diff --git a/onnxscript/function_libs/torch_lib/ops/core.py b/onnxscript/function_libs/torch_lib/ops/core.py
index d97f6da3b..d6c7029f6 100644
--- a/onnxscript/function_libs/torch_lib/ops/core.py
+++ b/onnxscript/function_libs/torch_lib/ops/core.py
@@ -2542,19 +2542,11 @@ def aten_diagonal(self: TReal, offset: int = 0, dim1: int = 0, dim2: int = 1) ->
     # This is because computing diagonal sum is on dim2 after transpose by perm
     axes = [self_rank - 2]
 
-    return _aten_diagonal_onnx(self, offset, dim1, dim2, perm, axes)
-
-
-@torch_op("aten::diagonal", private=True, traceable=True)
-def _aten_diagonal_onnx(
-    self: TTensor, offset: int, dim1: int, dim2: int, perm: Sequence[int], axes: Sequence[int]
-) -> TTensor:
     neg_1 = op.Constant(value_ints=[-1])
     dim1_size = op.Reshape(op.Gather(op.Shape(self), dim1), neg_1)  # row
     dim2_size = op.Reshape(op.Gather(op.Shape(self), dim2), neg_1)  # col
     mask_shape = op.Concat(dim1_size, dim2_size, axis=0)
-    tmp_tensor = op.ConstantOfShape(mask_shape)
-    mask = op.EyeLike(tmp_tensor, k=offset)
+    mask = op.EyeLike(op.ConstantOfShape(mask_shape), k=offset)
     mask = op.CastLike(mask, self)
     self_t = op.Transpose(self, perm=perm)
     result = op.Mul(self_t, mask)
@@ -2580,18 +2572,19 @@ def _aten_diagonal_onnx(
     # 6       0           4       0
 
     # From above table, we can get the logic below
+    offset_val = op.Constant(value_ints=[offset])
     if offset < 0:
         # row + offset
-        length = dim1_size + offset
+        length = op.Add(dim1_size, offset_val)
         start = op.Constant(value_ints=[0])
     else:  # offset >= 0
         # col - offset
-        length = dim2_size - offset
-        start = op.Reshape(op.Constant(value_int=offset), neg_1)
+        length = op.Sub(dim2_size, offset_val)
+        start = offset_val
 
     # max(min(length, min(row, col)), 0)
-    length = op.Max(op.Min(length, min_dim_size), 0)
-    end = start + length
+    length = op.Max(op.Min(length, min_dim_size), op.Constant(value_ints=[0]))
+    end = op.Add(start, length)
     result = op.Slice(result, start, end, axes=axes)
 
     return result
@@ -2621,19 +2614,11 @@ def aten_diagonal_bool(self: BOOL, offset: int = 0, dim1: int = 0, dim2: int = 1
     # This is because computing diagonal sum is on dim2 after transpose by perm
     axes = [self_rank - 2]
 
-    return _aten_diagonal_bool_onnx(self, offset, dim1, dim2, perm, axes)
-
-
-@torch_op("aten::diagonal", private=True)
-def _aten_diagonal_bool_onnx(
-    self: BOOL, offset: int, dim1: int, dim2: int, perm: Sequence[int], axes: Sequence[int]
-) -> BOOL:
     neg_1 = op.Constant(value_ints=[-1])
     dim1_size = op.Reshape(op.Gather(op.Shape(self), dim1), neg_1)  # row
     dim2_size = op.Reshape(op.Gather(op.Shape(self), dim2), neg_1)  # col
     mask_shape = op.Concat(dim1_size, dim2_size, axis=0)
-    tmp_tensor = op.ConstantOfShape(mask_shape)
-    mask = op.EyeLike(tmp_tensor, k=offset)
+    mask = op.EyeLike(op.ConstantOfShape(mask_shape), k=offset)
     self_int = op.Cast(self, to=INT64.dtype)
     mask_int = op.Cast(mask, to=INT64.dtype)
     self_int_t = op.Transpose(self_int, perm=perm)
@@ -2660,18 +2645,19 @@ def _aten_diagonal_bool_onnx(
     # 6       0           4       0
 
     # From above table, we can get the logic below
+    offset_val = op.Constant(value_ints=[offset])
     if offset < 0:
         # row + offset
-        length = dim1_size + offset
+        length = op.Add(dim1_size, offset_val)
         start = op.Constant(value_ints=[0])
     else:  # offset >= 0
         # col - offset
-        length = dim2_size - offset
-        start = op.Reshape(op.Constant(value_int=offset), neg_1)
+        length = op.Sub(dim2_size, offset_val)
+        start = offset_val
 
     # max(min(length, min(row, col)), 0)
-    length = op.Max(op.Min(length, min_dim_size), 0)
-    end = start + length
+    length = op.Max(op.Min(length, min_dim_size), op.Constant(value_ints=[0]))
+    end = op.Add(start, length)
     result = op.Slice(result, start, end, axes=axes)
     result = op.Cast(result, to=BOOL.dtype)