| import torch |
| import torch.optim as optim |
| import numpy as np |
| import copy |
| from ... import sync |
| from ...cfg_holder import cfg_unique_holder as cfguh |
|
|
| def singleton(class_): |
| instances = {} |
| def getinstance(*args, **kwargs): |
| if class_ not in instances: |
| instances[class_] = class_(*args, **kwargs) |
| return instances[class_] |
| return getinstance |
|
|
| @singleton |
| class get_scheduler(object): |
| def __init__(self): |
| self.lr_scheduler = {} |
|
|
| def register(self, lrsf, name): |
| self.lr_scheduler[name] = lrsf |
|
|
| def __call__(self, cfg): |
| if cfg is None: |
| return None |
| if isinstance(cfg, list): |
| schedulers = [] |
| for ci in cfg: |
| t = ci.type |
| schedulers.append( |
| self.lr_scheduler[t](**ci.args)) |
| if len(schedulers) == 0: |
| raise ValueError |
| else: |
| return compose_scheduler(schedulers) |
| t = cfg.type |
| return self.lr_scheduler[t](**cfg.args) |
| |
|
|
| def register(name): |
| def wrapper(class_): |
| get_scheduler().register(class_, name) |
| return class_ |
| return wrapper |
|
|
| class template_scheduler(object): |
| def __init__(self, step): |
| self.step = step |
|
|
| def __getitem__(self, idx): |
| raise ValueError |
|
|
| def set_lr(self, optim, new_lr, pg_lrscale=None): |
| """ |
| Set Each parameter_groups in optim with new_lr |
| New_lr can be find according to the idx. |
| pg_lrscale tells how to scale each pg. |
| """ |
| |
| pg_lrscale = copy.deepcopy(pg_lrscale) |
| for pg in optim.param_groups: |
| if pg_lrscale is None: |
| pg['lr'] = new_lr |
| else: |
| pg['lr'] = new_lr * pg_lrscale.pop(pg['name']) |
| assert (pg_lrscale is None) or (len(pg_lrscale)==0), \ |
| "pg_lrscale doesn't match pg" |
|
|
| @register('constant') |
| class constant_scheduler(template_scheduler): |
| def __init__(self, lr, step): |
| super().__init__(step) |
| self.lr = lr |
|
|
| def __getitem__(self, idx): |
| if idx >= self.step: |
| raise ValueError |
| return self.lr |
|
|
| @register('poly') |
| class poly_scheduler(template_scheduler): |
| def __init__(self, start_lr, end_lr, power, step): |
| super().__init__(step) |
| self.start_lr = start_lr |
| self.end_lr = end_lr |
| self.power = power |
|
|
| def __getitem__(self, idx): |
| if idx >= self.step: |
| raise ValueError |
| a, b = self.start_lr, self.end_lr |
| p, n = self.power, self.step |
| return b + (a-b)*((1-idx/n)**p) |
|
|
| @register('linear') |
| class linear_scheduler(template_scheduler): |
| def __init__(self, start_lr, end_lr, step): |
| super().__init__(step) |
| self.start_lr = start_lr |
| self.end_lr = end_lr |
|
|
| def __getitem__(self, idx): |
| if idx >= self.step: |
| raise ValueError |
| a, b, n = self.start_lr, self.end_lr, self.step |
| return b + (a-b)*(1-idx/n) |
|
|
| @register('multistage') |
| class constant_scheduler(template_scheduler): |
| def __init__(self, start_lr, milestones, gamma, step): |
| super().__init__(step) |
| self.start_lr = start_lr |
| m = [0] + milestones + [step] |
| lr_iter = start_lr |
| self.lr = [] |
| for ms, me in zip(m[0:-1], m[1:]): |
| for _ in range(ms, me): |
| self.lr.append(lr_iter) |
| lr_iter *= gamma |
|
|
| def __getitem__(self, idx): |
| if idx >= self.step: |
| raise ValueError |
| return self.lr[idx] |
|
|
| class compose_scheduler(template_scheduler): |
| def __init__(self, schedulers): |
| self.schedulers = schedulers |
| self.step = [si.step for si in schedulers] |
| self.step_milestone = [] |
| acc = 0 |
| for i in self.step: |
| acc += i |
| self.step_milestone.append(acc) |
| self.step = sum(self.step) |
|
|
| def __getitem__(self, idx): |
| if idx >= self.step: |
| raise ValueError |
| ms = self.step_milestone |
| for idx, (mi, mj) in enumerate(zip(ms[:-1], ms[1:])): |
| if mi <= idx < mj: |
| return self.schedulers[idx-mi] |
| raise ValueError |
|
|
| |
| |
| |
|
|
| class LambdaWarmUpCosineScheduler(template_scheduler): |
| """ |
| note: use with a base_lr of 1.0 |
| """ |
| def __init__(self, |
| base_lr, |
| warm_up_steps, |
| lr_min, lr_max, lr_start, max_decay_steps, verbosity_interval=0): |
| cfgt = cfguh().cfg.train |
| bs = cfgt.batch_size |
| if 'gradacc_every' not in cfgt: |
| print('Warning, gradacc_every is not found in xml, use 1 as default.') |
| acc = cfgt.get('gradacc_every', 1) |
| self.lr_multi = base_lr * bs * acc |
| 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. |
| self.verbosity_interval = verbosity_interval |
|
|
| def schedule(self, n): |
| 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 __getitem__(self, idx): |
| return self.schedule(idx) * self.lr_multi |
|
|
| class LambdaWarmUpCosineScheduler2(template_scheduler): |
| """ |
| supports repeated iterations, configurable via lists |
| note: use with a base_lr of 1.0. |
| """ |
| def __init__(self, |
| base_lr, |
| warm_up_steps, |
| f_min, f_max, f_start, cycle_lengths, verbosity_interval=0): |
| cfgt = cfguh().cfg.train |
| |
| |
| |
| |
| |
| self.lr_multi = base_lr |
| 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. |
| 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): |
| 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 __getitem__(self, idx): |
| return self.schedule(idx) * self.lr_multi |
|
|
| @register('stable_diffusion_linear') |
| class LambdaLinearScheduler(LambdaWarmUpCosineScheduler2): |
| def schedule(self, n): |
| 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 |
