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131 lines
4.3 KiB
131 lines
4.3 KiB
# Copyright (c) 2021 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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# Modified from espnet(https://github.com/espnet/espnet)
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from typing import Any
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from typing import Dict
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from typing import Text
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from typing import Union
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from paddle.optimizer.lr import LRScheduler
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from typeguard import check_argument_types
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from paddlespeech.s2t.utils.dynamic_import import dynamic_import
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from paddlespeech.s2t.utils.dynamic_import import instance_class
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from paddlespeech.s2t.utils.log import Log
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__all__ = ["WarmupLR", "LRSchedulerFactory"]
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logger = Log(__name__).getlog()
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SCHEDULER_DICT = {
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"noam": "paddle.optimizer.lr:NoamDecay",
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"expdecaylr": "paddle.optimizer.lr:ExponentialDecay",
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"piecewisedecay": "paddle.optimizer.lr:PiecewiseDecay",
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}
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def register_scheduler(cls):
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"""Register scheduler."""
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alias = cls.__name__.lower()
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SCHEDULER_DICT[cls.__name__.lower()] = cls.__module__ + ":" + cls.__name__
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return cls
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@register_scheduler
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class WarmupLR(LRScheduler):
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"""The WarmupLR scheduler
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This scheduler is almost same as NoamLR Scheduler except for following
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difference:
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NoamLR:
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lr = optimizer.lr * model_size ** -0.5
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* min(step ** -0.5, step * warmup_step ** -1.5)
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WarmupLR:
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lr = optimizer.lr * warmup_step ** 0.5
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* min(step ** -0.5, step * warmup_step ** -1.5)
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Note that the maximum lr equals to optimizer.lr in this scheduler.
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"""
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def __init__(self,
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warmup_steps: Union[int, float]=25000,
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learning_rate=1.0,
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last_epoch=-1,
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verbose=False,
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**kwargs):
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assert check_argument_types()
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self.warmup_steps = warmup_steps
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super().__init__(learning_rate, last_epoch, verbose)
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def __repr__(self):
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return f"{self.__class__.__name__}(warmup_steps={self.warmup_steps}, lr={self.base_lr}, last_epoch={self.last_epoch})"
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def get_lr(self):
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# self.last_epoch start from zero
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step_num = self.last_epoch + 1
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return self.base_lr * self.warmup_steps**0.5 * min(
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step_num**-0.5, step_num * self.warmup_steps**-1.5)
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def set_step(self, step: int=None):
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'''
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It will update the learning rate in optimizer according to current ``epoch`` .
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The new learning rate will take effect on next ``optimizer.step`` .
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Args:
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step (int, None): specify current epoch. Default: None. Auto-increment from last_epoch=-1.
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Returns:
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None
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'''
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self.step(epoch=step)
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@register_scheduler
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class ConstantLR(LRScheduler):
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"""
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Args:
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learning_rate (float): The initial learning rate. It is a python float number.
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last_epoch (int, optional): The index of last epoch. Can be set to restart training. Default: -1, means initial learning rate.
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verbose (bool, optional): If ``True``, prints a message to stdout for each update. Default: ``False`` .
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Returns:
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``ConstantLR`` instance to schedule learning rate.
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"""
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def __init__(self, learning_rate, last_epoch=-1, verbose=False):
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super().__init__(learning_rate, last_epoch, verbose)
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def get_lr(self):
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return self.base_lr
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def dynamic_import_scheduler(module):
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"""Import Scheduler class dynamically.
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Args:
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module (str): module_name:class_name or alias in `SCHEDULER_DICT`
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Returns:
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type: Scheduler class
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"""
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module_class = dynamic_import(module, SCHEDULER_DICT)
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assert issubclass(module_class,
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LRScheduler), f"{module} does not implement LRScheduler"
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return module_class
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class LRSchedulerFactory():
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@classmethod
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def from_args(cls, name: str, args: Dict[Text, Any]):
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module_class = dynamic_import_scheduler(name.lower())
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return instance_class(module_class, args)
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