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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
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