|
|
|
@ -29,20 +29,16 @@ class Lamb(Optimizer):
|
|
|
|
|
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):
|
|
|
|
|
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]))
|
|
|
|
|
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)
|
|
|
|
|
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)
|
|
|
|
|
|
|
|
|
@ -63,8 +59,7 @@ class Lamb(Optimizer):
|
|
|
|
|
continue
|
|
|
|
|
grad = p.grad.data
|
|
|
|
|
if grad.is_sparse:
|
|
|
|
|
raise RuntimeError(
|
|
|
|
|
'Lamb does not support sparse gradients, consider SparseAdam instad.')
|
|
|
|
|
raise RuntimeError('Lamb does not support sparse gradients, consider SparseAdam instad.')
|
|
|
|
|
|
|
|
|
|
state = self.state[p]
|
|
|
|
|
|
|
|
|
|