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| # 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): | |
| """ Constant learning rate schedule. | |
| """ | |
| def __init__(self, optimizer, last_epoch=-1): | |
| super(ConstantLRSchedule, self).__init__(optimizer, lambda _: 1.0, last_epoch=last_epoch) | |
| class WarmupConstantSchedule(LambdaLR): | |
| """ Linear warmup and then constant. | |
| Linearly increases learning rate schedule from 0 to 1 over `warmup_steps` training steps. | |
| Keeps learning rate schedule equal to 1. after warmup_steps. | |
| """ | |
| def __init__(self, optimizer, warmup_steps, last_epoch=-1): | |
| self.warmup_steps = warmup_steps | |
| super(WarmupConstantSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch) | |
| def lr_lambda(self, step): | |
| if step < self.warmup_steps: | |
| return float(step) / float(max(1.0, self.warmup_steps)) | |
| return 1. | |
| class WarmupLinearSchedule(LambdaLR): | |
| """ Linear warmup and then linear decay. | |
| Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps. | |
| Linearly decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps. | |
| """ | |
| def __init__(self, optimizer, warmup_steps, t_total, last_epoch=-1): | |
| self.warmup_steps = warmup_steps | |
| self.t_total = t_total | |
| super(WarmupLinearSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch) | |
| def lr_lambda(self, step): | |
| if step < self.warmup_steps: | |
| return float(step) / float(max(1, self.warmup_steps)) | |
| return max(0.0, float(self.t_total - step) / float(max(1.0, self.t_total - self.warmup_steps))) | |
| class WarmupCosineSchedule(LambdaLR): | |
| """ Linear warmup and then cosine decay. | |
| Linearly increases learning rate from 0 to 1 over `warmup_steps` training steps. | |
| Decreases learning rate from 1. to 0. over remaining `t_total - warmup_steps` steps following a cosine curve. | |
| If `cycles` (default=0.5) is different from default, learning rate follows cosine function after warmup. | |
| """ | |
| def __init__(self, optimizer, warmup_steps, t_total, cycles=.5, last_epoch=-1): | |
| self.warmup_steps = warmup_steps | |
| self.t_total = t_total | |
| self.cycles = cycles | |
| super(WarmupCosineSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch) | |
| def lr_lambda(self, step): | |
| if step < self.warmup_steps: | |
| return float(step) / float(max(1.0, self.warmup_steps)) | |
| # progress after warmup | |
| progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps)) | |
| return max(0.0, 0.5 * (1. + math.cos(math.pi * float(self.cycles) * 2.0 * progress))) | |
| class WarmupCosineWithHardRestartsSchedule(LambdaLR): | |
| """ Linear warmup and then cosine cycles with hard restarts. | |
| Linearly increases learning rate from 0 to 1 over `warmup_steps` 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): | |
| self.warmup_steps = warmup_steps | |
| self.t_total = t_total | |
| self.cycles = cycles | |
| super(WarmupCosineWithHardRestartsSchedule, self).__init__(optimizer, self.lr_lambda, last_epoch=last_epoch) | |
| def lr_lambda(self, step): | |
| if step < self.warmup_steps: | |
| return float(step) / float(max(1, self.warmup_steps)) | |
| # progress after warmup | |
| progress = float(step - self.warmup_steps) / float(max(1, self.t_total - self.warmup_steps)) | |
| if progress >= 1.0: | |
| return 0.0 | |
| return max(0.0, 0.5 * (1. + math.cos(math.pi * ((float(self.cycles) * progress) % 1.0)))) | |
| class AdamW(Optimizer): | |
| """ Implements Adam algorithm with weight decay fix. | |
| Parameters: | |
| lr (float): learning rate. Default 1e-3. | |
| betas (tuple of 2 floats): Adams beta parameters (b1, b2). Default: (0.9, 0.999) | |
| eps (float): Adams epsilon. Default: 1e-6 | |
| weight_decay (float): Weight decay. Default: 0.0 | |
| correct_bias (bool): can be set to False to avoid correcting bias in Adam (e.g. like in Bert TF repository). Default True. | |
| """ | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6, weight_decay=0.0, 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(eps)) | |
| 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_(1.0 - beta1, grad) | |
| exp_avg_sq.mul_(beta2).addcmul_(1.0 - 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 = 1.0 - beta1 ** state['step'] | |
| bias_correction2 = 1.0 - 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'] > 0.0: | |
| p.data.add_(-group['lr'] * group['weight_decay'], p.data) | |
| return loss | |