/usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor
Edit: /usr/local/lib64/python3.6/site-packages/torch/optim/_multi_tensor/asgd.py (3881B)
import torch
from . import _functional as F
from ..optimizer import Optimizer
from collections import defaultdict
class ASGD(Optimizer):
"""Implements Averaged Stochastic Gradient Descent.
It has been proposed in `Acceleration of stochastic approximation by
averaging`_.
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
lr (float, optional): learning rate (default: 1e-2)
lambd (float, optional): decay term (default: 1e-4)
alpha (float, optional): power for eta update (default: 0.75)
t0 (float, optional): point at which to start averaging (default: 1e6)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
.. _Acceleration of stochastic approximation by averaging:
https://dl.acm.org/citation.cfm?id=131098
"""
def __init__(self, params, lr=1e-2, lambd=1e-4, alpha=0.75, t0=1e6, weight_decay=0):
if not 0.0 <= lr:
raise ValueError("Invalid learning rate: {}".format(lr))
if not 0.0 <= weight_decay:
raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
defaults = dict(lr=lr, lambd=lambd, alpha=alpha, t0=t0,
weight_decay=weight_decay)
super(ASGD, self).__init__(params, defaults)
@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()
grads = []
params_with_grad = []
states = []
for group in self.param_groups:
for p in group['params']:
if p.grad is not None:
if p.grad.is_sparse:
raise RuntimeError('ASGD 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['eta'] = group['lr']
state['mu'] = 1
state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)
state['step'] += 1
states.append(state)
F.asgd(params_with_grad,
grads,
states,
lambd=group['lambd'],
lr=group['lr'],
t0=group['t0'],
alpha=group['alpha'],
weight_decay=group['weight_decay'])
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)