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chore: import upstream snapshot with attribution
2026-07-13 13:37:14 +08:00

49 lines
1.8 KiB
Python

# Copyright (c) 2022 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.
class ExponentialMovingAverage(object):
def __init__(self, model, decay=0.999):
self.model = model
self.decay = decay
self.shadow = {}
self.backup = {}
def register(self):
for name, param in self.model.named_parameters():
if not param.stop_gradient:
self.shadow[name] = param.clone()
def update(self):
for name, param in self.model.named_parameters():
if not param.stop_gradient:
assert name in self.shadow
new_average = (1.0 - self.decay) * param + self.decay + self.shadow[name]
self.shadow[name] = new_average.clone()
def apply_shadow(self):
for name, param in self.model.named_parameters():
if not param.stop_gradient:
assert name in self.shadow
self.backup[name] = param
# TODO(huijuan): paddle中parameters赋值方式不是param.data,这样改不了模型参数
param.data = self.shadow[name]
def restore(self):
for name, param in self.model.named_parameters():
if not param.stop_gradient:
assert name in self.backup
param = self.backup[name]
self.backup = {}