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# Copyright (c) 2025 Bytedance Ltd. 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.
from torch.optim.lr_scheduler import LambdaLR
def warmup_then_decay(optimizer, total_steps, warmup_steps, max_lr=1e-3, min_lr=1e-5, base_lr=1e-5):
"""
Create a learning rate scheduler with warmup followed by decay.
"""
def lr_lambda(current_step):
if current_step < warmup_steps:
# warmup: min_lr -> max_lr
progress = float(current_step) / float(max(1, warmup_steps))
# LR(t) = min_lr + (max_lr - min_lr)*progress
return (min_lr + (max_lr - min_lr)*progress) / base_lr
else:
# decay: warmup_steps -> total_steps
progress = float(current_step - warmup_steps) / float(max(1, total_steps - warmup_steps))
# LR(t) = max_lr + (min_lr - max_lr)*progress
return (max_lr + (min_lr - max_lr)*progress) / base_lr
scheduler = LambdaLR(optimizer, lr_lambda)
return scheduler |