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# Copyright 2024 ByteDance and/or its affiliates.
#
# 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.
import torch
class EMAWrapper(object):
"""A wrapper class for exponential moving average of model weights."""
def __init__(
self, model: torch.nn.Module, decay: float = 0.999, mutable_param_keywords=None
):
"""
model: a pytorch model to apply EMA
decay: a scaler to indicate the decay rate
mutable_param_keywords: keywords of parameters to apply EMA decay, other params will stay untouched
"""
self.model = model
self.decay = decay
self.mutable_param_keywords = [
s.strip() for s in mutable_param_keywords if s.strip()
]
self.shadow = {}
self.backup = {}
def register(self):
for name, param in self.model.named_parameters():
self.shadow[name] = param.data.clone()
def update(self):
for name, param in self.model.named_parameters():
if self.mutable_param_keywords and not any(
[keyword in name for keyword in self.mutable_param_keywords]
):
continue
assert name in self.shadow
new_average = (1.0 - self.decay) * param.data + self.decay * self.shadow[
name
]
self.shadow[name] = new_average.clone()
def apply_shadow(self):
for name, param in self.model.named_parameters():
assert name in self.shadow
self.backup[name] = param.data
param.data = self.shadow[name]
def restore(self):
for name, param in self.model.named_parameters():
assert name in self.backup
param.data = self.backup[name]
self.backup = {}
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