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PArallel Distributed Deep LEarning: Machine Learning Framework from Industrial Practice (『飞桨』核心框架,深度学习&机器学习高性能单机、分布式训练和跨平台部署)

# 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 types
from typing import Any
import paddle
from ._ops import import_module, load_library
PADDLE_CLASSES_MODULE_NAME = "paddle.classes"
class ClassesNameSpace(types.ModuleType):
def __init__(self, name: str):
super().__init__(f"{PADDLE_CLASSES_MODULE_NAME}.{name}")
self.name = name
def __getattr__(self, name: str) -> Any:
if name == "__file__":
return PADDLE_CLASSES_MODULE_NAME # type: ignore
return paddle.base.core.torch_compat._get_custom_class_python_wrapper(
self.name, name
)
class PaddleClassesModule(types.ModuleType):
__file__ = "_classes.py"
def __init__(self):
super().__init__(PADDLE_CLASSES_MODULE_NAME)
def __getattr__(self, name: str):
namespace = ClassesNameSpace(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)
classes = PaddleClassesModule()