diff --git a/flang/include/flang/Optimizer/Dialect/FIRTypes.td b/flang/include/flang/Optimizer/Dialect/FIRTypes.td
--- a/flang/include/flang/Optimizer/Dialect/FIRTypes.td
+++ b/flang/include/flang/Optimizer/Dialect/FIRTypes.td
@@ -128,7 +128,7 @@
     static constexpr LenType singleton() { return 1; }
 
     /// Character has a LEN value which is not a compile-time known constant.
-    static constexpr LenType unknownLen() { return -1; }
+    static constexpr LenType unknownLen() { return mlir::ShapedType::kDynamicSize; }
 
     /// Character LEN is a runtime value.
     bool hasDynamicLen() { return getLen() == unknownLen(); }
diff --git a/flang/lib/Optimizer/Transforms/AffinePromotion.cpp b/flang/lib/Optimizer/Transforms/AffinePromotion.cpp
--- a/flang/lib/Optimizer/Transforms/AffinePromotion.cpp
+++ b/flang/lib/Optimizer/Transforms/AffinePromotion.cpp
@@ -410,7 +410,8 @@
   auto affineApply = rewriter.create<mlir::AffineApplyOp>(acoOp.getLoc(),
                                                           affineMap, indexArgs);
   auto arrayElementType = coordinateArrayElement(acoOp);
-  auto newType = mlir::MemRefType::get({-1}, arrayElementType);
+  auto newType =
+      mlir::MemRefType::get({mlir::ShapedType::kDynamicSize}, arrayElementType);
   auto arrayConvert = rewriter.create<fir::ConvertOp>(acoOp.getLoc(), newType,
                                                       acoOp.getMemref());
   return std::make_pair(affineApply, arrayConvert);
diff --git a/mlir/include/mlir/IR/BuiltinTypeInterfaces.td b/mlir/include/mlir/IR/BuiltinTypeInterfaces.td
--- a/mlir/include/mlir/IR/BuiltinTypeInterfaces.td
+++ b/mlir/include/mlir/IR/BuiltinTypeInterfaces.td
@@ -87,7 +87,8 @@
     // TODO: merge these two special values in a single one used everywhere.
     // Unfortunately, uses of `-1` have crept deep into the codebase now and are
     // hard to track.
-    static constexpr int64_t kDynamicSize = -1;
+    static constexpr int64_t kDynamicSize =
+        std::numeric_limits<int64_t>::min();
     static constexpr int64_t kDynamicStrideOrOffset =
         std::numeric_limits<int64_t>::min();
 
diff --git a/mlir/lib/AsmParser/TypeParser.cpp b/mlir/lib/AsmParser/TypeParser.cpp
--- a/mlir/lib/AsmParser/TypeParser.cpp
+++ b/mlir/lib/AsmParser/TypeParser.cpp
@@ -510,7 +510,7 @@
     if (consumeIf(Token::question)) {
       if (!allowDynamic)
         return emitError(loc, "expected static shape");
-      dimensions.push_back(-1);
+      dimensions.push_back(ShapedType::kDynamicSize);
     } else {
       int64_t value;
       if (failed(parseIntegerInDimensionList(value)))
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
@@ -843,7 +843,7 @@
                                   bool isDynamic) {
   if (isDynamic) {
     // TODO (natashaknk): Make dynamic intermediate shape not always be rank-1
-    intermediateShape = {-1};
+    intermediateShape = {ShapedType::kDynamicSize};
     return true;
   }
 
@@ -1784,7 +1784,7 @@
 
     SmallVector<Value> dynDims;
     for (int i = 0; i < inputTy.getRank(); i++) {
-      if (inputTy.isDynamicDim(i) || multiples[i] == -1) {
+      if (inputTy.isDynamicDim(i) || multiples[i] == ShapedType::kDynamicSize) {
         dynDims.push_back(rewriter.create<tensor::DimOp>(loc, input, i));
       }
     }
diff --git a/mlir/lib/Dialect/Affine/Utils/Utils.cpp b/mlir/lib/Dialect/Affine/Utils/Utils.cpp
--- a/mlir/lib/Dialect/Affine/Utils/Utils.cpp
+++ b/mlir/lib/Dialect/Affine/Utils/Utils.cpp
@@ -1796,7 +1796,7 @@
     bool isDynDim =
         isNormalizedMemRefDynamicDim(d, layoutMap, memrefTypeDynDims, context);
     if (isDynDim) {
-      newShape[d] = -1;
+      newShape[d] = ShapedType::kDynamicSize;
     } else {
       // The lower bound for the shape is always zero.
       Optional<int64_t> ubConst =
diff --git a/mlir/lib/Dialect/LLVMIR/IR/LLVMTypeSyntax.cpp b/mlir/lib/Dialect/LLVMIR/IR/LLVMTypeSyntax.cpp
--- a/mlir/lib/Dialect/LLVMIR/IR/LLVMTypeSyntax.cpp
+++ b/mlir/lib/Dialect/LLVMIR/IR/LLVMTypeSyntax.cpp
@@ -266,11 +266,12 @@
 
   // We parsed a generic dimension list, but vectors only support two forms:
   //  - single non-dynamic entry in the list (fixed vector);
-  //  - two elements, the first dynamic (indicated by -1) and the second
+  //  - two elements, the first dynamic (indicated by ShapedType::kDynamicSize)
+  //  and the second
   //    non-dynamic (scalable vector).
   if (dims.empty() || dims.size() > 2 ||
-      ((dims.size() == 2) ^ (dims[0] == -1)) ||
-      (dims.size() == 2 && dims[1] == -1)) {
+      ((dims.size() == 2) ^ (dims[0] == ShapedType::kDynamicSize)) ||
+      (dims.size() == 2 && dims[1] == ShapedType::kDynamicSize)) {
     parser.emitError(dimPos)
         << "expected '? x <integer> x <type>' or '<integer> x <type>'";
     return Type();
diff --git a/mlir/lib/Dialect/Linalg/Transforms/Promotion.cpp b/mlir/lib/Dialect/Linalg/Transforms/Promotion.cpp
--- a/mlir/lib/Dialect/Linalg/Transforms/Promotion.cpp
+++ b/mlir/lib/Dialect/Linalg/Transforms/Promotion.cpp
@@ -64,7 +64,8 @@
   }
 
   // Fallback dynamic buffer.
-  auto dynamicBufferType = MemRefType::get(-1, b.getIntegerType(8));
+  auto dynamicBufferType =
+      MemRefType::get(ShapedType::kDynamicSize, b.getIntegerType(8));
   Value mul = b.createOrFold<arith::MulIOp>(
       b.create<arith::ConstantIndexOp>(width), allocSize);
   if (options.useAlloca)
@@ -242,7 +243,7 @@
     partialSizes.push_back(
         b.createOrFold<memref::DimOp>(loc, subView, resultDimIdx++));
   }
-  SmallVector<int64_t, 4> dynSizes(fullSizes.size(), -1);
+  SmallVector<int64_t, 4> dynSizes(fullSizes.size(), ShapedType::kDynamicSize);
   // If a callback is not specified, then use the default implementation for
   // allocating the promoted buffer.
   Optional<Value> fullLocalView = allocationFn(b, subView, fullSizes, layout);
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -185,7 +185,7 @@
     for (unsigned dim = 0, e = memrefType.getRank(); dim < e; ++dim) {
       int64_t dimSize = memrefType.getDimSize(dim);
       // If this is already static dimension, keep it.
-      if (dimSize != -1) {
+      if (dimSize != ShapedType::kDynamicSize) {
         newShapeConstants.push_back(dimSize);
         continue;
       }
@@ -197,7 +197,7 @@
         newShapeConstants.push_back(constantIndexOp.value());
       } else {
         // Dynamic shape dimension not folded; copy dynamicSize from old memref.
-        newShapeConstants.push_back(-1);
+        newShapeConstants.push_back(ShapedType::kDynamicSize);
         dynamicSizes.push_back(dynamicSize);
       }
       dynamicDimPos++;
@@ -666,7 +666,8 @@
 
     for (unsigned i = 0, e = aT.getRank(); i != e; ++i) {
       int64_t aDim = aT.getDimSize(i), bDim = bT.getDimSize(i);
-      if (aDim != -1 && bDim != -1 && aDim != bDim)
+      if (aDim != ShapedType::kDynamicSize &&
+          bDim != ShapedType::kDynamicSize && aDim != bDim)
         return false;
     }
     return true;
diff --git a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
--- a/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
+++ b/mlir/lib/Dialect/SparseTensor/IR/SparseTensorDialect.cpp
@@ -600,7 +600,7 @@
               "sum of all the concatenation dimensions of the input tensors.");
       }
     } else {
-      int prev = dstDim;
+      int64_t prev = dstDim;
       for (auto src : getInputs()) {
         auto d = src.getType().cast<RankedTensorType>().getShape()[i];
         if (prev != ShapedType::kDynamicSize && d != prev)
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
@@ -434,7 +434,7 @@
   }
 
   // Determine the dimension size along the concatenation axis.
-  int concatDimSize = 0;
+  int64_t concatDimSize = 0;
   for (auto operand : operands) {
     ShapeAdaptor operandShape = operands.getShape(operand);
 
@@ -645,7 +645,7 @@
   // Any non dynamic dimension can be multiplied to a known size.
   outputShape.reserve(multiples.size());
   for (int i = 0, s = inputShape.getRank(); i < s; i++) {
-    int dim = inputShape.getDimSize(i);
+    int64_t dim = inputShape.getDimSize(i);
     if (dim != ShapedType::kDynamicSize)
       dim *= multipleValues[i];
     outputShape.push_back(dim);
@@ -800,8 +800,8 @@
 
   outputShape[0] = inputShape.getDimSize(0);
   outputShape[3] = inputShape.getDimSize(3);
-  int32_t inputHeight = inputShape.getDimSize(1);
-  int32_t inputWidth = inputShape.getDimSize(2);
+  int64_t inputHeight = inputShape.getDimSize(1);
+  int64_t inputWidth = inputShape.getDimSize(2);
 
   if ((inputHeight == ShapedType::kDynamicSize) ||
       (inputWidth == ShapedType::kDynamicSize))
@@ -961,7 +961,7 @@
     SmallVectorImpl<ShapedTypeComponents> &inferredReturnShapes) {
   ShapeAdaptor inputShape = operands.getShape(0);
   llvm::SmallVector<int64_t> outputShape;
-  outputShape.resize(4, -1);
+  outputShape.resize(4, ShapedType::kDynamicSize);
 
   // We only know the rank if the input type is unranked.
   if (!inputShape) {
@@ -973,8 +973,8 @@
   outputShape[0] = inputShape.getDimSize(0);
   outputShape[3] = inputShape.getDimSize(3);
 
-  int32_t height = inputShape.getDimSize(1);
-  int32_t width = inputShape.getDimSize(2);
+  int64_t height = inputShape.getDimSize(1);
+  int64_t width = inputShape.getDimSize(2);
 
   llvm::SmallVector<int64_t> kernel;
   llvm::SmallVector<int64_t> stride;
@@ -984,12 +984,12 @@
   getI64Values(attributes.get("stride").cast<ArrayAttr>(), stride);
   getI64Values(attributes.get("pad").cast<ArrayAttr>(), pad);
 
-  if (height != -1) {
+  if (height != ShapedType::kDynamicSize) {
     int32_t padded = height + pad[0] + pad[1] - kernel[0];
     outputShape[1] = padded / stride[0] + 1;
   }
 
-  if (width != -1) {
+  if (width != ShapedType::kDynamicSize) {
     int32_t padded = width + pad[2] + pad[3] - kernel[1];
     outputShape[2] = padded / stride[1] + 1;
   }
@@ -1005,10 +1005,10 @@
   llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamicSize);
   Conv2DOp::Adaptor adaptor(operands.getValues(), attributes);
 
-  int32_t inputWidth = ShapedType::kDynamicSize;
-  int32_t inputHeight = ShapedType::kDynamicSize;
-  int32_t weightWidth = ShapedType::kDynamicSize;
-  int32_t weightHeight = ShapedType::kDynamicSize;
+  int64_t inputWidth = ShapedType::kDynamicSize;
+  int64_t inputHeight = ShapedType::kDynamicSize;
+  int64_t weightWidth = ShapedType::kDynamicSize;
+  int64_t weightHeight = ShapedType::kDynamicSize;
 
   // Input shape describes input width/height and batch.
 
@@ -1072,13 +1072,13 @@
   llvm::SmallVector<int64_t> outputShape(5, ShapedType::kDynamicSize);
   Conv3DOp::Adaptor adaptor(operands.getValues(), attributes);
 
-  int32_t inputWidth = ShapedType::kDynamicSize;
-  int32_t inputHeight = ShapedType::kDynamicSize;
-  int32_t inputDepth = ShapedType::kDynamicSize;
+  int64_t inputWidth = ShapedType::kDynamicSize;
+  int64_t inputHeight = ShapedType::kDynamicSize;
+  int64_t inputDepth = ShapedType::kDynamicSize;
 
-  int32_t weightWidth = ShapedType::kDynamicSize;
-  int32_t weightHeight = ShapedType::kDynamicSize;
-  int32_t weightDepth = ShapedType::kDynamicSize;
+  int64_t weightWidth = ShapedType::kDynamicSize;
+  int64_t weightHeight = ShapedType::kDynamicSize;
+  int64_t weightDepth = ShapedType::kDynamicSize;
 
   // Input shape describes input width/height and batch.
   ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
@@ -1163,13 +1163,13 @@
   llvm::SmallVector<int64_t> outputShape(4, ShapedType::kDynamicSize);
   DepthwiseConv2DOp::Adaptor adaptor(operands.getValues(), attributes);
 
-  int32_t inputWidth = ShapedType::kDynamicSize;
-  int32_t inputHeight = ShapedType::kDynamicSize;
-  int32_t inputChannels = ShapedType::kDynamicSize;
+  int64_t inputWidth = ShapedType::kDynamicSize;
+  int64_t inputHeight = ShapedType::kDynamicSize;
+  int64_t inputChannels = ShapedType::kDynamicSize;
 
-  int32_t weightWidth = ShapedType::kDynamicSize;
-  int32_t weightHeight = ShapedType::kDynamicSize;
-  int32_t depthChannels = ShapedType::kDynamicSize;
+  int64_t weightWidth = ShapedType::kDynamicSize;
+  int64_t weightHeight = ShapedType::kDynamicSize;
+  int64_t depthChannels = ShapedType::kDynamicSize;
 
   // Input shape describes input width/height and batch.
   ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
@@ -1244,10 +1244,10 @@
   llvm::SmallVector<int64_t> outputShape;
   getI64Values(adaptor.getOutShape(), outputShape);
 
-  int32_t inputWidth = ShapedType::kDynamicSize;
-  int32_t inputHeight = ShapedType::kDynamicSize;
-  int32_t weightWidth = ShapedType::kDynamicSize;
-  int32_t weightHeight = ShapedType::kDynamicSize;
+  int64_t inputWidth = ShapedType::kDynamicSize;
+  int64_t inputHeight = ShapedType::kDynamicSize;
+  int64_t weightWidth = ShapedType::kDynamicSize;
+  int64_t weightHeight = ShapedType::kDynamicSize;
 
   // Input shape describes input width/height and batch.
   ShapeAdaptor inputShape = operands.getShape(adaptor.getInput());
@@ -1287,14 +1287,18 @@
       !ShapedType::isDynamic(weightHeight)) {
     int32_t calculateSize =
         (inputHeight - 1) * stride[0] - padding[0] - padding[1] + weightHeight;
-    outputShape[1] = outputShape[1] == -1 ? calculateSize : outputShape[1];
+    outputShape[1] = outputShape[1] == ShapedType::kDynamicSize
+                         ? calculateSize
+                         : outputShape[1];
   }
 
   if (!ShapedType::isDynamic(inputWidth) &&
       !ShapedType::isDynamic(weightWidth)) {
     int32_t calculateSize =
         (inputWidth - 1) * stride[1] - padding[2] - padding[3] + weightWidth;
-    outputShape[2] = outputShape[2] == -1 ? calculateSize : outputShape[2];
+    outputShape[2] = outputShape[2] == ShapedType::kDynamicSize
+                         ? calculateSize
+                         : outputShape[2];
   }
 
   inferredReturnShapes.push_back(ShapedTypeComponents(outputShape));
diff --git a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp
--- a/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp
+++ b/mlir/lib/Dialect/Tosa/Transforms/TosaDecomposeConv2D.cpp
@@ -52,9 +52,10 @@
 
     // Reshape input to [N,IH,IW,IC] -> [N * IH * IW, IC].
     ArrayRef<int64_t> inputShape = inputType.getShape();
-    int64_t combined = inputShape[0] * inputShape[1] * inputShape[2];
-    if (combined < 0)
-      combined = ShapedType::kDynamicSize;
+    int64_t combined = ShapedType::kDynamicSize;
+    if (numDynamic == 0)
+      combined = inputShape[0] * inputShape[1] * inputShape[2];
+
     llvm::SmallVector<int64_t, 2> revisedInputShape{combined, inputShape[3]};
     auto revisedInputShapeType =
         RankedTensorType::get(revisedInputShape, inputType.getElementType());
diff --git a/mlir/lib/Dialect/Traits.cpp b/mlir/lib/Dialect/Traits.cpp
--- a/mlir/lib/Dialect/Traits.cpp
+++ b/mlir/lib/Dialect/Traits.cpp
@@ -80,7 +80,7 @@
 
   // Check each dimension is consistent.
   for (; i1 != e1 && i2 != e2; ++i1, ++i2, ++iR) {
-    if (*i1 == -1 || *i2 == -1) {
+    if (*i1 == ShapedType::kDynamicSize || *i2 == ShapedType::kDynamicSize) {
       // One or both dimensions is unknown. Follow TensorFlow behavior:
       // - If either dimension is greater than 1, we assume that the program is
       //   correct, and the other dimension will be broadcast to match it.
@@ -94,7 +94,7 @@
       } else if (*i2 == 1) {
         *iR = *i1;
       } else {
-        *iR = -1;
+        *iR = ShapedType::kDynamicSize;
       }
     } else {
       if (*i1 == *i2 || *i2 == 1) {
@@ -199,7 +199,8 @@
     // then it is compatible, else if the inferred dim is 1 then it is also
     // compatible. But if the existing dim is 1 and the inferred is greater than
     // 1 then flag.
-    return dim1 == dim2 || dim1 == -1 || dim2 == -1 || dim1 == 1;
+    return dim1 == dim2 || dim1 == ShapedType::kDynamicSize ||
+           dim2 == ShapedType::kDynamicSize || dim1 == 1;
   };
   if (inferred.size() != existing.size())
     return false;
diff --git a/mlir/lib/IR/BuiltinTypes.cpp b/mlir/lib/IR/BuiltinTypes.cpp
--- a/mlir/lib/IR/BuiltinTypes.cpp
+++ b/mlir/lib/IR/BuiltinTypes.cpp
@@ -335,7 +335,7 @@
                          ArrayRef<int64_t> shape, Type elementType,
                          Attribute encoding) {
   for (int64_t s : shape)
-    if (s < -1)
+    if (s < 0 && s != ShapedType::kDynamicSize)
       return emitError() << "invalid tensor dimension size";
   if (auto v = encoding.dyn_cast_or_null<VerifiableTensorEncoding>())
     if (failed(v.verifyEncoding(shape, elementType, emitError)))
@@ -656,9 +656,9 @@
   if (!BaseMemRefType::isValidElementType(elementType))
     return emitError() << "invalid memref element type";
 
-  // Negative sizes are not allowed except for `-1` that means dynamic size.
+  // Negative sizes are not allowed except for `kDynamicSize`.
   for (int64_t s : shape)
-    if (s < -1)
+    if (s < 0 && s != ShapedType::kDynamicSize)
       return emitError() << "invalid memref size";
 
   assert(layout && "missing layout specification");
diff --git a/mlir/python/mlir/dialects/_tensor_ops_ext.py b/mlir/python/mlir/dialects/_tensor_ops_ext.py
--- a/mlir/python/mlir/dialects/_tensor_ops_ext.py
+++ b/mlir/python/mlir/dialects/_tensor_ops_ext.py
@@ -4,6 +4,7 @@
 
 try:
   from ..ir import *
+  import numpy
 except ImportError as e:
   raise RuntimeError("Error loading imports from extension module") from e
 
@@ -30,7 +31,7 @@
       if isinstance(s, int):
         static_sizes.append(s)
       else:
-        static_sizes.append(-1)
+        static_sizes.append(ShapedType.get_dynamic_size())
         dynamic_sizes.append(s)
     result_type = RankedTensorType.get(static_sizes, element_type)
     op = self.build_generic(
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
@@ -559,7 +559,7 @@
 // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]
 func.func @test_reshape_downrank_dyn(%arg0: tensor<2x?xf32>) -> tensor<?xf32> {
   // CHECK: [[RESHAPE:%.+]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1]]
-  %0 = "tosa.reshape"(%arg0) {new_shape = [-1]} : (tensor<2x?xf32>) -> tensor<?xf32>
+  %0 = "tosa.reshape"(%arg0) {new_shape = [-9223372036854775808]} : (tensor<2x?xf32>) -> tensor<?xf32>
   // CHECK: return [[RESHAPE]]
   return %0 : tensor<?xf32>
 }
@@ -581,7 +581,7 @@
 // CHECK-SAME: (%[[ARG0:[0-9a-zA-Z_]*]]
 func.func @test_reshape_uprank_dyn(%arg0: tensor<?xf32>) -> tensor<2x?xf32> {
   // CHECK: [[RESHAPE:%.+]] = tensor.expand_shape %[[ARG0]] {{\[}}[0, 1]]
-  %0 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<?xf32>) -> tensor<2x?xf32>
+  %0 = "tosa.reshape"(%arg0) {new_shape = [2, -9223372036854775808]} : (tensor<?xf32>) -> tensor<2x?xf32>
   // CHECK: return [[RESHAPE]]
   return %0 : tensor<2x?xf32>
 }
@@ -605,7 +605,7 @@
 func.func @test_reshape_samerank_dyn(%arg0: tensor<?x2xf32>) -> tensor<2x?xf32> {
   // CHECK-NEXT: %[[RESHAPE1:.*]] = tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1]]
   // CHECK-NEXT: %[[RESHAPE2:.*]] = tensor.expand_shape %[[RESHAPE1]] {{\[}}[0, 1]]
-  %0 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<?x2xf32>) -> tensor<2x?xf32>
+  %0 = "tosa.reshape"(%arg0) {new_shape = [2, -9223372036854775808]} : (tensor<?x2xf32>) -> tensor<2x?xf32>
   // CHECK-NEXT: return %[[RESHAPE2]]
   return %0 : tensor<2x?xf32>
 }
@@ -627,7 +627,7 @@
 func.func @test_reshape_downrank_6D_dyn(%arg0: tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32> {
   // CHECK: tensor.collapse_shape %[[ARG0]] {{\[}}[0, 1, 2, 3, 4, 5]]
   // CHECK: tensor.expand_shape %{{.*}} {{\[}}[0, 1, 2]]
-  %0 = "tosa.reshape"(%arg0) {new_shape = [-1, 5, 77]} : (tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32>
+  %0 = "tosa.reshape"(%arg0) {new_shape = [-9223372036854775808, 5, 77]} : (tensor<1x2x?x5x7x11xf32>) -> tensor<?x5x77xf32>
   return %0 : tensor<?x5x77xf32>
 }
 
@@ -1302,7 +1302,7 @@
   // CHECK:   linalg.yield %[[ARG1]] : i8
   // CHECK: %[[COLLAPSED:.+]] = tensor.collapse_shape %[[GENERIC]] {{\[}}[0, 1, 2, 3]]
   // CHECK: tensor.expand_shape %[[COLLAPSED]] {{\[}}[0, 1]]
-  %0 = "tosa.tile"(%arg0) {multiples = [2, -1]} : (tensor<2x3xi8>)  -> (tensor<2x?xi8>)
+  %0 = "tosa.tile"(%arg0) {multiples = [2, -9223372036854775808]} : (tensor<2x3xi8>)  -> (tensor<2x?xi8>)
 
   return
 }
diff --git a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
--- a/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
+++ b/mlir/test/Conversion/TosaToTensor/tosa-to-tensor.mlir
@@ -16,6 +16,6 @@
   // CHECK: %[[C2:.+]] = arith.constant 2 : index
   // CHECK: %[[SUB:.+]] = arith.subi %[[DIM]], %[[C2]]
   // CHECK: tensor.extract_slice %arg0[2] [%[[SUB]]] [1]
-  %0 = "tosa.slice"(%arg0) {start = [2], size = [-1]} : (tensor<?xf32>)  -> (tensor<?xf32>)
+  %0 = "tosa.slice"(%arg0) {start = [2], size = [-9223372036854775808]} : (tensor<?xf32>)  -> (tensor<?xf32>)
   return %0 : tensor<?xf32>
 }
diff --git a/mlir/test/Dialect/Linalg/transform-op-split.mlir b/mlir/test/Dialect/Linalg/transform-op-split.mlir
--- a/mlir/test/Dialect/Linalg/transform-op-split.mlir
+++ b/mlir/test/Dialect/Linalg/transform-op-split.mlir
@@ -179,7 +179,7 @@
 transform.sequence failures(propagate) {
 ^bb1(%arg1: !pdl.operation):
   // expected-error @below {{expects either a dynamic or a static split point to be provided}}
-  %0:2 = "transform.structured.split"(%arg1) { dimension = 1, static_split_point = -1 } : (!pdl.operation) -> (!pdl.operation, !pdl.operation)
+  %0:2 = "transform.structured.split"(%arg1) { dimension = 1, static_split_point = -9223372036854775808 } : (!pdl.operation) -> (!pdl.operation, !pdl.operation)
 }
 
 // -----
diff --git a/mlir/test/Dialect/Shape/canonicalize.mlir b/mlir/test/Dialect/Shape/canonicalize.mlir
--- a/mlir/test/Dialect/Shape/canonicalize.mlir
+++ b/mlir/test/Dialect/Shape/canonicalize.mlir
@@ -815,7 +815,7 @@
   // CHECK-NEXT: consume.witness
   // CHECK-NEXT: return
   %cs0 = shape.const_shape [8, 1] : !shape.shape
-  %cs1 = shape.const_shape [1, -1] : !shape.shape
+  %cs1 = shape.const_shape [1, -9223372036854775808] : !shape.shape
   %0 = shape.cstr_broadcastable %cs0, %cs0, %cs1 : !shape.shape, !shape.shape, !shape.shape
   "consume.witness"(%0) : (!shape.witness) -> ()
   return
@@ -830,7 +830,7 @@
   // CHECK-NEXT: return
   %cs0 = shape.const_shape [8, 1] : !shape.shape
   %cs1 = shape.const_shape [1, 8] : !shape.shape
-  %cs2 = shape.const_shape [1, -1] : !shape.shape
+  %cs2 = shape.const_shape [1, -9223372036854775808] : !shape.shape
   %0 = shape.cstr_broadcastable %cs0, %cs1, %cs2 : !shape.shape, !shape.shape, !shape.shape
   "consume.witness"(%0) : (!shape.witness) -> ()
   return
@@ -844,8 +844,8 @@
   // CHECK-NEXT: consume.witness
   // CHECK-NEXT: return
   %cs0 = shape.const_shape [8, 1] : !shape.shape
-  %cs1 = shape.const_shape [1, -1] : !shape.shape
-  %cs2 = shape.const_shape [8, -1] : !shape.shape
+  %cs1 = shape.const_shape [1, -9223372036854775808] : !shape.shape
+  %cs2 = shape.const_shape [8, -9223372036854775808] : !shape.shape
   %0 = shape.cstr_broadcastable %cs0, %cs1, %cs2 : !shape.shape, !shape.shape, !shape.shape
   "consume.witness"(%0) : (!shape.witness) -> ()
   return
diff --git a/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir b/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir
--- a/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir
+++ b/mlir/test/Dialect/Tosa/tosa-decompose-conv2d.mlir
@@ -44,10 +44,10 @@
 // CHECK-SAME:                                     %[[VAL_1:.*]]: tensor<384x1x1x64xi8>,
 // CHECK-SAME:                                     %[[VAL_2:.*]]: tensor<384xi32>) -> tensor<?x14x14x384xi32> {
 func.func @conv_with_dynamic_dim(%arg0: tensor<?x14x14x64xi8>, %arg1: tensor<384x1x1x64xi8>, %arg2: tensor<384xi32>) -> tensor<?x14x14x384xi32> {
-// CHECK:           %[[VAL_3:.*]] = "tosa.reshape"(%[[VAL_0]]) {new_shape = [-1, 64]} : (tensor<?x14x14x64xi8>) -> tensor<?x64xi8>
+// CHECK:           %[[VAL_3:.*]] = "tosa.reshape"(%[[VAL_0]]) {new_shape = [-9223372036854775808, 64]} : (tensor<?x14x14x64xi8>) -> tensor<?x64xi8>
 // CHECK:           %[[VAL_4:.*]] = "tosa.reshape"(%[[VAL_1]]) {new_shape = [384, 64]} : (tensor<384x1x1x64xi8>) -> tensor<384x64xi8>
 // CHECK:           %[[VAL_5:.*]] = "tosa.fully_connected"(%[[VAL_3]], %[[VAL_4]], %[[VAL_2]]) {quantization_info = #tosa.conv_quant<input_zp = -6, weight_zp = 11>} : (tensor<?x64xi8>, tensor<384x64xi8>, tensor<384xi32>) -> tensor<?x384xi32>
-// CHECK:           %[[VAL_6:.*]] = "tosa.reshape"(%[[VAL_5]]) {new_shape = [-1, 14, 14, 384]} : (tensor<?x384xi32>) -> tensor<?x14x14x384xi32>
+// CHECK:           %[[VAL_6:.*]] = "tosa.reshape"(%[[VAL_5]]) {new_shape = [-9223372036854775808, 14, 14, 384]} : (tensor<?x384xi32>) -> tensor<?x14x14x384xi32>
 // CHECK:           return %[[VAL_6]] : tensor<?x14x14x384xi32>
 // CHECK:         }
   %0 = "tosa.conv2d"(%arg0, %arg1, %arg2) {dilation = [1, 1], pad = [0, 0, 0, 0], quantization_info = #tosa.conv_quant<input_zp = -6, weight_zp = 11>, stride = [1, 1]} : (tensor<?x14x14x64xi8>, tensor<384x1x1x64xi8>, tensor<384xi32>) -> tensor<?x14x14x384xi32>
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
@@ -375,11 +375,11 @@
   // CHECK: "tosa.reshape"(%arg0) {new_shape = [16]} : (tensor<4x4xi32>) -> tensor<16xi32>
   %0 = "tosa.reshape"(%arg0) {new_shape = [16]} : (tensor<4x4xi32>)  -> tensor<?xi32>
 
-  // CHECK: "tosa.reshape"(%arg0) {new_shape = [-1]} : (tensor<4x4xi32>) -> tensor<16xi32>
-  %1 = "tosa.reshape"(%arg0) {new_shape = [-1]} : (tensor<4x4xi32>)  -> tensor<?xi32>
+  // CHECK: "tosa.reshape"(%arg0) {new_shape = [-9223372036854775808]} : (tensor<4x4xi32>) -> tensor<16xi32>
+  %1 = "tosa.reshape"(%arg0) {new_shape = [-9223372036854775808]} : (tensor<4x4xi32>)  -> tensor<?xi32>
 
-  // CHECK: "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<4x4xi32>) -> tensor<2x8xi32>
-  %2 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<4x4xi32>)  -> tensor<?x?xi32>
+  // CHECK: "tosa.reshape"(%arg0) {new_shape = [2, -9223372036854775808]} : (tensor<4x4xi32>) -> tensor<2x8xi32>
+  %2 = "tosa.reshape"(%arg0) {new_shape = [2, -9223372036854775808]} : (tensor<4x4xi32>)  -> tensor<?x?xi32>
 
   return
 }
@@ -390,11 +390,11 @@
   // CHECK: %0 = "tosa.reshape"(%arg0) {new_shape = [16]} : (tensor<4x?xi32>) -> tensor<16xi32>
   %0 = "tosa.reshape"(%arg0) {new_shape = [16]} : (tensor<4x?xi32>)  -> tensor<?xi32>
 
-  // CHECK: %1 = "tosa.reshape"(%arg0) {new_shape = [-1]} : (tensor<4x?xi32>) -> tensor<?xi32>
-  %1 = "tosa.reshape"(%arg0) {new_shape = [-1]} : (tensor<4x?xi32>)  -> tensor<?xi32>
+  // CHECK: %1 = "tosa.reshape"(%arg0) {new_shape = [-9223372036854775808]} : (tensor<4x?xi32>) -> tensor<?xi32>
+  %1 = "tosa.reshape"(%arg0) {new_shape = [-9223372036854775808]} : (tensor<4x?xi32>)  -> tensor<?xi32>
 
-  // CHECK: %2 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<4x?xi32>) -> tensor<2x?xi32>
-  %2 = "tosa.reshape"(%arg0) {new_shape = [2, -1]} : (tensor<4x?xi32>)  -> tensor<?x?xi32>
+  // CHECK: %2 = "tosa.reshape"(%arg0) {new_shape = [2, -9223372036854775808]} : (tensor<4x?xi32>) -> tensor<2x?xi32>
+  %2 = "tosa.reshape"(%arg0) {new_shape = [2, -9223372036854775808]} : (tensor<4x?xi32>)  -> tensor<?x?xi32>
 
   return
 }
@@ -897,7 +897,7 @@
 // CHECK-LABEL: @transpose_conv2d_out_shape
 func.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) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, 8, 9, -9223372036854775808], stride = [1, 1]} : (tensor<2x?x?x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x8x9x5xf32>
   return
 }
 
@@ -906,7 +906,7 @@
 // CHECK-LABEL: @transpose_conv2d_static
 func.func @transpose_conv2d_static(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x18x19x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [1, 1]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -915,7 +915,7 @@
 // CHECK-LABEL: @transpose_conv2d_static_strided
 func.func @transpose_conv2d_static_strided(%arg0: tensor<2x16x14x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x33x45x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [2, 3]} : (tensor<2x16x14x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -924,7 +924,7 @@
 // CHECK-LABEL: @transpose_conv2d_dynamic_input
 func.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) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [1, 1]} : (tensor<?x?x?x?xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<?x?x?x5xf32>
   return
 }
 
@@ -933,7 +933,7 @@
 // CHECK-LABEL: @transpose_conv2d_dynamic_weights
 func.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) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [1, 1]} : (tensor<2x6x4x3xf32>, tensor<?x?x?x?xf32>, tensor<5xf32>) -> tensor<2x?x?x5xf32>
   return
 }
 
@@ -942,7 +942,7 @@
 // CHECK-LABEL: @transpose_conv2d_dynamic_bias
 func.func @transpose_conv2d_dynamic_bias(%arg0: tensor<2x6x4x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<?xf32>) {
   // CHECK: -> tensor<2x8x9x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [1, 1]} : (tensor<2x6x4x3xf32>, tensor<5x3x6x3xf32>, tensor<?xf32>) -> tensor<2x8x9x5xf32>
   return
 }
 
@@ -951,14 +951,14 @@
 // CHECK-LABEL: @transpose_conv2d_padded
 func.func @transpose_conv2d_padded(%arg0: tensor<2x9x11x3xf32>, %arg1: tensor<5x3x6x3xf32>, %arg2: tensor<5xf32>) {
   // CHECK: -> tensor<2x10x13x5xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [1, 0, 3, 0], out_shape = [-1, -1, -1, -1], stride = [1, 1]} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
+  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [1, 0, 3, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [1, 1]} : (tensor<2x9x11x3xf32>, tensor<5x3x6x3xf32>, tensor<5xf32>) -> tensor<2x10x13x5xf32>
   return
 }
 
 // CHECK-LABEL: @transpose_conv2d_strided
 func.func @transpose_conv2d_strided(%arg0: tensor<1x5x7x1xf32>, %arg1: tensor<1x1x1x1xf32>, %arg2: tensor<1xf32>) {
   // CHECK: -> tensor<1x13x13x1xf32>
-  %0 = "tosa.transpose_conv2d"(%arg0, %arg1, %arg2) {out_pad = [0, 0, 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) {out_pad = [0, 0, 0, 0], out_shape = [-9223372036854775808, -9223372036854775808, -9223372036854775808, -9223372036854775808], stride = [3, 2]} : (tensor<1x5x7x1xf32>, tensor<1x1x1x1xf32>, tensor<1xf32>) -> tensor<1x13x13x1xf32>
   return
 }
 
diff --git a/mlir/test/python/dialects/linalg/ops.py b/mlir/test/python/dialects/linalg/ops.py
--- a/mlir/test/python/dialects/linalg/ops.py
+++ b/mlir/test/python/dialects/linalg/ops.py
@@ -23,7 +23,8 @@
       #  CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
       #  CHECK-NEXT: %[[RES:.*]] = linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : tensor<12x?xf32>) -> tensor<12x?xf32>
       #  CHECK-NEXT: return %[[RES]] : tensor<12x?xf32>
-      @func.FuncOp.from_py_func(RankedTensorType.get((12, -1), f32))
+      @func.FuncOp.from_py_func(
+          RankedTensorType.get((12, ShapedType.get_dynamic_size()), f32))
       def fill_tensor(out):
         zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
         return linalg.fill(zero, outs=[out])
@@ -33,7 +34,8 @@
       #  CHECK-NEXT: %[[CST:.*]] = arith.constant 0.0{{.*}} : f32
       #  CHECK-NEXT: linalg.fill ins(%[[CST]] : f32) outs(%[[OUT]] : memref<12x?xf32>)
       #  CHECK-NEXT: return
-      @func.FuncOp.from_py_func(MemRefType.get((12, -1), f32))
+      @func.FuncOp.from_py_func(
+          MemRefType.get((12, ShapedType.get_dynamic_size()), f32))
       def fill_buffer(out):
         zero = arith.ConstantOp(value=FloatAttr.get(f32, 0.), result=f32).result
         linalg.fill(zero, outs=[out])
diff --git a/mlir/test/python/dialects/shape.py b/mlir/test/python/dialects/shape.py
--- a/mlir/test/python/dialects/shape.py
+++ b/mlir/test/python/dialects/shape.py
@@ -20,7 +20,7 @@
     f32 = F32Type.get()
     with InsertionPoint(module.body):
       @func.FuncOp.from_py_func(
-          RankedTensorType.get((12, -1), f32))
+          RankedTensorType.get((12, ShapedType.get_dynamic_size()), f32))
       def const_shape_tensor(arg):
         return shape.ConstShapeOp(
           DenseElementsAttr.get(np.array([10, 20], dtype=np.int64), type=IndexType.get()))
diff --git a/mlir/test/python/dialects/tensor.py b/mlir/test/python/dialects/tensor.py
--- a/mlir/test/python/dialects/tensor.py
+++ b/mlir/test/python/dialects/tensor.py
@@ -21,7 +21,10 @@
     indexType = IndexType.get()
     with InsertionPoint(module.body):
 
-      @func.FuncOp.from_py_func(RankedTensorType.get((-1, -1), f32Type))
+      @func.FuncOp.from_py_func(
+          RankedTensorType.get(
+              (ShapedType.get_dynamic_size(), ShapedType.get_dynamic_size()),
+              f32Type))
       #      CHECK: func @tensor_static_dim
       # CHECK-SAME:     %[[ARG0:.+]]: tensor<?x?xf32>
       #  CHECK-DAG:   %[[C0:.+]] = arith.constant 0 : index
diff --git a/mlir/test/python/dialects/vector.py b/mlir/test/python/dialects/vector.py
--- a/mlir/test/python/dialects/vector.py
+++ b/mlir/test/python/dialects/vector.py
@@ -35,7 +35,9 @@
   module = Module.create()
   with InsertionPoint(module.body):
     vector_type = VectorType.get([2, 3], F32Type.get())
-    memref_type = MemRefType.get([-1, -1], F32Type.get())
+    memref_type = MemRefType.get(
+        [ShapedType.get_dynamic_size(),
+         ShapedType.get_dynamic_size()], F32Type.get())
     index_type = IndexType.get()
     mask_type = VectorType.get(vector_type.shape, IntegerType.get_signless(1))
     identity_map = AffineMap.get_identity(vector_type.rank)
diff --git a/mlir/unittests/Dialect/BroadcastShapeTest.cpp b/mlir/unittests/Dialect/BroadcastShapeTest.cpp
--- a/mlir/unittests/Dialect/BroadcastShapeTest.cpp
+++ b/mlir/unittests/Dialect/BroadcastShapeTest.cpp
@@ -47,9 +47,10 @@
 
 TEST(BroadcastShapeTest, InterleavingUnknowns) {
   SmallVector<int64_t, 4> result;
-  ASSERT_TRUE(
-      getBroadcastedShape({1, 2, -1, -1, -1}, {-1, -1, -1, 4, 1}, result));
-  EXPECT_THAT(result, ElementsAre(-1, 2, -1, 4, -1));
+  int64_t dyn = mlir::ShapedType::kDynamicSize;
+  ASSERT_TRUE(getBroadcastedShape({1, 2, dyn, dyn, dyn}, {dyn, dyn, dyn, 4, 1},
+                                  result));
+  EXPECT_THAT(result, ElementsAre(dyn, 2, dyn, 4, dyn));
 }
 
 TEST(BroadcastShapeTest, IncompatibleLowDim) {