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Running
on
A10G
Running
on
A10G
import math | |
def get_cosine_schedule_with_warmup_lr_lambda( | |
current_step: int, | |
*, | |
num_warmup_steps: int | float, | |
num_training_steps: int, | |
num_cycles: float = 0.5, | |
final_lr_ratio: float = 0.0, | |
): | |
if 0 < num_warmup_steps < 1: # float mode | |
num_warmup_steps = int(num_warmup_steps * num_training_steps) | |
if current_step < num_warmup_steps: | |
return float(current_step) / float(max(1, num_warmup_steps)) | |
progress = float(current_step - num_warmup_steps) / float( | |
max(1, num_training_steps - num_warmup_steps) | |
) | |
return max( | |
final_lr_ratio, | |
0.5 * (1.0 + math.cos(math.pi * float(num_cycles) * 2.0 * progress)), | |
) | |
def get_constant_schedule_with_warmup_lr_lambda( | |
current_step: int, | |
*, | |
num_warmup_steps: int | float, | |
num_training_steps: int | None = None, | |
): | |
if 0 < num_warmup_steps < 1: # float mode | |
num_warmup_steps = int(num_warmup_steps * num_training_steps) | |
if current_step < num_warmup_steps: | |
return float(current_step) / float(max(1, num_warmup_steps)) | |
return 1.0 | |