diff --git a/python/tvm/relax/frontend/nn/op.py b/python/tvm/relax/frontend/nn/op.py
index e46553203fa4..45428692b830 100644
--- a/python/tvm/relax/frontend/nn/op.py
+++ b/python/tvm/relax/frontend/nn/op.py
@@ -2265,7 +2265,7 @@ def sort(x: Tensor, axis: int = -1, descending: bool = False, name="sort"):
     out : Tensor
         The sorted tensor.
     """
-    return wrap_nested(_op.sort(x, axis, descending), name=name)
+    return wrap_nested(_op.sort(x._expr, axis, descending), name=name)
 
 
 def argsort(
@@ -2296,7 +2296,7 @@ def argsort(
     out : Tensor
         The indices of the sorted tensor.
     """
-    return wrap_nested(_op.argsort(data, axis, descending, dtype), name=name)
+    return wrap_nested(_op.argsort(data._expr, axis, descending, dtype), name=name)
 
 
 def topk(
@@ -2344,7 +2344,7 @@ def topk(
     out : Tensor or Tuple[Tensor, Tensor]
         The computed result.
     """
-    return wrap_nested(_op.topk(data, k, axis, ret_type, largest, dtype), name=name)
+    return wrap_nested(_op.topk(data._expr, k, axis, ret_type, largest, dtype), name=name)
 
 
 def multinomial_from_uniform(
diff --git a/tests/python/relax/test_frontend_nn_op.py b/tests/python/relax/test_frontend_nn_op.py
index 7d78e47c945b..8bf52d7918e5 100644
--- a/tests/python/relax/test_frontend_nn_op.py
+++ b/tests/python/relax/test_frontend_nn_op.py
@@ -1188,5 +1188,34 @@ def foo(prob: R.Tensor((2, 3), dtype="float32"), sorted_prob: R.Tensor((2, 3), d
     )
 
 
+def test_sort_argsort_topk():
+    class Model(Module):
+        def foo(self, x: Tensor):
+            z0 = op.sort(x, axis=-1, descending=True)
+            z1 = op.argsort(x, axis=-1, descending=False)
+            z2 = op.topk(x, k=2, axis=-1)
+            return z0, z1, z2
+
+    @I.ir_module
+    class Expected:
+        @R.function
+        def foo(x: R.Tensor(("seq_len", 64), dtype="float16")):
+            R.func_attr({"num_input": 1})
+            with R.dataflow():
+                sort = R.sort(x, axis=-1, descending=True)
+                argsort = R.argsort(x, axis=-1, descending=False, dtype="int32")
+                topk = R.topk(x, k=2, axis=-1, ret_type="both", largest=True, dtype="int32")
+                topk_0 = topk[0]
+                topk_1 = topk[1]
+                gv = sort, argsort, (topk_0, topk_1)
+                R.output(gv)
+            return gv
+
+    m = Model()
+    mod, _ = m.export_tvm({"foo": {"x": spec.Tensor(("seq_len", 64), "float16")}})
+
+    tvm.ir.assert_structural_equal(mod, Expected)
+
+
 if __name__ == "__main__":
     tvm.testing.main()