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| """Lamb optimizer.""" | |
| import torch | |
| from torch.optim import Optimizer | |
| import math | |
| class Lamb(Optimizer): | |
| r"""Implements Lamb algorithm. | |
| It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 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. | |
| .. _Large Batch Optimization for Deep Learning: Training BERT in 76 minutes: | |
| https://arxiv.org/abs/1904.00962 | |
| """ | |
| def __init__( | |
| self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, 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 | |
| # Decay the first and second moment running average coefficient | |
| # m_t | |
| exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1) | |
| # v_t | |
| exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2) | |
| # Paper v3 does not use debiasing. | |
| bias_correction1 = 1 - beta1 ** state["step"] | |
| bias_correction2 = 1 - beta2 ** state["step"] | |
| exp_avg_hat = exp_avg / bias_correction1 | |
| exp_avg_sq_hat = exp_avg_sq / bias_correction2 | |
| # Apply bias to lr to avoid broadcast. | |
| step_size = group["lr"] | |
| do_layer_adaptation = ( | |
| group["layer_adaptation"] | |
| if "layer_adaptation" in group | |
| else group["weight_decay"] > 0 | |
| ) | |
| adam_step = exp_avg_hat / exp_avg_sq_hat.sqrt().add(group["eps"]) | |
| if group["weight_decay"] != 0: | |
| adam_step.add_(p.data, alpha=group["weight_decay"]) | |
| if do_layer_adaptation: | |
| weight_norm = p.data.norm(p=2) | |
| adam_norm = adam_step.norm(p=2) | |
| trust_ratio = torch.where( | |
| weight_norm.ne(0), | |
| torch.where(adam_norm.ne(0), weight_norm / adam_norm, 1), | |
| 1, | |
| ) | |
| if self.adam or not do_layer_adaptation: | |
| trust_ratio = 1 | |
| p.data.add_(adam_step, alpha=-step_size * trust_ratio) | |
| return loss | |