wehub-resource-sync
58 lines
2.4 KiB
Python
58 lines
2.4 KiB
Python
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# Copyright (c) Facebook, Inc. and its affiliates.
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#
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# This source code is licensed under the MIT license found in the
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# LICENSE file in the root directory of this source tree.
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from paddle.optimizer.lr import LRScheduler
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class InverseSquareRootSchedule(LRScheduler):
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"""
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Decay the LR based on the inverse square root of the update number.
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We also support a warmup phase where we linearly increase the learning rate
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from some initial learning rate until the configured learning rate. Thereafter
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we decay proportional to the number of updates, with a decay factor set to
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align with the configured learning rate.
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Args:
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warmup_steps(int):
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The number of warmup steps. A super parameter.
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learning_rate(float, optional):
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The learning rate. It is a python float number. Defaults to 1.0.
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last_epoch(int, optional):
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The index of last epoch. Can be set to restart training. Default: -1,
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means initial learning rate.
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verbose(bool, optional):
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If ``True``, prints a message to stdout for each
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update. Defaults to ``False``.
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"""
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def __init__(self, warmup_steps, learning_rate=1.0, last_epoch=-1, verbose=False):
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self.warmup_steps = warmup_steps
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warmup_end_lr = learning_rate
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self.warmup_init_lr = 0.0
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self.lr_step = (warmup_end_lr - self.warmup_init_lr) / self.warmup_steps
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self.decay_factor = warmup_end_lr * (self.warmup_steps**0.5)
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super(InverseSquareRootSchedule, self).__init__(learning_rate, last_epoch, verbose)
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def get_lr(self):
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if self.last_epoch < self.warmup_steps:
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return self.warmup_init_lr + self.last_epoch * self.lr_step
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else:
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return self.decay_factor * (self.last_epoch**-0.5)
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