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import torch
from ..optimizer import Optimizer, required
class SGD(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):
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)
if nesterov and (momentum <= 0 or dampening != 0):
raise ValueError("Nesterov momentum requires a momentum and zero dampening")
super(SGD, self).__init__(params, defaults)
def __setstate__(self, state):
super(SGD, 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']
grads = []
params_with_grad = []
states = []
has_sparse_grad = False
for p in group['params']:
if p.grad is not None:
grads.append(p.grad)
params_with_grad.append(p)
states.append(self.state[p])
if p.grad.is_sparse:
has_sparse_grad = True
if momentum != 0:
raise RuntimeError('SGD does not support momentum for sparse gradients')
if grads == []:
return loss
if weight_decay != 0:
grads = torch._foreach_add(grads, params_with_grad, alpha=weight_decay)
if momentum != 0:
bufs = []
all_states_with_momentum_buffer = True
for i in range(len(states)):
if 'momentum_buffer' not in states[i]:
all_states_with_momentum_buffer = False
break
else:
bufs.append(states[i]['momentum_buffer'])
if all_states_with_momentum_buffer:
torch._foreach_mul_(bufs, momentum)
torch._foreach_add_(bufs, grads, alpha=1 - dampening)
else:
bufs = []
for i in range(len(states)):
if 'momentum_buffer' not in states[i]:
buf = states[i]['momentum_buffer'] = torch.clone(grads[i]).detach()
else:
buf = states[i]['momentum_buffer']
buf.mul_(momentum).add_(grads[i], alpha=1 - dampening)
bufs.append(buf)
if nesterov:
torch._foreach_add_(grads, bufs, alpha=momentum)
else:
grads = bufs
if not has_sparse_grad:
torch._foreach_add_(params_with_grad, grads, alpha=-group['lr'])
else:
# foreach APIs dont support sparse
for i in range(len(params_with_grad)):
params_with_grad[i].add_(grads[i], alpha=-group['lr'])
return loss
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