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import torch
from ..optimizer import Optimizer
class Adamax(Optimizer):
"""Implements Adamax algorithm (a variant of Adam based on infinity norm).
It has been proposed in `Adam: A Method for Stochastic Optimization`__.
Arguments:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 2e-3)
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square
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)
__ https://arxiv.org/abs/1412.6980
"""
def __init__(self, params, lr=2e-3, betas=(0.9, 0.999), eps=1e-8,
weight_decay=0):
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]))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay)
super(Adamax, self).__init__(params, defaults)
@torch.no_grad()
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:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
grads = []
params_with_grad = []
states = []
exp_avgs = []
exp_infs = []
beta1, beta2 = group['betas']
eps = group['eps']
for p in group['params']:
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError('Adamax does not support sparse gradients')
grads.append(p.grad)
params_with_grad.append(p)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['exp_inf'] = torch.zeros_like(p, memory_format=torch.preserve_format)
exp_avgs.append(state['exp_avg'])
exp_infs.append(state['exp_inf'])
state['step'] += 1
states.append(state)
if group['weight_decay'] != 0:
torch._foreach_add_(grads, params_with_grad, alpha=group['weight_decay'])
# Update biased first moment estimate.
torch._foreach_mul_(exp_avgs, beta1)
torch._foreach_add_(exp_avgs, grads, alpha=1 - beta1)
# Update the exponentially weighted infinity norm.
torch._foreach_mul_(exp_infs, beta2)
for exp_inf, grad in zip(exp_infs, grads):
norm_buf = torch.cat([
exp_inf.unsqueeze(0),
grad.abs().add_(eps).unsqueeze_(0)
], 0)
torch.max(norm_buf, 0, keepdim=False, out=(exp_inf, exp_inf.new().long()))
bias_corrections = [1 - beta1 ** state['step'] for state in states]
clr = [group['lr'] / bias_correction for bias_correction in bias_corrections]
for i in range(len(params_with_grad)):
params_with_grad[i].addcdiv_(exp_avgs[i], exp_infs[i], value=-clr[i])
return loss
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