diff --git a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
index af7b91d17..de68e6146 100644
--- a/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
+++ b/lib/Dialect/Torch/Transforms/DecomposeComplexOps.cpp
@@ -2963,6 +2963,128 @@ public:
 };
 } // namespace
 
+namespace {
+// def slice_scatter(self, values, dim, start, end, step):
+//     size = self.size(dim)
+//     indices = torch.arange(size)
+//     shift_indices = indices - start
+//     mask = shift_indices % step == 0
+//     start_mask = shift_indices >= 0
+//     end_mask = shift_indices < end
+//     mask = mask * start_mask
+//     mask = mask * end_mask
+//     sizes = list(self.size())
+//     rank = len(sizes)
+//     shape = [1] * rank
+//     shape[dim] = size
+//     mask = mask.view(shape)
+//     return torch.where(mask, values, self)
+//
+class DecomposeAtenSliceScatterOp
+    : public OpRewritePattern<AtenSliceScatterOp> {
+public:
+  using OpRewritePattern::OpRewritePattern;
+  LogicalResult matchAndRewrite(AtenSliceScatterOp op,
+                                PatternRewriter &rewriter) const override {
+    int64_t inputRank = getTensorRank(op.self());
+    int64_t dimInt = 0;
+    if (matchPattern(op.dim(), m_TorchConstantInt(&dimInt))) {
+      dimInt = toPositiveDim(dimInt, inputRank);
+      if (!isValidDim(dimInt, inputRank))
+        return rewriter.notifyMatchFailure(op, "dim is not a valid dim");
+    } else {
+      return rewriter.notifyMatchFailure(op, "dim must be constant");
+    }
+
+    auto getOptionalVal = [&](Value val, Value defVal) -> Value {
+      if (val.getType().isa<Torch::NoneType>()) {
+        return defVal;
+      } else {
+        return val;
+      }
+    };
+
+    Value one = rewriter.create<Torch::ConstantIntOp>(
+        op.getLoc(), rewriter.getI64IntegerAttr(1));
+    Value zero = rewriter.create<Torch::ConstantIntOp>(
+        op.getLoc(), rewriter.getI64IntegerAttr(0));
+    Value none = rewriter.create<ConstantNoneOp>(op.getLoc());
+    Value dimSize =
+        rewriter.create<AtenSizeIntOp>(op.getLoc(), op.self(), op.dim());
+
+    Value start = getOptionalVal(op.start(), zero);
+    Value end = getOptionalVal(op.end(), dimSize);
+    Value step = getOptionalVal(op.step(), one);
+    // Step 0. create indices
+    Type indicesType = ValueTensorType::get(
+        op.getContext(), ArrayRef<int64_t>{ShapedType::kDynamicSize},
+        IntegerType::get(op.getContext(), 64, IntegerType::Signed));
+    Value indices = rewriter.create<AtenArangeOp>(
+        op.getLoc(), indicesType, dimSize, none, none, none, none);
+
+    // Step 1. make indices broadcastable to self's shape
+    SmallVector<int64_t> newIndicesShapeInt(inputRank, 1);
+    SmallVector<Value> newIndicesShape(inputRank, one);
+    newIndicesShape[dimInt] = dimSize;
+    newIndicesShapeInt[dimInt] = ShapedType::kDynamicSize;
+    Value newIndicesSizeList = rewriter.create<PrimListConstructOp>(
+        op.getLoc(), ListType::get(IntType::get(op.getContext())),
+        newIndicesShape);
+    Type indicesDtype = indices.getType().cast<ValueTensorType>().getDtype();
+    Type newIndicesType = ValueTensorType::get(
+        op.getContext(), llvm::makeArrayRef(newIndicesShapeInt), indicesDtype);
+    indices = rewriter.create<AtenViewOp>(op.getLoc(), newIndicesType,
+                                                 indices, newIndicesSizeList);
+
+    // Step 2. calculate scatter indices mask
+    Type maskType = ValueTensorType::get(
+        op.getContext(), newIndicesType.cast<ValueTensorType>().getSizes(),
+        IntegerType::get(op.getContext(), 1));
+    auto shiftIndices = rewriter.create<AtenSubScalarOp>(
+        op.getLoc(), indices.getType(), indices, start, one);
+    auto stepRemainder = rewriter.create<AtenRemainderScalarOp>(
+        op.getLoc(), indices.getType(), shiftIndices, step);
+    Value mask = rewriter.create<AtenEqScalarOp>(op.getLoc(), maskType,
+                                                 stepRemainder, zero);
+    auto maskStart = rewriter.create<AtenGeScalarOp>(op.getLoc(), maskType,
+                                                     shiftIndices, zero);
+    auto maskEnd =
+        rewriter.create<AtenLtScalarOp>(op.getLoc(), maskType, indices, end);
+    mask = rewriter.create<AtenBitwiseAndTensorOp>(op.getLoc(), maskType, mask,
+                                                   maskStart);
+    mask = rewriter.create<AtenBitwiseAndTensorOp>(op.getLoc(), maskType, mask,
+                                                   maskEnd);
+
+    // Step 3. make src broadcastable to self's shape
+    Value src = op.src();
+    BaseTensorType srcTensorType = src.getType().cast<BaseTensorType>();
+    if (!srcTensorType.hasSizes())
+      return rewriter.notifyMatchFailure(op, "src tensor must have size");
+
+    ArrayRef<int64_t> srcShape = srcTensorType.getSizes();
+    int64_t srcRank = srcShape.size();
+    if (srcRank != inputRank) {
+      if (srcRank + 1 == inputRank) {
+        SmallVector<int64_t> sizes;
+        sizes.append(srcShape.begin(), srcShape.end());
+        sizes.insert(sizes.begin() + dimInt, 1);
+        Type srcType = srcTensorType.getWithSizesAndDtype(
+            llvm::makeArrayRef(sizes), srcTensorType.getDtype());
+        src = rewriter.create<AtenUnsqueezeOp>(op.getLoc(), srcType, src,
+                                               op.dim());
+      } else {
+        return rewriter.notifyMatchFailure(op, "src's rank doesn't match");
+      }
+    }
+
+    // Step 4. replace output = mask? src: self
+    rewriter.replaceOpWithNewOp<AtenWhereSelfOp>(op, op.getType(), mask,
+                                                 src, op.self());
+    return success();
+  }
+};
+} // namespace
+
 namespace {
 class DecomposeAten_EmbeddingBagOp
     : public OpRewritePattern<Aten_EmbeddingBagOp> {
@@ -3354,6 +3476,8 @@ public:
     target.addIllegalOp<AtenNumpyTOp>();
     patterns.add<DecomposeAtenSelectScatterOp>(context);
     target.addIllegalOp<AtenSelectScatterOp>();
+    patterns.add<DecomposeAtenSliceScatterOp>(context);
+    target.addIllegalOp<AtenSliceScatterOp>();
     patterns.add<DecomposeAtenVarDimOp>(context);
     target.addIllegalOp<AtenVarDimOp>();
     patterns.add<DecomposeAtenVarCorrectionOp>(context);
diff --git a/python/torch_mlir/__init__.py b/python/torch_mlir/__init__.py
index 2cc6e984c..9b135ad24 100644
--- a/python/torch_mlir/__init__.py
+++ b/python/torch_mlir/__init__.py
@@ -240,7 +240,7 @@ class ExampleArgs:
 # compiler where each backend can "own" its set of legal ops.
 BACKEND_LEGAL_OPS = {
     OutputType.TOSA: ['torch.aten.flatten.using_ints', 'torch.aten.native_layer_norm', 'torch.aten.linear'],
-    OutputType.LINALG_ON_TENSORS: ['torch.aten.flatten.using_ints', ],
+    OutputType.LINALG_ON_TENSORS: ['torch.aten.flatten.using_ints', 'torch.aten.slice_scatter'],
     OutputType.MHLO: [],
 }
 
diff --git a/test/Dialect/Torch/decompose-complex-ops.mlir b/test/Dialect/Torch/decompose-complex-ops.mlir
index 9002894c1..123561b95 100644
--- a/test/Dialect/Torch/decompose-complex-ops.mlir
+++ b/test/Dialect/Torch/decompose-complex-ops.mlir
@@ -784,7 +784,7 @@ func.func @torch.aten.numpy_T$rank_three(%arg0: !torch.vtensor<[5,4,3],f32>) ->
 }
 
 // -----
-// CHECK-LABEL:   func.func @torch.aten.repeat(
+// CHECK-LABEL:   func @torch.aten.repeat(
 // CHECK-SAME:      %[[ARG0:.*]]: !torch.vtensor<[?,?],f32>, %[[ARG1:.*]]: !torch.int, %[[ARG2:.*]]: !torch.int, %[[ARG3:.*]]: !torch.int) -> !torch.vtensor<[?,?,?],f32> {                                                                                    
 // CHECK:     %[[T0:.*]] = torch.prim.ListConstruct %[[ARG1]], %[[ARG2]], %[[ARG3]] : (!torch.int, !torch.int, !torch.int) -> !torch.list<int>                                                                                                                                       
 // CHECK:     %[[INT1:.*]] = torch.constant.int 1
@@ -810,14 +810,29 @@ func.func @torch.aten.repeat(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.int
 // -----
 // CHECK-LABEL: func @torch.aten.select_scatter
 // CHECK-SAME:  (%[[SELF:.*]]: !torch.vtensor<[?,?],f32>, %[[SRC:.*]]: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?],f32> {
-// CHECK-NEXT:    %[[START:.*]] = torch.constant.int 0
-// CHECK-NEXT:    %[[DIM:.*]] = torch.constant.int 1
-// CHECK-NEXT:    %[[STEP:.*]] = torch.constant.int 1
-// CHECK-NEXT:    %[[END:.*]] = torch.aten.add.int %[[START]], %[[STEP]]
-// CHECK-NEXT:    %[[UNSQUEEZE_SRC:.*]] = torch.aten.unsqueeze %[[SRC]], %[[DIM]]
-// CHECK-NEXT:    %[[SLICE_SCATTER:.*]] = torch.aten.slice_scatter %[[SELF]], %[[UNSQUEEZE_SRC]], %[[DIM]], %[[START]], %[[END]], %[[STEP]]
-// CHECK-NEXT:    return %[[SLICE_SCATTER]]
-// CHECK-NEXT:    }
+// CHECK-NEXT:    %[[INT0:.*]] = torch.constant.int 0
+// CHECK-NEXT:    %[[INT1:.*]] = torch.constant.int 1
+// CHECK-NEXT:    %[[INT1_0:.*]] = torch.constant.int 1
+// CHECK-NEXT:    %[[T0:.*]] = torch.aten.add.int %[[INT0]], %[[INT1_0]] : !torch.int, !torch.int -> !torch.int
+// CHECK-NEXT:    %[[T1:.*]] = torch.aten.unsqueeze %[[SRC]], %[[INT1]] : !torch.vtensor<[?],f32>, !torch.int -> !torch.vtensor<[?,1],f32>
+// CHECK-NEXT:    %[[INT1_1:.*]] = torch.constant.int 1
+// CHECK-NEXT:    %[[INT0_2:.*]] = torch.constant.int 0
+// CHECK-NEXT:    %[[NONE:.*]] = torch.constant.none
+// CHECK-NEXT:    %[[T2:.*]] = torch.aten.size.int %[[SELF]], %[[INT1]] : !torch.vtensor<[?,?],f32>, !torch.int -> !torch.int
+// CHECK-NEXT:    %[[INT0_3:.*]] = torch.constant.int 0
+// CHECK-NEXT:    %[[INT1_4:.*]] = torch.constant.int 1
+// CHECK-NEXT:    %[[T3:.*]] = torch.aten.arange.start_step %[[INT0_3]], %[[T2]], %[[INT1_4]], %[[NONE]], %[[NONE]], %[[NONE]], %[[NONE]] : !torch.int, !torch.int, !torch.int, !torch.none, !torch.none, !torch.none, !torch.none -> !torch.vtensor<[?],si64>
+// CHECK-NEXT:    %[[T4:.*]] = torch.prim.ListConstruct %[[INT1_1]], %[[T2]] : (!torch.int, !torch.int) -> !torch.list<int>
+// CHECK-NEXT:    %[[T5:.*]] = torch.aten.view %[[T3]], %[[T4]] : !torch.vtensor<[?],si64>, !torch.list<int> -> !torch.vtensor<[1,?],si64>
+// CHECK-NEXT:    %[[T6:.*]] = torch.aten.sub.Scalar %[[T5]], %[[INT0]], %[[INT1_1]] : !torch.vtensor<[1,?],si64>, !torch.int, !torch.int -> !torch.vtensor<[1,?],si64>
+// CHECK-NEXT:    %[[T7:.*]] = torch.aten.remainder.Scalar %[[T6]], %[[INT1_0]] : !torch.vtensor<[1,?],si64>, !torch.int -> !torch.vtensor<[1,?],si64>
+// CHECK-NEXT:    %[[T8:.*]] = torch.aten.eq.Scalar %[[T7]], %[[INT0_2]] : !torch.vtensor<[1,?],si64>, !torch.int -> !torch.vtensor<[1,?],i1>
+// CHECK-NEXT:    %[[T9:.*]] = torch.aten.ge.Scalar %[[T6]], %[[INT0_2]] : !torch.vtensor<[1,?],si64>, !torch.int -> !torch.vtensor<[1,?],i1>
+// CHECK-NEXT:    %[[T10:.*]] = torch.aten.lt.Scalar %[[T5]], %[[T0]] : !torch.vtensor<[1,?],si64>, !torch.int -> !torch.vtensor<[1,?],i1>
+// CHECK-NEXT:    %[[T11:.*]] = torch.aten.bitwise_and.Tensor %[[T8]], %[[T9]] : !torch.vtensor<[1,?],i1>, !torch.vtensor<[1,?],i1> -> !torch.vtensor<[1,?],i1>
+// CHECK-NEXT:    %[[T12:.*]] = torch.aten.bitwise_and.Tensor %[[T11]], %[[T10]] : !torch.vtensor<[1,?],i1>, !torch.vtensor<[1,?],i1> -> !torch.vtensor<[1,?],i1>
+// CHECK-NEXT:    %[[T13:.*]] = torch.aten.where.self %[[T12]], %[[T1]], %[[SELF]] : !torch.vtensor<[1,?],i1>, !torch.vtensor<[?,1],f32>, !torch.vtensor<[?,?],f32> -> !torch.vtensor<[?,?],f32>
+// CHECK-NEXT:    return %[[T13]] : !torch.vtensor<[?,?],f32>
 func.func @torch.aten.select_scatter(%arg0: !torch.vtensor<[?,?],f32>, %arg1: !torch.vtensor<[?],f32>) -> !torch.vtensor<[?,?],f32> {
   %int0 = torch.constant.int 0 
   %int1 = torch.constant.int 1