diff --git a/backends/cadence/utils/facto_util.py b/backends/cadence/utils/facto_util.py
index 385193776a3..001fc882685 100644
--- a/backends/cadence/utils/facto_util.py
+++ b/backends/cadence/utils/facto_util.py
@@ -189,47 +189,37 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
             if index == 0:  # condition
                 tensor_constraints = [
                     cp.Dtype.In(lambda deps: [torch.bool]),
-                    cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
-                    cp.Value.Le(lambda deps, dtype, struct: 2**4),
+                    cp.Value.Ge(lambda deps, dtype, struct: 0),
+                    cp.Value.Le(lambda deps, dtype, struct: 1),
                     cp.Rank.Ge(lambda deps: 1),
                     cp.Size.Ge(lambda deps, r, d: 1),
                     max_size_constraint,
                 ]
             elif index == 1:  # input tensor(a)
                 tensor_constraints = [
-                    cp.Dtype.In(
-                        lambda deps: [
-                            torch.int8,
-                            torch.int16,
-                            torch.uint8,
-                            torch.uint16,
-                            torch.int32,
-                            torch.float32,
-                        ]
-                    ),
+                    cp.Dtype.In(lambda deps: [torch.float32]),
                     cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
                     cp.Value.Le(lambda deps, dtype, struct: 2**4),
                     cp.Rank.Ge(lambda deps: 1),
                     cp.Size.Ge(lambda deps, r, d: 1),
+                    cp.Size.In(
+                        lambda deps, r, d: fn.broadcast_with(deps[0].shape, r, d)
+                    ),
                     max_size_constraint,
                 ]
             else:  # input tensor(b)
                 tensor_constraints = [
-                    cp.Dtype.In(
-                        lambda deps: [
-                            torch.int8,
-                            torch.int16,
-                            torch.uint8,
-                            torch.uint16,
-                            torch.int32,
-                            torch.float32,
-                        ]
-                    ),
+                    cp.Dtype.In(lambda deps: [torch.float32]),
                     cp.Dtype.Eq(lambda deps: deps[1].dtype),
                     cp.Value.Ge(lambda deps, dtype, struct: -(2**4)),
                     cp.Value.Le(lambda deps, dtype, struct: 2**4),
                     cp.Rank.Ge(lambda deps: 1),
                     cp.Size.Ge(lambda deps, r, d: 1),
+                    cp.Size.In(
+                        lambda deps, r, d: fn.broadcast_with(
+                            fn.broadcasted_shape(deps[0].shape, deps[1].shape), r, d
+                        )
+                    ),
                     max_size_constraint,
                 ]
         case "embedding.default":