Source code for transformers.optimization

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# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.
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"""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__)


[docs]def get_constant_schedule(optimizer, last_epoch=-1): """ Create a schedule with a constant learning rate. """ return LambdaLR(optimizer, lambda _: 1, last_epoch=last_epoch)
[docs]def get_constant_schedule_with_warmup(optimizer, num_warmup_steps, last_epoch=-1): """ Create a schedule with a constant learning rate preceded by a warmup period during which the learning rate increases linearly between 0 and 1. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1.0, num_warmup_steps)) return 1. return LambdaLR(optimizer, lr_lambda, last_epoch=last_epoch)
[docs]def get_linear_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, last_epoch=-1): """ Create a schedule with a learning rate that decreases linearly after linearly increasing during a warmup period. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) return max(0.0, float(num_training_steps - current_step) / float(max(1, num_training_steps - num_warmup_steps))) return LambdaLR(optimizer, lr_lambda, last_epoch)
def get_cosine_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=.5, last_epoch=-1): """ Create a schedule with a learning rate that decreases following the values of the cosine function between 0 and `pi * cycles` after a warmup period during which it increases linearly between 0 and 1. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) return max(0., 0.5 * (1. + math.cos(math.pi * float(num_cycles) * 2. * progress))) return LambdaLR(optimizer, lr_lambda, last_epoch)
[docs]def get_cosine_with_hard_restarts_schedule_with_warmup(optimizer, num_warmup_steps, num_training_steps, num_cycles=1., last_epoch=-1): """ Create a schedule with a learning rate that decreases following the values of the cosine function with several hard restarts, after a warmup period during which it increases linearly between 0 and 1. """ def lr_lambda(current_step): if current_step < num_warmup_steps: return float(current_step) / float(max(1, num_warmup_steps)) progress = float(current_step - num_warmup_steps) / float(max(1, num_training_steps - num_warmup_steps)) if progress >= 1.: return 0. return max(0., 0.5 * (1. + math.cos(math.pi * ((float(num_cycles) * progress) % 1.)))) return LambdaLR(optimizer, lr_lambda, last_epoch)
[docs]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)
[docs] 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