diff --git a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
index 86f2455ca..7d7d588ad 100644
--- a/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
+++ b/lib/Conversion/TorchOnnxToTorch/DefaultDomainQtoZ.cpp
@@ -4016,4 +4016,83 @@ void mlir::torch::onnx_c::populateDefaultDomainQtoZ(
             binder.op, resultType, scatter, constZero, unflattenSizeList);
         return success();
       });
+  // split to sequence
+  // Arguments:
+  // - input: the tensor to split
+  // -Split(optional): Length of each output
+  // Attributes:
+  // - axis: the axis along which to split the input
+  // - keepdims: to keep the split dimension or not. Ignored when 'split' is
+  // specified Outputs:
+  // - outputs: sequence of tensor
+  //
+
+  patterns.onOp(
+      "SplitToSequence", 11,
+      [](OpBinder binder, ConversionPatternRewriter &rewriter) {
+        Value self;
+        Value split;
+        int64_t axis;
+        int64_t keepdims;
+        Torch::ListType resultType;
+
+        if (binder.op->getNumOperands() == 1)
+          return rewriter.notifyMatchFailure(
+              binder.op, "No of operands should be two.Keepdims attribute is "
+                         "not yet implemented");
+
+        if (binder.tensorOperandAtIndex(self, 0) ||
+            binder.tensorListResultType(resultType) ||
+            binder.s64IntegerAttr(keepdims, "keepdims", 1) ||
+            binder.tensorOperandAtIndex(split, 1) ||
+            binder.s64IntegerAttr(axis, "axis", 0))
+          return rewriter.notifyMatchFailure(
+              binder.op,
+              "Not converting to AtenSplitToSequenceOp due to inputs ");
+
+        Value axisValue = rewriter.create<Torch::ConstantIntOp>(
+            binder.getLoc(), rewriter.getType<Torch::IntType>(),
+            rewriter.getI64IntegerAttr(axis));
+        auto splitTy = cast<Torch::ValueTensorType>(split.getType());
+
+        if (!splitTy || !splitTy.hasSizes())
+          return failure();
+
+        auto splitSizes = splitTy.getSizes();
+        unsigned splitDim = splitTy.getSizes().size();
+
+        if (splitDim > 1)
+          return rewriter.notifyMatchFailure(
+              binder.op, "Split should be scalar or 1-D Tensor ");
+
+        if (splitDim == 1) {
+          if (splitSizes[0] == Torch::kUnknownSize) {
+            return rewriter.notifyMatchFailure(
+                binder.op, "Dynamic shapes for Split is not yet supported");
+          } else if (splitSizes[0] <=
+                     1) { // dealing with 1/0 element in 1-D tensor
+            Value splitInt = rewriter.create<Torch::AtenItemOp>(
+                binder.getLoc(), rewriter.getType<Torch::IntType>(), split);
+            rewriter.replaceOpWithNewOp<Torch::AtenSplitTensorOp>(
+                binder.op, resultType, self, splitInt, axisValue);
+            return success();
+          } else {
+            // Handling multiple elment in split
+            Value shapeList =
+                createConstantIntList(binder, rewriter, splitSizes);
+            rewriter.replaceOpWithNewOp<Torch::AtenSplitSizesOp>(
+                binder.op, resultType, self, shapeList, axisValue);
+            return success();
+          }
+        } else if (splitDim == 0) { // Handle 0-D tensor
+          Value splitInt = rewriter.create<Torch::AtenItemOp>(
+              binder.getLoc(), rewriter.getType<Torch::IntType>(), split);
+          rewriter.replaceOpWithNewOp<Torch::AtenSplitTensorOp>(
+              binder.op, resultType, self, splitInt, axisValue);
+          return success();
+        } else {
+          return rewriter.notifyMatchFailure(
+              binder.op, "Handling of this kind of inputs is not there");
+        }
+      });
 }
diff --git a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
index a9b6b7c66..6541f6f55 100644
--- a/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
+++ b/test/Conversion/TorchOnnxToTorch/simple_ops_q_to_z.mlir
@@ -3108,3 +3108,56 @@ func.func @test_scatternd_min(%arg0: !torch.vtensor<[4,4,4],f32>, %arg1: !torch.
   %0 = torch.operator "onnx.ScatterND"(%arg0, %arg1, %arg2) {torch.onnx.reduction = "min"} : (!torch.vtensor<[4,4,4],f32>, !torch.vtensor<[2,1],si64>, !torch.vtensor<[2,4,4],f32>) -> !torch.vtensor<[4,4,4],f32>
     return %0 : !torch.vtensor<[4,4,4],f32>
 }
+
+// ----
+
+// CHECK-LABEL: func.func @test_split_to_sequence_1
+func.func @test_split_to_sequence_1(%arg0: !torch.vtensor<[3,6],f32>, %arg1: !torch.vtensor<[1],si64>) -> !torch.list<vtensor<[3,6],f32>> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VAL_0:.*]]: !torch.vtensor<[3,6],f32>
+  // CHECK: %[[VAL_1:.*]]: !torch.vtensor<[1],si64>) -> !torch.list<vtensor<[3,6],f32>>
+  // CHECK: %[[VAL_2:.*]] = torch.constant.none
+  // CHECK: %[[VAL_3:.*]] = torch.constant.int 1
+  // CHECK: %[[VAL_4:.*]] = torch.aten.item %[[VAL_1]] : !torch.vtensor<[1],si64> -> !torch.int
+  // CHECK: %[[VAL_5:.*]] = torch.aten.split.Tensor %[[VAL_0]], %[[VAL_4]], %[[VAL_3]] : !torch.vtensor<[3,6],f32>, !torch.int, !torch.int -> !torch.list<vtensor<[3,6],f32>>
+  // CHECK: return %[[VAL_5]] : !torch.list<vtensor<[3,6],f32>>
+  %none = torch.constant.none
+  %int1 = torch.constant.int 1
+  %0 = torch.aten.item %arg1 : !torch.vtensor<[1],si64> -> !torch.int
+  %1 = torch.aten.split.Tensor %arg0, %0, %int1 : !torch.vtensor<[3,6],f32>, !torch.int, !torch.int -> !torch.list<vtensor<[3,6],f32>>
+  return %1 : !torch.list<vtensor<[3,6],f32>>
+}
+
+// ----
+
+// CHECK-LABEL: func.func @test_split_to_sequence_2
+func.func @test_split_to_sequence_2(%arg0: !torch.vtensor<[2,6],f32>, %arg1: !torch.vtensor<[],si64>) -> !torch.list<vtensor<[1,6],f32>> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+  // CHECK: %[[VAL_0:.*]]: !torch.vtensor<[2,6],f32>
+  // CHECK: %[[VAL_1:.*]]: !torch.vtensor<[],si64>) -> !torch.list<vtensor<[1,6],f32>>
+  // CHECK: %[[VAL_2:.*]] = torch.constant.none
+  // CHECK: %[[VAL_3:.*]] = torch.constant.int 0
+  // CHECK: %[[VAL_4:.*]] = torch.aten.item %[[VAL_1]] : !torch.vtensor<[],si64> -> !torch.int
+  // CHECK: %[[VAL_5:.*]] = torch.aten.split.Tensor %[[VAL_0]], %[[VAL_4]], %[[VAL_3]] : !torch.vtensor<[2,6],f32>, !torch.int, !torch.int -> !torch.list<vtensor<[1,6],f32>>
+  // CHECK: return %[[VAL_5]] : !torch.list<vtensor<[1,6],f32>>
+  %none = torch.constant.none
+  %int0 = torch.constant.int 0
+  %0 = torch.aten.item %arg1 : !torch.vtensor<[],si64> -> !torch.int
+  %1 = torch.aten.split.Tensor %arg0, %0, %int0 : !torch.vtensor<[2,6],f32>, !torch.int, !torch.int -> !torch.list<vtensor<[1,6],f32>>
+  return %1 : !torch.list<vtensor<[1,6],f32>>
+}
+
+// ----
+
+// CHECK-LABEL:   func.func @test_split_to_sequence_with_list(
+// CHECK-SAME:                                        %[[VAL_0:.*]]: !torch.vtensor<[4,6],f32>,
+// CHECK-SAME:                                        %[[VAL_1:.*]]: !torch.vtensor<[2],si64>) -> !torch.list<vtensor<[2,6],f32>> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+// CHECK:           %[[VAL_2:.*]] = torch.constant.none
+// CHECK:           %[[VAL_3:.*]] = torch.constant.int 0
+// CHECK:           %[[VAL_4:.*]] = torch.constant.int 2
+// CHECK:           %[[VAL_5:.*]] = torch.prim.ListConstruct %[[VAL_4]] : (!torch.int) -> !torch.list<int>
+// CHECK:           %[[VAL_6:.*]] = torch.aten.split.sizes %[[VAL_0]], %[[VAL_5]], %[[VAL_3]] : !torch.vtensor<[4,6],f32>, !torch.list<int>, !torch.int -> !torch.list<vtensor<[2,6],f32>>
+// CHECK:           return %[[VAL_6]] : !torch.list<vtensor<[2,6],f32>>
+  func.func @test_split_to_sequence_with_list(%arg0: !torch.vtensor<[4,6],f32>, %arg1: !torch.vtensor<[2],si64>) -> !torch.list<vtensor<[2,6],f32>> attributes {torch.onnx_meta.ir_version = 6 : si64, torch.onnx_meta.opset_version = 11 : si64, torch.onnx_meta.producer_name = "backend-test", torch.onnx_meta.producer_version = ""} {
+    %none = torch.constant.none
+    %0 = torch.operator "onnx.SplitToSequence"(%arg0, %arg1) {torch.onnx.axis = 0 : si64} : (!torch.vtensor<[4,6],f32>, !torch.vtensor<[2],si64>) -> !torch.list<vtensor<[2,6],f32>>
+    return %0 : !torch.list<vtensor<[2,6],f32>>
+  }