132 lines
5.0 KiB
Python
132 lines
5.0 KiB
Python
"""
|
|
Lamb optimizer.
|
|
|
|
Adapted from original source: https://github.com/cybertronai/pytorch-lamb/blob/master/pytorch_lamb/lamb.py
|
|
"""
|
|
|
|
import collections
|
|
import math
|
|
|
|
import torch
|
|
from torch.optim import Optimizer
|
|
|
|
|
|
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-6,
|
|
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']
|
|
# Apply bias to lr to avoid broadcast.
|
|
step_size = group['lr'] # * math.sqrt(bias_correction2) / bias_correction1
|
|
|
|
weight_norm = p.data.pow(2).sum().sqrt().clamp(0, 10)
|
|
|
|
adam_step = exp_avg / exp_avg_sq.sqrt().add(group['eps'])
|
|
if group['weight_decay'] != 0:
|
|
adam_step.add_(p.data, alpha=group['weight_decay'])
|
|
|
|
adam_norm = adam_step.pow(2).sum().sqrt()
|
|
if weight_norm == 0 or adam_norm == 0:
|
|
trust_ratio = 1
|
|
else:
|
|
trust_ratio = weight_norm / adam_norm
|
|
state['weight_norm'] = weight_norm
|
|
state['adam_norm'] = adam_norm
|
|
state['trust_ratio'] = trust_ratio
|
|
if self.adam:
|
|
trust_ratio = 1
|
|
|
|
p.data.add_(adam_step, alpha=-step_size * trust_ratio)
|
|
|
|
return loss
|
|
|
|
|
|
def debug(self):
|
|
"""Returns a histogram dict for recording various norms and the trust ratio."""
|
|
results = collections.defaultdict(list)
|
|
for group in self.param_groups:
|
|
for p in group['params']:
|
|
state = self.state[p]
|
|
for i in ('weight_norm', 'adam_norm', 'trust_ratio'):
|
|
if i in state:
|
|
results[i].append(state[i])
|
|
|
|
res = {}
|
|
for k, v in results.items():
|
|
res[f'histogram_lamb_{k}'] = torch.tensor(v)
|
|
return res
|