153 lines
5.4 KiB
Python
153 lines
5.4 KiB
Python
import math
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import torch
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from torch.optim.optimizer import Optimizer
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from src.utils.utils_types import Betas2, OptFloat, OptLossClosure, Params
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__all__ = ('Lamb',)
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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:
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Training BERT in 76 minutes`__.
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Arguments:
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params: iterable of parameters to optimize or dicts defining
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parameter groups
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lr: learning rate (default: 1e-3)
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betas: 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: term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay: weight decay (L2 penalty) (default: 0)
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clamp_value: clamp weight_norm in (0,clamp_value) (default: 10)
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set to a high value to avoid it (e.g 10e3)
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adam: always use trust ratio = 1, which turns this
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into Adam. Useful for comparison purposes. (default: False)
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debias: debias adam by (1 - beta**step) (default: False)
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Example:
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>>> optimizer = optim.Lamb(model.parameters(), lr=0.1)
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>>> optimizer.zero_grad()
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>>> loss_fn(model(input), target).backward()
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>>> optimizer.step()
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__ https://arxiv.org/abs/1904.00962
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Note:
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Reference code: https://github.com/cybertronai/pytorch-lamb
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"""
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def __init__(
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self,
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params: Params,
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lr: float = 1e-3,
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betas: Betas2 = (0.9, 0.999),
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eps: float = 1e-6,
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weight_decay: float = 0,
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clamp_value: float = 10,
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adam: bool = False,
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debias: bool = False,
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) -> None:
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if lr <= 0.0:
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raise ValueError('Invalid learning rate: {}'.format(lr))
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if eps < 0.0:
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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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)
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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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)
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if weight_decay < 0:
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raise ValueError(
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'Invalid weight_decay value: {}'.format(weight_decay)
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)
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if clamp_value < 0.0:
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raise ValueError('Invalid clamp value: {}'.format(clamp_value))
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defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
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self.clamp_value = clamp_value
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self.adam = adam
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self.debias = debias
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super(Lamb, self).__init__(params, defaults)
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def step(self, closure: OptLossClosure = None) -> OptFloat:
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r"""Performs a single optimization step.
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Arguments:
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closure: A closure that reevaluates the model 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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msg = (
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'Lamb does not support sparse gradients, '
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'please consider SparseAdam instead'
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)
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raise RuntimeError(msg)
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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)
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# Exponential moving average of squared gradient values
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state['exp_avg_sq'] = torch.zeros_like(p)
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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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if self.debias:
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bias_correction = math.sqrt(1 - beta2 ** state['step'])
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bias_correction /= 1 - beta1 ** state['step']
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else:
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bias_correction = 1
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# Apply bias to lr to avoid broadcast.
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step_size = group['lr'] * bias_correction
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weight_norm = torch.norm(p.data).clamp(0, self.clamp_value)
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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 = torch.norm(adam_step)
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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 |