# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # #The file has been adapted from pytorch project # #Licensed under BSD-style license - # https://github.com/pytorch/pytorch/blob/main/LICENSE from __future__ import annotations import contextlib import ctypes import importlib import os import sys import types from functools import cached_property from typing import Any, Callable, Generic, TypeVar from typing_extensions import ParamSpec import paddle _InputT = ParamSpec("_InputT") _RetT = TypeVar("_RetT") PADDLE_OPS_MODULE_NAME = "paddle.ops" # Query `hasattr` only once. _SET_GLOBAL_FLAGS = hasattr(sys, "getdlopenflags") and hasattr( sys, "setdlopenflags" ) @contextlib.contextmanager def dl_open_guard(): """ Context manager to set the RTLD_GLOBAL dynamic linker flag while we open a shared library to load custom operators. """ if not _SET_GLOBAL_FLAGS: yield return old_flags = sys.getdlopenflags() sys.setdlopenflags(old_flags | ctypes.RTLD_GLOBAL) try: yield finally: sys.setdlopenflags(old_flags) def import_module(module: str): return importlib.import_module(module) def load_library(path: str): """ Load a shared library at the specified path. """ path = os.path.realpath(path) with dl_open_guard(): ctypes.CDLL(path) class PythonOpRegistry: def __init__(self): self._registry: dict[str, Callable[..., object]] = {} def register(self, name: str, fn: Callable[..., object]): if name in self._registry: raise ValueError(f"Operator '{name}' is already registered.") self._registry[name] = fn def has_operator(self, name: str) -> bool: return name in self._registry def get_operator(self, name: str) -> Callable[..., object]: if name not in self._registry: raise ValueError(f"Operator '{name}' is not registered.") return self._registry[name] PYTHON_OP_REGISTRY = PythonOpRegistry() class OverloadedOpFunction(Generic[_InputT, _RetT]): def __init__(self, namespace: str, name: str): self.namespace = namespace self.name = name @cached_property def callable_fn(self) -> Callable[_InputT, _RetT]: if PYTHON_OP_REGISTRY.has_operator(f"{self.namespace}::{self.name}"): return PYTHON_OP_REGISTRY.get_operator( # type: ignore f"{self.namespace}::{self.name}" ) return paddle.base.core.torch_compat._get_operation( f"{self.namespace}::{self.name}" ) def __getattr__(self, name: str) -> Callable[_InputT, _RetT]: if name == "default": return self.callable_fn raise AttributeError( f"'{self.namespace}.{self.name}' has no attribute '{name}'" ) def __call__(self, *args: _InputT.args, **kwargs: _InputT.kwargs) -> _RetT: return self.callable_fn(*args, **kwargs) class OpNameSpace(types.ModuleType): def __init__(self, name): super().__init__(f"{PADDLE_OPS_MODULE_NAME}.{name}") self.name = name def __getattr__(self, name: str) -> OverloadedOpFunction[..., Any]: if name == "__file__": return PADDLE_OPS_MODULE_NAME # type: ignore return OverloadedOpFunction(self.name, name) class PaddleOpsModule(types.ModuleType): __file__ = "_ops.py" def __init__(self): super().__init__(PADDLE_OPS_MODULE_NAME) def __getattr__(self, name: str): namespace = OpNameSpace(name) # Insert to __dict__ to avoid repeatedly __getattr__ overhead setattr(self, name, namespace) return namespace def import_module(self, module): return import_module(module) def load_library(self, path): return load_library(path) ops = PaddleOpsModule()