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import math
from typing import NamedTuple, Literal, Callable
from functools import partial
import torch
from torch.optim.lr_scheduler import LambdaLR
from diffusers.optimization import get_scheduler as get_scheduler_, get_cosine_with_hard_restarts_schedule_with_warmup
from transformers.optimization import get_adafactor_schedule
class OneCyclePhase(NamedTuple):
step_min: int
step_max: int
min: float
max: float
func: Callable[[float], float]
def warmup_linear(progress: float):
return progress
def warmup_cos(exp: int, progress: float):
lr = 0.5 * (1.0 + math.cos(math.pi * (1 + progress)))
lr = lr ** (exp - (exp - 1) * progress)
return lr
def anneal_linear(progress: float):
return 1 - progress
def anneal_half_cos(exp: int, progress: float):
lr = 1.0 + math.cos(math.pi * (0.5 + 0.5 * progress))
lr = lr ** (exp - (exp - 1) * progress)
return lr
def anneal_cos(exp: int, progress: float):
lr = 0.5 * (1.0 + math.cos(math.pi * progress))
lr = lr ** (exp - (exp - 1) * progress)
return lr
def get_one_cycle_schedule(
optimizer: torch.optim.Optimizer,
num_training_steps: int,
warmup: Literal["cos", "linear"] = "cos",
annealing: Literal["cos", "half_cos", "linear"] = "cos",
warmup_exp: int = 1,
annealing_exp: int = 1,
min_lr: float = 0.04,
mid_point: float = 0.3,
last_epoch: int = -1
):
if warmup == "linear":
warmup_func = warmup_linear
else:
warmup_func = partial(warmup_cos, warmup_exp)
if annealing == "linear":
anneal_func = anneal_linear
elif annealing == "half_cos":
anneal_func = partial(anneal_half_cos, annealing_exp)
else:
anneal_func = partial(anneal_cos, annealing_exp)
thresh_up = int(num_training_steps * min(mid_point, 0.5))
if annealing == "linear":
thresh_down = thresh_up * 2
phases = [
OneCyclePhase(0, thresh_up, min_lr, 1, warmup_func),
OneCyclePhase(thresh_up, thresh_down, min_lr, 1, anneal_func),
OneCyclePhase(thresh_down, num_training_steps, 0, min_lr, anneal_func),
]
else:
phases = [
OneCyclePhase(0, thresh_up, min_lr, 1, warmup_func),
OneCyclePhase(thresh_up, num_training_steps, 0, 1, anneal_func),
]
def lr_lambda(current_step: int):
phase = [p for p in phases if current_step >= p.step_min][-1]
return phase.min + phase.func((current_step - phase.step_min) / (phase.step_max - phase.step_min)) * (phase.max - phase.min)
return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_exponential_growing_schedule(optimizer, end_lr: float, num_training_steps: int, last_epoch: int = -1):
def lr_lambda(base_lr: float, current_step: int):
return (end_lr / base_lr) ** (current_step / num_training_steps)
lr_lambdas = [partial(lr_lambda, group["lr"]) for group in optimizer.param_groups]
return LambdaLR(optimizer, lr_lambdas, last_epoch)
def get_scheduler(
id: str,
optimizer: torch.optim.Optimizer,
num_training_steps_per_epoch: int,
gradient_accumulation_steps: int = 1,
min_lr: float = 0.04,
mid_point: float = 0.3,
warmup_func: Literal["cos", "linear"] = "cos",
annealing_func: Literal["cos", "half_cos", "linear"] = "cos",
warmup_exp: int = 1,
annealing_exp: int = 1,
end_lr: float = 1e3,
cycles: int = 1,
train_epochs: int = 100,
warmup_epochs: int = 10,
):
num_training_steps_per_epoch = math.ceil(
num_training_steps_per_epoch / gradient_accumulation_steps
) # * gradient_accumulation_steps
num_training_steps = train_epochs * num_training_steps_per_epoch
num_warmup_steps = warmup_epochs * num_training_steps_per_epoch
if id == "one_cycle":
lr_scheduler = get_one_cycle_schedule(
optimizer=optimizer,
num_training_steps=num_training_steps,
warmup=warmup_func,
annealing=annealing_func,
warmup_exp=warmup_exp,
annealing_exp=annealing_exp,
min_lr=min_lr,
mid_point=mid_point,
)
elif id == "exponential_growth":
if cycles is None:
cycles = math.ceil(math.sqrt(((num_training_steps - num_warmup_steps) / num_training_steps_per_epoch)))
lr_scheduler = get_exponential_growing_schedule(
optimizer=optimizer,
end_lr=end_lr,
num_training_steps=num_training_steps,
)
elif id == "cosine_with_restarts":
if cycles is None:
cycles = math.ceil(math.sqrt(((num_training_steps - num_warmup_steps) / num_training_steps_per_epoch)))
lr_scheduler = get_cosine_with_hard_restarts_schedule_with_warmup(
optimizer=optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
num_cycles=cycles,
)
elif id == "adafactor":
lr_scheduler = get_adafactor_schedule(
optimizer,
initial_lr=min_lr
)
else:
lr_scheduler = get_scheduler_(
id,
optimizer=optimizer,
num_warmup_steps=num_warmup_steps,
num_training_steps=num_training_steps,
)
return lr_scheduler
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