# coding=utf-8 # Copyright (c) 2020, NVIDIA CORPORATION. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. """Learning rate decay and weight decay incr functions.""" import math from megatron import print_rank_0 class OptimizerParamScheduler(object): """Anneals learning rate and weight decay""" def __init__(self, optimizer, max_lr, min_lr, lr_warmup_steps, lr_decay_steps, lr_decay_style, start_wd, end_wd, wd_incr_steps, wd_incr_style, use_checkpoint_opt_param_scheduler=True, override_opt_param_scheduler=False): # Class values. self.optimizer = optimizer self.max_lr = float(max_lr) self.min_lr = min_lr assert self.min_lr >= 0.0 assert self.max_lr >= self.min_lr self.lr_warmup_steps = lr_warmup_steps self.num_steps = 0 self.lr_decay_steps = lr_decay_steps assert self.lr_decay_steps > 0 assert self.lr_warmup_steps < self.lr_decay_steps self.lr_decay_style = lr_decay_style self.start_wd = start_wd self.end_wd = end_wd assert self.start_wd >= 0.0 assert self.end_wd >= self.start_wd self.wd_incr_steps = wd_incr_steps self.wd_incr_style = wd_incr_style self.override_opt_param_scheduler = override_opt_param_scheduler self.use_checkpoint_opt_param_scheduler = use_checkpoint_opt_param_scheduler if self.override_opt_param_scheduler: assert not self.use_checkpoint_opt_param_scheduler, 'both override and '\ 'use-checkpoint are set.' # Set the learning rate self.step(0) print_rank_0('> learning rate decay style: {}'.format(self.lr_decay_style)) def get_wd(self): """ Weight decay incr functions""" if self.num_steps > self.wd_incr_steps: return self.end_wd if self.wd_incr_style == 'constant': assert self.start_wd == self.end_wd return self.end_wd incr_ratio = float(self.num_steps) / float(self.wd_incr_steps) assert incr_ratio >= 0.0 assert incr_ratio <= 1.0 delta_wd = self.end_wd - self.start_wd if self.wd_incr_style == 'linear': coeff = incr_ratio elif self.wd_incr_style == 'cosine': coeff = 0.5 * (math.cos(math.pi * (1 - incr_ratio)) + 1.0) else: raise Exception('{} weight decay increment style is not supported.'.format( self.wd_incr_style)) return self.start_wd + coeff * delta_wd def get_lr(self): """Learning rate decay functions from: https://openreview.net/pdf?id=BJYwwY9ll pg. 4""" # Use linear warmup for the initial part. if self.lr_warmup_steps > 0 and self.num_steps <= self.lr_warmup_steps: return self.max_lr * float(self.num_steps) / \ float(self.lr_warmup_steps) # If the learning rate is constant, just return the initial value. if self.lr_decay_style == 'constant': return self.max_lr # For any steps larger than `self.lr_decay_steps`, use `self.min_lr`. if self.num_steps > self.lr_decay_steps: return self.min_lr # If we are done with the warmup period, use the decay style. num_steps_ = self.num_steps - self.lr_warmup_steps decay_steps_ = self.lr_decay_steps - self.lr_warmup_steps decay_ratio = float(num_steps_) / float(decay_steps_) assert decay_ratio >= 0.0 assert decay_ratio <= 1.0 delta_lr = self.max_lr - self.min_lr if self.lr_decay_style == 'linear': coeff = (1.0 - decay_ratio) elif self.lr_decay_style == 'cosine': coeff = 0.5 * (math.cos(math.pi * decay_ratio) + 1.0) else: raise Exception('{} decay style is not supported.'.format( self.lr_decay_style)) return self.min_lr + coeff * delta_lr def step(self, increment): """Set lr for all parameters groups.""" self.num_steps += increment new_lr = self.get_lr() new_wd = self.get_wd() for group in self.optimizer.param_groups: group['lr'] = new_lr * group.get('lr_mult', 1.0) group['weight_decay'] = new_wd * group.get('wd_mult', 1.0) def state_dict(self): state_dict = { 'max_lr': self.max_lr, 'lr_warmup_steps': self.lr_warmup_steps, 'num_steps': self.num_steps, 'lr_decay_style': self.lr_decay_style, 'lr_decay_steps': self.lr_decay_steps, 'min_lr': self.min_lr, 'start_wd': self.start_wd, 'end_wd': self.end_wd, 'wd_incr_style': self.wd_incr_style, 'wd_incr_steps': self.wd_incr_steps } return state_dict def _check_and_set(self, cls_value, sd_value, name): """Auxiliary function for checking the values in the checkpoint and setting them.""" if self.override_opt_param_scheduler: print_rank_0(' > overriding {} value to {}'.format(name, cls_value)) return cls_value if not self.use_checkpoint_opt_param_scheduler: assert cls_value == sd_value, \ f'OptimizerParamScheduler: class input value {cls_value} and checkpoint' \ f'value {sd_value} for {name} do not match' print_rank_0(' > using checkpoint value {} for {}'.format(sd_value, name)) return sd_value def load_state_dict(self, sd): if 'start_lr' in sd: max_lr_ = sd['start_lr'] else: max_lr_ = sd['max_lr'] self.max_lr = self._check_and_set(self.max_lr, max_lr_, 'learning rate') self.min_lr = self._check_and_set(self.min_lr, sd['min_lr'], 'minimum learning rate') if 'warmup_iter' in sd: lr_warmup_steps_ = sd['warmup_iter'] elif 'warmup_steps' in sd: lr_warmup_steps_ = sd['warmup_steps'] else: lr_warmup_steps_ = sd['lr_warmup_steps'] self.lr_warmup_steps = self._check_and_set(self.lr_warmup_steps, lr_warmup_steps_, 'warmup iterations') if 'end_iter' in sd: lr_decay_steps_ = sd['end_iter'] elif 'decay_steps' in sd: lr_decay_steps_ = sd['decay_steps'] else: lr_decay_steps_ = sd['lr_decay_steps'] self.lr_decay_steps = self._check_and_set(self.lr_decay_steps, lr_decay_steps_, 'total number of iterations') if 'decay_style' in sd: lr_decay_style_ = sd['decay_style'] else: lr_decay_style_ = sd['lr_decay_style'] self.lr_decay_style = self._check_and_set(self.lr_decay_style, lr_decay_style_, 'learning rate decay style') if 'num_iters' in sd: num_steps = sd['num_iters'] else: num_steps = sd['num_steps'] self.step(increment=num_steps) if 'start_wd' in sd: self.start_wd = self._check_and_set(self.start_wd, sd['start_wd'], "start weight decay") self.end_wd = self._check_and_set(self.end_wd, sd['end_wd'], "end weight decay") self.wd_incr_steps = self._check_and_set(self.wd_incr_steps, sd['wd_incr_steps'], "total number of weight decay iterations") self.wd_incr_style = self._check_and_set(self.wd_incr_style, sd['wd_incr_style'], "weight decay incr style")