# MIT License
#
# Copyright (c) 2021 Soohwan Kim and Sangchun Ha and Soyoung Cho
#
# Permission is hereby granted, free of charge, to any person obtaining a copy
# of this software and associated documentation files (the "Software"), to deal
# in the Software without restriction, including without limitation the rights
# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
# copies of the Software, and to permit persons to whom the Software is
# furnished to do so, subject to the following conditions:
#
# The above copyright notice and this permission notice shall be included in all
# copies or substantial portions of the Software.
#
# THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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# SOFTWARE.
import math
import torch
from dataclasses import dataclass, field
from typing import Optional
from omegaconf import DictConfig
from torch.optim import Optimizer
from openspeech.dataclass.configurations import LearningRateSchedulerConfigs
from openspeech.optim.scheduler import register_scheduler
from openspeech.optim.scheduler.lr_scheduler import LearningRateScheduler
[docs]@dataclass
class TriStageLRSchedulerConfigs(LearningRateSchedulerConfigs):
scheduler_name: str = field(
default="tri_stage", metadata={"help": "Name of learning rate scheduler."}
)
init_lr: float = field(
default=1e-7, metadata={"help": "Initial learning rate."}
)
init_lr_scale: float = field(
default=0.01, metadata={"help": "Initial learning rate scale."}
)
final_lr_scale: float = field(
default=0.01, metadata={"help": "Final learning rate scale"}
)
phase_ratio: str = field(
default="(0.1, 0.4, 0.5)", metadata={"help": "Automatically sets warmup/hold/decay steps to the ratio "
"specified here from max_updates. the ratios must add up to 1.0"}
)
total_steps: int = field(
default=400000, metadata={"help": "Total training steps."}
)
[docs]@register_scheduler("tri_stage", dataclass=TriStageLRSchedulerConfigs)
class TriStageLRScheduler(LearningRateScheduler):
r"""
Tri-Stage Learning Rate Scheduler. Implement the learning rate scheduler in "SpecAugment"
Similar to inverse_squre_root scheduler, but tri_stage learning rate employs
three stages LR scheduling:
- warmup stage, starting from `lr` * `init_lr_scale`, linearly
increased to `lr` in `warmup_steps` iterations
- hold stage, after `warmup_steps`, keep the LR as `lr` for `hold_steps`
iterations
- decay stage, after hold stage, decay LR exponetially to
`lr` * `final_lr_scale` in `decay_steps`;
after that LR is keep as `final_lr_scale` * `lr`
During warmup::
init_lr = cfg.init_lr_scale * cfg.lr
lrs = torch.linspace(init_lr, cfg.lr, cfg.warmup_steps)
lr = lrs[update_num]
During hold::
lr = cfg.lr
During decay::
decay_factor = - math.log(cfg.final_lr_scale) / cfg.decay_steps
lr = cfg.lr * exp(- (update_num - warmup_steps - decay_steps) * decay_factor)
After that::
lr = cfg.lr * cfg.final_lr_scale
Args:
optimizer (Optimizer): wrapped optimizer.
configs (DictConfig): configuration set.
"""
def __init__(
self,
optimizer: Optimizer,
configs: DictConfig,
):
super(TriStageLRScheduler, self).__init__(optimizer, configs.lr_scheduler.init_lr)
self.phase_ratio = eval(configs.lr_scheduler.phase_ratio)
self.warmup_steps = int(configs.lr_scheduler.total_steps * self.phase_ratio[0])
self.hold_steps = int(configs.lr_scheduler.total_steps * self.phase_ratio[1])
self.decay_steps = int(configs.lr_scheduler.total_steps * self.phase_ratio[2])
self.peak_lr = configs.lr_scheduler.lr
self.init_lr = configs.lr_scheduler.init_lr_scale * configs.lr_scheduler.lr
self.final_lr = configs.lr_scheduler.final_lr_scale * configs.lr_scheduler.lr
self.warmup_rate = (
(self.peak_lr - self.init_lr) / self.warmup_steps
if self.warmup_steps != 0
else 0
)
self.decay_factor = -math.log(configs.lr_scheduler.final_lr_scale) / self.decay_steps
self.update_step = 0
self.lr = self.init_lr
def _decide_stage(self):
if self.update_step < self.warmup_steps:
return 0, self.update_step
offset = self.warmup_steps
if self.update_step < offset + self.hold_steps:
return 1, self.update_step - offset
offset += self.hold_steps
if self.update_step <= offset + self.decay_steps:
# decay stage
return 2, self.update_step - offset
offset += self.decay_steps
return 3, self.update_step - offset
def step(self, val_loss: Optional[torch.FloatTensor] = None):
stage, steps_in_stage = self._decide_stage()
if stage == 0:
self.lr = self.init_lr + self.warmup_rate * steps_in_stage
elif stage == 1:
self.lr = self.peak_lr
elif stage == 2:
self.lr = self.peak_lr * math.exp(-self.decay_factor * steps_in_stage)
elif stage == 3:
self.lr = self.final_lr
else:
raise ValueError("Undefined stage")
self.set_lr(self.optimizer, self.lr)
self.update_step += 1
return self.lr