diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaOpBase.td
@@ -115,7 +115,8 @@
 // bitwidth of the output given the bit width of the input & weight content.
 def Tosa_ConvOpQuantInfoBuilder : OpBuilder<
   (ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias,
-       "ArrayAttr":$pad, "ArrayAttr":$stride, "ArrayAttr":$dilation),
+       "DimensionListAttr":$pad, "DimensionListAttr":$stride,
+       "DimensionListAttr":$dilation),
   [{
     buildConvOpWithQuantInfo($_builder, $_state, outputType,
                              input, weight, bias,
@@ -125,8 +126,8 @@
 // Handles tosa.transpose_conv2d which has an outpad and output shape attribute.
 def Tosa_TransConvOpQuantInfoBuilder : OpBuilder<
   (ins "Type":$outputType, "Value":$input, "Value":$weight, "Value":$bias,
-       "ArrayAttr":$outpad, "ArrayAttr":$stride, "ArrayAttr":$dilation,
-       "ArrayAttr":$outputShape),
+       "DimensionListAttr":$outpad, "DimensionListAttr":$stride,
+       "DimensionListAttr":$dilation, "DimensionListAttr":$outputShape),
   [{
     buildTransConvOpWithQuantInfo($_builder, $_state, outputType,
                                   input, weight, bias,
@@ -158,8 +159,8 @@
 // UnaruOpQuantizationAttr but the avg_pool operator has its own builder as it
 // has additional parameters not part of the unary ops.
 def Tosa_AvgPool2dOpQuantInfoBuilder : OpBuilder<
-  (ins "Type":$outputType, "Value":$input, "ArrayAttr":$kernel,
-       "ArrayAttr":$stride, "ArrayAttr":$pad),
+  (ins "Type":$outputType, "Value":$input, "DimensionListAttr":$kernel,
+       "DimensionListAttr":$stride, "DimensionListAttr":$pad),
   [{
     buildAvgPool2dOpWithQuantInfo($_builder, $_state, outputType,
                                   input, kernel, stride, pad);
diff --git a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
--- a/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
+++ b/mlir/include/mlir/Dialect/Tosa/IR/TosaTypesBase.td
@@ -14,6 +14,8 @@
 #define TOSA_TYPES_BASE
 
 include "mlir/IR/OpBase.td"
+include "mlir/IR/BuiltinAttributes.td"
+
 
 //===----------------------------------------------------------------------===//
 // Tosa Type Definitions.
@@ -162,9 +164,6 @@
 //===----------------------------------------------------------------------===//
 // Attribute predicates and classes.
 //===----------------------------------------------------------------------===//
-class ArrayMaxCt<int n> : AttrConstraint<
-    CPred<"$_self.cast<::mlir::ArrayAttr>().size() <= " # n>,
-    "with at least " # n # " elements">;
 
 def Tosa_Fp32ArrayAttr2 : Confined<F32ArrayAttr, [ArrayCount<2>]>;
 def Tosa_Fp32ArrayAttr3 : Confined<F32ArrayAttr, [ArrayCount<3>]>;
@@ -172,15 +171,15 @@
 def Tosa_Fp32ArrayAttr5 : Confined<F32ArrayAttr, [ArrayCount<5>]>;
 def Tosa_Fp32ArrayAttr6 : Confined<F32ArrayAttr, [ArrayCount<6>]>;
 
-def Tosa_IntArrayAttr2 : Confined<I64ArrayAttr, [ArrayCount<2>]>;
-def Tosa_IntArrayAttr3 : Confined<I64ArrayAttr, [ArrayCount<3>]>;
-def Tosa_IntArrayAttr4 : Confined<I64ArrayAttr, [ArrayCount<4>]>;
-def Tosa_IntArrayAttr5 : Confined<I64ArrayAttr, [ArrayCount<5>]>;
-def Tosa_IntArrayAttr6 : Confined<I64ArrayAttr, [ArrayCount<6>]>;
+def Tosa_IntArrayAttr2 : DimensionListAttrOfSize<2>;
+def Tosa_IntArrayAttr3 : DimensionListAttrOfSize<3>;
+def Tosa_IntArrayAttr4 : DimensionListAttrOfSize<4>;
+def Tosa_IntArrayAttr5 : DimensionListAttrOfSize<5>;
+def Tosa_IntArrayAttr6 : DimensionListAttrOfSize<6>;
 
-def Tosa_IntArrayAttrUpto2 : Confined<I64ArrayAttr, [ArrayMaxCt<2>]>;
-def Tosa_IntArrayAttrUpto4 : Confined<I64ArrayAttr, [ArrayMaxCt<4>]>;
-def Tosa_IntArrayAttrUpto5 : Confined<I64ArrayAttr, [ArrayMaxCt<5>]>;
+def Tosa_IntArrayAttrUpto2 : DimensionListAttrOfMaxSize<2>;
+def Tosa_IntArrayAttrUpto4 : DimensionListAttrOfMaxSize<4>;
+def Tosa_IntArrayAttrUpto5 : DimensionListAttrOfMaxSize<5>;
 
 //===----------------------------------------------------------------------===//
 // Iterable attributes.
diff --git a/mlir/include/mlir/IR/AttributeSupport.h b/mlir/include/mlir/IR/AttributeSupport.h
--- a/mlir/include/mlir/IR/AttributeSupport.h
+++ b/mlir/include/mlir/IR/AttributeSupport.h
@@ -254,17 +254,17 @@
 namespace AttributeTrait {
 
 /// This trait provides an "ArrayRef-like" behavior by defining all the methods
-/// from ArrayRef. This expects the attribute to define an `arr()` accessor for
-/// the underlying ArrayRef.
+/// from ArrayRef. This expects the attribute to define an `getArray()` accessor
+/// for the underlying ArrayRef.
 template <typename T>
 struct ArrayRefAttr {
   template <typename ConcreteType>
   class Impl {
-    ConcreteType *crtp() { return static_cast<ConcreteType *>(this)->arr(); }
+    ArrayRef<T> crtp() const {
+      return static_cast<const ConcreteType *>(this)->getArray();
+    }
 
   public:
-    //  using T = typename
-    //  decltype(std::declval<ConcreteType>().arr())::value_type;
     using value_type = T;
     using pointer = value_type *;
     using const_pointer = const value_type *;
@@ -279,41 +279,41 @@
 
     operator ArrayRef<T>() { return crtp(); }
 
-    iterator begin() const { return crtp()->begin(); }
-    iterator end() const { return crtp()->end(); }
-    reverse_iterator rbegin() const { return crtp()->rbegin(); }
-    reverse_iterator rend() const { return crtp()->rend(); }
-    bool empty() const { return crtp()->empty(); }
-    const T *data() const { return crtp()->data(); }
-    size_t size() const { return crtp()->size(); }
-    const T &front() const { return crtp()->front(); }
-    const T &back() const { return crtp()->back(); }
+    iterator begin() const { return crtp().begin(); }
+    iterator end() const { return crtp().end(); }
+    reverse_iterator rbegin() const { return crtp().rbegin(); }
+    reverse_iterator rend() const { return crtp().rend(); }
+    bool empty() const { return crtp().empty(); }
+    const T *data() const { return crtp().data(); }
+    size_t size() const { return crtp().size(); }
+    const T &front() const { return crtp().front(); }
+    const T &back() const { return crtp().back(); }
     template <typename Allocator>
     ArrayRef<T> copy(Allocator &A) {
-      return crtp()->copy(A);
+      return crtp().copy(A);
     }
-    bool equals(ArrayRefAttr RHS) const { return crtp()->equals(RHS->crtp()); }
-    ArrayRef<T> slice(size_t N, size_t M) const { return crtp()->slice(N, M); }
-    ArrayRef<T> slice(size_t N) const { return crtp()->slice(N); }
-    ArrayRef<T> drop_front(size_t N = 1) const { return crtp()->drop_front(N); }
-    ArrayRef<T> drop_back(size_t N = 1) const { return crtp()->drop_back(N); }
+    bool equals(ArrayRefAttr RHS) const { return crtp().equals(RHS->crtp()); }
+    ArrayRef<T> slice(size_t N, size_t M) const { return crtp().slice(N, M); }
+    ArrayRef<T> slice(size_t N) const { return crtp().slice(N); }
+    ArrayRef<T> drop_front(size_t N = 1) const { return crtp().drop_front(N); }
+    ArrayRef<T> drop_back(size_t N = 1) const { return crtp().drop_back(N); }
     template <class PredicateT>
     ArrayRef<T> drop_while(PredicateT Pred) const {
-      return crtp()->drop_while(Pred);
+      return crtp().drop_while(Pred);
     }
     template <class PredicateT>
     ArrayRef<T> drop_until(PredicateT Pred) const {
-      return crtp()->drop_until(Pred);
+      return crtp().drop_until(Pred);
     }
-    ArrayRef<T> take_front(size_t N = 1) const { return crtp()->take_front(N); }
-    ArrayRef<T> take_back(size_t N = 1) const { return crtp()->take_back(N); }
+    ArrayRef<T> take_front(size_t N = 1) const { return crtp().take_front(N); }
+    ArrayRef<T> take_back(size_t N = 1) const { return crtp().take_back(N); }
     template <class PredicateT>
     ArrayRef<T> take_while(PredicateT Pred) const {
-      return crtp()->take_while(Pred);
+      return crtp().take_while(Pred);
     }
     template <class PredicateT>
     ArrayRef<T> take_until(PredicateT Pred) const {
-      return crtp()->take_until(Pred);
+      return crtp().take_until(Pred);
     }
     const T &operator[](size_t Index) const { return (*crtp())[Index]; }
   };
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -1466,9 +1466,8 @@
         yOffset = rewriter.create<arith::ConstantOp>(loc, op.offset_fp()[0]);
         xOffset = rewriter.create<arith::ConstantOp>(loc, op.offset_fp()[1]);
       } else {
-        SmallVector<int32_t> stride, offset;
-        getValuesFromIntArrayAttribute(op.stride(), stride);
-        getValuesFromIntArrayAttribute(op.offset(), offset);
+        ArrayRef<int64_t> stride = op.stride();
+        ArrayRef<int64_t> offset = op.offset();
 
         yStride = rewriter.create<arith::ConstantOp>(
             loc, rewriter.getI32IntegerAttr(stride[0]));
@@ -1649,28 +1648,27 @@
           dy = rewriter.create<arith::ExtSIOp>(loc, resultElementTy, dy);
         }
 
-          auto unitVal = rewriter.create<arith::ConstantOp>(
-              loc, rewriter.getIntegerAttr(resultElementTy, 1 << shift));
-          Value rightPart = dx;
-          Value leftPart = rewriter.create<arith::SubIOp>(loc, unitVal, dx);
+        auto unitVal = rewriter.create<arith::ConstantOp>(
+            loc, rewriter.getIntegerAttr(resultElementTy, 1 << shift));
+        Value rightPart = dx;
+        Value leftPart = rewriter.create<arith::SubIOp>(loc, unitVal, dx);
 
-          y0x0 = rewriter.create<arith::MulIOp>(loc, y0x0, leftPart);
-          y0x1 = rewriter.create<arith::MulIOp>(loc, y0x1, rightPart);
-          Value topAcc = rewriter.create<arith::AddIOp>(loc, y0x0, y0x1);
+        y0x0 = rewriter.create<arith::MulIOp>(loc, y0x0, leftPart);
+        y0x1 = rewriter.create<arith::MulIOp>(loc, y0x1, rightPart);
+        Value topAcc = rewriter.create<arith::AddIOp>(loc, y0x0, y0x1);
 
-          y1x0 = rewriter.create<arith::MulIOp>(loc, y1x0, leftPart);
-          y1x1 = rewriter.create<arith::MulIOp>(loc, y1x1, rightPart);
-          Value bottomAcc = rewriter.create<arith::AddIOp>(loc, y1x0, y1x1);
+        y1x0 = rewriter.create<arith::MulIOp>(loc, y1x0, leftPart);
+        y1x1 = rewriter.create<arith::MulIOp>(loc, y1x1, rightPart);
+        Value bottomAcc = rewriter.create<arith::AddIOp>(loc, y1x0, y1x1);
 
-          Value bottomPart = dy;
-          Value topPart = rewriter.create<arith::SubIOp>(loc, unitVal, dy);
-          topAcc = rewriter.create<arith::MulIOp>(loc, topAcc, topPart);
-          bottomAcc =
-              rewriter.create<arith::MulIOp>(loc, bottomAcc, bottomPart);
-          Value result = rewriter.create<arith::AddIOp>(loc, topAcc, bottomAcc);
+        Value bottomPart = dy;
+        Value topPart = rewriter.create<arith::SubIOp>(loc, unitVal, dy);
+        topAcc = rewriter.create<arith::MulIOp>(loc, topAcc, topPart);
+        bottomAcc = rewriter.create<arith::MulIOp>(loc, bottomAcc, bottomPart);
+        Value result = rewriter.create<arith::AddIOp>(loc, topAcc, bottomAcc);
 
-          rewriter.create<linalg::YieldOp>(loc, result);
-          return success();
+        rewriter.create<linalg::YieldOp>(loc, result);
+        return success();
       }
 
       return failure();
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalgNamed.cpp
@@ -111,11 +111,6 @@
     Type inputETy = inputTy.getElementType();
     Type resultETy = resultTy.getElementType();
 
-    auto padAttr = op->getAttr("pad").cast<ArrayAttr>();
-    auto strideTosaAttr = op->getAttr("stride").cast<ArrayAttr>();
-    auto dilationTosaAttr = op->getAttr("dilation").cast<ArrayAttr>();
-    bool isQuantized = op->hasAttr("quantization_info");
-
     if (!inputTy.hasStaticShape() || !weightTy.hasStaticShape() ||
         !biasTy.hasStaticShape() || !resultTy.hasStaticShape())
       return rewriter.notifyMatchFailure(op,
@@ -129,6 +124,7 @@
 
     // Apply padding as necessary.
     Attribute zeroAttr = rewriter.getZeroAttr(inputETy);
+    bool isQuantized = op->hasAttr("quantization_info");
     if (isQuantized) {
       auto quantizationInfo =
           op->getAttr("quantization_info").cast<tosa::ConvOpQuantizationAttr>();
@@ -147,10 +143,10 @@
 
       zeroAttr = rewriter.getIntegerAttr(inputETy, iZp);
     }
-
     llvm::SmallVector<int64_t> pad;
     pad.resize(2, 0);
-    getValuesFromIntArrayAttribute(padAttr, pad);
+    ArrayRef<int64_t> originalPad = op.pad();
+    pad.append(originalPad.begin(), originalPad.end());
     pad.resize(pad.size() + 2, 0);
     input = applyPad(loc, input, pad, zeroAttr, rewriter);
 
@@ -178,9 +174,8 @@
         rewriter.create<linalg::FillOp>(loc, zero, initTensor).getResult(0);
 
     // Extract the attributes for convolution.
-    llvm::SmallVector<int64_t> stride, dilation;
-    getValuesFromIntArrayAttribute(strideTosaAttr, stride);
-    getValuesFromIntArrayAttribute(dilationTosaAttr, dilation);
+    ArrayRef<int64_t> stride = op.stride();
+    ArrayRef<int64_t> dilation = op.dilation();
 
     // Create the convolution op.
     auto strideAttr = DenseIntElementsAttr::get(
@@ -276,10 +271,6 @@
     Type inputETy = inputTy.getElementType();
     Type resultETy = resultTy.getElementType();
 
-    auto padAttr = op->getAttr("pad").cast<ArrayAttr>();
-    auto strideTosaAttr = op->getAttr("stride").cast<ArrayAttr>();
-    auto dilationTosaAttr = op->getAttr("dilation").cast<ArrayAttr>();
-
     bool isQuantized = op->hasAttr("quantization_info");
     IntegerAttr iZp;
     IntegerAttr kZp;
@@ -324,15 +315,15 @@
 
     llvm::SmallVector<int64_t> pad;
     pad.resize(2, 0);
-    getValuesFromIntArrayAttribute(padAttr, pad);
+    ArrayRef<int64_t> originalPad = op.pad();
+    pad.append(originalPad.begin(), originalPad.end());
     pad.resize(pad.size() + 2, 0);
 
     input = applyPad(loc, input, pad, zeroAttr, rewriter);
 
     // Extract the attributes for convolution.
-    llvm::SmallVector<int64_t> stride, dilation;
-    getValuesFromIntArrayAttribute(strideTosaAttr, stride);
-    getValuesFromIntArrayAttribute(dilationTosaAttr, dilation);
+    ArrayRef<int64_t> stride = op.stride();
+    ArrayRef<int64_t> dilation = op.dilation();
 
     // Create the convolution op.
     auto strideAttr = DenseIntElementsAttr::get(
@@ -634,15 +625,15 @@
     // Apply padding as necessary.
     llvm::SmallVector<int64_t> pad;
     pad.resize(2, 0);
-    getValuesFromIntArrayAttribute(op.pad(), pad);
+    ArrayRef<int64_t> originalPad = op.pad();
+    pad.append(originalPad.begin(), originalPad.end());
     pad.resize(pad.size() + 2, 0);
     Value paddedInput = applyPad(loc, input, pad, initialAttr, rewriter);
 
     Value initialValue = rewriter.create<arith::ConstantOp>(loc, initialAttr);
 
-    SmallVector<int64_t> kernel, stride;
-    getValuesFromIntArrayAttribute(op.kernel(), kernel);
-    getValuesFromIntArrayAttribute(op.stride(), stride);
+    ArrayRef<int64_t> kernel = op.kernel();
+    ArrayRef<int64_t> stride = op.stride();
 
     Attribute strideAttr = rewriter.getI64VectorAttr(stride);
     Attribute dilationAttr = rewriter.getI64VectorAttr({1, 1});
@@ -688,7 +679,8 @@
     // Apply padding as necessary.
     llvm::SmallVector<int64_t> pad;
     pad.resize(2, 0);
-    getValuesFromIntArrayAttribute(op.pad(), pad);
+    ArrayRef<int64_t> originalPad = op.pad();
+    pad.append(originalPad.begin(), originalPad.end());
     pad.resize(pad.size() + 2, 0);
     Attribute padAttr = rewriter.getZeroAttr(inElementTy);
     Value paddedInput = applyPad(loc, input, pad, padAttr, rewriter);
@@ -696,9 +688,8 @@
     Attribute initialAttr = rewriter.getZeroAttr(accETy);
     Value initialValue = rewriter.create<arith::ConstantOp>(loc, initialAttr);
 
-    SmallVector<int64_t> kernel, stride;
-    getValuesFromIntArrayAttribute(op.kernel(), kernel);
-    getValuesFromIntArrayAttribute(op.stride(), stride);
+    ArrayRef<int64_t> kernel = op.kernel();
+    ArrayRef<int64_t> stride = op.stride();
 
     Attribute strideAttr = rewriter.getI64VectorAttr(stride);
     Attribute dilationAttr = rewriter.getI64VectorAttr({1, 1});
diff --git a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
--- a/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
+++ b/mlir/lib/Dialect/Tosa/IR/TosaOps.cpp
@@ -627,8 +627,9 @@
 /// bitwidth of the output given the bit width of the input & weight content.
 static void buildConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
                                      Type outputType, Value input, Value weight,
-                                     Value bias, ArrayAttr pad,
-                                     ArrayAttr stride, ArrayAttr dilation) {
+                                     Value bias, DimensionListAttr pad,
+                                     DimensionListAttr stride,
+                                     DimensionListAttr dilation) {
 
   result.addOperands({input, weight, bias});
   result.addAttribute("pad", pad);
@@ -646,11 +647,11 @@
 }
 
 /// Handles tosa.transpose_conv2d which has outpad and output shape attributes.
-static void
-buildTransConvOpWithQuantInfo(OpBuilder &builder, OperationState &result,
-                              Type outputType, Value input, Value weight,
-                              Value bias, ArrayAttr outpad, ArrayAttr stride,
-                              ArrayAttr dilation, ArrayAttr outputShape) {
+static void buildTransConvOpWithQuantInfo(
+    OpBuilder &builder, OperationState &result, Type outputType, Value input,
+    Value weight, Value bias, DimensionListAttr outpad,
+    DimensionListAttr stride, DimensionListAttr dilation,
+    DimensionListAttr outputShape) {
   result.addOperands({input, weight, bias});
   result.addAttribute("out_pad", outpad);
   result.addAttribute("stride", stride);
@@ -724,11 +725,9 @@
 /// Both the tosa.avg_pool2d and unary ops use the same UnaruOpQuantizationAttr
 /// but avg_pool operator has its own builder as it has additional parameters
 /// not part of the unary ops.
-static void buildAvgPool2dOpWithQuantInfo(OpBuilder &builder,
-                                          OperationState &result,
-                                          Type outputType, Value input,
-                                          ArrayAttr kernel, ArrayAttr stride,
-                                          ArrayAttr pad) {
+static void buildAvgPool2dOpWithQuantInfo(
+    OpBuilder &builder, OperationState &result, Type outputType, Value input,
+    DimensionListAttr kernel, DimensionListAttr stride, DimensionListAttr pad) {
   result.addOperands(input);
   result.addAttribute("kernel", kernel);
   result.addAttribute("stride", stride);
@@ -1207,18 +1206,15 @@
   }
 
   int32_t shift = adaptor.shift().getValue().getSExtValue();
-  llvm::SmallVector<int64_t> newShape;
-  getI64Values(adaptor.output_size(), newShape);
-  outputShape[1] = newShape[0];
-  outputShape[2] = newShape[1];
+  ArrayRef<int64_t> outputSize = adaptor.output_size();
+  outputShape[1] = outputSize[0];
+  outputShape[2] = outputSize[1];
 
-  llvm::SmallVector<int64_t> strideInt;
-  llvm::SmallVector<int64_t> offsetInt;
+  ArrayRef<int64_t> strideInt = adaptor.stride();
+  ArrayRef<int64_t> offsetInt = adaptor.offset();
   llvm::SmallVector<double> strideFp;
   llvm::SmallVector<double> offsetFp;
-  getI64Values(adaptor.offset(), offsetInt);
   getF64Values(adaptor.offset_fp(), offsetFp);
-  getI64Values(adaptor.stride(), strideInt);
   getF64Values(adaptor.stride_fp(), strideFp);
 
   // If we have a 0 zero in integers we know that the resize indexing needs to
@@ -1442,13 +1438,9 @@
   int32_t height = inputShape.getDimSize(1);
   int32_t width = inputShape.getDimSize(2);
 
-  llvm::SmallVector<int64_t> kernel;
-  llvm::SmallVector<int64_t> stride;
-  llvm::SmallVector<int64_t> pad;
-
-  getI64Values(attributes.get("kernel").cast<ArrayAttr>(), kernel);
-  getI64Values(attributes.get("stride").cast<ArrayAttr>(), stride);
-  getI64Values(attributes.get("pad").cast<ArrayAttr>(), pad);
+  ArrayRef<int64_t> kernel = attributes.get("kernel").cast<DimensionListAttr>();
+  ArrayRef<int64_t> stride = attributes.get("stride").cast<DimensionListAttr>();
+  ArrayRef<int64_t> pad = attributes.get("pad").cast<DimensionListAttr>();
 
   if (height != -1) {
     int32_t padded = height + pad[0] + pad[1] - kernel[0];
@@ -1501,13 +1493,9 @@
                          : outputShape[3];
   }
 
-  llvm::SmallVector<int64_t> dilation;
-  llvm::SmallVector<int64_t> padding;
-  llvm::SmallVector<int64_t> stride;
-
-  getI64Values(adaptor.dilation(), dilation);
-  getI64Values(adaptor.pad(), padding);
-  getI64Values(adaptor.stride(), stride);
+  ArrayRef<int64_t> dilation = adaptor.dilation();
+  ArrayRef<int64_t> padding = adaptor.pad();
+  ArrayRef<int64_t> stride = adaptor.stride();
 
   if (!ShapedType::isDynamic(inputHeight) &&
       !ShapedType::isDynamic(weightHeight)) {
@@ -1569,13 +1557,9 @@
         (outputShape[4] == -1) ? biasShape.getDimSize(0) : outputShape[4];
   }
 
-  llvm::SmallVector<int64_t> dilation;
-  llvm::SmallVector<int64_t> padding;
-  llvm::SmallVector<int64_t> stride;
-
-  getI64Values(adaptor.dilation(), dilation);
-  getI64Values(adaptor.pad(), padding);
-  getI64Values(adaptor.stride(), stride);
+  ArrayRef<int64_t> dilation = adaptor.dilation();
+  ArrayRef<int64_t> padding = adaptor.pad();
+  ArrayRef<int64_t> stride = adaptor.stride();
 
   if (!ShapedType::isDynamic(inputHeight) &&
       !ShapedType::isDynamic(weightHeight)) {
@@ -1669,13 +1653,9 @@
                          : outputShape[3];
   }
 
-  llvm::SmallVector<int64_t> dilation;
-  llvm::SmallVector<int64_t> padding;
-  llvm::SmallVector<int64_t> stride;
-
-  getI64Values(adaptor.dilation(), dilation);
-  getI64Values(adaptor.pad(), padding);
-  getI64Values(adaptor.stride(), stride);
+  ArrayRef<int64_t> dilation = adaptor.dilation();
+  ArrayRef<int64_t> padding = adaptor.pad();
+  ArrayRef<int64_t> stride = adaptor.stride();
 
   if (!ShapedType::isDynamic(inputHeight) &&
       !ShapedType::isDynamic(weightHeight)) {
@@ -1702,8 +1682,7 @@
     ValueShapeRange operands, DictionaryAttr attributes, RegionRange regions,
     SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
   TransposeConv2DOp::Adaptor adaptor(operands.getValues(), attributes);
-  llvm::SmallVector<int64_t> outputShape;
-  getI64Values(adaptor.out_shape(), outputShape);
+  SmallVector<int64_t> outputShape = llvm::to_vector(adaptor.out_shape());
 
   int32_t inputWidth = ShapedType::kDynamicSize;
   int32_t inputHeight = ShapedType::kDynamicSize;
@@ -1738,13 +1717,9 @@
                          : outputShape[3];
   }
 
-  llvm::SmallVector<int64_t> dilation;
-  llvm::SmallVector<int64_t> padding;
-  llvm::SmallVector<int64_t> stride;
-
-  getI64Values(adaptor.dilation(), dilation);
-  getI64Values(adaptor.out_pad(), padding);
-  getI64Values(adaptor.stride(), stride);
+  ArrayRef<int64_t> dilation = adaptor.dilation();
+  ArrayRef<int64_t> padding = adaptor.out_pad();
+  ArrayRef<int64_t> stride = adaptor.stride();
 
   if (!ShapedType::isDynamic(inputHeight) &&
       !ShapedType::isDynamic(weightHeight)) {
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeTransposeConv.cpp
@@ -24,14 +24,6 @@
 
 namespace {
 
-template <typename T>
-static void getValuesFromIntArrayAttribute(ArrayAttr attr,
-                                           SmallVector<T> &arrayValues) {
-  for (Attribute val : attr.getValue()) {
-    arrayValues.push_back(val.cast<IntegerAttr>().getValue().getSExtValue());
-  }
-}
-
 template <typename TosaOp, typename... Args>
 TosaOp createOpAndInfer(PatternRewriter &rewriter, Location loc, Type resultTy,
                         Args &&...args) {
@@ -94,13 +86,9 @@
     ShapedType biasTy = bias.getType().cast<ShapedType>();
     ShapedType resultTy = op->getResult(0).getType().cast<ShapedType>();
 
-    llvm::SmallVector<int64_t> pad;
-    llvm::SmallVector<int64_t> stride;
-    llvm::SmallVector<int64_t> dilation;
-
-    getValuesFromIntArrayAttribute(op.out_pad().cast<ArrayAttr>(), pad);
-    getValuesFromIntArrayAttribute(op.stride().cast<ArrayAttr>(), stride);
-    getValuesFromIntArrayAttribute(op.dilation().cast<ArrayAttr>(), dilation);
+    ArrayRef<int64_t> pad = op.out_pad();
+    ArrayRef<int64_t> stride = op.stride();
+    ArrayRef<int64_t> dilation = op.dilation();
 
     // If striding is all 1 we can modify padding and reverse the kernel along
     // the x/y direction to make it a regular convolution. This is much simpler
@@ -132,14 +120,13 @@
     if (op.quantization_info().hasValue()) {
       conv2d = rewriter.create<tosa::Conv2DOp>(
           loc, resultTy, input, reverse2, bias,
-          rewriter.getI64ArrayAttr(convPad), rewriter.getI64ArrayAttr(stride),
-          rewriter.getI64ArrayAttr(dilation),
-          op.quantization_info().getValue());
+          rewriter.getDimListAttr(convPad), rewriter.getDimListAttr(stride),
+          rewriter.getDimListAttr(dilation), op.quantization_info().getValue());
     } else {
       conv2d = rewriter.create<tosa::Conv2DOp>(
           loc, resultTy, input, reverse2, bias,
-          rewriter.getI64ArrayAttr(convPad), rewriter.getI64ArrayAttr(stride),
-          rewriter.getI64ArrayAttr(dilation));
+          rewriter.getDimListAttr(convPad), rewriter.getDimListAttr(stride),
+          rewriter.getDimListAttr(dilation));
     }
 
     rewriter.replaceOp(op, conv2d);
@@ -168,13 +155,9 @@
     Type biasETy = biasTy.getElementType();
     Type resultETy = resultTy.getElementType();
 
-    llvm::SmallVector<int64_t> pad;
-    llvm::SmallVector<int64_t> stride;
-    llvm::SmallVector<int64_t> dilation;
-
-    getValuesFromIntArrayAttribute(op.out_pad().cast<ArrayAttr>(), pad);
-    getValuesFromIntArrayAttribute(op.stride().cast<ArrayAttr>(), stride);
-    getValuesFromIntArrayAttribute(op.dilation().cast<ArrayAttr>(), dilation);
+    ArrayRef<int64_t> pad = op.out_pad();
+    ArrayRef<int64_t> stride = op.stride();
+    ArrayRef<int64_t> dilation = op.dilation();
 
     // If striding is all 1 we can modify padding and reverse the kernel along
     // the x/y direction to make it a regular convolution. This is much simpler
@@ -302,18 +285,18 @@
       conv2d = createOpAndInfer<tosa::Conv2DOp>(
                    rewriter, loc, UnrankedTensorType::get(resultETy), input,
                    weight, zeroBias,
-                   /*pad=*/rewriter.getI64ArrayAttr({0, 0, 0, 0}),
-                   /*stride=*/rewriter.getI64ArrayAttr({1, 1}),
-                   /*dilation=*/rewriter.getI64ArrayAttr({1, 1}),
+                   /*pad=*/rewriter.getDimListAttr({0, 0, 0, 0}),
+                   /*stride=*/rewriter.getDimListAttr({1, 1}),
+                   /*dilation=*/rewriter.getDimListAttr({1, 1}),
                    op.quantization_info().getValue())
                    .getResult();
     } else {
       conv2d = createOpAndInfer<tosa::Conv2DOp>(
                    rewriter, loc, UnrankedTensorType::get(resultETy), input,
                    weight, zeroBias,
-                   /*pad=*/rewriter.getI64ArrayAttr({0, 0, 0, 0}),
-                   /*stride=*/rewriter.getI64ArrayAttr({1, 1}),
-                   /*dilation=*/rewriter.getI64ArrayAttr({1, 1}))
+                   /*pad=*/rewriter.getDimListAttr({0, 0, 0, 0}),
+                   /*stride=*/rewriter.getDimListAttr({1, 1}),
+                   /*dilation=*/rewriter.getDimListAttr({1, 1}))
                    .getResult();
     }
 
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaOptimization.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaOptimization.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaOptimization.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaOptimization.cpp
@@ -51,11 +51,8 @@
     }
 
     // Stride must be 1 for this optimization.
-    for (Attribute stride : op.stride().getValue()) {
-      if (!stride.cast<IntegerAttr>().getValue().isOne()) {
-        return failure();
-      }
-    }
+    if (!llvm::all_of(op.stride(), [](int64_t dim) { return dim == 1; }))
+      return failure();
 
     // Only works for a 1x1 kernel.
     ArrayRef<int64_t> weightShape = weightType.getShape();
@@ -145,11 +142,8 @@
     }
 
     // Stride must be 1 for this optimization.
-    for (Attribute stride : op.stride().getValue()) {
-      if (!stride.cast<IntegerAttr>().getValue().isOne()) {
-        return failure();
-      }
-    }
+    if (!llvm::all_of(op.stride(), [](int64_t dim) { return dim == 1; }))
+      return failure();
 
     // Only works for a 1x1 kernel.
     ArrayRef<int64_t> weightShape = weightType.getShape();
diff --git a/mlir/lib/IR/AsmPrinter.cpp b/mlir/lib/IR/AsmPrinter.cpp
--- a/mlir/lib/IR/AsmPrinter.cpp
+++ b/mlir/lib/IR/AsmPrinter.cpp
@@ -1907,7 +1907,7 @@
 }
 
 void AsmPrinter::Impl::printDimensionListAttr(DimensionListAttr attr) {
-  llvm::interleaveComma(attr.getDims(), os);
+  llvm::interleaveComma(attr, os);
 }
 
 void AsmPrinter::Impl::printDenseStringElementsAttr(
diff --git a/mlir/lib/IR/Builders.cpp b/mlir/lib/IR/Builders.cpp
--- a/mlir/lib/IR/Builders.cpp
+++ b/mlir/lib/IR/Builders.cpp
@@ -227,6 +227,10 @@
   return getArrayAttr(attrs);
 }
 
+DimensionListAttr Builder::getDimListAttr(ArrayRef<int64_t> values) {
+  return DimensionListAttr::get(getContext(), values);
+}
+
 ArrayAttr Builder::getIndexArrayAttr(ArrayRef<int64_t> values) {
   auto attrs = llvm::to_vector<8>(
       llvm::map_range(values, [this](int64_t v) -> Attribute {
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg-named.mlir
@@ -146,7 +146,7 @@
   // CHECK-DAG: [[FILL:%.+]] = linalg.fill([[CONST]], [[INIT]])
   // CHECK-DAG: [[KERNEL:%.+]] = linalg.init_tensor [3, 3]
   // CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins(%arg0, [[KERNEL]] : tensor<1x6x34x62xf32>, tensor<3x3xf32>) outs([[FILL]] : tensor<1x4x32x62xf32>)
-  %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xf32>)  -> (tensor<1x4x32x62xf32>)
+  %0 = "tosa.max_pool2d"(%arg0) {pad = dims<0, 0, 0, 0>, kernel = dims<3, 3>, stride = dims<1, 1>} : (tensor<1x6x34x62xf32>)  -> (tensor<1x4x32x62xf32>)
   return
 }
 
@@ -160,7 +160,7 @@
   // CHECK-DAG: [[FILL:%.+]] = linalg.fill([[INITVAL]], [[INIT]])
   // CHECK-DAG: [[KERNEL:%.+]] = linalg.init_tensor [3, 3]
   // CHECK: linalg.pooling_nhwc_max {dilations = dense<1> : vector<2xi64>, strides = dense<1> : vector<2xi64>} ins([[PAD]], [[KERNEL]] : tensor<1x6x35x62xf32>, tensor<3x3xf32>) outs([[FILL]] : tensor<1x4x33x62xf32>)
-  %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 1], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xf32>)  -> (tensor<1x4x33x62xf32>)
+  %0 = "tosa.max_pool2d"(%arg0) {pad = dims<0, 0, 0, 1>, kernel = dims<3, 3>, stride = dims<1, 1>} : (tensor<1x6x34x62xf32>)  -> (tensor<1x4x33x62xf32>)
   return
 }
 
@@ -168,7 +168,7 @@
 func @max_pool_i8(%arg0: tensor<1x6x34x62xi8>) -> () {
   // CHECK: arith.constant -128
   // CHECK: linalg.pooling_nhwc_max
-  %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xi8>)  -> (tensor<1x4x32x62xi8>)
+  %0 = "tosa.max_pool2d"(%arg0) {pad = dims<0, 0, 0, 0>, kernel = dims<3, 3>, stride = dims<1, 1>} : (tensor<1x6x34x62xi8>)  -> (tensor<1x4x32x62xi8>)
   return
 }
 
@@ -176,7 +176,7 @@
 func @max_pool_i16(%arg0: tensor<1x6x34x62xi16>) -> () {
   // CHECK: arith.constant -32768
   // CHECK: linalg.pooling_nhwc_max
-  %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xi16>)  -> (tensor<1x4x32x62xi16>)
+  %0 = "tosa.max_pool2d"(%arg0) {pad = dims<0, 0, 0, 0>, kernel = dims<3, 3>, stride = dims<1, 1>} : (tensor<1x6x34x62xi16>)  -> (tensor<1x4x32x62xi16>)
   return
 }
 
@@ -184,7 +184,7 @@
 func @max_pool_i32(%arg0: tensor<1x6x34x62xi32>) -> () {
   // CHECK: arith.constant -2147483648
   // CHECK: linalg.pooling_nhwc_max
-  %0 = "tosa.max_pool2d"(%arg0) {pad = [0, 0, 0, 0], kernel = [3, 3], stride = [1, 1]} : (tensor<1x6x34x62xi32>)  -> (tensor<1x4x32x62xi32>)
+  %0 = "tosa.max_pool2d"(%arg0) {pad = dims<0, 0, 0, 0>, kernel = dims<3, 3>, stride = dims<1, 1>} : (tensor<1x6x34x62xi32>)  -> (tensor<1x4x32x62xi32>)
   return
 }
 // -----
@@ -244,7 +244,7 @@
   // CHECK:   [[CF:%.+]] = arith.sitofp [[CI]]
   // CHECK:   [[RESULT:%.+]] = arith.divf %arg1, [[CF]]
   // CHECK:   linalg.yield [[RESULT]]
-  %0 = "tosa.avg_pool2d"(%arg0) {pad = [1, 1, 1, 1], kernel = [4, 4], stride = [1, 1]} : (tensor<1x6x34x62xf32>)  -> (tensor<1x5x33x62xf32>)
+  %0 = "tosa.avg_pool2d"(%arg0) {pad = dims<1, 1, 1, 1>, kernel = dims<4, 4>, stride = dims<1, 1>} : (tensor<1x6x34x62xf32>)  -> (tensor<1x5x33x62xf32>)
   return %0 : tensor<1x5x33x62xf32>
 }
 
@@ -273,7 +273,7 @@
   // CHECK: %[[CLMP_MAX:.+]] = select %[[CMP_MAX]], %[[MAX]], %[[CLMP_MIN]]
   // CHECK: %[[TRUNC:.+]] = arith.trunci %[[CLMP_MAX]]
   // CHECK: linalg.yield %[[TRUNC]]
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = [4, 4], pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, output_zp = -128 : i32}, stride = [4, 4]} : (tensor<1x128x128x2xi8>) -> tensor<1x32x32x2xi8>
+  %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<4, 4>, pad = dims<0, 0, 0, 0>, quantization_info = {input_zp = -128 : i32, output_zp = -128 : i32}, stride = dims<4, 4>} : (tensor<1x128x128x2xi8>) -> tensor<1x32x32x2xi8>
   return
 }
 
@@ -302,7 +302,7 @@
   // CHECK: %[[CLMP_MAX:.+]] = select %[[CMP_MAX]], %[[MAX]], %[[CLMP_MIN]]
   // CHECK: %[[TRUNC:.+]] = arith.trunci %[[CLMP_MAX]]
   // CHECK: linalg.yield %[[TRUNC]]
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = [4, 4], pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, output_zp = -128 : i32}, stride = [4, 4]} : (tensor<1x128x128x2xi16>) -> tensor<1x32x32x2xi16>
+  %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<4, 4>, pad = dims<0, 0, 0, 0>, quantization_info = {input_zp = -128 : i32, output_zp = -128 : i32}, stride = dims<4, 4>} : (tensor<1x128x128x2xi16>) -> tensor<1x32x32x2xi16>
   return
 }
 
@@ -323,7 +323,7 @@
   // CHECK: %[[B:.+]] = linalg.generic {indexing_maps = [#[[$MAP1]], #[[$MAP2]], #[[$MAP2]]], iterator_types = ["parallel", "parallel", "parallel", "parallel"]} ins(%arg2, %[[CONV]] : tensor<28xf32>, tensor<1x45x40x28xf32>) outs(%[[B_IN]] : tensor<1x45x40x28xf32>)
   // CHECK:   arith.addf
   // CHECK:   linalg.yield
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [2, 1]} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>)  -> (tensor<1x45x40x28xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<2, 1>} : (tensor<1x49x42x27xf32>, tensor<28x3x3x27xf32>, tensor<28xf32>)  -> (tensor<1x45x40x28xf32>)
   return
 }
 
@@ -335,7 +335,7 @@
   // CHECK: linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
   // CHECK:   linalg.yield %[[C0]]
   // CHECK: linalg.conv_2d_nhwc_hwcf
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [1, 1, 1, 1], stride = [1, 1], dilation = [2, 1]} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>)  -> (tensor<1x45x40x28xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<1, 1, 1, 1>, stride = dims<1, 1>, dilation = dims<2, 1>} : (tensor<1x47x40x28xf32>, tensor<28x3x3x28xf32>, tensor<28xf32>)  -> (tensor<1x45x40x28xf32>)
   return
 }
 
@@ -347,7 +347,7 @@
   // CHECK: linalg.pad_tensor %arg0 low[0, 1, 1, 0] high[0, 1, 1, 0]
   // CHECK:   linalg.yield %[[C22]]
   // CHECK: linalg.conv_2d_nhwc_hwcf_q
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [1, 1, 1, 1], quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = [1, 1]} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x12x12x1024xi32>
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<1, 1, 1, 1>, quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = dims<1, 1>} : (tensor<1x12x12x1xi8>, tensor<1024x3x3x1xi8>, tensor<1024xi32>) -> tensor<1x12x12x1024xi32>
   return
 }
 
@@ -369,7 +369,7 @@
   // CHECK:   [[ADD:%.+]] = arith.addf %arg3, %arg4 : f32
   // CHECK:   linalg.yield [[ADD]] : f32
   // CHECK: } -> tensor<1x5x5x33xf32>
-  %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1] } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>)  -> (tensor<1x5x5x33xf32>)
+  %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1> } : (tensor<1x7x5x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>)  -> (tensor<1x5x5x33xf32>)
   return
 }
 
@@ -391,7 +391,7 @@
   // CHECK:   [[ADD:%.+]] = arith.addf %arg3, %arg4 : f32
   // CHECK:   linalg.yield [[ADD]] : f32
   // CHECK: } -> tensor<1x5x5x33xf32>
-  %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = [0, 0, 0, 0], stride = [2, 2], dilation = [1, 1] } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>)  -> (tensor<1x5x5x33xf32>)
+  %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) { pad = dims<0, 0, 0, 0>, stride = dims<2, 2>, dilation = dims<1, 1> } : (tensor<1x11x9x3xf32>, tensor<3x1x3x11xf32>, tensor<33xf32>)  -> (tensor<1x5x5x33xf32>)
   return
 }
 
@@ -419,7 +419,7 @@
   // CHECK:   [[ADD:%.+]] = arith.addi %arg3, %arg4 : i32
   // CHECK:   linalg.yield [[ADD]] : i32
   // CHECK: } -> tensor<1x12x12x512xi32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [1, 1, 1, 1], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1], dilation = [1, 1] } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>)  -> tensor<1x12x12x512xi32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = dims<1, 1, 1, 1>, quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = dims<1, 1>, dilation = dims<1, 1> } : (tensor<1x12x12x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>)  -> tensor<1x12x12x512xi32>
   return
 }
 
@@ -443,6 +443,6 @@
   // CHECK:   [[ADD:%.+]] = arith.addi %arg3, %arg4 : i32
   // CHECK:   linalg.yield [[ADD]] : i32
   // CHECK: } -> tensor<1x10x10x512xi32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = [1, 1], dilation = [2, 2] } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>)  -> tensor<1x10x10x512xi32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, quantization_info = {input_zp = -128 : i32, weight_zp = 42 : i32}, stride = dims<1, 1>, dilation = dims<2, 2> } : (tensor<1x14x14x4xi8>, tensor<3x3x4x128xi8>, tensor<512xi32>)  -> tensor<1x10x10x512xi32>
   return
 }
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -1309,7 +1309,7 @@
   // CHECK-DAG: %[[IDX:.+]] = arith.index_cast %[[VAL29]]
   // CHECK-DAG: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[IDX0]], %[[IDY]], %[[IDX]], %[[IDX3]]]
   // CHECK: linalg.yield %[[EXTRACT]]
-  %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [0, 0], offset = [0, 0], stride_fp = [0.5 : f32, 0.5 : f32], offset_fp = [0.1 : f32, 0.2 : f32], shift = 0 : i32, mode = "NEAREST_NEIGHBOR" } : (tensor<1x2x2x1xf32>)  -> (tensor<1x4x4x1xf32>)
+  %output = "tosa.resize"(%input) { output_size = dims<4, 4>, stride = dims<0, 0>, offset = dims<0, 0>, stride_fp = [0.5 : f32, 0.5 : f32], offset_fp = [0.1 : f32, 0.2 : f32], shift = 0 : i32, mode = "NEAREST_NEIGHBOR" } : (tensor<1x2x2x1xf32>)  -> (tensor<1x4x4x1xf32>)
 
   return
 }
@@ -1392,7 +1392,7 @@
   // CHECK: %[[WHI:.+]] = arith.mulf %[[HI]], %[[DY]]
   // CHECK: %[[RESULT:.+]] = arith.addf %[[WLO]], %[[WHI]]
   // CHECK: linalg.yield %[[RESULT]]
-  %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [0, 0], offset = [0, 0], stride_fp = [0.5 : f32, 0.5 : f32], offset_fp = [0.1 : f32, 0.2 : f32], shift = 0 : i32, mode = "BILINEAR" } : (tensor<1x2x2x1xf32>)  -> (tensor<1x4x4x1xf32>)
+  %output = "tosa.resize"(%input) { output_size = dims<4, 4>, stride = dims<0, 0>, offset = dims<0, 0>, stride_fp = [0.5 : f32, 0.5 : f32], offset_fp = [0.1 : f32, 0.2 : f32], shift = 0 : i32, mode = "BILINEAR" } : (tensor<1x2x2x1xf32>)  -> (tensor<1x4x4x1xf32>)
   return
 }
 
@@ -1460,7 +1460,7 @@
   // CHECK-DAG: %[[IDX:.+]] = arith.index_cast %[[VAL29]]
   // CHECK: %[[EXTRACT:.+]] = tensor.extract %arg0[%[[IDX0]], %[[IDY]], %[[IDX]], %[[IDX3]]]
   // CHECK: linalg.yield %[[EXTRACT]]
-  %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [128, 128], offset = [1, 2], stride_fp = [0. : f32, 0. : f32], offset_fp = [0. : f32, 0. : f32], shift = 8 : i32, mode = "NEAREST_NEIGHBOR" } : (tensor<1x2x2x1xi32>)  -> (tensor<1x4x4x1xi32>)
+  %output = "tosa.resize"(%input) { output_size = dims<4, 4>, stride = dims<128, 128>, offset = dims<1, 2>, stride_fp = [0. : f32, 0. : f32], offset_fp = [0. : f32, 0. : f32], shift = 8 : i32, mode = "NEAREST_NEIGHBOR" } : (tensor<1x2x2x1xi32>)  -> (tensor<1x4x4x1xi32>)
   return
 }
 
@@ -1545,6 +1545,6 @@
   // CHECK: %[[WHI:.+]] = arith.muli %[[HI]], %[[DY]]
   // CHECK: %[[RESULT:.+]] = arith.addi %[[WLO]], %[[WHI]]
   // CHECK: linalg.yield %[[RESULT]]
-  %output = "tosa.resize"(%input) { output_size = [4, 4], stride = [128, 128], offset = [1, 2], stride_fp = [0. : f32, 0. : f32], offset_fp = [0. : f32, 0. : f32], shift = 8 : i32, mode = "BILINEAR" } : (tensor<1x2x2x1xi8>)  -> (tensor<1x4x4x1xi32>)
+  %output = "tosa.resize"(%input) { output_size = dims<4, 4>, stride = dims<128, 128>, offset = dims<1, 2>, stride_fp = [0. : f32, 0. : f32], offset_fp = [0. : f32, 0. : f32], shift = 8 : i32, mode = "BILINEAR" } : (tensor<1x2x2x1xi8>)  -> (tensor<1x4x4x1xi32>)
   return
 }
diff --git a/mlir/test/Dialect/Tosa/canonicalize.mlir b/mlir/test/Dialect/Tosa/canonicalize.mlir
--- a/mlir/test/Dialect/Tosa/canonicalize.mlir
+++ b/mlir/test/Dialect/Tosa/canonicalize.mlir
@@ -73,7 +73,7 @@
   // CHECK: "tosa.conv2d"
   %weight = "tosa.const"() {value = dense<[[[[1.0, 1.0]]], [[[1.0, 1.0]]], [[[1.0, 1.0]]]]> : tensor<3x1x1x2xf32>} : ()-> tensor<3x1x1x2xf32>
   %bias = "tosa.const"() {value = dense<0.0> : tensor<3xf32>} : ()-> tensor<3xf32>
-  %0 = "tosa.conv2d"(%arg0, %weight, %bias) {pad = [0, 0, 0, 0], stride = [2, 2], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
+  %0 = "tosa.conv2d"(%arg0, %weight, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<2, 2>, dilation = dims<1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
   return %0 : tensor<4x10x10x3xf32>
 }
 
@@ -84,7 +84,7 @@
   // CHECK: "tosa.conv2d"
   %weight = "tosa.const"() {value = dense<[[[[1.0], [1.0]], [[1.0], [1.0]]]]> : tensor<1x2x2x1xf32>} : ()-> tensor<1x2x2x1xf32>
   %bias = "tosa.const"() {value = dense<0.0> : tensor<1xf32>} : ()-> tensor<1xf32>
-  %0 = "tosa.conv2d"(%arg0, %weight, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>) -> tensor<4x10x10x1xf32>
+  %0 = "tosa.conv2d"(%arg0, %weight, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<4x10x10x1xf32>, tensor<1x2x2x1xf32>, tensor<1xf32>) -> tensor<4x10x10x1xf32>
   return %0 : tensor<4x10x10x1xf32>
 }
 
@@ -93,7 +93,7 @@
 // CHECK-LABEL: @depthwise_conv2d_stride_2
 func @depthwise_conv2d_stride_2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<1x1x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
   // CHECK: "tosa.depthwise_conv2d"
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [2, 2], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, stride = dims<2, 2>, dilation = dims<1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
   return %0 : tensor<4x10x10x6xf32>
 }
 
@@ -102,7 +102,7 @@
 // CHECK-LABEL: @depthwise_conv2d_weight_2x2
 func @depthwise_conv2d_weight_2x2(%arg0: tensor<4x10x10x2xf32>, %arg1: tensor<2x2x2x3xf32>, %arg2: tensor<6xf32>) -> tensor<4x10x10x6xf32> {
   // CHECK: "tosa.depthwise_conv2d"
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<4x10x10x2xf32>, tensor<2x2x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
   return %0 : tensor<4x10x10x6xf32>
 }
 
@@ -112,7 +112,7 @@
 func @max_pool2d_is_noop(%arg0: tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32> {
   // CHECK-NOT: "tosa.max_pool2d"
   // CHECK: return %arg0
-  %0 = "tosa.max_pool2d"(%arg0) {kernel = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32>
+  %0 = "tosa.max_pool2d"(%arg0) {kernel = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<10x1x1x3xf32>) -> tensor<10x1x1x3xf32>
   return %0 : tensor<10x1x1x3xf32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/invalid.mlir b/mlir/test/Dialect/Tosa/invalid.mlir
--- a/mlir/test/Dialect/Tosa/invalid.mlir
+++ b/mlir/test/Dialect/Tosa/invalid.mlir
@@ -3,7 +3,7 @@
 
 func @test_conv2d(%arg0: tensor<1x29x29x4xf32>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
   // expected-error@+1 {{expect both input and weight to be float or not together, got 'f32' and 'i8'}}
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>}
            : (tensor<1x29x29x4xf32>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
   return %0 : tensor<1x27x27x16xi8>
 }
@@ -12,7 +12,7 @@
 
 func @test_conv2d(%arg0: tensor<*xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
   // expected-error@+1 {{expect a ranked tensor for input, got <block argument> of type 'tensor<*xi8>' at index: 0}}
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>}
            : (tensor<*xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
   return %0 : tensor<1x27x27x16xi8>
 }
@@ -21,7 +21,7 @@
 
 func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<*xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
   // expected-error@+1 {{expect a ranked tensor for weight, got <block argument> of type 'tensor<*xi8>' at index: 1}}
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>}
            : (tensor<1x29x29x4xi8>, tensor<*xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
   return %0 : tensor<1x27x27x16xi8>
 }
@@ -31,7 +31,7 @@
 
 func @test_conv2d(%arg0: tensor<1x29x29x4xi8>, %arg1: tensor<16x3x3x4xi8>, %arg2: tensor<16xi8>) -> tensor<1x27x27x16xi8> {
   // expected-error@+1 {{'tosa.conv2d' op quantizationattr is required for quantized type, and not allowed for float type}}
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>}
            : (tensor<1x29x29x4xi8>, tensor<16x3x3x4xi8>, tensor<16xi8>) -> tensor<1x27x27x16xi8>
   return %0 : tensor<1x27x27x16xi8>
 }
diff --git a/mlir/test/Dialect/Tosa/operation_optimization.mlir b/mlir/test/Dialect/Tosa/operation_optimization.mlir
--- a/mlir/test/Dialect/Tosa/operation_optimization.mlir
+++ b/mlir/test/Dialect/Tosa/operation_optimization.mlir
@@ -14,7 +14,7 @@
   // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 3]}
   // CHECK-SAME: -> tensor<4x10x10x3xf32>
   // CHECK: return %[[VAR3]]
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<4x10x10x2xf32>, tensor<3x1x1x2xf32>, tensor<3xf32>) -> tensor<4x10x10x3xf32>
   return %0 : tensor<4x10x10x3xf32>
 }
 
@@ -33,7 +33,7 @@
   // CHECK: %[[VAR3:.*]] = "tosa.reshape"(%[[VAR2]]) {new_shape = [4, 10, 10, 3]}
   // CHECK-SAME: -> tensor<4x10x10x3xi32>
   // CHECK: return %[[VAR3]]
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1], quantization_info = {input_zp = 42 : i32, weight_zp = 24 : i32}} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x10x10x3xi32>
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>, quantization_info = {input_zp = 42 : i32, weight_zp = 24 : i32}} : (tensor<4x10x10x2xi8>, tensor<3x1x1x2xi8>, tensor<3xi32>) -> tensor<4x10x10x3xi32>
   return %0 : tensor<4x10x10x3xi32>
 }
 
@@ -53,7 +53,7 @@
   // CHECK: %[[VAR4:.*]] = "tosa.add"(%[[VAR3]], %arg2)
   // CHECK-SAME: -> tensor<4x10x10x6xf32>
   // CHECK: return %[[VAR4]]
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<4x10x10x2xf32>, tensor<1x1x2x3xf32>, tensor<6xf32>) -> tensor<4x10x10x6xf32>
   return %0 : tensor<4x10x10x6xf32>
 }
 
@@ -62,7 +62,7 @@
 // CHECK-LABEL: @depthwise_conv2d_as_mul_q
 func @depthwise_conv2d_as_mul_q(%arg0: tensor<4x10x10x2xi8>, %arg1: tensor<1x1x2x3xi8>, %arg2: tensor<6xi32>) -> tensor<4x10x10x6xi32> {
   // CHECK: "tosa.depthwise_conv2d"
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1], quantization_info = {input_zp = 0 : i32, weight_zp = 0 : i32}} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>) -> tensor<4x10x10x6xi32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>, quantization_info = {input_zp = 0 : i32, weight_zp = 0 : i32}} : (tensor<4x10x10x2xi8>, tensor<1x1x2x3xi8>, tensor<6xi32>) -> tensor<4x10x10x6xi32>
   return %0 : tensor<4x10x10x6xi32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/ops.mlir b/mlir/test/Dialect/Tosa/ops.mlir
--- a/mlir/test/Dialect/Tosa/ops.mlir
+++ b/mlir/test/Dialect/Tosa/ops.mlir
@@ -12,42 +12,42 @@
 // -----
 // CHECK-LABEL: avg_pool2d_f32
 func @test_avg_pool2d_f32(%arg0: tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = [2, 2], pad = [0, 1, 0, 1], stride = [1, 1]} : (tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32>
+    %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<2, 2>, pad = dims<0, 1, 0, 1>, stride = dims<1, 1>} : (tensor<1x7x7x9xf32>) -> tensor<1x7x7x9xf32>
     return %0 : tensor<1x7x7x9xf32>
 }
 
 // -----
 // CHECK-LABEL: avg_pool2d_i8
 func @test_avg_pool2d_i8(%arg0: tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = [2, 2], pad = [0, 1, 0, 1], stride = [1, 1]} : (tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8>
+    %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<2, 2>, pad = dims<0, 1, 0, 1>, stride = dims<1, 1>} : (tensor<1x7x7x9xi8>) -> tensor<1x7x7x9xi8>
     return %0 : tensor<1x7x7x9xi8>
 }
 
 // -----
 // CHECK-LABEL: avg_pool2d_i16
 func @test_avg_pool2d_i16(%arg0: tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = [2, 2], pad = [0, 1, 0, 1], stride = [1, 1]} : (tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16>
+    %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<2, 2>, pad = dims<0, 1, 0, 1>, stride = dims<1, 1>} : (tensor<1x7x7x9xi16>) -> tensor<1x7x7x9xi16>
     return %0 : tensor<1x7x7x9xi16>
 }
 
 // -----
 // CHECK-LABEL: avg_pool2d_q8
 func @test_avg_pool2d_q8(%arg0: tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>> {
-    %0 = "tosa.avg_pool2d"(%arg0) {kernel = [2, 2], pad = [0, 1, 0, 1], stride = [1, 1]} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
+    %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<2, 2>, pad = dims<0, 1, 0, 1>, stride = dims<1, 1>} : (tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>) -> tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
     return %0 : tensor<1x7x7x9x!quant.uniform<i8:f32, 0.01>>
 }
 
 // -----
 // CHECK-LABEL: conv2d
 func @test_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<8x1x1x4xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
-    %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
+    %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<1x4x4x4xf32>, tensor<8x1x1x4xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
     return %0 : tensor<1x4x4x8xf32>
 }
 
 // -----
 // CHECK-LABEL: depthwise_conv2d
 func @test_depthwise_conv2d(%arg0: tensor<1x4x4x4xf32>, %arg1: tensor<1x1x4x2xf32>, %arg2: tensor<8xf32>) -> tensor<1x4x4x8xf32> {
-  %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
+  %2 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<1x4x4x4xf32>, tensor<1x1x4x2xf32>, tensor<8xf32>) -> tensor<1x4x4x8xf32>
   return %2 : tensor<1x4x4x8xf32>
 }
 
@@ -68,14 +68,14 @@
 // -----
 // CHECK-LABEL: max_pool2d
 func @test_max_pool2d(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32> {
-  %0 = "tosa.max_pool2d"(%arg0) {kernel = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
+  %0 = "tosa.max_pool2d"(%arg0) {kernel = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<1x32x32x8xf32>) -> tensor<1x32x32x8xf32>
   return %0 : tensor<1x32x32x8xf32>
 }
 
 // -----
 /// CHECK-LABEL: transpose_conv2d
 func @test_transpose_conv2d(%arg0: tensor<1x32x32x8xf32>, %arg1: tensor<16x1x1x8xf32>, %arg2: tensor<16xf32>) -> tensor<1x32x32x16xf32> {
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [1, 32, 32, 16], stride = [1, 1]} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<1, 32, 32, 16>, stride = dims<1, 1>} : (tensor<1x32x32x8xf32>, tensor<16x1x1x8xf32>, tensor<16xf32>) -> tensor<1x32x32x16xf32>
   return %0 : tensor<1x32x32x16xf32>
 }
 
@@ -456,7 +456,7 @@
 // -----
 // CHECK-LABEL: resize
 func @test_resize(%arg0: tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32> {
-  %1 = "tosa.resize"(%arg0) {output_size = [64, 64], stride = [1024, 1024], offset = [0, 0], shift = 10 : i32, stride_fp = [0.0 : f32, 0.0 : f32], offset_fp = [0.0 : f32, 0.0 : f32], mode = "BILINEAR"} : (tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32>
+  %1 = "tosa.resize"(%arg0) {output_size = dims<64, 64>, stride = dims<1024, 1024>, offset = dims<0, 0>, shift = 10 : i32, stride_fp = [0.0 : f32, 0.0 : f32], offset_fp = [0.0 : f32, 0.0 : f32], mode = "BILINEAR"} : (tensor<1x32x32x8xf32>) -> tensor<1x64x64x8xf32>
   return %1 : tensor<1x64x64x8xf32>
 }
 
diff --git a/mlir/test/Dialect/Tosa/quant-test.mlir b/mlir/test/Dialect/Tosa/quant-test.mlir
--- a/mlir/test/Dialect/Tosa/quant-test.mlir
+++ b/mlir/test/Dialect/Tosa/quant-test.mlir
@@ -12,7 +12,7 @@
 // CHECK-LABEL: test_build_mult_and_shift
 func @test_build_mult_and_shift(%arg0: tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, %arg1 : tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, %arg2 : tensor<16xi32>) -> tensor<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>> {
   // CHECK: tosa.conv2d
-  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = [1, 1, 2, 2], dilation = [2, 1], stride = [1, 1], quantization_info = {input_zp = -1 : i32, weight_zp = 0 : i32}} : (tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<16xi32>) -> tensor<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>>
+  %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {pad = dims<1, 1, 2, 2>, dilation = dims<2, 1>, stride = dims<1, 1>, quantization_info = {input_zp = -1 : i32, weight_zp = 0 : i32}} : (tensor<1x32x32x8x!quant.uniform<i8:f32, 0.015684768557548523>>, tensor<16x1x1x8x!quant.uniform<i8<-127:127>:f32, 0.015680249780416489>>, tensor<16xi32>) -> tensor<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>>
   return %0 : tensor<1x32x32x16x!quant.uniform<i8:f32, 0.078431375324726104>>
 
 }
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
--- a/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-transpose-conv.mlir
@@ -4,8 +4,8 @@
 func @transpose_conv2d(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {
   // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
   // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
-  // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = [1, 1], pad = [2, 2, 5, 5], stride = [1, 1]}
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x18x19x5xf32>
+  // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = dims<1, 1>, pad = dims<2, 2, 5, 5>, stride = dims<1, 1>}
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>, stride = dims<1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x18x19x5xf32>
   %1 = tensor.cast %0 : tensor<2x18x19x5xf32> to tensor<2x?x?x5xf32>
   return %1 : tensor<2x?x?x5xf32>
 }
@@ -16,24 +16,24 @@
 func @transpose_conv2d_quantized(%arg0: tensor<2x16x14x3xi8>, %arg1: tensor<5x3x6x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x18x19x5xi32>) {
   // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
   // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
-  // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = [1, 1], pad = [2, 2, 5, 5], quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = [1, 1]}
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>) -> tensor<2x18x19x5xi32>
+  // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = dims<1, 1>, pad = dims<2, 2, 5, 5>, quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = dims<1, 1>}
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, out_shape = dims<-1, -1, -1, -1>, stride = dims<1, 1>} : (tensor<2x16x14x3xi8>, tensor<5x3x6x3xi8>, tensor<5xi32>) -> tensor<2x18x19x5xi32>
   return %0 : tensor<2x18x19x5xi32>
 }
 
-// ----
+// -----
 
 // CHECK-LABEL: @transpose_conv2d_dilated
 func @transpose_conv2d_dilated(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {
   // CHECK: %[[REV1:.+]] = "tosa.reverse"(%arg1) {axis = 1 : i64}
   // CHECK: %[[REV2:.+]] = "tosa.reverse"(%[[REV1]]) {axis = 2 : i64}
-  // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = [2, 3], pad = [4, 4, 15, 15], stride = [1, 1]}
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [2, 3], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x20x29x5xf32>
+  // CHECK: "tosa.conv2d"(%arg0, %[[REV2]], %arg2) {dilation = dims<2, 3>, pad = dims<4, 4, 15, 15>, stride = dims<1, 1>}
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<2, 3>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>, stride = dims<1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x20x29x5xf32>
   %1 = tensor.cast %0 : tensor<2x20x29x5xf32> to tensor<2x?x?x5xf32>
   return %1 : tensor<2x?x?x5xf32>
 }
 
-// ----
+// -----
 
 // CHECK-LABEL: @transpose_conv2d_strided
 func @transpose_conv2d_strided(%arg0: tensor<2x17x15x3xf32>, %arg1: tensor<5x3x5x3xf32>, %arg2: tensor<5xf32>) -> tensor<2x?x?x5xf32> {
@@ -54,18 +54,18 @@
 
   // Manipulate the final shape.
   // CHECK-DAG: %[[BIAS:.+]]  = "tosa.const"() {value = dense<0.000000e+00> : tensor<30xf32>}
-  // CHECK-DAG: %[[CONV:.+]] = "tosa.conv2d"(%[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]]) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]}
+  // CHECK-DAG: %[[CONV:.+]] = "tosa.conv2d"(%[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]]) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>}
   // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = "tosa.reshape"(%[[CONV]]) {new_shape = [2, 18, 16, 2, 3, 5]}
   // CHECK-DAG: %[[TRANS_OUT:.+]] = "tosa.transpose"(%[[RESHAPE_OUT_1]], %[[TRANS2]])
   // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = "tosa.reshape"(%[[TRANS_OUT]]) {new_shape = [2, 36, 48, 5]}
   // CHECK-DAG: %[[SLICE:.+]] = "tosa.slice"(%[[RESHAPE_OUT_2]]) {size = [2, 35, 47, 5], start = [0, 0, 0, 0]}
   // CHECK: %[[ADD:.+]] = "tosa.add"(%[[SLICE]], %arg2)
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [2, 3]} : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>) -> tensor<2x35x47x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>, stride = dims<2, 3>} : (tensor<2x17x15x3xf32>, tensor<5x3x5x3xf32>, tensor<5xf32>) -> tensor<2x35x47x5xf32>
   %1 = tensor.cast %0 : tensor<2x35x47x5xf32> to tensor<2x?x?x5xf32>
   return %1 : tensor<2x?x?x5xf32>
 }
 
-// ----
+// -----
 
 // CHECK-LABEL: @transpose_conv2d_strided_quantized
 func @transpose_conv2d_strided_quantized(%arg0: tensor<2x17x15x3xi8>, %arg1: tensor<5x3x5x3xi8>, %arg2: tensor<5xi32>) -> (tensor<2x35x47x5xi32>) {
@@ -86,12 +86,12 @@
 
   // Manipulate the final shape.
   // CHECK-DAG: %[[BIAS:.+]]  = "tosa.const"() {value = dense<0> : tensor<30xi32>}
-  // CHECK-DAG: %[[CONV:.+]] = "tosa.conv2d"(%[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]]) {dilation = [1, 1], pad = [0, 0, 0, 0], quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = [1, 1]}
+  // CHECK-DAG: %[[CONV:.+]] = "tosa.conv2d"(%[[NEWINPUT]], %[[NEWWEIGHT]], %[[BIAS]]) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>, quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, stride = dims<1, 1>}
   // CHECK-DAG: %[[RESHAPE_OUT_1:.+]] = "tosa.reshape"(%[[CONV]]) {new_shape = [2, 18, 16, 2, 3, 5]}
   // CHECK-DAG: %[[TRANS_OUT:.+]] = "tosa.transpose"(%[[RESHAPE_OUT_1]], %[[TRANS2]])
   // CHECK-DAG: %[[RESHAPE_OUT_2:.+]] = "tosa.reshape"(%[[TRANS_OUT]]) {new_shape = [2, 36, 48, 5]}
   // CHECK-DAG: %[[SLICE:.+]] = "tosa.slice"(%[[RESHAPE_OUT_2]]) {size = [2, 35, 47, 5], start = [0, 0, 0, 0]}
   // CHECK: %[[ADD:.+]] = "tosa.add"(%[[SLICE]], %arg2)
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, out_shape = [-1, -1, -1, -1], stride = [2, 3]} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>) -> tensor<2x35x47x5xi32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, quantization_info = {input_zp = -22 : i32, weight_zp = 42 : i32}, out_shape = dims<-1, -1, -1, -1>, stride = dims<2, 3>} : (tensor<2x17x15x3xi8>, tensor<5x3x5x3xi8>, tensor<5xi32>) -> tensor<2x35x47x5xi32>
   return %0 : tensor<2x35x47x5xi32>
 }
diff --git a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
--- a/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-infer-shapes.mlir
@@ -675,10 +675,10 @@
 // CHECK-LABEL: @test_pool_static
 func @test_pool_static(%arg0: tensor<3x5x6x7xf32>) {
   // CHECK: -> tensor<3x2x4x7xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = [4, 3], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<3x2x4x7xf32>
-  %1 = "tosa.max_pool2d"(%arg0) {kernel = [4, 3], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+  %1 = "tosa.max_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
   return
 }
 
@@ -687,7 +687,7 @@
 // CHECK-LABEL: @conv2d_static
 func @conv2d_static(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
   // CHECK: -> tensor<2x6x4x5xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -696,7 +696,7 @@
 // CHECK-LABEL: @conv2d_dynamic_input
 func @conv2d_dynamic_input(%input: tensor<?x?x?x?xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
   // CHECK: -> tensor<?x?x?x5xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -705,10 +705,10 @@
 // CHECK-LABEL: @test_pool_dynamic_input
 func @test_pool_dynamic_input(%arg0: tensor<?x?x?x?xf32>) {
   // CHECK: -> tensor<?x?x?x?xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = [4, 3], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<?x?x?x?xf32>
-  %1 = "tosa.max_pool2d"(%arg0) {kernel = [4, 3], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
+  %1 = "tosa.max_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<0, 0, 0, 0>, stride = dims<1, 1>} : (tensor<?x?x?x?xf32>) -> tensor<?x?x?x?xf32>
   return
 }
 
@@ -717,10 +717,10 @@
 // CHECK-LABEL: @test_pool_padded
 func @test_pool_padded(%arg0: tensor<3x5x6x7xf32>) {
   // CHECK: -> tensor<3x5x11x7xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = [4, 3], pad = [1, 2, 3, 4], stride = [1, 1]} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<1, 2, 3, 4>, stride = dims<1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<3x5x11x7xf32>
-  %1 = "tosa.max_pool2d"(%arg0) {kernel = [4, 3], pad = [1, 2, 3, 4], stride = [1, 1]} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
+  %1 = "tosa.max_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<1, 2, 3, 4>, stride = dims<1, 1>} : (tensor<3x5x6x7xf32>) -> tensor<?x?x?x?xf32>
   return
 }
 
@@ -729,7 +729,7 @@
 // CHECK-LABEL: @conv2d_dynamic_weight
 func @conv2d_dynamic_weight(%input: tensor<2x8x9x3xf32>, %weights: tensor<?x?x?x?xf32>, %bias: tensor<5xf32>) -> () {
   // CHECK: -> tensor<2x?x?x5xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -738,7 +738,7 @@
 // CHECK-LABEL: @conv2d_dynamic_bias
 func @conv2d_dynamic_bias(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<?xf32>) -> () {
   // CHECK: -> tensor<2x6x4x5xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -747,10 +747,10 @@
 // CHECK-LABEL: @test_pool_stride
 func @test_pool_stride(%arg0: tensor<3x11x12x7xf32>) {
   // CHECK: -> tensor<3x4x4x7xf32>
-  %0 = "tosa.avg_pool2d"(%arg0) {kernel = [4, 3], pad = [0, 0, 0, 0], stride = [2, 3]} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>
+  %0 = "tosa.avg_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<0, 0, 0, 0>, stride = dims<2, 3>} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>
 
   // CHECK: -> tensor<3x4x4x7xf32>
-  %1 = "tosa.max_pool2d"(%arg0) {kernel = [4, 3], pad = [0, 0, 0, 0], stride = [2, 3]} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>
+  %1 = "tosa.max_pool2d"(%arg0) {kernel = dims<4, 3>, pad = dims<0, 0, 0, 0>, stride = dims<2, 3>} : (tensor<3x11x12x7xf32>) -> tensor<?x?x?x?xf32>
   return
 }
 
@@ -759,7 +759,7 @@
 // CHECK-LABEL: @conv2d_padded
 func @conv2d_padded(%input: tensor<2x8x9x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
   // CHECK: -> tensor<2x9x11x5xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [1, 2, 3, 4], stride = [1, 1], dilation = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<1, 2, 3, 4>, stride = dims<1, 1>, dilation = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -768,7 +768,7 @@
 // CHECK-LABEL: @conv2d_dilated
 func @conv2d_dilated(%input: tensor<2x12x14x3xf32>, %weights: tensor<5x3x6x3xf32>, %bias: tensor<5xf32>) -> () {
   // CHECK: -> tensor<2x6x4x5xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [1, 1], dilation = [3, 2]} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<1, 1>, dilation = dims<3, 2>} : (tensor<2x12x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -777,7 +777,7 @@
 // CHECK-LABEL: @conv2d_strided
 func @conv2d_strided(%input: tensor<1x13x14x1xf32>, %weights: tensor<1x1x1x1xf32>, %bias: tensor<1xf32>) -> () {
   // CHECK: -> tensor<1x5x7x1xf32>
-  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = [0, 0, 0, 0], stride = [3, 2], dilation = [1, 1]} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>)  -> (tensor<?x?x?x?xf32>)
+  %0 = "tosa.conv2d"(%input, %weights, %bias) {pad = dims<0, 0, 0, 0>, stride = dims<3, 2>, dilation = dims<1, 1>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>)  -> (tensor<?x?x?x?xf32>)
   return
 }
 
@@ -786,7 +786,7 @@
 // CHECK-LABEL: @conv3d_static
 func @conv3d_static(%input: tensor<2x8x9x10x3xf32>, %weights: tensor<5x3x6x4x3xf32>, %bias: tensor<5xf32>) -> () {
   // CHECK: -> tensor<2x6x4x7x5xf32>
-  %0 = "tosa.conv3d"(%input, %weights, %bias) {dilation = [1, 1, 1], pad = [0, 0, 0, 0, 0, 0], stride = [1, 1, 1]} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?x?xf32>)
+  %0 = "tosa.conv3d"(%input, %weights, %bias) {dilation = dims<1, 1, 1>, pad = dims<0, 0, 0, 0, 0, 0>,  stride = dims<1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>)  -> (tensor<?x?x?x?x?xf32>)
   return
 }
 
@@ -795,7 +795,7 @@
 // CHECK-LABEL: @conv3d_dynamic_input
 func @conv3d_dynamic_input(%arg0: tensor<?x?x?x?x?xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<?x?x?x?x5xf32>
-  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = [1, 1, 1], pad = [0, 0, 0, 0, 0, 0], stride = [1, 1, 1]} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1, 1>, pad = dims<0, 0, 0, 0, 0, 0>,  stride = dims<1, 1, 1>} : (tensor<?x?x?x?x?xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
   return
 }
 
@@ -804,7 +804,7 @@
 // CHECK-LABEL: @conv3d_dynamic_weight
 func @conv3d_dynamic_weight(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<?x?x?x?x?xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x?x?x?x5xf32>
-  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = [1, 1, 1], pad = [0, 0, 0, 0, 0, 0], stride = [1, 1, 1]} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1, 1>, pad = dims<0, 0, 0, 0, 0, 0>,  stride = dims<1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<?x?x?x?x?xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
   return
 }
 
@@ -813,7 +813,7 @@
 // CHECK-LABEL: @conv3d_dynamic_bias
 func @conv3d_dynamic_bias(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<?xf32>) {
   // CHECK: -> tensor<2x6x4x7x5xf32>
-  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = [1, 1, 1], pad = [0, 0, 0, 0, 0, 0], stride = [1, 1, 1]} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?x?xf32>
+  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1, 1>, pad = dims<0, 0, 0, 0, 0, 0>,  stride = dims<1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<?xf32>) -> tensor<?x?x?x?x?xf32>
   return
 }
 
@@ -822,7 +822,7 @@
 // CHECK-LABEL: @conv3d_padded
 func @conv3d_padded(%arg0: tensor<2x8x9x10x3xf32>, %arg1: tensor<5x3x6x4x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x9x11x18x5xf32>
-  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = [1, 1, 1], pad = [1, 2, 3, 4, 5, 6], stride = [1, 1, 1]} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1, 1>, pad = dims<1, 2, 3, 4, 5, 6>,  stride = dims<1, 1, 1>} : (tensor<2x8x9x10x3xf32>, tensor<5x3x6x4x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
   return
 }
 
@@ -831,7 +831,7 @@
 // CHECK-LABEL: @conv3d_dilated
 func @conv3d_dilated(%arg0: tensor<2x12x14x16x3xf32>, %arg1: tensor<5x3x6x2x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x6x4x12x5xf32>
-  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = [3, 2, 4], pad = [0, 0, 0, 0, 0, 0], stride = [1, 1, 1]} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
+  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = dims<3, 2, 4>, pad = dims<0, 0, 0, 0, 0, 0>,  stride = dims<1, 1, 1>} : (tensor<2x12x14x16x3xf32>, tensor<5x3x6x2x3xf32>, tensor<5xf32>) -> tensor<?x?x?x?x?xf32>
   return
 }
 
@@ -840,7 +840,7 @@
 // CHECK-LABEL: @conv3d_strided
 func @conv3d_strided(%arg0: tensor<1x13x14x15x1xf32>, %arg1: tensor<1x1x1x1x1xf32>, %arg2: tensor<1xf32>) {
   // CHECK: -> tensor<1x5x7x4x1xf32>
-  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = [1, 1, 1], pad = [0, 0, 0, 0, 0, 0], stride = [3, 2, 4]} : (tensor<1x13x14x15x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
+  %0 = "tosa.conv3d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1, 1>, pad = dims<0, 0, 0, 0, 0, 0>,  stride = dims<3, 2, 4>} : (tensor<1x13x14x15x1xf32>, tensor<1x1x1x1x1xf32>, tensor<1xf32>) -> tensor<?x?x?x?x?xf32>
   return
 }
 
@@ -849,7 +849,7 @@
 // CHECK-LABEL: @depthwise_conv2d_static
 func @depthwise_conv2d_static(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
   // CHECK: -> tensor<2x6x4x15xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>,  stride = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
   return
 }
 
@@ -858,7 +858,7 @@
 // CHECK-LABEL: @depthwise_conv2d_dynamic_input
 func @depthwise_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
   // CHECK: -> tensor<?x?x?x15xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<?x?x?x15xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>,  stride = dims<1, 1>} : (tensor<?x?x?x?xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<?x?x?x15xf32>
   return
 }
 
@@ -867,7 +867,7 @@
 // CHECK-LABEL: @depthwise_conv2d_dynamic_weight
 func @depthwise_conv2d_dynamic_weight(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<15xf32>) {
   // CHECK: -> tensor<2x?x?x15xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>,  stride = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<?x?x?x?xf32>, tensor<15xf32>) -> tensor<2x?x?x15xf32>
   return
 }
 
@@ -876,7 +876,7 @@
 // CHECK-LABEL: @depthwise_conv2d_dynamic_bias
 func @depthwise_conv2d_dynamic_bias(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<?xf32>) {
   // CHECK: -> tensor<2x6x4x15xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>) -> tensor<2x6x4x15xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>,  stride = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<?xf32>) -> tensor<2x6x4x15xf32>
   return
 }
 
@@ -885,7 +885,7 @@
 // CHECK-LABEL: @depthwise_conv2d_padded
 func @depthwise_conv2d_padded(%arg0: tensor<2x8x9x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
   // CHECK: -> tensor<2x9x11x15xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [1, 2, 3, 4], stride = [1, 1]} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x9x11x15xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<1, 2, 3, 4>,  stride = dims<1, 1>} : (tensor<2x8x9x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x9x11x15xf32>
   return
 }
 
@@ -894,7 +894,7 @@
 // CHECK-LABEL: @depthwise_conv2d_dilated
 func @depthwise_conv2d_dilated(%arg0: tensor<2x12x14x3xf32>, %arg1: tensor<3x6x3x5xf32>, %arg2: tensor<15xf32>) {
   // CHECK: -> tensor<2x6x4x15xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [3, 2], pad = [0, 0, 0, 0], stride = [1, 1]} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<3, 2>, pad = dims<0, 0, 0, 0>,  stride = dims<1, 1>} : (tensor<2x12x14x3xf32>, tensor<3x6x3x5xf32>, tensor<15xf32>) -> tensor<2x6x4x15xf32>
   return
 }
 
@@ -903,7 +903,7 @@
 // CHECK-LABEL: @depthwise_conv2d_strided
 func @depthwise_conv2d_strided(%arg0: tensor<1x13x14x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
   // CHECK: -> tensor<1x5x7x1xf32>
-  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], stride = [3, 2]} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x5x7x1xf32>
+  %0 = "tosa.depthwise_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, pad = dims<0, 0, 0, 0>,  stride = dims<3, 2>} : (tensor<1x13x14x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x5x7x1xf32>
   return
 }
 
@@ -912,7 +912,7 @@
 // CHECK-LABEL: @transpose_conv2d_out_shape
 func @transpose_conv2d_out_shape(%arg0: tensor<2x?x?x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x8x9x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, 8, 9, -1], stride = [1, 1]} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x8x9x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, 8, 9, -1>,  stride = dims<1, 1>} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x8x9x5xf32>
   return
 }
 
@@ -921,7 +921,7 @@
 // CHECK-LABEL: @transpose_conv2d_static
 func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x18x19x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -930,7 +930,7 @@
 // CHECK-LABEL: @transpose_conv2d_static_dilated
 func @transpose_conv2d_static_dilated(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x20x29x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [2, 3], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<2, 3>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -939,7 +939,7 @@
 // CHECK-LABEL: @transpose_conv2d_static_strided
 func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x33x45x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [2, 3]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<2, 3>} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -948,7 +948,7 @@
 // CHECK-LABEL: @transpose_conv2d_dynamic_input
 func @transpose_conv2d_dynamic_input(%arg0: tensor<?x?x?x?xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<?x?x?x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x5xf32>
   return
 }
 
@@ -957,7 +957,7 @@
 // CHECK-LABEL: @transpose_conv2d_dynamic_weights
 func @transpose_conv2d_dynamic_weights(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<?x?x?x?xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x?x?x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -966,7 +966,7 @@
 // CHECK-LABEL: @transpose_conv2d_dynamic_bias
 func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<?xf32>) {
   // CHECK: -> tensor<2x8x9x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<2x8x9x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<2x8x9x5xf32>
   return
 }
 
@@ -975,7 +975,7 @@
 // CHECK-LABEL: @transpose_conv2d_padded
 func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x10x13x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [1, 3], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<1, 3>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
   return
 }
 
@@ -984,7 +984,7 @@
 // CHECK-LABEL: @transpose_conv2d_dilated
 func @transpose_conv2d_dilated(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x12x14x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [3, 2], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x12x14x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<3, 2>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<1, 1>} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x12x14x5xf32>
   return
 }
 
@@ -993,7 +993,7 @@
 // CHECK-LABEL: @transpose_conv2d_strided
 func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
   // CHECK: -> tensor<1x13x13x1xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], out_pad = [0, 0], out_shape = [-1, -1, -1, -1], stride = [3, 2]} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {dilation = dims<1, 1>, out_pad = dims<0, 0>, out_shape = dims<-1, -1, -1, -1>,  stride = dims<3, 2>} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
   return
 }
 
@@ -1002,7 +1002,7 @@
 // CHECK-LABEL: @resize_output_size
 func @resize_output_size(%arg0: tensor<2x?x?x3xi32>) {
   // CHECK: -> tensor<2x4x5x3xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [0, 1], offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = [4, 5], shift = 8 : i32, stride = [1, 1], stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<2x?x?x3xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<0, 1>, offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = dims<4, 5>, shift = 8 : i32, stride = dims<1, 1>, stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<2x?x?x3xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 
@@ -1011,7 +1011,7 @@
 // CHECK-LABEL: @resize_int_horizontal
 func @resize_int_horizontal(%arg0: tensor<1x2x4x1xi32>) {
   // CHECK: -> tensor<1x2x7x1xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [0, 0], offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = [-1, -1], shift = 8 : i32, stride = [256, 128], stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<0, 0>, offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = dims<-1, -1>, shift = 8 : i32, stride = dims<256, 128>, stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 
@@ -1020,7 +1020,7 @@
 // CHECK-LABEL: @resize_int_vertical
 func @resize_int_vertical(%arg0: tensor<1x2x4x1xi32>) {
   // CHECK: -> tensor<1x3x4x1xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [0, 0], offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = [-1, -1], shift = 8 : i32, stride = [128, 256], stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<0, 0>, offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = dims<-1, -1>, shift = 8 : i32, stride = dims<128, 256>, stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 
@@ -1029,7 +1029,7 @@
 // CHECK-LABEL: @resize_int_offsetted
 func @resize_int_offsetted(%arg0: tensor<1x2x4x1xi32>) {
   // CHECK: -> tensor<1x4x6x1xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [64, 64], offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = [-1, -1], shift = 8 : i32, stride = [64, 128], stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<64, 64>, offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = dims<-1, -1>, shift = 8 : i32, stride = dims<64, 128>, stride_fp = [0.000000e+00 : f32, 0.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 
@@ -1038,7 +1038,7 @@
 // CHECK-LABEL: @resize_fp_horizontal
 func @resize_fp_horizontal(%arg0: tensor<1x2x4x1xi32>) {
   // CHECK: -> tensor<1x2x7x1xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [0, 0], offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = [-1, -1], shift = 0 : i32, stride = [0, 0], stride_fp = [1.000000e+00 : f32, 5.000000e-01 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<0, 0>, offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = dims<-1, -1>, shift = 0 : i32, stride = dims<0, 0>, stride_fp = [1.000000e+00 : f32, 5.000000e-01 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 
@@ -1047,13 +1047,13 @@
 // CHECK-LABEL: @resize_fp_vertical
 func @resize_fp_vertical(%arg0: tensor<1x2x4x1xi32>) {
   // CHECK: -> tensor<1x3x4x1xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [0, 0], offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = [-1, -1], shift = 0 : i32, stride = [0, 0], stride_fp = [5.000000e-01 : f32, 1.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<0, 0>, offset_fp = [0.000000e+00 : f32, 0.000000e+00 : f32], output_size = dims<-1, -1>, shift = 0 : i32, stride = dims<0, 0>, stride_fp = [5.000000e-01 : f32, 1.000000e+00 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 // CHECK-LABEL: @resize_fp_offsetted
 func @resize_fp_offsetted(%arg0: tensor<1x2x4x1xi32>) {
   // CHECK: -> tensor<1x4x6x1xi32>
-  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = [0, 0], offset_fp = [2.500000e-01 : f32, 2.500000e-01 : f32], output_size = [-1, -1], shift = 0 : i32, stride = [0, 0], stride_fp = [2.500000e-01 : f32, 5.000000e-01 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
+  %0 = "tosa.resize"(%arg0) {mode = "NEAREST_NEIGHBOR", offset = dims<0, 0>, offset_fp = [2.500000e-01 : f32, 2.500000e-01 : f32], output_size = dims<-1, -1>, shift = 0 : i32, stride = dims<0, 0>, stride_fp = [2.500000e-01 : f32, 5.000000e-01 : f32]} : (tensor<1x2x4x1xi32>) -> tensor<?x?x?x?xi32>
   return
 }
 
diff --git a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
--- a/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
+++ b/mlir/test/lib/Dialect/Tosa/TosaTestPasses.cpp
@@ -148,8 +148,8 @@
 
   auto newTosaConv2DOp = rewriter.create<tosa::Conv2DOp>(
       op->getLoc(), newTosaConv2DOpType, tosaConv2DOp.input(),
-      tosaConv2DOp.weight(), tosaConv2DOp.bias(), tosaConv2DOp.pad(),
-      tosaConv2DOp.stride(), tosaConv2DOp.dilation());
+      tosaConv2DOp.weight(), tosaConv2DOp.bias(), tosaConv2DOp.padAttr(),
+      tosaConv2DOp.strideAttr(), tosaConv2DOp.dilationAttr());
 
   // Create rescale to quantized type
   double inputScale = inputQType.getScale();
diff --git a/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel b/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
--- a/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
+++ b/utils/bazel/llvm-project-overlay/mlir/BUILD.bazel
@@ -6902,6 +6902,7 @@
     name = "TosaDialectTdFiles",
     srcs = glob(["include/mlir/Dialect/Tosa/IR/*.td"]),
     deps = [
+        ":BuiltinDialectTdFiles",
         ":InferTypeOpInterfaceTdFiles",
         ":LoopLikeInterfaceTdFiles",
         ":OpBaseTdFiles",
@@ -6930,11 +6931,17 @@
             "include/mlir/Dialect/Tosa/IR/TosaStructs.cpp.inc",
         ),
         (
-            ["-gen-dialect-decls"],
+            [
+                "-gen-dialect-decls",
+                "-dialect=tosa",
+            ],
             "include/mlir/Dialect/Tosa/IR/TosaOpsDialect.h.inc",
         ),
         (
-            ["-gen-dialect-defs"],
+            [
+                "-gen-dialect-defs",
+                "-dialect=tosa",
+            ],
             "include/mlir/Dialect/Tosa/IR/TosaOpsDialect.cpp.inc",
         ),
         (