/usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor
Edit: /usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor/rmsprop.py (6280B)
import torch
from ..optimizer import Optimizer
from collections import defaultdict
class RMSprop(Optimizer):
r"""Implements RMSprop algorithm.
Proposed by G. Hinton in his
`course
`_.
The centered version first appears in `Generating Sequences
With Recurrent Neural Networks `_.
The implementation here takes the square root of the gradient average before
adding epsilon (note that TensorFlow interchanges these two operations). The effective
learning rate is thus :math:`\alpha/(\sqrt{v} + \epsilon)` where :math:`\alpha`
is the scheduled learning rate and :math:`v` is the weighted moving average
of the squared gradient.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
momentum (float, optional): momentum factor (default: 0)
alpha (float, optional): smoothing constant (default: 0.99)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
centered (bool, optional) : if ``True``, compute the centered RMSProp,
the gradient is normalized by an estimation of its variance
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
"""
def __init__(self, params, lr=1e-2, alpha=0.99, eps=1e-8, weight_decay=0, momentum=0, centered=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 <= momentum:
raise ValueError("Invalid momentum value: {}".format(momentum))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
if not 0.0 <= alpha:
raise ValueError("Invalid alpha value: {}".format(alpha))
defaults = dict(lr=lr, momentum=momentum, alpha=alpha, eps=eps, centered=centered, weight_decay=weight_decay)
super(RMSprop, self).__init__(params, defaults)
def __setstate__(self, state):
super(RMSprop, self).__setstate__(state)
for group in self.param_groups:
group.setdefault('momentum', 0)
group.setdefault('centered', False)
@torch.no_grad()
def step(self, closure=None):
"""Performs a single optimization step.
Args:
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 = []
alpha = group['alpha']
square_avg = []
for p in group['params']:
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError('RMSprop 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['square_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
if group['momentum'] > 0:
state['momentum_buffer'] = torch.zeros_like(p, memory_format=torch.preserve_format)
if group['centered']:
state['grad_avg'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['step'] += 1
states.append(state)
square_avg.append(state['square_avg'])
if group['weight_decay'] != 0:
torch._foreach_add_(grads, params_with_grad, alpha=group['weight_decay'])
torch._foreach_mul_(square_avg, alpha)
torch._foreach_addcmul_(square_avg, grads, grads, value=1 - alpha)
if group['centered']:
grad_avgs = [s['grad_avg'] for s in states]
torch._foreach_mul_(grad_avgs, alpha)
torch._foreach_add_(grad_avgs, grads, alpha=1 - alpha)
avg = torch._foreach_addcmul(square_avg, grad_avgs, grad_avgs, value=-1)
torch._foreach_sqrt_(avg)
torch._foreach_add_(avg, group['eps'])
else:
avg = torch._foreach_sqrt(square_avg)
torch._foreach_add_(avg, group['eps'])
if group['momentum'] > 0:
buf = [s['momentum_buffer'] for s in states]
torch._foreach_mul_(buf, group['momentum'])
torch._foreach_addcdiv_(buf, grads, avg)
torch._foreach_add_(params_with_grad, buf, alpha=-group['lr'])
else:
torch._foreach_addcdiv_(params_with_grad, grads, avg, value=-group['lr'])
return loss
# TODO: refactor to a base class once foreach ops are in a good shape.
def zero_grad(self, set_to_none: bool = False):
per_device_and_dtype_grads = defaultdict(lambda: defaultdict(list))
for group in self.param_groups:
for p in group['params']:
if p.grad is not None:
if set_to_none:
p.grad = None
else:
if p.grad.grad_fn is not None:
p.grad.detach_()
else:
p.grad.requires_grad_(False)
if p.grad.is_sparse:
p.grad.zero_()
else:
per_device_and_dtype_grads[p.grad.device][p.grad.dtype].append(p.grad)
for _, per_dtype_grads in per_device_and_dtype_grads.items():
for grads in per_dtype_grads.values():
torch._foreach_zero_(grads)