diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index 92b8abb36d..da16bba726 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -1647,29 +1647,12 @@ def aten_choose_qparams_optimized(
     raise NotImplementedError()
 
 
-@torch_op("aten::chunk")
+@torch_op("aten::chunk", trace_only=True)
 def aten_chunk(self: TTensor, chunks: int, dim: int = 0) -> Sequence[TTensor]:
     """chunk(Tensor(a -> *) self, int chunks, int dim=0) -> Tensor(a)[]"""
-    # This will create a Sequence of tensors
-    neg_1 = op.Constant(value_ints=[-1])
-    # Get size of specified dim
-    self_shape = op.Shape(self)
-    dim_size = op.Gather(self_shape, dim, axis=0)
-    # Compute size/chunk to get the number of data in one chunk
-    num_per_chunk = op.Div(dim_size, chunks)
-    num_per_chunk = op.Cast(op.Mod(dim_size, chunks) > 0, to=INT64.dtype) + num_per_chunk  # type: ignore[operator]
-
-    # Compute real chunk number
-    num_chunk = op.Div(dim_size, num_per_chunk)
-    # Get something like [n, n, n, n, ...], total num_chunk
-    list_split = op.Expand(num_per_chunk, op.Reshape(num_chunk, neg_1))
-
-    remainder = op.Mod(dim_size, num_per_chunk)
-    if remainder > 0:  # type: ignore[operator]
-        # Append the remainder to the [n, n, n, n, ..., r]
-        list_split = op.Concat(list_split, op.Reshape(remainder, neg_1), axis=0)
-
-    return op.SplitToSequence(self, list_split, axis=dim)
+    if chunks == 1:
+        return op.Identity(self)
+    return op.Split(self, axis=dim, num_outputs=chunks)
 
 
 @torch_op("aten::clamp", trace_only=True)