diff --git a/backends/cadence/utils/facto_util.py b/backends/cadence/utils/facto_util.py
index a09f3578391..e49cf412c19 100644
--- a/backends/cadence/utils/facto_util.py
+++ b/backends/cadence/utils/facto_util.py
@@ -222,6 +222,34 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
                     cp.Value.Le(lambda deps, dtype, struct: 2),
                 ]
             )
+        case "transpose_copy.int":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
+                ]
+            )
+        case "permute_copy.default":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32, torch.int8, torch.uint8]),
+                    cp.Rank.Le(
+                        lambda deps: 5
+                    ),  # xa_nn_transpose only supports up to 5D
+                    cp.Rank.Ge(lambda deps: 1),  # Must have at least 1 dimension
+                ]
+            )
+        case "sqrt.default":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
+                ]
+            )
+        case "clamp.default":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
+                ]
+            )
         case "rsqrt.default":
             tensor_constraints.extend(
                 [
@@ -232,6 +260,12 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
                     cp.Value.Le(lambda deps, dtype, struct: 2**2),
                 ]
             )
+        case "relu.default":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32]),
+                ]
+            )
         case "mean.dim":
             tensor_constraints.extend(
                 [
@@ -241,10 +275,17 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
         case "exp.default":
             tensor_constraints.extend(
                 [
+                    cp.Dtype.In(lambda deps: [torch.float32]),
                     cp.Value.Ge(lambda deps, dtype, struct: -(2**2)),
                     cp.Value.Le(lambda deps, dtype, struct: 2**2),
                 ]
             )
+        case "tanh.default":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32]),
+                ]
+            )
         case "slice_copy.Tensor":
             tensor_constraints.extend(
                 [
@@ -253,6 +294,34 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
                     cp.Value.Le(lambda deps, dtype, struct: 2),
                 ]
             )
+        case "div.Scalar" | "add.Tensor" | "mul.Tensor" | "sub.Tensor":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(
+                        lambda deps: [
+                            torch.int32,
+                            torch.int64,
+                            torch.float32,
+                        ]
+                    ),
+                ]
+            )
+        case "split_copy.Tensor":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(
+                        lambda deps: [
+                            torch.int32,
+                            torch.int64,
+                            torch.float32,
+                        ]
+                    ),
+                    cp.Value.Ge(lambda deps, dtype, struct: 1),
+                    cp.Value.Le(lambda deps, dtype, struct: 2**3),
+                    cp.Rank.Le(lambda deps: 3),
+                    cp.Size.Le(lambda deps, r, d: 2**2),
+                ]
+            )
         case "constant_pad_nd.default":
             tensor_constraints.extend(
                 [
@@ -283,6 +352,12 @@ def random_size_constraint(deps: object, r: int, d: int) -> int:
                     cp.Rank.Le(lambda deps: 2**2),
                 ]
             )
+        case "pow.Tensor_Scalar":
+            tensor_constraints.extend(
+                [
+                    cp.Dtype.In(lambda deps: [torch.float32, torch.int32]),
+                ]
+            )
         case "div.Tensor_mode" | "minimum.default":
             if index == 0:
                 tensor_constraints = [