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3cd1076 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import torch
import torch.optim as optim
class NovoGrad(optim.Optimizer):
def __init__(self, params, lr=0.01, betas=(0.9, 0.999), eps=1e-8, weight_decay=0, grad_averaging=False, amsgrad=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]))
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, grad_averaging=grad_averaging, amsgrad=amsgrad)
super(NovoGrad, self).__init__(params, defaults)
def __setstate__(self, state):
super(NovoGrad, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('amsgrad', False)
def step(self, closure=None):
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('NovoGrad does not support sparse gradients')
amsgrad = group['amsgrad']
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = 0
state['exp_avg'] = torch.zeros_like(p.data)
state['exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
if amsgrad:
state['max_exp_avg_sq'] = torch.zeros([]).to(state['exp_avg'].device)
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
norm = torch.sum(torch.pow(grad, 2))
if exp_avg_sq == 0:
exp_avg_sq.copy_(norm)
else:
exp_avg_sq.mul_(beta2).add_(1 - beta2, norm)
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().add_(group['eps'])
else:
denom = exp_avg_sq.sqrt().add_(group['eps'])
grad.div_(denom)
if group['weight_decay'] != 0:
grad.add_(group['weight_decay'], p.data)
if group['grad_averaging']:
grad.mul_(1 - beta1)
exp_avg.mul_(beta1).add_(grad)
p.data.add_(-group['lr'], exp_avg)
return loss |