diff --git a/vllm_ascend/patch/worker/patch_common/__init__.py b/vllm_ascend/patch/worker/patch_common/__init__.py
index 2ba9917..af19d4a 100644
--- a/vllm_ascend/patch/worker/patch_common/__init__.py
+++ b/vllm_ascend/patch/worker/patch_common/__init__.py
@@ -14,7 +14,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 #
-
+# patch_utils should be the first import, because it will be used by other
+# patch files.
+import vllm_ascend.patch.worker.patch_common.patch_utils  # noqa isort:skip
 import vllm_ascend.patch.worker.patch_common.patch_metrics  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_minicpm  # noqa
 import vllm_ascend.patch.worker.patch_common.patch_multi_step_worker  # noqa
diff --git a/vllm_ascend/patch/worker/patch_common/patch_utils.py b/vllm_ascend/patch/worker/patch_common/patch_utils.py
new file mode 100644
index 0000000..dec618c
--- /dev/null
+++ b/vllm_ascend/patch/worker/patch_common/patch_utils.py
@@ -0,0 +1,38 @@
+from typing import Callable, List, Optional, Tuple
+
+import torch
+from torch.library import Library
+from vllm import utils
+from vllm.utils import vllm_lib
+
+
+def ascend_direct_register_custom_op(
+        op_name: str,
+        op_func: Callable,
+        mutates_args: list[str],
+        fake_impl: Optional[Callable] = None,
+        target_lib: Optional[Library] = None,
+        dispatch_key: str = "CUDA",
+        tags: Tuple[torch.Tag, ...] = (),
+):
+    # In pytorch 2.5.1, torch.library.infer_schema require the input function to
+    # have annotations supported by typing library. But in pytorch 2.7.0 which
+    # vllm using, torch.library.infer_schema require the python builtin type. In
+    # this case, we should revert built type to typing type for 2.5.1 backward
+    # compatibility.
+    for k, v in op_func.__annotations__.items():
+        if v == list[int]:
+            op_func.__annotations__[k] = List[int]
+        if v == Optional[list[int]]:
+            op_func.__annotations__[k] = Optional[List[int]]
+        # TODO: add more type convert here if needed.
+    import torch.library
+    schema_str = torch.library.infer_schema(op_func, mutates_args=mutates_args)
+    my_lib = target_lib or vllm_lib
+    my_lib.define(op_name + schema_str, tags=tags)
+    my_lib.impl(op_name, op_func, dispatch_key=dispatch_key)
+    if fake_impl is not None:
+        my_lib._register_fake(op_name, fake_impl)
+
+
+utils.direct_register_custom_op = ascend_direct_register_custom_op