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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) 2023 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.
from paddle.base.wrapped_decorator import signature_safe_contextmanager
from . import Program
_already_patch_program = False
def monkey_patch_program():
@signature_safe_contextmanager
def _lr_schedule_guard(self, is_with_opt=False):
# TODO(dev): Currently there has not equivalent of op_role in PIR
# mode, so we simply remove _lr_schedule_guard here, this should
# be fixed in the future.
yield
def state_dict(self, mode="all", scope=None):
from paddle.base import core
from paddle.base.executor import global_scope
if scope is not None and not isinstance(scope, core._Scope):
raise TypeError(
f"`scope` should be None or `paddle.static.Scope'` type, but received {type(scope)}."
)
if scope is None:
scope = global_scope()
return self._state_dict(mode, scope)
program_attrs = {
"_lr_schedule_guard": _lr_schedule_guard,
"state_dict": state_dict,
}
global _already_patch_program
if not _already_patch_program:
for attr, value in program_attrs.items():
setattr(Program, attr, value)
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_already_patch_program = True