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gRDA.py
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gRDA.py
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import math
import torch
from torch.optim.optimizer import Optimizer, required
class gRDA_momentum(Optimizer):
def __init__(self,
params,
lr=required,
c=0.005,
mu=0.7,
momentum=0,
reg='l1'):
if lr is not required and lr < 0.0:
raise ValueError("Invalid learning rate: {}".format(lr))
if momentum < 0.0:
raise ValueError("Invalid momentum value: {}".format(momentum))
defaults = dict(lr=lr,
c=c,
mu=mu,
momentum=momentum,
dampening=0,
reg=reg)
super(gRDA_momentum, self).__init__(params, defaults)
def __setstate__(self, state):
super(gRDA_momentum, self).__setstate__(state)
def step(self, closure=None):
loss = None
if closure is not None:
loss = closure()
for group in self.param_groups:
reg = group['reg']
lr = group['lr']
c = group['c']
mu = group['mu']
momentum = group['momentum']
dampening = group['dampening']
for p in group['params']:
if p.grad is None:
continue
d_p = p.grad.data
if momentum != 0:
param_state = self.state[p]
if 'momentum_buffer' not in param_state:
buf = param_state['momentum_buffer'] = torch.clone(
d_p).detach()
else:
buf = param_state['momentum_buffer']
buf.mul_(momentum).add_(1 - dampening, d_p)
d_p = buf
param_state = self.state[p]
if 'iter_num' not in param_state:
iter_num = param_state['iter_num'] = torch.zeros(1)
accumulator = param_state[
'accumulator'] = torch.FloatTensor(p.shape).to(
p.device)
l1_accumulation = param_state[
'l1_accumulation'] = torch.zeros(1)
accumulator.data = p.clone()
else:
iter_num = param_state['iter_num']
accumulator = param_state['accumulator']
l1_accumulation = param_state['l1_accumulation']
iter_num.add_(1)
accumulator.data.add_(-lr, d_p)
l1_diff = c * torch.pow(torch.tensor(
lr), mu + 0.5) * torch.pow(iter_num, mu) - c * torch.pow(
torch.tensor(lr), mu + 0.5) * torch.pow(
iter_num - 1, mu)
l1_accumulation += l1_diff
new_a_l1 = torch.abs(accumulator.data) - l1_accumulation.to(
p.device)
if reg == 'l1':
p.data = torch.sign(
accumulator.data) * new_a_l1.clamp(min=0)
elif reg == 'elasticnet':
p.data = 1 / (
1 + 20 * l1_accumulation.to(p.device)) * torch.sign(
accumulator.data) * new_a_l1.clamp(min=0)
elif reg == 'g_lasso':
p.data = (1 - l1_accumulation.to(p.device) / torch.norm(
accumulator.data, p=2)) * accumulator.data
return loss
class gRDAAdam(Optimizer):
r"""Implements Adam algorithm.
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: 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)
amsgrad (boolean, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
.. _Adam\: A Method for Stochastic Optimization:
https://arxiv.org/abs/1412.6980
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
def __init__(self,
params,
lr=1e-3,
betas=(0.9, 0.999),
eps=1e-8,
weight_decay=0,
amsgrad=False,
c=0.005,
mu=0.7,
reg='l1'):
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,
amsgrad=amsgrad,
c=c,
mu=mu,
reg=reg)
super(gRDAAdam, self).__init__(params, defaults)
def __setstate__(self, state):
super(gRDAAdam, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
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:
reg = group['reg']
lr = group['lr']
c = group['c']
mu = group['mu']
for p in group['params']:
if p.grad is None:
continue
grad = p.grad.data
if grad.is_sparse:
raise RuntimeError(
'Adam does not support sparse gradients, please consider SparseAdam instead'
)
amsgrad = group['amsgrad']
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, memory_format=torch.preserve_format)
# Exponential moving average of squared gradient values
state['exp_avg_sq'] = torch.zeros_like(
p.data, memory_format=torch.preserve_format)
if amsgrad:
# Maintains max of all exp. moving avg. of sq. grad. values
state['max_exp_avg_sq'] = torch.zeros_like(
p.data, memory_format=torch.preserve_format)
exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
if amsgrad:
max_exp_avg_sq = state['max_exp_avg_sq']
beta1, beta2 = group['betas']
state['step'] += 1
bias_correction1 = 1 - beta1**state['step']
bias_correction2 = 1 - beta2**state['step']
if group['weight_decay'] != 0:
grad.add_(group['weight_decay'], p.data)
# Decay the first and second moment running average coefficient
exp_avg.mul_(beta1).add_(1 - beta1, grad)
exp_avg_sq.mul_(beta2).addcmul_(1 - beta2, grad, grad)
if amsgrad:
# Maintains the maximum of all 2nd moment running avg. till now
torch.max(max_exp_avg_sq, exp_avg_sq, out=max_exp_avg_sq)
# Use the max. for normalizing running avg. of gradient
denom = (max_exp_avg_sq.sqrt() /
math.sqrt(bias_correction2)).add_(group['eps'])
else:
denom = (exp_avg_sq.sqrt() /
math.sqrt(bias_correction2)).add_(group['eps'])
step_size = group['lr'] / bias_correction1
grad = p.data.addcdiv_(-step_size, exp_avg, denom)
# compute RDA grads
if 'iter_num' not in state:
iter_num = state['iter_num'] = torch.zeros(1)
accumulator = state['accumulator'] = torch.FloatTensor(
p.shape).to(p.device)
l1_accumulation = state['l1_accumulation'] = torch.zeros(1)
accumulator.data = p.clone()
else:
iter_num = state['iter_num']
accumulator = state['accumulator']
l1_accumulation = state['l1_accumulation']
iter_num.add_(1)
accumulator.data.add_(-lr, grad)
# l1 = c * torch.pow(torch.tensor(lr), 0.5 + mu) * torch.pow(iter_num, mu)
l1_diff = c * torch.pow(torch.tensor(
lr), mu + 0.5) * torch.pow(iter_num, mu) - c * torch.pow(
torch.tensor(lr), mu + 0.5) * torch.pow(
iter_num - 1, mu)
l1_accumulation += l1_diff
if reg == 'l1':
new_a_l1 = torch.abs(
accumulator.data) - l1_accumulation.to(p.device)
p.data = torch.sign(
accumulator.data) * new_a_l1.clamp(min=0)
elif reg == 'elasticnet':
new_a_l1 = torch.abs(
accumulator.data) - l1_accumulation.to(p.device)
p.data = 1 / (
1 + 0.5 * l1_accumulation.to(p.device)) * torch.sign(
accumulator.data) * new_a_l1.clamp(min=0)
elif reg == 'g_lasso':
new_a_l1 = torch.abs(
accumulator.data) - l1_accumulation.to(p.device)
p.data = (1 - l1_accumulation.to(p.device) / torch.norm(
accumulator.data, p=2)) * accumulator.data
return loss