Created
March 23, 2020 11:25
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import torch | |
from torch.optim.optimizer import Optimizer, required | |
class SGD_MC(Optimizer): | |
r"""Implements stochastic gradient descent (optionally with momentum). | |
Nesterov momentum is based on the formula from | |
`On the importance of initialization and momentum in deep learning`__. | |
Args: | |
params (iterable): iterable of parameters to optimize or dicts defining | |
parameter groups | |
lr (float): learning rate | |
momentum (float, optional): momentum factor (default: 0) | |
weight_decay (float, optional): weight decay (L2 penalty) (default: 0) | |
dampening (float, optional): dampening for momentum (default: 0) | |
nesterov (bool, optional): enables Nesterov momentum (default: False) | |
Example: | |
>>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9) | |
>>> optimizer.zero_grad() | |
>>> loss_fn(model(input), target).backward() | |
>>> optimizer.step() | |
__ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf | |
.. note:: | |
The implementation of SGD with Momentum/Nesterov subtly differs from | |
Sutskever et. al. and implementations in some other frameworks. | |
Considering the specific case of Momentum, the update can be written as | |
.. math:: | |
\begin{aligned} | |
v_{t+1} & = \mu * v_{t} + g_{t+1}, \\ | |
p_{t+1} & = p_{t} - \text{lr} * v_{t+1}, | |
\end{aligned} | |
where :math:`p`, :math:`g`, :math:`v` and :math:`\mu` denote the | |
parameters, gradient, velocity, and momentum respectively. | |
This is in contrast to Sutskever et. al. and | |
other frameworks which employ an update of the form | |
.. math:: | |
\begin{aligned} | |
v_{t+1} & = \mu * v_{t} + \text{lr} * g_{t+1}, \\ | |
p_{t+1} & = p_{t} - v_{t+1}. | |
\end{aligned} | |
The Nesterov version is analogously modified. | |
""" | |
def __init__(self, params, lr=required, momentum=0, dampening=0, | |
weight_decay=0, nesterov=False, max_change_per_layer=0.75, max_change=1.5): | |
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)) | |
if weight_decay < 0.0: | |
raise ValueError("Invalid weight_decay value: {}".format(weight_decay)) | |
defaults = dict(lr=lr, momentum=momentum, dampening=dampening, | |
weight_decay=weight_decay, nesterov=nesterov, | |
max_change_per_layer=max_change_per_layer, | |
max_change=max_change) | |
if nesterov and (momentum <= 0 or dampening != 0): | |
raise ValueError("Nesterov momentum requires a momentum and zero dampening") | |
super(SGD_MC, self).__init__(params, defaults) | |
def __setstate__(self, state): | |
super(SGD_MC, self).__setstate__(state) | |
for group in self.param_groups: | |
group.setdefault('nesterov', False) | |
@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: | |
weight_decay = group['weight_decay'] | |
momentum = group['momentum'] | |
dampening = group['dampening'] | |
nesterov = group['nesterov'] | |
max_change_per_layer = group['max_change_per_layer'] | |
max_change = group['max_change'] | |
delta = [] | |
total_norm = 0 | |
for i in range(len(group['params'])): | |
p = group['params'][i] | |
if p.grad is None: | |
continue | |
d_p = p.grad | |
if weight_decay != 0: | |
d_p = d_p.add(p, alpha=weight_decay) | |
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_(d_p, alpha=1 - dampening) | |
if nesterov: | |
d_p = d_p.add(buf, alpha=momentum) | |
else: | |
d_p = buf | |
norm = d_p.norm(2).item() | |
if norm > max_change_per_layer: | |
d_p.mul_(max_change_per_layer / norm) | |
delta.append(d_p) | |
total_norm += d_p.norm(2).item() ** 2. | |
total_norm = total_norm ** 0.5 | |
for i in range(len(group['params'])): | |
p = group['params'][i] | |
if p.grad is None: | |
continue | |
if total_norm > max_change: | |
p.add_(delta[i], alpha=-group['lr'] * max_change / total_norm) | |
else: | |
p.add_(delta[i], alpha=-group['lr']) | |
return loss |
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