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| # SOURCE: https://github.com/cybertronai/pytorch-lamb/ | |
| import collections | |
| import math | |
| import torch | |
| from torch.optim import Optimizer | |
| class Lamb(Optimizer): | |
| r"""Implements Lamb algorithm. | |
| It has been proposed in `Reducing BERT Pre-Training Time from 3 Days to 76 Minutes`_. | |
| Arguments: | |
| params (iterable): iterable of parameters to optimize or dicts defining | |
| parameter groups | |
| lr (float, optional): learning rate (default: 1e-3) | |
| betas (Tuple[float, float], optional): coefficients used for computing | |
| running averages of gradient and its square (default: (0.9, 0.999)) | |
| eps (float, optional): term added to the denominator to improve | |
| numerical stability (default: 1e-8) | |
| weight_decay (float, optional): weight decay (L2 penalty) (default: 0) | |
| adam (bool, optional): always use trust ratio = 1, which turns this into | |
| Adam. Useful for comparison purposes. | |
| .. _Reducing BERT Pre-Training Time from 3 Days to 76 Minutes: | |
| https://arxiv.org/abs/1904.00962 | |
| """ | |
| def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-4, | |
| weight_decay=0, adam=False): | |
| if not 0.0 <= lr: | |
| raise ValueError("Invalid learning rate: {}".format(lr)) | |
| if not 0.0 <= eps: | |
| raise ValueError("Invalid epsilon value: {}".format(eps)) | |
| if not 0.0 <= betas[0] < 1.0: | |
| raise ValueError("Invalid beta parameter at index 0: {}".format(betas[0])) | |
| if not 0.0 <= betas[1] < 1.0: | |
| raise ValueError("Invalid beta parameter at index 1: {}".format(betas[1])) | |
| defaults = dict(lr=lr, betas=betas, eps=eps, | |
| weight_decay=weight_decay) | |
| self.adam = adam | |
| super(Lamb, 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('Lamb does not support sparse gradients, consider SparseAdam instad.') | |
| 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 | |
| if group['weight_decay'] != 0: | |
| grad.add_(group['weight_decay'], p.data) | |
| # Decay the first and second moment running average coefficient | |
| 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']) | |
| bias_correction1 = 1 - beta1 ** state['step'] | |
| bias_correction2 = 1 - beta2 ** state['step'] | |
| # Apply bias to lr to avoid broadcast. | |
| step_size = group['lr'] * math.sqrt(bias_correction2) / bias_correction1 | |
| adam_step = exp_avg / denom | |
| # L2 norm uses sum, but here since we're dividing, use mean to avoid overflow. | |
| r1 = p.data.pow(2).mean().sqrt() | |
| r2 = adam_step.pow(2).mean().sqrt() | |
| r = 1 if r1 == 0 or r2 == 0 else min(r1/r2, 10) | |
| state['r1'] = r1 | |
| state['r2'] = r2 | |
| state['r'] = r | |
| if self.adam: | |
| r = 1 | |
| p.data.add_(-step_size * r, adam_step) | |
| return loss | |