| | 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 |
