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# Author: Bingxin Ke
# Last modified: 2024-02-22
import numpy as np
class IterExponential:
def __init__(self, total_iter_length, final_ratio, warmup_steps=0) -> None:
"""
Customized iteration-wise exponential scheduler.
Re-calculate for every step, to reduce error accumulation
Args:
total_iter_length (int): Expected total iteration number
final_ratio (float): Expected LR ratio at n_iter = total_iter_length
"""
self.total_length = total_iter_length
self.effective_length = total_iter_length - warmup_steps
self.final_ratio = final_ratio
self.warmup_steps = warmup_steps
def __call__(self, n_iter) -> float:
if n_iter < self.warmup_steps:
alpha = 1.0 * n_iter / self.warmup_steps
elif n_iter >= self.total_length:
alpha = self.final_ratio
else:
actual_iter = n_iter - self.warmup_steps
alpha = np.exp(
actual_iter / self.effective_length * np.log(self.final_ratio)
)
return alpha
if "__main__" == __name__:
lr_scheduler = IterExponential(
total_iter_length=50000, final_ratio=0.01, warmup_steps=200
)
lr_scheduler = IterExponential(
total_iter_length=50000, final_ratio=0.01, warmup_steps=0
)
x = np.arange(100000)
alphas = [lr_scheduler(i) for i in x]
import matplotlib.pyplot as plt
plt.plot(alphas)
plt.savefig("lr_scheduler.png")
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