23 lines
1.1 KiB
Python
23 lines
1.1 KiB
Python
from tinygrad import Tensor, dtypes
|
|
from tinygrad.nn.optim import Optimizer
|
|
|
|
from extra.lr_scheduler import LR_Scheduler
|
|
|
|
# https://github.com/mlcommons/training/blob/e237206991d10449d9675d95606459a3cb6c21ad/image_classification/tensorflow2/lars_util.py
|
|
class PolynomialDecayWithWarmup(LR_Scheduler):
|
|
def __init__(self, optimizer: Optimizer, initial_lr, end_lr, train_steps, warmup, power=2):
|
|
super().__init__(optimizer)
|
|
self.epoch_counter = self.epoch_counter.cast(dtypes.float32)
|
|
assert train_steps > 0 and warmup > 0
|
|
self.warmup = min(warmup, train_steps)
|
|
self.initial_lr, self.end_lr, self.epochs, self.power = initial_lr, end_lr, train_steps, power
|
|
|
|
# set lr for first warmup step
|
|
self.optimizer.lr.assign(self.get_lr()).realize()
|
|
|
|
def get_lr(self):
|
|
# LR is 0 on the first step, matching the reference.
|
|
warmup_lr = (self.epoch_counter * (1.0 / self.warmup)) * self.initial_lr
|
|
x = (1 - (self.epoch_counter - self.warmup) / (self.epochs - self.warmup + 1))
|
|
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)
|