# coding=utf-8 # Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team. # # 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. """PyTorch optimization for BERT model.""" import logging import math import torch from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR logger = logging.getLogger(__name__) class ConstantLRSchedule(LambdaLR): def __init__(self, optimizer, last_epoch=-1): super(ConstantLRSchedule, self).__init__(optimizer, lambda _: 1.0, last_epoch=last_epoch) class WarmupCosineSchedule(LambdaLR): """ Linearly increases learning rate from 0 to 1 over `warmup` training steps. Decreases learning rate from 1. to 0. over remaining `t_total - warmup` steps following a cosine curve. If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup. :param warmup: see LRSchedule :param t_total: see LRSchedule :param cycles: number of cycles. Default: 0.5, corresponding to cosine decay from 1. at progress==warmup and 0 at progress==1. :param kw: """ warn_t_total = True def __init__(self, optimizer, warmup_steps, t_total, cycles=.5, last_epoch=-1): def lr_lambda(step): if step < warmup_steps: return float(step) / float(max(1.0, warmup_steps)) else: progress = float(step - warmup_steps) / float(max(1, t_total - warmup_steps)) # progress after warmup return 0.5 * (1. + math.cos(math.pi * float(cycles) * 2.0 * progress)) super(WarmupCosineSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch) class WarmupCosineWithHardRestartsSchedule(LambdaLR): """ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps. If `cycles` (default=1.) is different from default, learning rate follows `cycles` times a cosine decaying learning rate (with hard restarts). """ def __init__(self, optimizer, warmup_steps, t_total, cycles=1., last_epoch=-1): def lr_lambda(step): if step < warmup_steps: return float(step) / float(max(1, warmup_steps)) else: progress = float(step - warmup_steps) / float(max(1, t_total - warmup_steps)) # progress after warmup if progress >= 1.0: return 0.0 return 0.5 * (1. + math.cos(math.pi * ((float(cycles) * progress) % 1.0))) super(WarmupCosineWithHardRestartsSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch) class WarmupConstantSchedule(LambdaLR): """ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps. Keeps learning rate equal to 1. after warmup. """ def __init__(self, optimizer, warmup_steps, last_epoch=-1): def lr_lambda(step): if step < warmup_steps: return float(step) / float(max(1.0, warmup_steps)) return 1. super(WarmupConstantSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch) class WarmupLinearSchedule(LambdaLR): """ Linearly increases learning rate from 0 to 1 over `warmup` fraction of training steps. Linearly decreases learning rate from 1. to 0. over remaining `1 - warmup` steps. """ def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1): def lr_lambda(step): if step < warmup_steps: return float(step) / float(max(1, warmup_steps)) return float(t_total - step) / float(max(1.0, t_total - warmup_steps)) super(WarmupLinearSchedule, self).__init__(optimizer, lr_lambda, last_epoch=last_epoch) class AdamW(Optimizer): """ Implements Adam algorithm with weight decay fix. Parameters: lr: learning rate warmup: portion of t_total for the warmup, -1 means no warmup. Default: -1 t_total: total number of training steps for the learning rate schedule, -1 means constant learning rate of 1. (no warmup regardless of warmup setting). Default: -1 schedule: schedule to use for the warmup (see above). Can be `'warmup_linear'`, `'warmup_constant'`, `'warmup_cosine'`, `'none'`, `None` or a `_LRSchedule` object (see below). If `None` or `'none'`, learning rate is always kept constant. Default : `'warmup_linear'` b1: Adams b1. Default: 0.9 b2: Adams b2. Default: 0.999 e: Adams epsilon. Default: 1e-6 weight_decay: Weight decay. Default: 0.01 max_grad_norm: Maximum norm for the gradients (-1 means no clipping). Default: 1.0 correct_bias: can be set to False to avoid correcting bias in Adam (e.g. like in Bert repository) """ def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01, correct_bias=True): if lr < 0.0: raise ValueError("Invalid learning rate: {} - should be >= 0.0".format(lr)) if not 0.0 <= betas[0] < 1.0: raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[0])) if not 0.0 <= betas[1] < 1.0: raise ValueError("Invalid beta parameter: {} - should be in [0.0, 1.0[".format(betas[1] )) if not 0.0 <= eps: raise ValueError("Invalid epsilon value: {} - should be >= 0.0".format(e)) defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay, correct_bias=correct_bias) super(AdamW, self).__init__(params, defaults) def step(self, closure=None): """Performs a single optimization step. Arguments: closure (callable, optional): A closure that reevaluates the model and returns the loss. """ loss = None if closure is not None: loss = closure() for group in self.param_groups: for p in group['params']: if p.grad is None: continue grad = p.grad.data if grad.is_sparse: raise RuntimeError('Adam does not support sparse gradients, please consider SparseAdam instead') state = self.state[p] # State initialization if len(state) == 0: state['step'] = 0 # Exponential moving average of gradient values state['exp_avg'] = torch.zeros_like(p.data) # Exponential moving average of squared gradient values state['exp_avg_sq'] = torch.zeros_like(p.data) exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq'] beta1, beta2 = group['betas'] state['step'] += 1 # Decay the first and second moment running average coefficient # In-place operations to update the averages at the same time exp_avg.mul_(beta1).add_(1 - beta1, grad) exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad) denom = exp_avg_sq.sqrt().add_(group['eps']) step_size = group['lr'] if group['correct_bias']: # No bias correction for Bert bias_correction1 = 1 - beta1 ** state['step'] bias_correction2 = 1 - beta2 ** state['step'] step_size = step_size * math.sqrt(bias_correction2) / bias_correction1 p.data.addcdiv_(-step_size, exp_avg, denom) # Just adding the square of the weights to the loss function is *not* # the correct way of using L2 regularization/weight decay with Adam, # since that will interact with the m and v parameters in strange ways. # # Instead we want to decay the weights in a manner that doesn't interact # with the m/v parameters. This is equivalent to adding the square # of the weights to the loss with plain (non-momentum) SGD. # Add weight decay at the end (fixed version) if group['weight_decay'] > 0: p.data.add_(-group['lr'] * group['weight_decay'], p.data) return loss