InvokeAI/ldm/lr_scheduler.py

import numpy as np


class LambdaWarmUpCosineScheduler:
    """
    note: use with a base_lr of 1.0
    """

    def __init__(
        self,
        warm_up_steps,
        lr_min,
        lr_max,
        lr_start,
        max_decay_steps,
        verbosity_interval=0,
    ):
        self.lr_warm_up_steps = warm_up_steps
        self.lr_start = lr_start
        self.lr_min = lr_min
        self.lr_max = lr_max
        self.lr_max_decay_steps = max_decay_steps
        self.last_lr = 0.0
        self.verbosity_interval = verbosity_interval

    def schedule(self, n, **kwargs):
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0:
                print(
                    f'current step: {n}, recent lr-multiplier: {self.last_lr}'
                )
        if n < self.lr_warm_up_steps:
            lr = (
                self.lr_max - self.lr_start
            ) / self.lr_warm_up_steps * n + self.lr_start
            self.last_lr = lr
            return lr
        else:
            t = (n - self.lr_warm_up_steps) / (
                self.lr_max_decay_steps - self.lr_warm_up_steps
            )
            t = min(t, 1.0)
            lr = self.lr_min + 0.5 * (self.lr_max - self.lr_min) * (
                1 + np.cos(t * np.pi)
            )
            self.last_lr = lr
            return lr

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaWarmUpCosineScheduler2:
    """
    supports repeated iterations, configurable via lists
    note: use with a base_lr of 1.0.
    """

    def __init__(
        self,
        warm_up_steps,
        f_min,
        f_max,
        f_start,
        cycle_lengths,
        verbosity_interval=0,
    ):
        assert (
            len(warm_up_steps)
            == len(f_min)
            == len(f_max)
            == len(f_start)
            == len(cycle_lengths)
        )
        self.lr_warm_up_steps = warm_up_steps
        self.f_start = f_start
        self.f_min = f_min
        self.f_max = f_max
        self.cycle_lengths = cycle_lengths
        self.cum_cycles = np.cumsum([0] + list(self.cycle_lengths))
        self.last_f = 0.0
        self.verbosity_interval = verbosity_interval

    def find_in_interval(self, n):
        interval = 0
        for cl in self.cum_cycles[1:]:
            if n <= cl:
                return interval
            interval += 1

    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0:
                print(
                    f'current step: {n}, recent lr-multiplier: {self.last_f}, '
                    f'current cycle {cycle}'
                )
        if n < self.lr_warm_up_steps[cycle]:
            f = (
                self.f_max[cycle] - self.f_start[cycle]
            ) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            t = (n - self.lr_warm_up_steps[cycle]) / (
                self.cycle_lengths[cycle] - self.lr_warm_up_steps[cycle]
            )
            t = min(t, 1.0)
            f = self.f_min[cycle] + 0.5 * (
                self.f_max[cycle] - self.f_min[cycle]
            ) * (1 + np.cos(t * np.pi))
            self.last_f = f
            return f

    def __call__(self, n, **kwargs):
        return self.schedule(n, **kwargs)


class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2):
    def schedule(self, n, **kwargs):
        cycle = self.find_in_interval(n)
        n = n - self.cum_cycles[cycle]
        if self.verbosity_interval > 0:
            if n % self.verbosity_interval == 0:
                print(
                    f'current step: {n}, recent lr-multiplier: {self.last_f}, '
                    f'current cycle {cycle}'
                )

        if n < self.lr_warm_up_steps[cycle]:
            f = (
                self.f_max[cycle] - self.f_start[cycle]
            ) / self.lr_warm_up_steps[cycle] * n + self.f_start[cycle]
            self.last_f = f
            return f
        else:
            f = self.f_min[cycle] + (self.f_max[cycle] - self.f_min[cycle]) * (
                self.cycle_lengths[cycle] - n
            ) / (self.cycle_lengths[cycle])
            self.last_f = f
            return f