : tensor<3xi32>}> : () -> tensor<3xi32>
   %shape = tosa.const_shape {value = dense<[1, 64, 1]> : tensor<3xindex>} : () -> !tosa.shape<3>
   %2 = tosa.reshape %arg0, %shape : (tensor<64xf32>, !tosa.shape<3>) -> tensor<1x64x1xf32>
   %3 = tosa.clamp %2 {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<1x64x1xf32>) -> tensor<1x64x1xf32>
-  %4 = tosa.transpose %2, %1 : (tensor<1x64x1xf32>, tensor<3xi32>) -> tensor<1x1x64xf32>
-  %5 = tosa.transpose %3, %0 : (tensor<1x64x1xf32>, tensor<3xi32>) -> tensor<64x1x1xf32>
+  %4 = tosa.transpose %2 {perms = array<i32: 0, 2, 1>}: (tensor<1x64x1xf32>) -> tensor<1x1x64xf32>
+  %5 = tosa.transpose %3 {perms = array<i32: 1, 2, 0>}: (tensor<1x64x1xf32>) -> tensor<64x1x1xf32>
   return %4, %5 : tensor<1x1x64xf32>, tensor<64x1x1xf32>
 }
 
@@ -328,16 +301,15 @@ func.func @test_two_different_downstream_converge_to_reshape_different_perms(%ar
 
 // COM: no transform
 // CHECK-LABEL: @test_outside_perms_usage_of_fan_in
-// CHECK: tosa.const
 // CHECK: tosa.transpose
 // CHECK: tosa.clamp
 // CHECK: %[[RES1:.*]] = tosa.transpose
 // CHECK: %[[RES2:.*]] = tosa.add
 // CHECK: return %[[RES1]], %[[RES2]]
-func.func @test_outside_perms_usage_of_fan_in(%arg0: tensor<2x3xf32>, %arg1: tensor<3x2xf32>) -> (tensor<2x3xf32>, tensor<3x2xf32>) {        %0 = "tosa.const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<2xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+func.func @test_outside_perms_usage_of_fan_in(%arg0: tensor<2x3xf32>, %arg1: tensor<3x2xf32>) -> (tensor<2x3xf32>, tensor<3x2xf32>) {
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xf32>) -> tensor<3x2xf32>
   %2 = tosa.clamp %1  {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<3x2xf32>) -> tensor<3x2xf32>
-  %3 = tosa.transpose %2, %0 : (tensor<3x2xf32>, tensor<2xi32>) -> tensor<2x3xf32>
+  %3 = tosa.transpose %2 {perms = array<i32: 1, 0>}: (tensor<3x2xf32>) -> tensor<2x3xf32>
   %4 = tosa.add %arg1, %2 : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
   return %3, %4: tensor<2x3xf32>, tensor<3x2xf32>
 }
@@ -351,13 +323,12 @@ func.func @test_outside_perms_usage_of_fan_in(%arg0: tensor<2x3xf32>, %arg1: ten
 // CHECK-DAG: %[[NEW_ADD:.*]] = tosa.add %arg1, %[[NEW_CLAMP]]
 // CHECK: return %[[NEW_CLAMP]], %[[NEW_ADD]]
 func.func @test_use_present_in_another_valid_perms_fan_in(%arg0: tensor<2x3xf32>, %arg1: tensor<2x3xf32>) -> (tensor<2x3xf32>, tensor<2x3xf32>) {
-  %0 = "tosa.const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<2xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xf32>) -> tensor<3x2xf32>
   %2 = tosa.clamp %1  {max_val = 3.40282347E+38 : f32, min_val = 0.000000e+00 : f32} : (tensor<3x2xf32>) -> tensor<3x2xf32>
-  %3 = tosa.transpose %2, %0 : (tensor<3x2xf32>, tensor<2xi32>) -> tensor<2x3xf32>
-  %4 = tosa.transpose %arg1, %0 : (tensor<2x3xf32>, tensor<2xi32>) -> tensor<3x2xf32>
+  %3 = tosa.transpose %2 {perms = array<i32: 1, 0>}: (tensor<3x2xf32>) -> tensor<2x3xf32>
+  %4 = tosa.transpose %arg1 {perms = array<i32: 1, 0>}: (tensor<2x3xf32>) -> tensor<3x2xf32>
   %5 = tosa.add %4, %2 : (tensor<3x2xf32>, tensor<3x2xf32>) -> tensor<3x2xf32>
-  %6 = tosa.transpose %5, %0 : (tensor<3x2xf32>, tensor<2xi32>) -> tensor<2x3xf32>
+  %6 = tosa.transpose %5 {perms = array<i32: 1, 0>}: (tensor<3x2xf32>) -> tensor<2x3xf32>
   return %3, %6: tensor<2x3xf32>, tensor<2x3xf32>
 }
 
@@ -365,7 +336,6 @@ func.func @test_use_present_in_another_valid_perms_fan_in(%arg0: tensor<2x3xf32>
 
 // COM: no transform, since we would get duplicates
 // CHECK-LABEL: @test_two_same_perms_fan_in_but_one_doesnt_convert_dependents
-// CHECK: tosa.const
 // CHECK: tosa.transpose
 // CHECK: %[[CEIL:.*]] = tosa.ceil
 // CHECK: %[[ADD:.*]] = tosa.add %[[CEIL]]
@@ -373,12 +343,11 @@ func.func @test_use_present_in_another_valid_perms_fan_in(%arg0: tensor<2x3xf32>
 // CHECK: %[[RES2:.*]] = tosa.transpose %[[ADD]]
 // CHECK: return %[[RES1]], %[[RES2]]
 func.func @test_two_same_perms_fan_in_but_one_doesnt_convert_dependents(%arg0: tensor<2x3xi32>, %arg1: tensor<3x2xi32>) -> (tensor<2x3xi32>, tensor<2x3xi32>) {
-  %0 = "tosa.const"() <{value = dense<[1, 0]> : tensor<2xi32>}> : () -> tensor<2xi32>
-  %1 = tosa.transpose %arg0, %0 : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
+  %1 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
   %2 = tosa.ceil %1 : (tensor<3x2xi32>) -> tensor<3x2xi32>
   %3 = tosa.add %2, %arg1 : (tensor<3x2xi32>, tensor<3x2xi32>) -> tensor<3x2xi32>
-  %4 = tosa.transpose %2, %0 : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<2x3xi32>
-  %5 = tosa.transpose %3, %0 : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<2x3xi32>
+  %4 = tosa.transpose %2 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
+  %5 = tosa.transpose %3 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
   return %4, %5 : tensor<2x3xi32>, tensor<2x3xi32>
 }
 
@@ -388,12 +357,10 @@ func.func @test_two_same_perms_fan_in_but_one_doesnt_convert_dependents(%arg0: t
 // CHECK-NEXT: %[[RES:.*]] = tosa.clamp %arg0
 // CHECK-NEXT: return %[[RES]], %[[RES]]
 func.func @test_direct_use_in_other_transpose_with_same_perms(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
-  %2 = tosa.transpose %clamp, %perms1 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+  %2 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   return %1, %2 : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
 }
 
@@ -405,8 +372,7 @@ func.func @test_direct_use_in_other_transpose_with_same_perms(%arg0: tensor<3x3x
 // CHECK: return %[[NEW]]
 func.func @test_const_transpose() -> tensor<2x3xi32> {
   %0 = "tosa.const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<2x3xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<2x3xi32>
   return %1 : tensor<2x3xi32>
 }
 
@@ -420,8 +386,7 @@ func.func @test_const_transpose() -> tensor<2x3xi32> {
 func.func @test_transpose_tracks_to_const_single_step() -> tensor<1x2x3x4xi32> {
   %0 = "tosa.const"() {value = dense<0> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
   %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %1 : tensor<1x2x3x4xi32>
 }
 
@@ -434,13 +399,11 @@ func.func @test_transpose_tracks_to_const_single_step() -> tensor<1x2x3x4xi32> {
 // CHECK: %[[NEW_NOT:.*]] = tosa.bitwise_not %[[NEW_ABS]] : (tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
 // CHECK: return %[[NEW_NOT]]
 func.func @test_static_unary_path_to_const() -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
   %0 = "tosa.const"() {value = dense<1> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
   %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %abs = tosa.abs %clamp : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %bitwise_not = tosa.bitwise_not %abs : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %bitwise_not, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %1 = tosa.transpose %bitwise_not {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %1 : tensor<1x2x3x4xi32>
 }
 
@@ -455,16 +418,14 @@ func.func @test_static_unary_path_to_const() -> tensor<1x2x3x4xi32> {
 // CHECK: %[[NEW_ADD:.*]] = tosa.add %[[NEW_ABS]], %[[NEW_CLAMP]] : (tensor<1x2x3x4xi32>, tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32>
 // CHECK: return %[[NEW_ADD]]
 func.func @test_static_diverges_to_non_splat_const_and_nullifying(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
   %const = "tosa.const"() {value = dense<[[[[1, 2], [3, 4], [5, 6], [7, 8]],
    [[9, 10], [11, 12], [13, 14], [15, 16]],
    [[17, 18], [19, 20], [21, 22], [23, 24]]]]> : tensor<1x3x4x2xi32>} : () -> tensor<1x3x4x2xi32>
   %clamp = tosa.clamp %transpose0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %abs = tosa.abs %const : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %add = tosa.add %abs, %clamp : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms2 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %result = tosa.transpose %add, %perms2 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %result : tensor<1x2x3x4xi32>
 }
 
@@ -474,12 +435,10 @@ func.func @test_static_diverges_to_non_splat_const_and_nullifying(%arg0: tensor<
 // CHECK-NEXT: %[[RES:.*]] = tosa.clamp %arg0
 // CHECK-NEXT: return %[[RES]], %[[RES]]
 func.func @test_multi_downstream_both_nullify(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
-  %2 = tosa.transpose %clamp, %perms1 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+  %2 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   return %1, %2 : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
 }
 
@@ -487,19 +446,15 @@ func.func @test_multi_downstream_both_nullify(%arg0: tensor<3x3x3x3xi32>) -> (te
 
 // COM: we don't perform this transformation intentionally, since we would then get duplicates
 // CHECK-LABEL: @test_multi_downstream_one_nullifies_upstream_other_does_not
-// CHECK: tosa.const
 // CHECK: tosa.transpose
 // CHECK: tosa.clamp
-// CHECK: tosa.const
 // CHECK: tosa.transpose
 // CHECK: tosa.transpose
 func.func @test_multi_downstream_one_nullifies_upstream_other_does_not(%arg0: tensor<3x3x3x3xi32>) -> (tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>) {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   %clamp = tosa.clamp %0 {max_val = 2147483647 : i32, min_val = 0 : i32} : (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
-  %2 = tosa.transpose %clamp, %perms0 : (tensor<3x3x3x3xi32>, tensor<4xi32>) -> tensor<3x3x3x3xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
+  %2 = tosa.transpose %clamp {perms = array<i32: 0, 2, 3, 1>}: (tensor<3x3x3x3xi32>) -> tensor<3x3x3x3xi32>
   return %1, %2 : tensor<3x3x3x3xi32>, tensor<3x3x3x3xi32>
 }
 
@@ -508,9 +463,8 @@ func.func @test_multi_downstream_one_nullifies_upstream_other_does_not(%arg0: te
 // CHECK-LABEL: @test_unknown_dim_inner_replacement_matches
 // CHECK-NEXT: return %arg0
 func.func @test_unknown_dim_inner_replacement_matches(%arg0: tensor<3x2xi32>) -> tensor<3x2xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<?x3xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<?x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x2xi32>) -> tensor<?x3xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<?x3xi32>) -> tensor<3x2xi32>
   return %1 : tensor<3x2xi32>
 }
 
@@ -520,9 +474,8 @@ func.func @test_unknown_dim_inner_replacement_matches(%arg0: tensor<3x2xi32>) ->
 // CHECK-LABEL: @test_unknown_dim_outer_replacement_matches
 // CHECK-NEXT: return %arg0
 func.func @test_unknown_dim_outer_replacement_matches(%arg0: tensor<3x?xi32>) -> tensor<3x?xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<3x?xi32>, tensor<2xi32>) -> tensor<2x3xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x?xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x?xi32>) -> tensor<2x3xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x?xi32>
   return %1 : tensor<3x?xi32>
 }
 
@@ -534,47 +487,28 @@ func.func @test_unknown_dim_outer_replacement_matches(%arg0: tensor<3x?xi32>) ->
 // CHECK-NEXT: %[[ADD:.*]] = tosa.add %[[CLAMP]], %[[ABS]]
 // CHECK-NEXT: return %[[ADD]]
 func.func @test_transpose_tracks_to_nullifying_diverging_binary_unknown_dim_replacements_match(%arg0: tensor<1x?x3x4xi32>, %arg1: tensor<1x2x?x4xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<1x?x3x4xi32>, tensor<4xi32>) -> tensor<?x3x4x?xi32>
-  %transpose1 = tosa.transpose %arg1, %perms0 : (tensor<1x2x?x4xi32>, tensor<4xi32>) -> tensor<1x?x?x2xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x?x3x4xi32>) -> tensor<?x3x4x?xi32>
+  %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x?x4xi32>) -> tensor<1x?x?x2xi32>
   %clamp = tosa.clamp %transpose0 {min_val = 0 : i32, max_val = 1 : i32} : (tensor<?x3x4x?xi32>) -> tensor<?x3x4x?xi32>
   %abs = tosa.abs %transpose1 : (tensor<1x?x?x2xi32>) -> tensor<1x?x?x2xi32>
   %add = tosa.add %clamp, %abs : (tensor<?x3x4x?xi32>, tensor<1x?x?x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %result = tosa.transpose %add, %perms1 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %result : tensor<1x2x3x4xi32>
 }
 
 // -----
 
-// COM: we cannot do anything to the transpose in this case.
-// CHECK-LABEL: @test_unimplemented_non_const_perms
-// CHECK: tosa.const
-// CHECK-NEXT: tosa.transpose
-// CHECK-NEXT: return
-func.func @test_unimplemented_non_const_perms(%perms: tensor<2xi32>) -> tensor<?x?xi32> {
-  %0 = "tosa.const"() {value = dense<0> : tensor<3x2xi32>} : () -> tensor<3x2xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<?x?xi32>
-  return %1 : tensor<?x?xi32>
-}
-
-// -----
-
 // COM: due to tracking back to a non-nullifying transpose, we can't get rid of the transposes entirely.
 // COM: later editions of the pass may wish to fold these into a single transpose.
 // CHECK-LABEL: @test_unimplemented_transpose_tracks_to_non_nullifying_transpose_single_step
-// CHECK: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.clamp
-// CHECK-NEXT: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: return
 func.func @test_unimplemented_transpose_tracks_to_non_nullifying_transpose_single_step(%arg0: tensor<1x2x3x4xi32>) -> tensor<1x2x4x3xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 3, 2, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x4x3x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 3, 2, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x4x3x2xi32>
   %clamp = tosa.clamp %0 {min_val = 0 : i32, max_val = 1 : i32} : (tensor<1x4x3x2xi32>) -> tensor<1x4x3x2xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %1 = tosa.transpose %clamp, %perms1 : (tensor<1x4x3x2xi32>, tensor<4xi32>) -> tensor<1x2x4x3xi32>
+  %1 = tosa.transpose %clamp {perms = array<i32: 0, 3, 1, 2>} : (tensor<1x4x3x2xi32>) -> tensor<1x2x4x3xi32>
   return %1 : tensor<1x2x4x3xi32>
 }
 
@@ -582,28 +516,24 @@ func.func @test_unimplemented_transpose_tracks_to_non_nullifying_transpose_singl
 
 // COM: we don't deal with this case. resolution of shapes required.
 // CHECK-LABEL: @test_unimplemented_unknown_dim_input_nullifying_pair
-// CHECK: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: return
 func.func @test_unimplemented_unknown_dim_input_nullifying_pair(%arg0: tensor<3x?xi32>) -> tensor<3x2xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<3x?xi32>, tensor<2xi32>) -> tensor<2x3xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<2x3xi32>, tensor<2xi32>) -> tensor<3x2xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x?xi32>) -> tensor<2x3xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<2x3xi32>) -> tensor<3x2xi32>
   return %1 : tensor<3x2xi32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_unimplemented_unknown_dim_replacement_does_not_match
-// CHECK: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: return
 func.func @test_unimplemented_unknown_dim_replacement_does_not_match(%arg0: tensor<3x?xi32>) -> tensor<?x?xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<3x?xi32>, tensor<2xi32>) -> tensor<?x3xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<?x3xi32>, tensor<2xi32>) -> tensor<?x?xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<3x?xi32>) -> tensor<?x3xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<?x3xi32>) -> tensor<?x?xi32>
   return %1 : tensor<?x?xi32>
 }
 
@@ -611,53 +541,43 @@ func.func @test_unimplemented_unknown_dim_replacement_does_not_match(%arg0: tens
 
 // COM: this would be able to be converted if --tosa-infer-shapes was run beforehand
 // CHECK-LABEL: @test_unimplemented_unranked_tensors_present
-// CHECK: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: return
 func.func @test_unimplemented_unranked_tensors_present(%arg0: tensor<3x2xi32>) -> tensor<*xi32> {
-  %perms = "tosa.const"() {value = dense<[0, 1]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<3x2xi32>, tensor<2xi32>) -> tensor<*xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<*xi32>, tensor<2xi32>) -> tensor<*xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 0, 1>}: (tensor<3x2xi32>) -> tensor<*xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 0, 1>}: (tensor<*xi32>) -> tensor<*xi32>
   return %1 : tensor<*xi32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_unimplemented_unranked_everything
-// CHECK: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: return
 func.func @test_unimplemented_unranked_everything(%arg0: tensor<*xi32>) -> tensor<*xi32> {
-  %perms = "tosa.const"() {value = dense<[1, 0]> : tensor<2xi32>} : () -> tensor<2xi32>
-  %0 = tosa.transpose %arg0, %perms : (tensor<*xi32>, tensor<2xi32>) -> tensor<*xi32>
-  %1 = tosa.transpose %0, %perms : (tensor<*xi32>, tensor<2xi32>) -> tensor<*xi32>
+  %0 = tosa.transpose %arg0 {perms = array<i32: 1, 0>}: (tensor<*xi32>) -> tensor<*xi32>
+  %1 = tosa.transpose %0 {perms = array<i32: 1, 0>}: (tensor<*xi32>) -> tensor<*xi32>
   return %1 : tensor<*xi32>
 }
 
 // -----
 
 // CHECK-LABEL: @test_unimplemented_static_diverges_to_one_nullifying_one_non_nullifying
-// CHECK: tosa.const
-// CHECK-NEXT: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: tosa.clamp
 // CHECK-NEXT: tosa.abs
 // CHECK-NEXT: tosa.add
-// CHECK-NEXT: tosa.const
 // CHECK-NEXT: tosa.transpose
 // CHECK-NEXT: return
 func.func @test_unimplemented_static_diverges_to_one_nullifying_one_non_nullifying(%arg0: tensor<1x2x3x4xi32>, %arg1: tensor<1x2x4x3xi32>) -> tensor<1x2x3x4xi32> {
-  %perms0 = "tosa.const"() {value = dense<[0, 2, 3, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %perms1 = "tosa.const"() {value = dense<[0, 3, 2, 1]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %transpose0 = tosa.transpose %arg0, %perms0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
-  %transpose1 = tosa.transpose %arg1, %perms1 : (tensor<1x2x4x3xi32>, tensor<4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose0 = tosa.transpose %arg0 {perms = array<i32: 0, 2, 3, 1>}: (tensor<1x2x3x4xi32>) -> tensor<1x3x4x2xi32>
+  %transpose1 = tosa.transpose %arg1 {perms = array<i32: 0, 3, 2, 1>}: (tensor<1x2x4x3xi32>) -> tensor<1x3x4x2xi32>
   %clamp = tosa.clamp %transpose0 {min_val = 0 : i32, max_val = 1 : i32} : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %abs = tosa.abs %transpose1 : (tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
   %add = tosa.add %clamp, %abs : (tensor<1x3x4x2xi32>, tensor<1x3x4x2xi32>) -> tensor<1x3x4x2xi32>
-  %perms2 = "tosa.const"() {value = dense<[0, 3, 1, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-  %result = tosa.transpose %add, %perms2 : (tensor<1x3x4x2xi32>, tensor<4xi32>) -> tensor<1x2x3x4xi32>
+  %result = tosa.transpose %add {perms = array<i32: 0, 3, 1, 2>}: (tensor<1x3x4x2xi32>) -> tensor<1x2x3x4xi32>
   return %result : tensor<1x2x3x4xi32>
 }
diff --git a/mlir/test/Dialect/Tosa/transpose-fold.mlir b/mlir/test/Dialect/Tosa/transpose-fold.mlir
index a7e9982c8db70..d29cb54eb2117 100644
--- a/mlir/test/Dialect/Tosa/transpose-fold.mlir
+++ b/mlir/test/Dialect/Tosa/transpose-fold.mlir
@@ -6,10 +6,8 @@
 // CHECK:         }
 
 func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) {
-	%0 = "tosa.const"() {value = dense<[1, 2, 0]> : tensor<3xi32>} : () -> tensor<3xi32>
-	%1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>) -> tensor<2x3x1xi32>
-	%2 = "tosa.const"() {value = dense<[2, 0, 1]> : tensor<3xi32>} : () -> tensor<3xi32>
-	%3 = tosa.transpose %1, %2 : (tensor<2x3x1xi32>, tensor<3xi32>) -> tensor<1x2x3xi32>
+	%1 = tosa.transpose %arg0 { perms = array<i32: 1, 2, 0> }: (tensor<1x2x3xi32>) -> tensor<2x3x1xi32>
+	%3 = tosa.transpose %1 { perms = array<i32: 2, 0, 1> }: (tensor<2x3x1xi32>) -> tensor<1x2x3xi32>
   return %3 : tensor<1x2x3xi32>
 }
 
@@ -21,8 +19,7 @@ func.func @test_cancel_transpose_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<
 // CHECK:         }
 
 func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1x2x3xi32>) {
-	%0 = "tosa.const"() {value = dense<[0, 1, 2]> : tensor<3xi32>} : () -> tensor<3xi32>
-	%1 = tosa.transpose %arg0, %0 : (tensor<1x2x3xi32>, tensor<3xi32>) -> tensor<1x2x3xi32>
+	%1 = tosa.transpose %arg0 { perms = array<i32: 0, 1, 2> }: (tensor<1x2x3xi32>) -> tensor<1x2x3xi32>
   return %1 : tensor<1x2x3xi32>
 }
 
@@ -30,16 +27,13 @@ func.func @test_remove_identity_transpose(%arg0: tensor<1x2x3xi32>) -> (tensor<1
 
 // CHECK-LABEL:   func.func @test_do_not_cancel_different_transpose(
 // CHECK-SAME:                                                      %[[VAL_0:.*]]: tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32> {
-// CHECK:           %[[VAL_1:.*]] = "tosa.const"() <{value = dense<[3, 2, 1, 0]> : tensor<4xi32>}> : () -> tensor<4xi32>
-// CHECK:           %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_1]] : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<5x4x3x2xi32>
+// CHECK:           %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]] {perms = array<i32: 3, 2, 1, 0>} : (tensor<2x3x4x5xi32>) -> tensor<5x4x3x2xi32>
 // CHECK:           return %[[VAL_2]] : tensor<5x4x3x2xi32>
 // CHECK:         }
 
 func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) -> (tensor<5x4x3x2xi32>) {
-	%0 = "tosa.const"() {value = dense<[1, 2, 0, 3]> : tensor<4xi32>} : () -> tensor<4xi32>
-	%1 = tosa.transpose %arg0, %0 : (tensor<2x3x4x5xi32>, tensor<4xi32>) -> tensor<3x4x2x5xi32>
-	%2 = "tosa.const"() {value = dense<[3, 1, 0, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-	%3 = tosa.transpose %1, %2 : (tensor<3x4x2x5xi32>, tensor<4xi32>) -> tensor<5x4x3x2xi32>
+	%1 = tosa.transpose %arg0 { perms = array<i32: 1, 2, 0, 3> }: (tensor<2x3x4x5xi32>) -> tensor<3x4x2x5xi32>
+	%3 = tosa.transpose %1 { perms = array<i32: 3, 1, 0, 2> }: (tensor<3x4x2x5xi32>) -> tensor<5x4x3x2xi32>
   return %3 : tensor<5x4x3x2xi32>
 }
 
@@ -47,15 +41,12 @@ func.func @test_do_not_cancel_different_transpose(%arg0: tensor<2x3x4x5xi32>) ->
 
 // CHECK-LABEL:   func.func @test_prefer_compose_transpose(
 // CHECK-SAME:                                                      %[[VAL_0:.*]]: tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32> {
-// CHECK:           %[[VAL_1:.*]] = "tosa.const"() <{value = dense<[3, 2, 1, 0]> : tensor<4xi32>}> : () -> tensor<4xi32>
-// CHECK:           %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]], %[[VAL_1]] : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<4x3x2x1xi32>
+// CHECK:           %[[VAL_2:.*]] = tosa.transpose %[[VAL_0]] {perms = array<i32: 3, 2, 1, 0>} : (tensor<1x2x3x4xi32>) -> tensor<4x3x2x1xi32>
 // CHECK:           return %[[VAL_2]] : tensor<4x3x2x1xi32>
 // CHECK:         }
 
 func.func @test_prefer_compose_transpose(%arg0: tensor<1x2x3x4xi32>) -> (tensor<4x3x2x1xi32>) {
-	%0 = "tosa.const"() {value = dense<[1, 2, 0, 3]> : tensor<4xi32>} : () -> tensor<4xi32>
-	%1 = tosa.transpose %arg0, %0 : (tensor<1x2x3x4xi32>, tensor<4xi32>) -> tensor<2x3x1x4xi32>
-	%2 = "tosa.const"() {value = dense<[3, 1, 0, 2]> : tensor<4xi32>} : () -> tensor<4xi32>
-	%3 = tosa.transpose %1, %2 : (tensor<2x3x1x4xi32>, tensor<4xi32>) -> tensor<4x3x2x1xi32>
+	%1 = tosa.transpose %arg0 { perms = array<i32: 1, 2, 0, 3> }: (tensor<1x2x3x4xi32>) -> tensor<2x3x1x4xi32>
+	%3 = tosa.transpose %1 { perms = array<i32: 3, 1, 0, 2> }: (tensor<2x3x1x4xi32>) -> tensor<4x3x2x1xi32>
   return %3 : tensor<4x3x2x1xi32>
 }