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117 lines
4.5 KiB
117 lines
4.5 KiB
"""
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Adapted from the pytorch-lamb library at https://github.com/cybertronai/pytorch-lamb
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"""
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import torch
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from torch.optim import Optimizer
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from colossalai.registry import OPTIMIZERS
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@OPTIMIZERS.register_module
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class Lamb(Optimizer):
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r"""Implements Lamb algorithm.
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It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
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Arguments:
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params (iterable): iterable of parameters to optimize or dicts defining
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parameter groups
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lr (float, optional): learning rate (default: 1e-3)
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-6)
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weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
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adam (bool, optional): always use trust ratio = 1, which turns this into
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Adam. Useful for comparison purposes.
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.. _Large Batch Optimization for Deep Learning\: Training BERT in 76 minutes:
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https://arxiv.org/abs/1904.00962
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"""
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def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-6,
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weight_decay=0, adam=False):
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if not 0.0 <= lr:
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raise ValueError("Invalid learning rate: {}".format(lr))
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if not 0.0 <= eps:
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raise ValueError("Invalid epsilon value: {}".format(eps))
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if not 0.0 <= betas[0] < 1.0:
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raise ValueError(
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"Invalid beta parameter at index 0: {}".format(betas[0]))
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if not 0.0 <= betas[1] < 1.0:
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raise ValueError(
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"Invalid beta parameter at index 1: {}".format(betas[1]))
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defaults = dict(lr=lr, betas=betas, eps=eps,
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weight_decay=weight_decay)
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self.adam = adam
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super(Lamb, self).__init__(params, defaults)
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def step(self, closure=None):
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"""Performs a single optimization step.
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Arguments:
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closure (callable, optional): A closure that reevaluates the model
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and returns the loss.
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"""
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loss = None
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if closure is not None:
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loss = closure()
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for group in self.param_groups:
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for p in group['params']:
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if p.grad is None:
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continue
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grad = p.grad.data
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if grad.is_sparse:
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raise RuntimeError(
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'Lamb does not support sparse gradients, consider SparseAdam instad.')
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state = self.state[p]
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# State initialization
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if len(state) == 0:
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state['step'] = 0
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# Exponential moving average of gradient values
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state['exp_avg'] = torch.zeros_like(p.data)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p.data)
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exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
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beta1, beta2 = group['betas']
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state['step'] += 1
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# Decay the first and second moment running average coefficient
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# m_t
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exp_avg.mul_(beta1).add_(grad, alpha=1 - beta1)
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# v_t
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exp_avg_sq.mul_(beta2).addcmul_(grad, grad, value=1 - beta2)
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# Paper v3 does not use debiasing.
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# bias_correction1 = 1 - beta1 ** state['step']
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# bias_correction2 = 1 - beta2 ** state['step']
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# Apply bias to lr to avoid broadcast.
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# * math.sqrt(bias_correction2) / bias_correction1
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step_size = group['lr']
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weight_norm = p.data.pow(2).sum().sqrt()
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adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])
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if group['weight_decay'] != 0:
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adam_step.add_(p.data, alpha=group['weight_decay'])
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adam_norm = adam_step.pow(2).sum().sqrt()
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if weight_norm == 0 or adam_norm == 0:
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trust_ratio = 1
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else:
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trust_ratio = weight_norm / adam_norm
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state['weight_norm'] = weight_norm
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state['adam_norm'] = adam_norm
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state['trust_ratio'] = trust_ratio
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if self.adam:
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trust_ratio = 1
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p.data.add_(adam_step, alpha=-step_size * trust_ratio)
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return loss
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