diff --git a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
index 2151606..5442fed 100644
--- a/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
+++ b/mlir/include/mlir/Dialect/Tensor/IR/TensorOps.td
@@ -184,11 +184,14 @@
     ShapedType::kDynamicStrideOrOffset encodes that the corresponding entry has
     a dynamic value.
 
-    After buffer-allocation, the "extract_slice" op is expected to lower into a
-    "subview" op.
+    After buffer allocation, the "extract_slice" op is expected to lower into a
+    memref.subview op.
 
     An extract_slice operation may additionally reduce the rank of the resulting
     tensor by removing dimensions that are statically known to be of size 1.
+    This rank-reduction behavior is not required by the op semantics: this
+    flexibility allows to progressively drop unit dimensions while lowering
+    between different flavors of ops on that operate on tensors.
 
     Example:
 
@@ -196,8 +199,8 @@
     // Rank-reducing extract_slice.
     %1 = tensor.extract_slice %0[0, 0, 0][1, 16, 4][1, 1, 1] :
       tensor<8x16x4xf32> to tensor<16x4xf32>
-    %3 = tensor.extract_slice %2[3, 4, 2][1, 6, 3][1, 1, 1] :
-      tensor<8x16x4xf32> to tensor<6x3xf32>
+    %3 = tensor.extract_slice %2[%o0, 4, %o2][1, %sz1, 1][1, %st1, 1] :
+      tensor<8x16x4xf32> to tensor<1x?xf32>
     ```
   }];
 
@@ -257,24 +260,28 @@
     /// An extract_slice result type can be fully inferred from the source type
     /// and the static representation of offsets, sizes and strides. Special
     /// sentinels encode the dynamic case.
-    static Type inferResultType(RankedTensorType sourceRankedTensorType,
-                                ArrayRef<int64_t> staticOffsets,
-                                ArrayRef<int64_t> staticSizes,
-                                ArrayRef<int64_t> staticStrides);
-    static Type inferResultType(RankedTensorType sourceRankedTensorType,
-                                ArrayRef<OpFoldResult> staticOffsets,
-                                ArrayRef<OpFoldResult> staticSizes,
-                                ArrayRef<OpFoldResult> staticStrides);
-    static Type inferRankReducedResultType(unsigned resultRank,
-                                           RankedTensorType sourceRankedTensorType,
-                                           ArrayRef<int64_t> staticOffsets,
-                                           ArrayRef<int64_t> staticSizes,
-                                           ArrayRef<int64_t> staticStrides);
-    static Type inferRankReducedResultType(unsigned resultRank,
-                                           RankedTensorType sourceRankedTensorType,
-                                           ArrayRef<OpFoldResult> staticOffsets,
-                                           ArrayRef<OpFoldResult> staticSizes,
-                                           ArrayRef<OpFoldResult> staticStrides);
+    static RankedTensorType inferResultType(
+      RankedTensorType sourceRankedTensorType,
+      ArrayRef<int64_t> staticOffsets,
+      ArrayRef<int64_t> staticSizes,
+      ArrayRef<int64_t> staticStrides);
+    static RankedTensorType inferResultType(
+      RankedTensorType sourceRankedTensorType,
+      ArrayRef<OpFoldResult> staticOffsets,
+      ArrayRef<OpFoldResult> staticSizes,
+      ArrayRef<OpFoldResult> staticStrides);
+    static RankedTensorType inferRankReducedResultType(
+      unsigned resultRank,
+      RankedTensorType sourceRankedTensorType,
+      ArrayRef<int64_t> staticOffsets,
+      ArrayRef<int64_t> staticSizes,
+      ArrayRef<int64_t> staticStrides);
+    static RankedTensorType inferRankReducedResultType(
+      unsigned resultRank,
+      RankedTensorType sourceRankedTensorType,
+      ArrayRef<OpFoldResult> staticOffsets,
+      ArrayRef<OpFoldResult> staticSizes,
+      ArrayRef<OpFoldResult> staticStrides);
 
     /// Return the expected rank of each of the`static_offsets`, `static_sizes`
     /// and `static_strides` attributes.
@@ -469,8 +476,27 @@
     ShapedType::kDynamicStrideOrOffset encodes that the corresponding entry has
     a dynamic value.
 
-    After buffer-allocation, the "insert_slice" op is expected to become an
-    in-place buffer update.
+    After buffer allocation, the "insert_slice" op is expected to lower into a
+    memref.subview op.
+
+    An insert_slice operation may additionally specify insertion into a tensor
+    of higher rank than the source tensor, along dimensions that are statically
+    known to be of size 1.
+    This rank-altering behavior is not required by the op semantics: this
+    flexibility allows to progressively drop unit dimensions while lowering
+    between different flavors of ops on that operate on tensors.
+    The rank-altering behavior of tensor.insert_slice matches the rank-reducing
+    behavior of tensor.extract_slice.
+
+    Example:
+
+    ```
+    // Rank-reducing extract_slice.
+    %1 = tensor.insert_slice %t into %0[0, 0, 0][1, 16, 4][1, 1, 1] :
+      tensor<16x4xf32> into tensor<8x16x4xf32>
+    %3 = tensor.insert_slice %tt into %2[%o0, 4, %o2][1, %sz1, 1][1, %st1, 1] :
+      tensor<1x?xf32> into tensor<8x16x4xf32>
+    ```
   }];
 
   let arguments = (ins
@@ -493,8 +519,6 @@
     attr-dict `:` type($source) `into` type($dest)
   }];
 
-  let verifier = ?;
-
   let builders = [
     // Build a InsertSliceOp with mixed static and dynamic entries.
     OpBuilder<(ins "Value":$source, "Value":$dest,
diff --git a/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h b/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
index 5838f1d..bf5d0a8 100644
--- a/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
+++ b/mlir/include/mlir/Dialect/Utils/StaticValueUtils.h
@@ -21,9 +21,10 @@
 
 namespace mlir {
 
-/// Helper function to dispatch an OpFoldResult into either the `dynamicVec` if
-/// it is a Value or into `staticVec` if it is an IntegerAttr.
-/// In the case of a Value, a copy of the `sentinel` value is also pushed to
+/// Helper function to dispatch an OpFoldResult into `staticVec` if:
+///   a) it is an IntegerAttr
+/// In other cases, the OpFoldResult is dispached to the `dynamicVec`.
+/// In such dynamic cases, a copy of the `sentinel` value is also pushed to
 /// `staticVec`. This is useful to extract mixed static and dynamic entries that
 /// come from an AttrSizedOperandSegments trait.
 void dispatchIndexOpFoldResult(OpFoldResult ofr,
@@ -31,11 +32,8 @@
                                SmallVectorImpl<int64_t> &staticVec,
                                int64_t sentinel);
 
-/// Helper function to dispatch multiple OpFoldResults into either the
-/// `dynamicVec` (for Values) or into `staticVec` (for IntegerAttrs).
-/// In the case of a Value, a copy of the `sentinel` value is also pushed to
-/// `staticVec`. This is useful to extract mixed static and dynamic entries that
-/// come from an AttrSizedOperandSegments trait.
+/// Helper function to dispatch multiple OpFoldResults according to the behavior
+/// of `dispatchIndexOpFoldResult(OpFoldResult ofr` for a single OpFoldResult.
 void dispatchIndexOpFoldResults(ArrayRef<OpFoldResult> ofrs,
                                 SmallVectorImpl<Value> &dynamicVec,
                                 SmallVectorImpl<int64_t> &staticVec,
diff --git a/mlir/include/mlir/IR/BuiltinTypes.h b/mlir/include/mlir/IR/BuiltinTypes.h
index f3d2c24..10d8a58 100644
--- a/mlir/include/mlir/IR/BuiltinTypes.h
+++ b/mlir/include/mlir/IR/BuiltinTypes.h
@@ -369,6 +369,25 @@
 computeRankReductionMask(ArrayRef<int64_t> originalShape,
                          ArrayRef<int64_t> reducedShape);
 
+/// Enum that captures information related to verifier error conditions on
+/// slice insert/extract type of ops.
+enum class SliceVerificationResult {
+  Success,
+  RankTooLarge,
+  SizeMismatch,
+  ElemTypeMismatch,
+  // Error codes to ops with a memory space and a layout annotation.
+  MemSpaceMismatch,
+  LayoutMismatch
+};
+
+/// Check if `originalType` can be rank reduced to `candidateReducedType` type
+/// by dropping some dimensions with static size `1`.
+/// Return `SliceVerificationResult::Success` on success or an appropriate error
+/// code.
+SliceVerificationResult isRankReducedType(ShapedType originalType,
+                                          ShapedType candidateReducedType);
+
 //===----------------------------------------------------------------------===//
 // Deferred Method Definitions
 //===----------------------------------------------------------------------===//
diff --git a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
index 77cf563..7961638 100644
--- a/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
+++ b/mlir/lib/Conversion/TosaToLinalg/TosaToLinalg.cpp
@@ -2248,8 +2248,8 @@
 
     Location loc = op.getLoc();
     int axis = op.axis();
-    Value axisValue =
-        rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(axis));
+    Value axisValue = rewriter.createOrFold<arith::ConstantOp>(
+        loc, rewriter.getIndexAttr(axis));
     int rank = resultType.getRank();
     SmallVector<Value, 3> offsets, sizes, strides;
     sizes.reserve(rank);
@@ -2257,31 +2257,41 @@
     offsets.resize(rank, rewriter.create<arith::ConstantIndexOp>(loc, 0));
 
     for (int i = 0; i < rank; ++i) {
-      sizes.push_back(
-          rewriter.create<tensor::DimOp>(loc, adaptor.getOperands()[0], i));
+      sizes.push_back(rewriter.createOrFold<tensor::DimOp>(
+          loc, adaptor.getOperands()[0], i));
     }
 
     Value resultDimSize = sizes[axis];
     for (auto arg : adaptor.getOperands().drop_front()) {
-      auto size = rewriter.create<tensor::DimOp>(loc, arg, axisValue);
-      resultDimSize = rewriter.create<arith::AddIOp>(loc, resultDimSize, size);
+      auto size = rewriter.createOrFold<tensor::DimOp>(loc, arg, axisValue);
+      resultDimSize =
+          rewriter.createOrFold<arith::AddIOp>(loc, resultDimSize, size);
     }
     sizes[axis] = resultDimSize;
 
     Value init = rewriter.create<linalg::InitTensorOp>(
         loc, resultType.getShape(), resultType.getElementType());
 
-    Value zeroVal = rewriter.create<arith::ConstantOp>(
+    Value zeroVal = rewriter.createOrFold<arith::ConstantOp>(
         loc, rewriter.getZeroAttr(resultType.getElementType()));
     Value result =
         rewriter.create<linalg::FillOp>(loc, zeroVal, init).getResult(0);
 
+    auto toOpFoldResult = [](Value v) -> OpFoldResult {
+      auto op = v.getDefiningOp<arith::ConstantIndexOp>();
+      if (!op)
+        return v;
+      return op.getValue();
+    };
     for (auto arg : adaptor.getOperands()) {
-      sizes[axis] = rewriter.create<tensor::DimOp>(loc, arg, axisValue);
-      result = rewriter.create<tensor::InsertSliceOp>(loc, arg, result, offsets,
-                                                      sizes, strides);
+      sizes[axis] = rewriter.createOrFold<tensor::DimOp>(loc, arg, axisValue);
+      result = rewriter.createOrFold<tensor::InsertSliceOp>(
+          loc, arg, result,
+          llvm::to_vector(llvm::map_range(offsets, toOpFoldResult)),
+          llvm::to_vector(llvm::map_range(sizes, toOpFoldResult)),
+          llvm::to_vector(llvm::map_range(strides, toOpFoldResult)));
       offsets[axis] =
-          rewriter.create<arith::AddIOp>(loc, offsets[axis], sizes[axis]);
+          rewriter.createOrFold<arith::AddIOp>(loc, offsets[axis], sizes[axis]);
     }
     rewriter.replaceOp(op, result);
     return success();
diff --git a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
index 9ef930c..36828ea 100644
--- a/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
+++ b/mlir/lib/Dialect/Linalg/IR/LinalgOps.cpp
@@ -835,16 +835,14 @@
 //===----------------------------------------------------------------------===//
 // InitTensorOp
 //===----------------------------------------------------------------------===//
+
 void InitTensorOp::build(OpBuilder &b, OperationState &result,
                          ArrayRef<OpFoldResult> sizes, Type elementType,
                          ArrayRef<NamedAttribute> attrs) {
-  unsigned rank = sizes.size();
   SmallVector<Value, 4> dynamicSizes;
   SmallVector<int64_t, 4> staticSizes;
-  for (unsigned i = 0; i < rank; ++i) {
-    dispatchIndexOpFoldResult(sizes[i], dynamicSizes, staticSizes,
-                              ShapedType::kDynamicSize);
-  }
+  dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
+                             ShapedType::kDynamicSize);
   auto resultType = RankedTensorType ::get(staticSizes, elementType);
   build(b, result, resultType, dynamicSizes, b.getI64ArrayAttr(staticSizes));
   result.addAttributes(attrs);
@@ -1127,19 +1125,16 @@
                         ArrayRef<NamedAttribute> attrs) {
   assert(resultType.isa<RankedTensorType>());
   auto sourceType = source.getType().cast<RankedTensorType>();
-  unsigned rank = sourceType.getRank();
   SmallVector<Value, 4> dynamicLow, dynamicHigh;
   SmallVector<int64_t, 4> staticLow, staticHigh;
-  for (unsigned i = 0; i < rank; ++i) {
-    // staticLow and staticHigh have full information of the padding config.
-    // This will grow staticLow and staticHigh with 1 value. If the config is
-    // dynamic (ie not a constant), dynamicLow and dynamicHigh will grow with 1
-    // value as well.
-    dispatchIndexOpFoldResult(low[i], dynamicLow, staticLow,
-                              ShapedType::kDynamicSize);
-    dispatchIndexOpFoldResult(high[i], dynamicHigh, staticHigh,
-                              ShapedType::kDynamicSize);
-  }
+  // staticLow and staticHigh have full information of the padding config.
+  // This will grow staticLow and staticHigh with 1 value. If the config is
+  // dynamic (ie not a constant), dynamicLow and dynamicHigh will grow with 1
+  // value as well.
+  dispatchIndexOpFoldResults(low, dynamicLow, staticLow,
+                             ShapedType::kDynamicSize);
+  dispatchIndexOpFoldResults(high, dynamicHigh, staticHigh,
+                             ShapedType::kDynamicSize);
   if (!resultType) {
     resultType =
         PadTensorOp::inferResultType(sourceType, staticLow, staticHigh);
diff --git a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
index b9bd01b..938197d 100644
--- a/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
+++ b/mlir/lib/Dialect/MemRef/IR/MemRefOps.cpp
@@ -504,11 +504,13 @@
   return numOccurences;
 }
 
-/// Given the type of the un-rank reduced subview result type and the
-/// rank-reduced result type, computes the dropped dimensions. This accounts for
-/// cases where there are multiple unit-dims, but only a subset of those are
-/// dropped. For MemRefTypes these can be disambiguated using the strides. If a
-/// dimension is dropped the stride must be dropped too.
+/// Given the `originalType` and a `candidateReducedType` whose shape is assumed
+/// to be a subset of `originalType` with some `1` entries erased, return the
+/// set of indices that specifies which of the entries of `originalShape` are
+/// dropped to obtain `reducedShape`.
+/// This accounts for cases where there are multiple unit-dims, but only a
+/// subset of those are dropped. For MemRefTypes these can be disambiguated
+/// using the strides. If a dimension is dropped the stride must be dropped too.
 static llvm::Optional<llvm::SmallDenseSet<unsigned>>
 computeMemRefRankReductionMask(MemRefType originalType, MemRefType reducedType,
                                ArrayRef<OpFoldResult> sizes) {
@@ -1548,8 +1550,7 @@
   dispatchIndexOpFoldResults(leadingStaticStrides, dynamicStrides,
                              staticStrides, ShapedType::kDynamicStrideOrOffset);
   return SubViewOp::inferResultType(sourceMemRefType, staticOffsets,
-                                    staticSizes, staticStrides)
-      .cast<MemRefType>();
+                                    staticSizes, staticStrides);
 }
 
 Type SubViewOp::inferRankReducedResultType(
@@ -1706,88 +1707,58 @@
 /// For ViewLikeOpInterface.
 Value SubViewOp::getViewSource() { return source(); }
 
-enum SubViewVerificationResult {
-  Success,
-  RankTooLarge,
-  SizeMismatch,
-  ElemTypeMismatch,
-  MemSpaceMismatch,
-  AffineMapMismatch
-};
-
 /// Checks if `original` Type type can be rank reduced to `reduced` type.
 /// This function is slight variant of `is subsequence` algorithm where
 /// not matching dimension must be 1.
-static SubViewVerificationResult
-isRankReducedType(Type originalType, Type candidateReducedType,
-                  ArrayRef<OpFoldResult> sizes, std::string *errMsg = nullptr) {
-  if (originalType == candidateReducedType)
-    return SubViewVerificationResult::Success;
-  if (!originalType.isa<MemRefType>())
-    return SubViewVerificationResult::Success;
-  if (originalType.isa<MemRefType>() && !candidateReducedType.isa<MemRefType>())
-    return SubViewVerificationResult::Success;
-
-  ShapedType originalShapedType = originalType.cast<ShapedType>();
-  ShapedType candidateReducedShapedType =
-      candidateReducedType.cast<ShapedType>();
-
-  // Rank and size logic is valid for all ShapedTypes.
-  ArrayRef<int64_t> originalShape = originalShapedType.getShape();
-  ArrayRef<int64_t> candidateReducedShape =
-      candidateReducedShapedType.getShape();
-  unsigned originalRank = originalShape.size(),
-           candidateReducedRank = candidateReducedShape.size();
-  if (candidateReducedRank > originalRank)
-    return SubViewVerificationResult::RankTooLarge;
+static SliceVerificationResult
+isRankReducedMemRefType(MemRefType originalType,
+                        MemRefType candidatecandidateReducedType,
+                        ArrayRef<OpFoldResult> sizes) {
+  auto partialRes =
+      isRankReducedType(originalType, candidatecandidateReducedType);
+  if (partialRes != SliceVerificationResult::Success)
+    return partialRes;
 
   MemRefType original = originalType.cast<MemRefType>();
-  MemRefType candidateReduced = candidateReducedType.cast<MemRefType>();
+  MemRefType candidateReduced =
+      candidatecandidateReducedType.cast<MemRefType>();
 
   auto optionalUnusedDimsMask =
       computeMemRefRankReductionMask(original, candidateReduced, sizes);
 
   // Sizes cannot be matched in case empty vector is returned.
   if (!optionalUnusedDimsMask.hasValue())
-    return SubViewVerificationResult::SizeMismatch;
+    return SliceVerificationResult::LayoutMismatch;
 
-  if (originalShapedType.getElementType() !=
-      candidateReducedShapedType.getElementType())
-    return SubViewVerificationResult::ElemTypeMismatch;
-
-  // Strided layout logic is relevant for MemRefType only.
   if (original.getMemorySpace() != candidateReduced.getMemorySpace())
-    return SubViewVerificationResult::MemSpaceMismatch;
-  return SubViewVerificationResult::Success;
+    return SliceVerificationResult::MemSpaceMismatch;
+
+  return SliceVerificationResult::Success;
 }
 
 template <typename OpTy>
-static LogicalResult produceSubViewErrorMsg(SubViewVerificationResult result,
-                                            OpTy op, Type expectedType,
-                                            StringRef errMsg = "") {
+static LogicalResult produceSubViewErrorMsg(SliceVerificationResult result,
+                                            OpTy op, Type expectedType) {
   auto memrefType = expectedType.cast<ShapedType>();
   switch (result) {
-  case SubViewVerificationResult::Success:
+  case SliceVerificationResult::Success:
     return success();
-  case SubViewVerificationResult::RankTooLarge:
+  case SliceVerificationResult::RankTooLarge:
     return op.emitError("expected result rank to be smaller or equal to ")
-           << "the source rank. " << errMsg;
-  case SubViewVerificationResult::SizeMismatch:
+           << "the source rank. ";
+  case SliceVerificationResult::SizeMismatch:
     return op.emitError("expected result type to be ")
            << expectedType
-           << " or a rank-reduced version. (mismatch of result sizes) "
-           << errMsg;
-  case SubViewVerificationResult::ElemTypeMismatch:
+           << " or a rank-reduced version. (mismatch of result sizes) ";
+  case SliceVerificationResult::ElemTypeMismatch:
     return op.emitError("expected result element type to be ")
-           << memrefType.getElementType() << errMsg;
-  case SubViewVerificationResult::MemSpaceMismatch:
-    return op.emitError("expected result and source memory spaces to match.")
-           << errMsg;
-  case SubViewVerificationResult::AffineMapMismatch:
+           << memrefType.getElementType();
+  case SliceVerificationResult::MemSpaceMismatch:
+    return op.emitError("expected result and source memory spaces to match.");
+  case SliceVerificationResult::LayoutMismatch:
     return op.emitError("expected result type to be ")
            << expectedType
-           << " or a rank-reduced version. (mismatch of result affine map) "
-           << errMsg;
+           << " or a rank-reduced version. (mismatch of result layout) ";
   }
   llvm_unreachable("unexpected subview verification result");
 }
@@ -1813,10 +1784,9 @@
       extractFromI64ArrayAttr(op.static_sizes()),
       extractFromI64ArrayAttr(op.static_strides()));
 
-  std::string errMsg;
-  auto result =
-      isRankReducedType(expectedType, subViewType, op.getMixedSizes(), &errMsg);
-  return produceSubViewErrorMsg(result, op, expectedType, errMsg);
+  auto result = isRankReducedMemRefType(expectedType.cast<MemRefType>(),
+                                        subViewType, op.getMixedSizes());
+  return produceSubViewErrorMsg(result, op, expectedType);
 }
 
 raw_ostream &mlir::operator<<(raw_ostream &os, const Range &range) {
diff --git a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
index 0eff26b..7f1bd74 100644
--- a/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
+++ b/mlir/lib/Dialect/Tensor/IR/TensorOps.cpp
@@ -12,6 +12,7 @@
 #include "mlir/Dialect/Utils/StaticValueUtils.h"
 #include "mlir/IR/BlockAndValueMapping.h"
 #include "mlir/IR/Builders.h"
+#include "mlir/IR/BuiltinAttributeInterfaces.h"
 #include "mlir/IR/Matchers.h"
 #include "mlir/IR/PatternMatch.h"
 #include "mlir/IR/TypeUtilities.h"
@@ -655,10 +656,11 @@
 /// An extract_slice op result type can be fully inferred from the source type
 /// and the static representation of offsets, sizes and strides. Special
 /// sentinels encode the dynamic case.
-Type ExtractSliceOp::inferResultType(RankedTensorType sourceRankedTensorType,
-                                     ArrayRef<int64_t> leadingStaticOffsets,
-                                     ArrayRef<int64_t> leadingStaticSizes,
-                                     ArrayRef<int64_t> leadingStaticStrides) {
+RankedTensorType
+ExtractSliceOp::inferResultType(RankedTensorType sourceRankedTensorType,
+                                ArrayRef<int64_t> leadingStaticOffsets,
+                                ArrayRef<int64_t> leadingStaticSizes,
+                                ArrayRef<int64_t> leadingStaticStrides) {
   // An extract_slice op may specify only a leading subset of offset/sizes/
   // strides in which case we complete with offset=0, sizes from memref type and
   // strides=1.
@@ -673,11 +675,11 @@
                                sourceRankedTensorType.getElementType());
 }
 
-Type ExtractSliceOp::inferResultType(
-    RankedTensorType sourceRankedTensorType,
-    ArrayRef<OpFoldResult> leadingStaticOffsets,
-    ArrayRef<OpFoldResult> leadingStaticSizes,
-    ArrayRef<OpFoldResult> leadingStaticStrides) {
+RankedTensorType
+ExtractSliceOp::inferResultType(RankedTensorType sourceRankedTensorType,
+                                ArrayRef<OpFoldResult> leadingStaticOffsets,
+                                ArrayRef<OpFoldResult> leadingStaticSizes,
+                                ArrayRef<OpFoldResult> leadingStaticStrides) {
   SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
   SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
   dispatchIndexOpFoldResults(leadingStaticOffsets, dynamicOffsets,
@@ -693,7 +695,7 @@
 /// An extract_slice op result type can be fully inferred from the source type
 /// and the static representation of offsets, sizes and strides. Special
 /// sentinels encode the dynamic case.
-Type ExtractSliceOp::inferRankReducedResultType(
+RankedTensorType ExtractSliceOp::inferRankReducedResultType(
     unsigned resultRank, RankedTensorType sourceRankedTensorType,
     ArrayRef<int64_t> leadingStaticOffsets,
     ArrayRef<int64_t> leadingStaticSizes,
@@ -717,7 +719,7 @@
   return inferredType;
 }
 
-Type ExtractSliceOp::inferRankReducedResultType(
+RankedTensorType ExtractSliceOp::inferRankReducedResultType(
     unsigned resultRank, RankedTensorType sourceRankedTensorType,
     ArrayRef<OpFoldResult> leadingStaticOffsets,
     ArrayRef<OpFoldResult> leadingStaticSizes,
@@ -746,10 +748,12 @@
   SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
   SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
   dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
+
                              ShapedType::kDynamicStrideOrOffset);
   dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                              ShapedType::kDynamicSize);
   dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
+
                              ShapedType::kDynamicStrideOrOffset);
   auto sourceRankedTensorType = source.getType().cast<RankedTensorType>();
   // Structuring implementation this way avoids duplication between builders.
@@ -797,89 +801,35 @@
   build(b, result, RankedTensorType(), source, offsets, sizes, strides, attrs);
 }
 
-enum SliceVerificationResult {
-  Success,
-  RankTooLarge,
-  SizeMismatch,
-  ElemTypeMismatch,
-};
-
-/// Checks if `original` Type type can be rank reduced to `reduced` type.
-/// This function is slight variant of `is subsequence` algorithm where
-/// not matching dimension must be 1.
-static SliceVerificationResult
-isRankReducedType(Type originalType, Type candidateReducedType,
-                  std::string *errMsg = nullptr) {
-  if (originalType == candidateReducedType)
-    return SliceVerificationResult::Success;
-  if (!originalType.isa<RankedTensorType>())
-    return SliceVerificationResult::Success;
-  if (originalType.isa<RankedTensorType>() &&
-      !candidateReducedType.isa<RankedTensorType>())
-    return SliceVerificationResult::Success;
-
-  ShapedType originalShapedType = originalType.cast<ShapedType>();
-  ShapedType candidateReducedShapedType =
-      candidateReducedType.cast<ShapedType>();
-
-  // Rank and size logic is valid for all ShapedTypes.
-  ArrayRef<int64_t> originalShape = originalShapedType.getShape();
-  ArrayRef<int64_t> candidateReducedShape =
-      candidateReducedShapedType.getShape();
-  unsigned originalRank = originalShape.size(),
-           candidateReducedRank = candidateReducedShape.size();
-  if (candidateReducedRank > originalRank)
-    return SliceVerificationResult::RankTooLarge;
-
-  auto optionalUnusedDimsMask =
-      computeRankReductionMask(originalShape, candidateReducedShape);
-
-  // Sizes cannot be matched in case empty vector is returned.
-  if (!optionalUnusedDimsMask.hasValue())
-    return SliceVerificationResult::SizeMismatch;
-
-  if (originalShapedType.getElementType() !=
-      candidateReducedShapedType.getElementType())
-    return SliceVerificationResult::ElemTypeMismatch;
-
-  // We are done for the tensor case.
-  if (originalType.isa<RankedTensorType>())
-    return SliceVerificationResult::Success;
-
-  return SliceVerificationResult::Success;
-}
-
 template <typename OpTy>
 static LogicalResult produceSliceErrorMsg(SliceVerificationResult result,
-                                          OpTy op, Type expectedType,
-                                          StringRef errMsg = "") {
+                                          OpTy op, Type expectedType) {
   auto memrefType = expectedType.cast<ShapedType>();
   switch (result) {
   case SliceVerificationResult::Success:
     return success();
   case SliceVerificationResult::RankTooLarge:
-    return op.emitError("expected result rank to be smaller or equal to ")
-           << "the source rank. " << errMsg;
+    return op.emitError("expected rank to be smaller or equal to ")
+           << "the other rank. ";
   case SliceVerificationResult::SizeMismatch:
-    return op.emitError("expected result type to be ")
-           << expectedType
-           << " or a rank-reduced version. (mismatch of result sizes) "
-           << errMsg;
+    return op.emitError("expected type to be ")
+           << expectedType << " or a rank-reduced version. (size mismatch) ";
   case SliceVerificationResult::ElemTypeMismatch:
-    return op.emitError("expected result element type to be ")
-           << memrefType.getElementType() << errMsg;
+    return op.emitError("expected element type to be ")
+           << memrefType.getElementType();
+  default:
+    llvm_unreachable("unexpected extract_slice op verification result");
   }
-  llvm_unreachable("unexpected extract_slice op verification result");
 }
 
 /// Verifier for ExtractSliceOp.
 static LogicalResult verify(ExtractSliceOp op) {
   // Verify result type against inferred type.
-  auto expectedType = ExtractSliceOp::inferResultType(
-      op.getSourceType(), extractFromI64ArrayAttr(op.static_offsets()),
-      extractFromI64ArrayAttr(op.static_sizes()),
-      extractFromI64ArrayAttr(op.static_strides()));
-  auto result = isRankReducedType(expectedType, op.getType());
+  auto expectedType =
+      ExtractSliceOp::inferResultType(op.getSourceType(), op.getMixedOffsets(),
+                                      op.getMixedSizes(), op.getMixedStrides());
+  auto result =
+      isRankReducedType(expectedType.cast<ShapedType>(), op.getType());
   return produceSliceErrorMsg(result, op, expectedType);
 }
 
@@ -1104,10 +1054,12 @@
   SmallVector<int64_t> staticOffsets, staticSizes, staticStrides;
   SmallVector<Value> dynamicOffsets, dynamicSizes, dynamicStrides;
   dispatchIndexOpFoldResults(offsets, dynamicOffsets, staticOffsets,
+
                              ShapedType::kDynamicStrideOrOffset);
   dispatchIndexOpFoldResults(sizes, dynamicSizes, staticSizes,
                              ShapedType::kDynamicSize);
   dispatchIndexOpFoldResults(strides, dynamicStrides, staticStrides,
+
                              ShapedType::kDynamicStrideOrOffset);
   build(b, result, dest.getType(), source, dest, dynamicOffsets, dynamicSizes,
         dynamicStrides, b.getI64ArrayAttr(staticOffsets),
@@ -1128,6 +1080,19 @@
   build(b, result, source, dest, offsetValues, sizeValues, strideValues);
 }
 
+/// Verifier for InsertSliceOp.
+static LogicalResult verify(InsertSliceOp op) {
+  // insert_slice is the inverse of extract_slice, use the same type inference.
+  auto expectedType = ExtractSliceOp::inferRankReducedResultType(
+      op.getSourceType().getRank(), op.getType(),
+      extractFromI64ArrayAttr(op.static_offsets()),
+      extractFromI64ArrayAttr(op.static_sizes()),
+      extractFromI64ArrayAttr(op.static_strides()));
+  auto result =
+      isRankReducedType(expectedType.cast<ShapedType>(), op.getSourceType());
+  return produceSliceErrorMsg(result, op, expectedType);
+}
+
 /// If we have two consecutive InsertSliceOp writing to the same slice, we
 /// can mutate the second InsertSliceOp's destination to the first one's.
 ///
@@ -1202,9 +1167,16 @@
     canonicalizeSubViewPart(mixedStrides, ShapedType::isDynamicStrideOrOffset);
 
     // Create the new op in canonical form.
-    rewriter.replaceOpWithNewOp<InsertSliceOp>(
-        insertSliceOp, insertSliceOp.source(), insertSliceOp.dest(),
+    auto sourceType = ExtractSliceOp::inferRankReducedResultType(
+        insertSliceOp.getSourceType().getRank(), insertSliceOp.getType(),
         mixedOffsets, mixedSizes, mixedStrides);
+    Value toInsert = insertSliceOp.source();
+    if (sourceType != insertSliceOp.getSourceType())
+      toInsert = rewriter.create<tensor::CastOp>(insertSliceOp.getLoc(),
+                                                 sourceType, toInsert);
+    rewriter.replaceOpWithNewOp<InsertSliceOp>(
+        insertSliceOp, toInsert, insertSliceOp.dest(), mixedOffsets, mixedSizes,
+        mixedStrides);
     return success();
   }
 };
diff --git a/mlir/lib/Dialect/Utils/StaticValueUtils.cpp b/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
index 24d8ac0..3e50fac 100644
--- a/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
+++ b/mlir/lib/Dialect/Utils/StaticValueUtils.cpp
@@ -13,22 +13,24 @@
 
 namespace mlir {
 
-/// Helper function to dispatch an OpFoldResult into either the `dynamicVec` if
-/// it is a Value or into `staticVec` if it is an IntegerAttr.
-/// In the case of a Value, a copy of the `sentinel` value is also pushed to
+/// Helper function to dispatch an OpFoldResult into `staticVec` if:
+///   a) it is an IntegerAttr
+/// In other cases, the OpFoldResult is dispached to the `dynamicVec`.
+/// In such dynamic cases, a copy of the `sentinel` value is also pushed to
 /// `staticVec`. This is useful to extract mixed static and dynamic entries that
 /// come from an AttrSizedOperandSegments trait.
 void dispatchIndexOpFoldResult(OpFoldResult ofr,
                                SmallVectorImpl<Value> &dynamicVec,
                                SmallVectorImpl<int64_t> &staticVec,
                                int64_t sentinel) {
-  if (auto v = ofr.dyn_cast<Value>()) {
-    dynamicVec.push_back(v);
-    staticVec.push_back(sentinel);
+  auto v = ofr.dyn_cast<Value>();
+  if (!v) {
+    APInt apInt = ofr.get<Attribute>().cast<IntegerAttr>().getValue();
+    staticVec.push_back(apInt.getSExtValue());
     return;
   }
-  APInt apInt = ofr.dyn_cast<Attribute>().cast<IntegerAttr>().getValue();
-  staticVec.push_back(apInt.getSExtValue());
+  dynamicVec.push_back(v);
+  staticVec.push_back(sentinel);
 }
 
 void dispatchIndexOpFoldResults(ArrayRef<OpFoldResult> ofrs,
diff --git a/mlir/lib/IR/BuiltinTypes.cpp b/mlir/lib/IR/BuiltinTypes.cpp
index 64dceaa..33ed6b6 100644
--- a/mlir/lib/IR/BuiltinTypes.cpp
+++ b/mlir/lib/IR/BuiltinTypes.cpp
@@ -571,7 +571,7 @@
   llvm::SmallDenseSet<unsigned> unusedDims;
   unsigned reducedIdx = 0;
   for (unsigned originalIdx = 0; originalIdx < originalRank; ++originalIdx) {
-    // Greedily insert `originalIdx` if no match.
+    // Greedily insert `originalIdx` if match.
     if (reducedIdx < reducedRank &&
         originalShape[originalIdx] == reducedShape[reducedIdx]) {
       reducedIdx++;
@@ -590,6 +590,39 @@
   return unusedDims;
 }
 
+SliceVerificationResult
+mlir::isRankReducedType(ShapedType originalType,
+                        ShapedType candidateReducedType) {
+  if (originalType == candidateReducedType)
+    return SliceVerificationResult::Success;
+
+  ShapedType originalShapedType = originalType.cast<ShapedType>();
+  ShapedType candidateReducedShapedType =
+      candidateReducedType.cast<ShapedType>();
+
+  // Rank and size logic is valid for all ShapedTypes.
+  ArrayRef<int64_t> originalShape = originalShapedType.getShape();
+  ArrayRef<int64_t> candidateReducedShape =
+      candidateReducedShapedType.getShape();
+  unsigned originalRank = originalShape.size(),
+           candidateReducedRank = candidateReducedShape.size();
+  if (candidateReducedRank > originalRank)
+    return SliceVerificationResult::RankTooLarge;
+
+  auto optionalUnusedDimsMask =
+      computeRankReductionMask(originalShape, candidateReducedShape);
+
+  // Sizes cannot be matched in case empty vector is returned.
+  if (!optionalUnusedDimsMask.hasValue())
+    return SliceVerificationResult::SizeMismatch;
+
+  if (originalShapedType.getElementType() !=
+      candidateReducedShapedType.getElementType())
+    return SliceVerificationResult::ElemTypeMismatch;
+
+  return SliceVerificationResult::Success;
+}
+
 bool mlir::detail::isSupportedMemorySpace(Attribute memorySpace) {
   // Empty attribute is allowed as default memory space.
   if (!memorySpace)
diff --git a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
index 1cf88f9..1540970 100644
--- a/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
+++ b/mlir/test/Conversion/TosaToLinalg/tosa-to-linalg.mlir
@@ -820,38 +820,24 @@
   // CHECK: [[STRIDE:%.+]]   = arith.constant 1
   // CHECK: [[OFFSET:%.+]] = arith.constant 0 : index
   // CHECK: [[IDX0:%.+]] = arith.constant 0 : index
-  // CHECK: [[ARG0_DIM0:%.+]] = tensor.dim %arg0, [[IDX0]]
   // CHECK: [[IDX1:%.+]] = arith.constant 1 : index
-  // CHECK: [[ARG0_DIM1:%.+]] = tensor.dim %arg0, [[IDX1]]
-  // CHECK: [[ARG1_AXIS:%.+]] = tensor.dim %arg1, [[AXIS]]
-  // CHECK: [[RESULT_AXIS:%.+]] = arith.addi [[ARG0_DIM0]], [[ARG1_AXIS]]
   // CHECK: [[INIT:%.+]] = linalg.init_tensor [11, 1]
   // CHECK: [[CST:%.+]] = arith.constant 0.0
   // CHECK: [[FILL:%.+]] = linalg.fill([[CST]], [[INIT]])
-  // CHECK: [[ARG0_DIM0:%.+]] = tensor.dim %arg0, [[AXIS]]
-  // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]]{{\[}}[[OFFSET]], [[OFFSET]]] {{\[}}[[ARG0_DIM0]], [[ARG0_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]]
-  // CHECK: [[NEW_OFFSET:%.+]] = arith.addi [[OFFSET]], [[ARG0_DIM0]]
-  // CHECK: [[ARG1_DIM0:%.+]] = tensor.dim %arg1, [[AXIS]]
-  // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg1 into [[INSERT0]]{{\[}}[[NEW_OFFSET]], [[OFFSET]]] {{\[}}[[ARG1_DIM0]], [[ARG0_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]]
+  // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]][0, 0] [5, 1] [1, 1]
+  // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg1 into [[INSERT0]][5, 0] [6, 1] [1, 1]
   %0 = "tosa.concat"(%arg0, %arg1) { axis = 0 : i64} : (tensor<5x1xf32>, tensor<6x1xf32>)  -> (tensor<11x1xf32>)
 
   // CHECK: [[AXIS:%.+]] = arith.constant 1
   // CHECK: [[STRIDE:%.+]]   = arith.constant 1
   // CHECK: [[OFFSET:%.+]] = arith.constant 0 : index
   // CHECK: [[IDX0:%.+]] = arith.constant 0 : index
-  // CHECK: [[ARG0_DIM0:%.+]] = tensor.dim %arg0, [[IDX0]]
   // CHECK: [[IDX1:%.+]] = arith.constant 1 : index
-  // CHECK: [[ARG0_DIM1:%.+]] = tensor.dim %arg0, [[IDX1]]
-  // CHECK: [[ARG1_AXIS:%.+]] = tensor.dim %arg0, [[AXIS]]
-  // CHECK: [[RESULT_AXIS:%.+]] = arith.addi [[ARG0_DIM1]], [[ARG1_AXIS]]
   // CHECK: [[INIT:%.+]] = linalg.init_tensor [5, 2]
   // CHECK: [[CST:%.+]] = arith.constant 0.0
   // CHECK: [[FILL:%.+]] = linalg.fill([[CST]], [[INIT]])
-  // CHECK: [[ARG0_DIM1:%.+]] = tensor.dim %arg0, [[AXIS]]
-  // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]]{{\[}}[[OFFSET]], [[OFFSET]]] {{\[}}[[ARG0_DIM0]], [[ARG0_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]]
-  // CHECK: [[NEW_OFFSET:%.+]] = arith.addi [[OFFSET]], [[ARG0_DIM1]]
-  // CHECK: [[ARG1_DIM1:%.+]] = tensor.dim %arg0, [[AXIS]]
-  // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg0 into [[INSERT0]]{{\[}}[[OFFSET]], [[NEW_OFFSET]]] {{\[}}[[ARG0_DIM0]], [[ARG1_DIM1]]] {{\[}}[[STRIDE]], [[STRIDE]]]
+  // CHECK: [[INSERT0:%.+]] = tensor.insert_slice %arg0 into [[FILL]][0, 0] [5, 1] [1, 1]
+  // CHECK: [[INSERT1:%.+]] = tensor.insert_slice %arg0 into [[INSERT0]][0, 1] [5, 1] [1, 1]
   %1 = "tosa.concat"(%arg0, %arg0) { axis = 1 : i64} : (tensor<5x1xf32>, tensor<5x1xf32>)  -> (tensor<5x2xf32>)
   return
 }
diff --git a/mlir/test/Dialect/Linalg/comprehensive-module-bufferize-analysis.mlir b/mlir/test/Dialect/Linalg/comprehensive-module-bufferize-analysis.mlir
index 68fe343..4a8d2e4 100644
--- a/mlir/test/Dialect/Linalg/comprehensive-module-bufferize-analysis.mlir
+++ b/mlir/test/Dialect/Linalg/comprehensive-module-bufferize-analysis.mlir
@@ -428,7 +428,9 @@
     %A : tensor<?x?xf32>,
     %B : tensor<?x?xf32> {linalg.inplaceable = true},
     %C : tensor<?x?xf32> {linalg.inplaceable = true},
-    %idx : index)
+    %idx : index,
+    %sz1 : index,
+    %sz2 : index)
   ->  (tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>)
 {
   %f0 = arith.constant 0.0 : f32
@@ -497,9 +499,9 @@
   // CHECK-NEXT: tensor.insert_slice
   // CHECK-SAME: {__inplace_results_attr__ = ["true"]}
   %sC = tensor.extract_slice %C[0, 0][%idx, %idx][1, 1] : tensor<?x?xf32> to tensor<?x?xf32>
-  %ssC = tensor.extract_slice %sC[0, 0][4, 4][1, 1] : tensor<?x?xf32> to tensor<4x4xf32>
-  %FC = linalg.fill(%f0, %ssC) : f32, tensor<4x4xf32> -> tensor<4x4xf32>
-  %rsC = tensor.insert_slice %FC into %sC[0, 0][12345, 67890][1, 1] : tensor<4x4xf32> into tensor<?x?xf32>
+  %ssC = tensor.extract_slice %sC[0, 0][%sz1, 4][1, 1] : tensor<?x?xf32> to tensor<?x4xf32>
+  %FC = linalg.fill(%f0, %ssC) : f32, tensor<?x4xf32> -> tensor<?x4xf32>
+  %rsC = tensor.insert_slice %FC into %sC[0, 0][%sz2, 4][1, 1] : tensor<?x4xf32> into tensor<?x?xf32>
   %rC = tensor.insert_slice %rsC into %C[0, 0][%idx, %idx][1, 1] : tensor<?x?xf32> into tensor<?x?xf32>
 
   return %rA, %rB, %rC: tensor<?x?xf32>, tensor<?x?xf32>, tensor<?x?xf32>
diff --git a/mlir/test/Dialect/Linalg/roundtrip.mlir b/mlir/test/Dialect/Linalg/roundtrip.mlir
index b0cddb2..20cd860 100644
--- a/mlir/test/Dialect/Linalg/roundtrip.mlir
+++ b/mlir/test/Dialect/Linalg/roundtrip.mlir
@@ -592,7 +592,7 @@
       linalg.yield %1 : f32
     } -> tensor<4xf32>
 
-    %sum_sub = tensor.insert_slice %acc into %o_[%j][%c4][1]
+    %sum_sub = tensor.insert_slice %acc into %o_[%j][4][1]
       : tensor<4xf32> into tensor<24xf32>
     linalg.yield %sum_sub : tensor<24xf32>
   }
diff --git a/mlir/test/Dialect/MemRef/invalid.mlir b/mlir/test/Dialect/MemRef/invalid.mlir
index 861478d..7300614 100644
--- a/mlir/test/Dialect/MemRef/invalid.mlir
+++ b/mlir/test/Dialect/MemRef/invalid.mlir
@@ -644,7 +644,7 @@
 // -----
 
 func @invalid_rank_reducing_subview(%arg0 : memref<?x?xf32>, %arg1 : index, %arg2 : index) {
-  // expected-error@+1 {{expected result type to be 'memref<?x1xf32, affine_map<(d0, d1)[s0, s1] -> (d0 * s1 + s0 + d1)>>' or a rank-reduced version. (mismatch of result sizes)}}
+  // expected-error@+1 {{expected result type to be 'memref<?x1xf32, affine_map<(d0, d1)[s0, s1] -> (d0 * s1 + s0 + d1)>>' or a rank-reduced version. (mismatch of result layout)}}
   %0 = memref.subview %arg0[0, %arg1][%arg2, 1][1, 1] : memref<?x?xf32> to memref<?xf32>
   return
 }
@@ -653,7 +653,7 @@
 
 func @static_stride_to_dynamic_stride(%arg0 : memref<?x?x?xf32>, %arg1 : index,
     %arg2 : index) -> memref<?x?xf32, offset:?, strides: [?, ?]> {
-  // expected-error @+1 {{expected result type to be 'memref<1x?x?xf32, affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0 * s1 + s0 + d1 * s2 + d2)>>' or a rank-reduced version. (mismatch of result sizes)}}
+  // expected-error @+1 {{expected result type to be 'memref<1x?x?xf32, affine_map<(d0, d1, d2)[s0, s1, s2] -> (d0 * s1 + s0 + d1 * s2 + d2)>>' or a rank-reduced version. (mismatch of result layout)}}
   %0 = memref.subview %arg0[0, 0, 0] [1, %arg1, %arg2] [1, 1, 1] : memref<?x?x?xf32> to memref<?x?xf32, offset: ?, strides: [?, ?]>
   return %0 : memref<?x?xf32, offset: ?, strides: [?, ?]>
 }
diff --git a/mlir/test/Dialect/SCF/for-loop-canonicalization.mlir b/mlir/test/Dialect/SCF/for-loop-canonicalization.mlir
index a07e568..79a692f 100644
--- a/mlir/test/Dialect/SCF/for-loop-canonicalization.mlir
+++ b/mlir/test/Dialect/SCF/for-loop-canonicalization.mlir
@@ -250,7 +250,7 @@
 //       CHECK:   scf.for
 //       CHECK:     tensor.dim %[[t]]
 func @tensor_dim_of_iter_arg_insertslice(%t : tensor<?x?xf32>,
-                                         %t2 : tensor<?x?xf32>) -> index {
+                                         %t2 : tensor<10x10xf32>) -> index {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %c10 = arith.constant 10 : index
@@ -258,9 +258,9 @@
       -> (tensor<?x?xf32>, index) {
     %dim = tensor.dim %arg0, %c0 : tensor<?x?xf32>
     %2 = tensor.insert_slice %t2 into %arg0[0, 0] [10, 10] [1, 1]
-        : tensor<?x?xf32> into tensor<?x?xf32>
+        : tensor<10x10xf32> into tensor<?x?xf32>
     %3 = tensor.insert_slice %t2 into %2[1, 1] [10, 10] [1, 1]
-        : tensor<?x?xf32> into tensor<?x?xf32>
+        : tensor<10x10xf32> into tensor<?x?xf32>
     scf.yield %3, %dim : tensor<?x?xf32>, index
   }
   return %1 : index
@@ -274,7 +274,7 @@
 //       CHECK:     scf.for
 //       CHECK:       tensor.dim %[[t]]
 func @tensor_dim_of_iter_arg_nested_for(%t : tensor<?x?xf32>,
-                                        %t2 : tensor<?x?xf32>) -> index {
+                                        %t2 : tensor<10x10xf32>) -> index {
   %c0 = arith.constant 0 : index
   %c1 = arith.constant 1 : index
   %c10 = arith.constant 10 : index
@@ -284,7 +284,7 @@
         -> (tensor<?x?xf32>, index) {
       %dim = tensor.dim %arg2, %c0 : tensor<?x?xf32>
       %4 = tensor.insert_slice %t2 into %arg2[0, 0] [10, 10] [1, 1]
-          : tensor<?x?xf32> into tensor<?x?xf32>
+          : tensor<10x10xf32> into tensor<?x?xf32>
       scf.yield %4, %dim : tensor<?x?xf32>, index
     }
     scf.yield %2, %3 : tensor<?x?xf32>, index
@@ -292,6 +292,7 @@
   return %1 : index
 }
 
+
 // -----
 
 // A test case that should not canonicalize because the loop is not shape
diff --git a/mlir/test/Dialect/Tensor/canonicalize.mlir b/mlir/test/Dialect/Tensor/canonicalize.mlir
index 9d9da02..1aa4008 100644
--- a/mlir/test/Dialect/Tensor/canonicalize.mlir
+++ b/mlir/test/Dialect/Tensor/canonicalize.mlir
@@ -348,8 +348,10 @@
 //   CHECK-NOT:   tensor.cast
 //       CHECK:   return %[[S]] : tensor<4x6x16x32xi8>
 func @rank_reducing_insert_slice_of_cast(%a : tensor<16x32xi8>, %b : tensor<4x6x16x32xi8>) -> tensor<4x6x16x32xi8> {
+  %c0 = arith.constant 0: index
   %cast = tensor.cast %a : tensor<16x32xi8> to tensor<?x32xi8>
-  %res = tensor.insert_slice %cast into %b[0, 1, 0] [1, 1, 16] [1, 1, 1] : tensor<?x32xi8> into tensor<4x6x16x32xi8>
+  %sz = tensor.dim %cast, %c0: tensor<?x32xi8>
+  %res = tensor.insert_slice %cast into %b[0, 1, 0] [1, 1, %sz] [1, 1, 1] : tensor<?x32xi8> into tensor<4x6x16x32xi8>
   return %res : tensor<4x6x16x32xi8>
 }
 
@@ -408,9 +410,10 @@
 }
 // CHECK-LABEL: func @rank_reducing_insert_slice_canonicalize
 //  CHECK-SAME:   %[[ARG0:.+]]: tensor<?x?xf32>
-//       CHECK:   %[[RESULT:.+]] = tensor.insert_slice %[[ARG0]]
+//       CHECK:   %[[CAST:.*]] = tensor.cast %[[ARG0]] : tensor<?x?xf32> to tensor<4x?xf32>
+//       CHECK:   %[[RESULT:.+]] = tensor.insert_slice %[[CAST]]
 //  CHECK-SAME:      [0, %{{.+}}, 1] [4, 1, %{{.+}}] [1, 1, 1]
-//  CHECK-SAME:      : tensor<?x?xf32> into tensor<?x?x?xf32>
+//  CHECK-SAME:      : tensor<4x?xf32> into tensor<?x?x?xf32>
 //       CHEKC:   return %[[RESULT]]
 
 // -----
@@ -450,7 +453,7 @@
   ^bb0(%arg4: index, %arg5: index):
     tensor.yield %1 : i32
   } : tensor<?x?xi32>
-  %3 = tensor.insert_slice %arg0 into %2[%c0, %arg3] [%c2, %0] [%c1, %c1] : tensor<2x?xi32> into tensor<?x?xi32>
+  %3 = tensor.insert_slice %arg0 into %2[0, %arg3] [2, %0] [1, 1] : tensor<2x?xi32> into tensor<?x?xi32>
   return %3 : tensor<?x?xi32>
 }
 // CHECK-LABEL: func @insert_slice_propagate_dest_cast
@@ -462,9 +465,6 @@
 // -----
 
 func @insert_slice_output_dest_canonicalize(%arg0 : tensor<2x3xi32>, %arg1 : tensor<i32>) -> tensor<3x9xi32> {
-  %c0 = arith.constant 0 : index
-  %c1 = arith.constant 1 : index
-  %c2 = arith.constant 2 : index
   %c9 = arith.constant 9 : index
   %c3 = arith.constant 3 : index
   %2 = tensor.extract %arg1[] : tensor<i32>
@@ -472,7 +472,7 @@
   ^bb0(%arg2: index, %arg3: index):
     tensor.yield %2 : i32
   } : tensor<?x?xi32>
-  %5 = tensor.insert_slice %arg0 into %4[%c0, %c1] [%c2, %c3] [1, 1] : tensor<2x3xi32> into tensor<?x?xi32>
+  %5 = tensor.insert_slice %arg0 into %4[0, 1] [2, 3] [1, 1] : tensor<2x3xi32> into tensor<?x?xi32>
   %6 = tensor.cast %5 : tensor<?x?xi32> to tensor<3x9xi32>
   return %6 : tensor<3x9xi32>
 }
@@ -527,8 +527,9 @@
 //      CHECK:    %[[r:.*]] =  tensor.insert_slice %[[cast]] into %[[arg1]][0, 1, 2] [64, 5, 64] [1, 1, 1] : tensor<64x5x64xf32> into tensor<?x?x?xf32>
 //      CHECK:    return %[[r]]
 func @insert_tensor_cast_on_insert_slice_src(
-  %arg0 : tensor<?x5x?xf32>,  %arg1 : tensor<?x?x?xf32>) -> tensor<?x?x?xf32> {
-  %r = tensor.insert_slice %arg0 into %arg1[0, 1, 2] [64, 5, 64] [1, 1, 1]
+    %arg0 : tensor<?x5x?xf32>,  %arg1 : tensor<?x?x?xf32>, %sz0: index, %sz2: index) -> tensor<?x?x?xf32> {
+  %c64 = arith.constant 64: index
+  %r = tensor.insert_slice %arg0 into %arg1[0, 1, 2] [%c64, 5, %c64] [1, 1, 1]
     : tensor<?x5x?xf32> into tensor<?x?x?xf32>
   return %r : tensor<?x?x?xf32>
 }
@@ -559,13 +560,3 @@
   // CHECK: return %[[INSERT]]
   return %1 : tensor<?x?x?xf32>
 }
-
-// -----
-
-// CHECK-LABEL: func @folding_incorrect_ir_triggers_infinite_loop
-func @folding_incorrect_ir_triggers_infinite_loop(
-  %A : tensor<4x4xf32>, %C : tensor<?x?xf32>) -> tensor<?x?xf32> {
-  %rC = tensor.insert_slice %A into %C[0, 0][12345, 67890][1, 1] :
-    tensor<4x4xf32> into tensor<?x?xf32>
-  return %rC: tensor<?x?xf32>
-}
diff --git a/mlir/test/Dialect/Tensor/invalid.mlir b/mlir/test/Dialect/Tensor/invalid.mlir
index 51b67a7..f3c8ba2 100644
--- a/mlir/test/Dialect/Tensor/invalid.mlir
+++ b/mlir/test/Dialect/Tensor/invalid.mlir
@@ -149,8 +149,36 @@
 
 // -----
 
-func @slice_wrong_dynamic_type(%t: tensor<8x16x4xf32>, %idx : index) {
-      // expected-error @+1 {{expected result type to be 'tensor<4x4x4xf32>' or a rank-reduced version. (mismatch of result sizes)}}
+func @extract_slice_wrong_result_rank(%t: tensor<?xf32>, %idx : index) {
+  // expected-error @+1 {{expected rank to be smaller or equal to the other rank.}}
+  %0 = tensor.extract_slice %t[0][4][1] : tensor<?xf32> to tensor<?x?xf32>
+
+  return
+}
+
+// -----
+
+func @extract_slice_wrong_result_rank(%t: tensor<?xf32>, %idx : index) {
+  // expected-error @+1 {{expected element type to be 'f32'}}
+  %0 = tensor.extract_slice %t[0][4][1] : tensor<?xf32> to tensor<4xi8>
+
+  return
+}
+
+// -----
+
+func @extract_slice_wrong_static_type(%t: tensor<8x16x4xf32>, %idx : index) {
+  // expected-error @+1 {{expected type to be 'tensor<?x4x4xf32>' or a rank-reduced version. (size mismatch)}}
+  %0 = tensor.extract_slice %t[0, 0, 0][%idx, 4, 4][1, 1, 1]
+    : tensor<8x16x4xf32> to tensor<4x4x4xf32>
+
+  return
+}
+
+// -----
+
+func @extract_slice_wrong_dynamic_type(%t: tensor<8x16x4xf32>, %idx : index) {
+  // expected-error @+1 {{expected type to be 'tensor<4x4x4xf32>' or a rank-reduced version. (size mismatch)}}
   %0 = tensor.extract_slice %t[0, 2, 0][4, 4, 4][1, 1, 1]
     : tensor<8x16x4xf32> to tensor<?x4x4xf32>
 
@@ -159,10 +187,38 @@
 
 // -----
 
-func @slice_wrong_static_type(%t: tensor<8x16x4xf32>, %idx : index) {
-      // expected-error @+1 {{expected result type to be 'tensor<?x3x?xf32>' or a rank-reduced version. (mismatch of result sizes)}}
-  %0 = tensor.extract_slice %t[0, 0, 0][%idx, 3, %idx][1, 1, 1]
-    : tensor<8x16x4xf32> to tensor<4x4x4xf32>
+func @insert_slice_wrong_result_rank(%t1: tensor<?xf32>, %t2: tensor<?x?xf32>, %idx : index) {
+  // expected-error @+1 {{expected rank to be smaller or equal to the other rank.}}
+  %0 = tensor.insert_slice %t2 into %t1[0][4][1] : tensor<?x?xf32> into tensor<?xf32>
+
+  return
+}
+
+// -----
+
+func @insert_slice_wrong_result_rank(%t1: tensor<4xi8>, %t2: tensor<?xf32>, %idx : index) {
+  // expected-error @+1 {{expected element type to be 'f32'}}
+  %0 = tensor.insert_slice %t1 into %t2[0][4][1] : tensor<4xi8> into tensor<?xf32>
+
+  return
+}
+
+// -----
+
+func @insert_slice_wrong_static_type(%t1: tensor<4x4x4xf32>, %t2: tensor<8x16x4xf32>, %idx : index) {
+  // expected-error @+1 {{expected type to be 'tensor<?x4x4xf32>' or a rank-reduced version. (size mismatch)}}
+  %0 = tensor.insert_slice %t1 into %t2[0, 0, 0][%idx, 4, 4][1, 1, 1]
+    : tensor<4x4x4xf32> into tensor<8x16x4xf32>
+
+  return
+}
+
+// -----
+
+func @insert_slice_wrong_dynamic_type(%t1: tensor<?x4x4xf32>, %t2: tensor<8x16x4xf32>, %idx : index) {
+  // expected-error @+1 {{expected type to be 'tensor<4x4x4xf32>' or a rank-reduced version. (size mismatch)}}
+  %0 = tensor.insert_slice %t1 into %t2[0, 2, 0][4, 4, 4][1, 1, 1]
+    : tensor<?x4x4xf32> into tensor<8x16x4xf32>
 
   return
 }
diff --git a/mlir/test/Dialect/Tensor/ops.mlir b/mlir/test/Dialect/Tensor/ops.mlir
index 2353d03..d8c5a41 100644
--- a/mlir/test/Dialect/Tensor/ops.mlir
+++ b/mlir/test/Dialect/Tensor/ops.mlir
@@ -78,3 +78,60 @@
                : (tensor<?x?xf32>, tensor<?xi32>) -> tensor<*xf32>
   return %new_unranked : tensor<*xf32>
 }
+
+// CHECK-LABEL: func @slice({{.*}}) {
+func @slice(%t: tensor<8x16x4xf32>, %idx : index) {
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+
+  // CHECK: tensor.extract_slice
+  // CHECK-SAME: tensor<8x16x4xf32> to tensor<?x?x?xf32>
+  %1 = tensor.extract_slice %t[%c0, %c0, %c0][%idx, %idx, %idx][%c1, %c1, %c1]
+    : tensor<8x16x4xf32> to tensor<?x?x?xf32>
+
+  // CHECK: tensor.extract_slice
+  // CHECK-SAME: tensor<8x16x4xf32> to tensor<4x4x4xf32>
+  %2 = tensor.extract_slice %t[0, 2, 0][4, 4, 4][1, 1, 1]
+    : tensor<8x16x4xf32> to tensor<4x4x4xf32>
+
+  // CHECK: tensor.extract_slice
+  // CHECK-SAME: tensor<8x16x4xf32> to tensor<4x4xf32>
+  %3 = tensor.extract_slice %t[0, 2, 0][4, 1, 4][1, 1, 1]
+    : tensor<8x16x4xf32> to tensor<4x4xf32>
+
+  return
+}
+
+// CHECK-LABEL: func @insert_slice({{.*}}) {
+func @insert_slice(
+    %t: tensor<8x16x4xf32>,
+    %td: tensor<8x?x4xf32>,
+    %t2: tensor<16x32x8xf32>,
+    %t3: tensor<4x4xf32>,
+    %idx : index,
+    %sz : index) {
+  %c0 = arith.constant 0 : index
+  %c1 = arith.constant 1 : index
+
+  // CHECK: tensor.insert_slice
+  // CHECK-SAME: tensor<8x16x4xf32> into tensor<16x32x8xf32>
+  %1 = tensor.insert_slice %t into %t2[%c0, %c0, %c0][8, 16, 4][%c1, %c1, %c1]
+    : tensor<8x16x4xf32> into tensor<16x32x8xf32>
+
+  // CHECK: tensor.insert_slice
+  // CHECK-SAME: tensor<8x16x4xf32> into tensor<16x32x8xf32>
+  %2 = tensor.insert_slice %t into %t2[%c0, %idx, %c0][8, 16, 4][%c1, 1, %c1]
+    : tensor<8x16x4xf32> into tensor<16x32x8xf32>
+
+  // CHECK: tensor.insert_slice
+  // CHECK-SAME: tensor<4x4xf32> into tensor<8x16x4xf32>
+  %3 = tensor.insert_slice %t3 into %t[0, 2, 0][4, 1, 4][1, 1, 1]
+    : tensor<4x4xf32> into tensor<8x16x4xf32>
+
+  // CHECK: tensor.insert_slice
+  // CHECK-SAME: tensor<8x?x4xf32> into tensor<8x16x4xf32>
+  %4 = tensor.insert_slice %td into %t[0, %idx, 0][8, %sz, 4][1, 1, 1]
+    : tensor<8x?x4xf32> into tensor<8x16x4xf32>
+
+  return
+}
diff --git a/mlir/test/IR/core-ops.mlir b/mlir/test/IR/core-ops.mlir
index 029d26c..300c254 100644
--- a/mlir/test/IR/core-ops.mlir
+++ b/mlir/test/IR/core-ops.mlir
@@ -486,53 +486,3 @@
   memref.assume_alignment %0, 16 : memref<4x4xf16>
   return
 }
-
-// CHECK-LABEL: func @slice({{.*}}) {
-func @slice(%t: tensor<8x16x4xf32>, %idx : index) {
-  %c0 = arith.constant 0 : index
-  %c1 = arith.constant 1 : index
-
-  // CHECK: tensor.extract_slice
-  // CHECK-SAME: tensor<8x16x4xf32> to tensor<?x?x?xf32>
-  %1 = tensor.extract_slice %t[%c0, %c0, %c0][%idx, %idx, %idx][%c1, %c1, %c1]
-    : tensor<8x16x4xf32> to tensor<?x?x?xf32>
-
-  // CHECK: tensor.extract_slice
-  // CHECK-SAME: tensor<8x16x4xf32> to tensor<4x4x4xf32>
-  %2 = tensor.extract_slice %t[0, 2, 0][4, 4, 4][1, 1, 1]
-    : tensor<8x16x4xf32> to tensor<4x4x4xf32>
-
-  // CHECK: tensor.extract_slice
-  // CHECK-SAME: tensor<8x16x4xf32> to tensor<4x4xf32>
-  %3 = tensor.extract_slice %t[0, 2, 0][4, 1, 4][1, 1, 1]
-    : tensor<8x16x4xf32> to tensor<4x4xf32>
-
-  return
-}
-
-// CHECK-LABEL: func @insert_slice({{.*}}) {
-func @insert_slice(
-    %t: tensor<8x16x4xf32>,
-    %t2: tensor<16x32x8xf32>,
-    %t3: tensor<4x4xf32>,
-    %idx : index) {
-  %c0 = arith.constant 0 : index
-  %c1 = arith.constant 1 : index
-
-  // CHECK: tensor.insert_slice
-  // CHECK-SAME: tensor<8x16x4xf32> into tensor<16x32x8xf32>
-  %1 = tensor.insert_slice %t into %t2[%c0, %c0, %c0][%idx, %idx, %idx][%c1, %c1, %c1]
-    : tensor<8x16x4xf32> into tensor<16x32x8xf32>
-
-  // CHECK: tensor.insert_slice
-  // CHECK-SAME: tensor<8x16x4xf32> into tensor<16x32x8xf32>
-  %2 = tensor.insert_slice %t into %t2[%c0, %idx, %c0][%idx, 4, %idx][%c1, 1, %c1]
-    : tensor<8x16x4xf32> into tensor<16x32x8xf32>
-
-  // CHECK: tensor.insert_slice
-  // CHECK-SAME: tensor<4x4xf32> into tensor<8x16x4xf32>
-  %3 = tensor.insert_slice %t3 into %t[0, 2, 0][4, 1, 4][1, 1, 1]
-    : tensor<4x4xf32> into tensor<8x16x4xf32>
-
-  return
-}