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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
# Copyright 2022 The HuggingFace Team. 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.
import paddle
from paddle import nn
class LitEma(nn.Layer):
"""
Exponential Moving Average (EMA) of model updates
Parameters:
model: The model architecture for apply EMA.
decay: The exponential decay. Default 0.9999.
use_num_updates: Whether to use number of updates when computing
averages.
"""
def __init__(self, model, decay=0.9999, use_num_upates=True):
super().__init__()
if decay < 0.0 or decay > 1.0:
raise ValueError("Decay must be between 0 and 1")
self.m_name2s_name = {}
self.register_buffer("decay", paddle.to_tensor(decay, dtype=paddle.float32))
self.register_buffer(
"num_updates",
paddle.to_tensor(0, dtype=paddle.int64) if use_num_upates else paddle.to_tensor(-1, dtype=paddle.int64),
)
for name, p in model.named_parameters():
if not p.stop_gradient:
# remove as '.'-character is not allowed in buffers
s_name = name.replace(".", "")
self.m_name2s_name.update({name: s_name})
self.register_buffer(s_name, p.clone().detach())
self.collected_params = []
def forward(self, model):
decay = self.decay
if self.num_updates >= 0:
self.num_updates += 1
decay = min(self.decay, (1 + self.num_updates) / (10 + self.num_updates))
one_minus_decay = 1.0 - decay
with paddle.no_grad():
m_param = dict(model.named_parameters())
shadow_params = dict(self.named_buffers())
for key in m_param:
if not m_param[key].stop_gradient:
sname = self.m_name2s_name[key]
shadow_params[sname].scale_(decay)
shadow_params[sname].add_(m_param[key] * one_minus_decay)
else:
assert key not in self.m_name2s_name
def copy_to(self, model):
m_param = dict(model.named_parameters())
shadow_params = dict(self.named_buffers())
for key in m_param:
if not m_param[key].stop_gradient:
m_param[key].copy_(shadow_params[self.m_name2s_name[key]], True)
else:
assert key not in self.m_name2s_name
def store(self, parameters):
"""
Save the current parameters for restoring later.
Args:
parameters: Iterable of `EagerParamBase`; the parameters to be
temporarily stored.
"""
self.collected_params = [param.clone() for param in parameters]
def restore(self, parameters):
"""
Restore the parameters stored with the `store` method.
Useful to validate the model with EMA parameters without affecting the
original optimization process. Store the parameters before the
`copy_to` method. After validation (or model saving), use this to
restore the former parameters.
Args:
parameters: Iterable of `EagerParamBase`; the parameters to be
updated with the stored parameters.
"""
for c_param, param in zip(self.collected_params, parameters):
param.copy_(c_param, True)
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