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39 lines
1.9 KiB
39 lines
1.9 KiB
import math
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from tinygrad import dtypes
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from tinygrad.nn.optim import Optimizer
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from extra.lr_scheduler import LR_Scheduler
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# https://github.com/mlcommons/training/blob/e237206991d10449d9675d95606459a3cb6c21ad/image_classification/tensorflow2/lars_util.py
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class PolynomialDecayWithWarmup(LR_Scheduler):
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def __init__(self, optimizer: Optimizer, initial_lr, end_lr, train_steps, warmup, power=2):
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super().__init__(optimizer)
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self.epoch_counter = self.epoch_counter.cast(dtypes.float32)
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assert train_steps > 0 and warmup > 0
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self.warmup = min(warmup, train_steps)
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self.initial_lr, self.end_lr, self.epochs, self.power = initial_lr, end_lr, train_steps, power
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# set lr for first warmup step
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self.optimizer.lr.assign(self.get_lr()).realize()
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def get_lr(self):
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# LR is 0 on the first step, matching the reference.
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warmup_lr = (self.epoch_counter * (1.0 / self.warmup)) * self.initial_lr
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x = (1 - (self.epoch_counter - self.warmup) / (self.epochs - self.warmup + 1))
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return (self.epoch_counter <= self.warmup).where(warmup_lr, (self.initial_lr - self.end_lr) * x ** self.power + self.end_lr).cast(self.optimizer.lr.dtype)
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class CosineAnnealingLRWithWarmup(LR_Scheduler):
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def __init__(self, optimizer:Optimizer, base_lr, end_lr, warmup_steps:int, decay_steps:int):
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assert warmup_steps > 0 and decay_steps > 0
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super().__init__(optimizer)
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self.base_lr = base_lr
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self.end_lr = end_lr
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self.warmup_steps = warmup_steps
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self.decay_steps = decay_steps
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# set lr for first warmup step
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self.optimizer.lr.assign(self.get_lr()).realize()
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def get_lr(self):
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warmup_lr = ((self.epoch_counter+1) / self.warmup_steps) * self.base_lr
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decay_lr = self.end_lr + 0.5 * (self.base_lr-self.end_lr) * (1 + (((self.epoch_counter+1-self.warmup_steps)/self.decay_steps) * math.pi).cos())
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return (self.epoch_counter < self.warmup_steps).where(warmup_lr, decay_lr).cast(self.optimizer.lr.dtype) |