diff --git a/backends/cadence/aot/TARGETS b/backends/cadence/aot/TARGETS
index 8456c50f6c7..9876e59dbfa 100644
--- a/backends/cadence/aot/TARGETS
+++ b/backends/cadence/aot/TARGETS
@@ -62,6 +62,21 @@ python_library(
     ],
 )
 
+python_library(
+    name = "pass_utils",
+    srcs = [
+        "pass_utils.py",
+    ],
+    deps = [
+        ":utils",
+        "//caffe2:torch",
+        "//executorch/exir:pass_base",
+        "//executorch/exir/dialects:lib",
+        "//executorch/exir/passes:lib",
+        "//executorch/exir/passes:spec_prop_pass",
+    ],
+)
+
 python_library(
     name = "ops_registrations",
     srcs = [
diff --git a/backends/cadence/aot/pass_utils.py b/backends/cadence/aot/pass_utils.py
new file mode 100644
index 00000000000..3aa6f48a31c
--- /dev/null
+++ b/backends/cadence/aot/pass_utils.py
@@ -0,0 +1,91 @@
+# (c) Meta Platforms, Inc. and affiliates. Confidential and proprietary.
+
+# pyre-strict
+
+from dataclasses import dataclass
+from typing import Callable, Optional, Set, Union
+
+import torch
+from executorch.backends.cadence.aot.utils import get_edge_overload_packet
+
+from executorch.exir.dialects.edge._ops import EdgeOpOverload, EdgeOpOverloadPacket
+
+from executorch.exir.pass_base import ExportPass
+from torch._ops import OpOverloadPacket
+
+
+# Is an overlap in tensor lifetime and storage allowed at the current opt level?
+# We allow overlap at opt level >= 2.
+def allow_lifetime_and_storage_overlap(opt_level: int) -> bool:
+    return opt_level >= 2
+
+
+# A dataclass that stores the attributes of an ExportPass.
+@dataclass
+class CadencePassAttribute:
+    opt_level: Optional[int] = None
+    debug_pass: bool = False
+
+
+# A dictionary that maps an ExportPass to its attributes.
+_ALL_CADENCE_PASSES: dict[ExportPass, CadencePassAttribute] = {}
+
+
+def get_cadence_pass_attribute(p: ExportPass) -> CadencePassAttribute:
+    return _ALL_CADENCE_PASSES[p]
+
+
+# A decorator that registers a pass.
+def register_cadence_pass(
+    pass_attribute: CadencePassAttribute,
+) -> Callable[[ExportPass], ExportPass]:
+    def wrapper(cls: ExportPass) -> ExportPass:
+        _ALL_CADENCE_PASSES[cls] = pass_attribute
+        return cls
+
+    return wrapper
+
+
+def get_all_available_cadence_passes() -> Set[ExportPass]:
+    return set(_ALL_CADENCE_PASSES.keys())
+
+
+# Create a new filter to filter out relevant passes from all Jarvis passes.
+def create_cadence_pass_filter(
+    opt_level: int, debug: bool = False
+) -> Callable[[ExportPass], bool]:
+    def _filter(p: ExportPass) -> bool:
+        pass_attribute = get_cadence_pass_attribute(p)
+        return (
+            pass_attribute.opt_level is not None
+            and pass_attribute.opt_level <= opt_level
+            and (not pass_attribute.debug_pass or debug)
+        )
+
+    return _filter
+
+
+# Return the overload packet for the edge or torch op.
+def get_overload_packet(
+    op: Union[Callable[..., str], str],
+) -> Union[OpOverloadPacket, EdgeOpOverloadPacket, None]:
+    return (
+        get_edge_overload_packet(op)
+        if isinstance(op, EdgeOpOverload)
+        else getattr(op, "overloadpacket", None)
+    )
+
+
+# Get the list of node names in a graph module (only for "call_function" ops and
+# EdgeOpOverload targets). This should be used only after to_edge is called.
+def get_node_names_list_from_gm(
+    graph_module: torch.fx.GraphModule,
+) -> list[torch.fx.Node]:
+    graph_nodes = []
+    for node in graph_module.graph.nodes:
+        if node.op != "call_function":
+            continue
+        if not isinstance(node.target, EdgeOpOverload):
+            continue
+        graph_nodes.append(node.name)
+    return graph_nodes