diff --git a/include/torch-mlir/Conversion/TorchToLinalg/Utils.h b/include/torch-mlir/Conversion/TorchToLinalg/Utils.h
index 14e9202222c6..b59d183b4084 100644
--- a/include/torch-mlir/Conversion/TorchToLinalg/Utils.h
+++ b/include/torch-mlir/Conversion/TorchToLinalg/Utils.h
@@ -101,6 +101,10 @@ LogicalResult permuteTensor(Operation *op, PatternRewriter &rewriter,
                             Location loc, SmallVector<int64_t> dimensions,
                             Value input, Value &result);
 
+// Flips an input tensor based on the values of axis list.
+Value flipTensor(PatternRewriter &rewriter, Location loc, Value input,
+                 SmallVector<int64_t> axis);
+
 } // namespace torch_to_linalg
 } // namespace torch
 } // namespace mlir
diff --git a/lib/Conversion/TorchToLinalg/DataMovement.cpp b/lib/Conversion/TorchToLinalg/DataMovement.cpp
index 5542e0fc642f..ac1707ec23a6 100644
--- a/lib/Conversion/TorchToLinalg/DataMovement.cpp
+++ b/lib/Conversion/TorchToLinalg/DataMovement.cpp
@@ -40,6 +40,7 @@ static int64_t productReduce(ArrayRef<int64_t> a) {
 template <typename OpTy, typename OpAdaptor>
 LogicalResult prepareArgumentsForSlicingOp(OpTy op, OpAdaptor adaptor,
                                            ConversionPatternRewriter &rewriter,
+                                           int64_t &dim,
                                            SmallVector<Value> &resultShape,
                                            SmallVector<Value> &offsets,
                                            SmallVector<Value> &strides) {
@@ -51,7 +52,6 @@ LogicalResult prepareArgumentsForSlicingOp(OpTy op, OpAdaptor adaptor,
   Value one = rewriter.create<arith::ConstantIndexOp>(loc, 1);
   Value negone = rewriter.create<arith::ConstantIndexOp>(loc, -1);
 
-  int64_t dim;
   if (!matchPattern(op.getDim(), m_TorchConstantInt(&dim)))
     return op->emitError("unimplemented: dim is not constant");
 
@@ -1857,14 +1857,46 @@ class ConvertAtenSliceTensorOp : public OpConversionPattern<AtenSliceTensorOp> {
     RankedTensorType resultType = cast<RankedTensorType>(
         typeConverter->convertType(op->getResult(0).getType()));
 
-    SmallVector<Value> resultShape;
-    SmallVector<Value> offsets;
-    SmallVector<Value> strides;
+    SmallVector<Value> resultShape, offsets, strides;
+    int64_t dim;
     if (failed(prepareArgumentsForSlicingOp<AtenSliceTensorOp,
                                             AtenSliceTensorOpAdaptor>(
-            op, adaptor, rewriter, resultShape, offsets, strides))) {
+            op, adaptor, rewriter, dim, resultShape, offsets, strides))) {
       return failure();
     }
+
+    // If stride is negative, then flip the input tensor corresponding to that
+    // dim, update the stride for flipped tensor by multiplying it by -1, and
+    // update the offset as follows:
+    // flipped_offset = input_shape[dim] - (result_shape[dim] * flipped_stride)
+    //
+    // For example:
+    // Input = [0, 1, 2, 3, 4, 5]
+    // stride = [-2], result_shape = [2], offset = [3]
+    // Result = [3, 1]
+    // After flipping:
+    // Input = [5, 4, 3, 2, 1, 0]
+    // stride = [2], result_shape = [2], offset = [6 - (2 * 2)] = [2]
+    // Result = [3, 1]
+
+    Value flippedInput = torch_to_linalg::flipTensor(rewriter, loc, input,
+                                                     SmallVector<int64_t>{dim});
+    Value cstDim = rewriter.create<arith::ConstantIndexOp>(loc, dim);
+    Value zero = rewriter.create<arith::ConstantIndexOp>(loc, 0);
+    Value isNegativeStride = rewriter.create<arith::CmpIOp>(
+        loc, arith::CmpIPredicate::slt, strides[dim], zero);
+    strides[dim] = rewriter.create<math::AbsIOp>(loc, strides[dim]);
+    Value resShapeMulStride =
+        rewriter.create<arith::MulIOp>(loc, resultShape[dim], strides[dim]);
+    Value inputDim = rewriter.create<tensor::DimOp>(loc, input, cstDim);
+    Value flippedOffset =
+        rewriter.create<arith::SubIOp>(loc, inputDim, resShapeMulStride);
+    offsets[dim] = rewriter.create<arith::SelectOp>(
+        loc, isNegativeStride, flippedOffset, offsets[dim]);
+
+    input = rewriter.create<arith::SelectOp>(loc, isNegativeStride,
+                                             flippedInput, input);
+
     SmallVector<int64_t> dynShape(resultType.getRank(), ShapedType::kDynamic);
     auto sliceType = RankedTensorType::get(
         dynShape, resultType.getElementType(), resultType.getEncoding());
@@ -2095,12 +2127,11 @@ class ConvertAtenSliceScatterOp
     RankedTensorType resultType = cast<RankedTensorType>(
         typeConverter->convertType(op->getResult(0).getType()));
 
-    SmallVector<Value> resultShape;
-    SmallVector<Value> offsets;
-    SmallVector<Value> strides;
+    SmallVector<Value> resultShape, offsets, strides;
+    int64_t dim;
     if (failed(prepareArgumentsForSlicingOp<AtenSliceScatterOp,
                                             AtenSliceScatterOpAdaptor>(
-            op, adaptor, rewriter, resultShape, offsets, strides))) {
+            op, adaptor, rewriter, dim, resultShape, offsets, strides))) {
       return failure();
     }
 
diff --git a/lib/Conversion/TorchToLinalg/Linear.cpp b/lib/Conversion/TorchToLinalg/Linear.cpp
index 52765411bd73..fc910fa9d3f2 100644
--- a/lib/Conversion/TorchToLinalg/Linear.cpp
+++ b/lib/Conversion/TorchToLinalg/Linear.cpp
@@ -222,14 +222,9 @@ class ConvertAtenFlipOp : public OpConversionPattern<AtenFlipOp> {
                   ConversionPatternRewriter &rewriter) const override {
 
     Location loc = op->getLoc();
-    MLIRContext *context = op.getContext();
     Value self = adaptor.getSelf();
     auto selfRank =
         cast<RankedTensorType>(adaptor.getSelf().getType()).getRank();
-    Type elementType =
-        cast<RankedTensorType>(adaptor.getSelf().getType()).getElementType();
-    Value c1 =
-        rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(1));
 
     SmallVector<int64_t> axis;
     if (!matchPattern(adaptor.getDims(), m_TorchListOfConstantInts(axis)))
@@ -242,40 +237,8 @@ class ConvertAtenFlipOp : public OpConversionPattern<AtenFlipOp> {
       }
     }
 
-    // Only used to calculate flipped values, i.e. those on the flip axes. Other
-    // dims won't be used.
-    SmallVector<Value> dims = getTensorSizes(rewriter, loc, self);
-    for (auto flipDim : axis)
-      dims[flipDim] = rewriter.create<arith::SubIOp>(loc, dims[flipDim], c1);
-
-    Value initTensor = createZeroInitTensor(
-        rewriter, loc, getTensorSizes(rewriter, loc, self), elementType);
-
-    SmallVector<utils::IteratorType> iteratorTypes(
-        selfRank, utils::IteratorType::parallel);
-    SmallVector<AffineMap> indexingMaps(
-        2, AffineMap::getMultiDimIdentityMap(selfRank, context));
-    Value flipped =
-        rewriter
-            .create<linalg::GenericOp>(
-                loc, self.getType(), self, initTensor, indexingMaps,
-                iteratorTypes,
-                [&](OpBuilder &b, Location loc, ValueRange args) {
-                  SmallVector<Value> indices;
-                  for (auto i = 0; i < selfRank; i++)
-                    indices.push_back(b.create<linalg::IndexOp>(loc, i));
-                  for (auto flipDim : axis) {
-                    indices[flipDim] = b.create<arith::SubIOp>(
-                        loc, dims[flipDim], indices[flipDim]);
-                  }
-                  Value res = b.create<tensor::ExtractOp>(loc, self, indices)
-                                  .getResult();
-                  b.create<linalg::YieldOp>(loc, res);
-                })
-            .getResult(0);
-
+    Value flipped = torch_to_linalg::flipTensor(rewriter, loc, self, axis);
     rewriter.replaceOpWithNewOp<tensor::CastOp>(op, self.getType(), flipped);
-
     return success();
   }
 };
diff --git a/lib/Conversion/TorchToLinalg/Utils.cpp b/lib/Conversion/TorchToLinalg/Utils.cpp
index 6ef947d890cd..18e8fb449ef5 100644
--- a/lib/Conversion/TorchToLinalg/Utils.cpp
+++ b/lib/Conversion/TorchToLinalg/Utils.cpp
@@ -620,3 +620,44 @@ LogicalResult torch_to_linalg::permuteTensor(Operation *op,
                .getResult(0);
   return success();
 }
+
+// Flips an input tensor based on the values of axis list.
+Value torch_to_linalg::flipTensor(PatternRewriter &rewriter, Location loc,
+                                  Value input, SmallVector<int64_t> axis) {
+  Value c1 = rewriter.create<arith::ConstantOp>(loc, rewriter.getIndexAttr(1));
+  Type elementType = cast<RankedTensorType>(input.getType()).getElementType();
+  auto selfRank = cast<RankedTensorType>(input.getType()).getRank();
+
+  // Only used to calculate flipped values, i.e. those on the flip axes. Other
+  // dims won't be used.
+  SmallVector<Value> dims = getTensorSizes(rewriter, loc, input);
+  for (auto flipDim : axis)
+    dims[flipDim] = rewriter.create<arith::SubIOp>(loc, dims[flipDim], c1);
+
+  Value initTensor = createZeroInitTensor(
+      rewriter, loc, getTensorSizes(rewriter, loc, input), elementType);
+
+  SmallVector<utils::IteratorType> iteratorTypes(selfRank,
+                                                 utils::IteratorType::parallel);
+  SmallVector<AffineMap> indexingMaps(
+      2, AffineMap::getMultiDimIdentityMap(selfRank, rewriter.getContext()));
+  Value flipped =
+      rewriter
+          .create<linalg::GenericOp>(
+              loc, input.getType(), input, initTensor, indexingMaps,
+              iteratorTypes,
+              [&](OpBuilder &b, Location loc, ValueRange args) {
+                SmallVector<Value> indices;
+                for (auto i = 0; i < selfRank; i++)
+                  indices.push_back(b.create<linalg::IndexOp>(loc, i));
+                for (auto flipDim : axis) {
+                  indices[flipDim] = b.create<arith::SubIOp>(loc, dims[flipDim],
+                                                             indices[flipDim]);
+                }
+                Value res = b.create<tensor::ExtractOp>(loc, input, indices)
+                                .getResult();
+                b.create<linalg::YieldOp>(loc, res);
+              })
+          .getResult(0);
+  return flipped;
+}