import math
import torch
from torch import Tensor
from .optimizer import Optimizer
from typing import List, Optional
__all__ = ['ASGD', 'asgd']
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)
foreach (bool, optional): whether foreach implementation of optimizer
is used (default: None)
maximize (bool, optional): maximize the params based on the objective, instead of
minimizing (default: False)
.. _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,
foreach: Optional[bool] = None, maximize: bool = False):
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, foreach=foreach, maximize=maximize)
super(ASGD, self).__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault('foreach', None)
group.setdefault('maximize', False)
state_values = list(self.state.values())
step_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['step'])
if not step_is_tensor:
for s in state_values:
s['step'] = torch.tensor(float(s['step']))
eta_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['eta'])
if not eta_is_tensor:
for s in state_values:
s['eta'] = torch.tensor(s['eta'])
mu_is_tensor = (len(state_values) != 0) and torch.is_tensor(state_values[0]['mu'])
if not mu_is_tensor:
for s in state_values:
s['mu'] = torch.tensor(float(s['mu']))
@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:
params_with_grad = []
grads = []
mus = []
axs = []
etas = []
state_steps = []
for p in group['params']:
if p.grad is not None:
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError('ASGD does not support sparse gradients')
grads.append(p.grad)
state = self.state[p]
# State initialization
if len(state) == 0:
state['step'] = torch.tensor(0.)
state['eta'] = torch.tensor(group['lr'])
state['mu'] = torch.tensor(1.)
state['ax'] = torch.zeros_like(p, memory_format=torch.preserve_format)
mus.append(state['mu'])
axs.append(state['ax'])
etas.append(state['eta'])
state_steps.append(state['step'])
asgd(params_with_grad,
grads,
axs,
mus,
etas,
state_steps,
lambd=group['lambd'],
lr=group['lr'],
t0=group['t0'],
alpha=group['alpha'],
weight_decay=group['weight_decay'],
foreach=group['foreach'],
maximize=group['maximize'])
return loss
def asgd(params: List[Tensor],
grads: List[Tensor],
axs: List[Tensor],
mus: List[Tensor],
etas: List[Tensor],
state_steps: List[Tensor],
# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
foreach: bool = None,
maximize: bool = False,
*,
lambd: float,
lr: float,
t0: float,
alpha: float,
weight_decay: float):
r"""Functional API that performs asgd algorithm computation.
See :class:`~torch.optim.ASGD` for details.
"""
if foreach is None:
# Placeholder for more complex foreach logic to be added when value is not set
foreach = False
if foreach and torch.jit.is_scripting():
raise RuntimeError('torch.jit.script not supported with foreach optimizers')
if foreach and not torch.jit.is_scripting():
func = _multi_tensor_asgd
else:
func = _single_tensor_asgd
func(params,
grads,
axs,
mus,
etas,
state_steps,
lambd=lambd,
lr=lr,
t0=t0,
alpha=alpha,
weight_decay=weight_decay,
maximize=maximize)
def _single_tensor_asgd(params: List[Tensor],
grads: List[Tensor],
axs: List[Tensor],
mus: List[Tensor],
etas: List[Tensor],
state_steps: List[Tensor],
*,
lambd: float,
lr: float,
t0: float,
alpha: float,
weight_decay: float,
maximize: bool):
for i, param in enumerate(params):
grad = grads[i]
grad = grad if not maximize else -grad
mu = mus[i]
ax = axs[i]
eta = etas[i]
step_t = state_steps[i]
if torch.is_complex(param):
grad = torch.view_as_real(grad)
param = torch.view_as_real(param)
ax = torch.view_as_real(ax)
# update step
step_t += 1
step = step_t.item()
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
# decay term
param.mul_(1 - lambd * eta.item())
# update parameter
param.add_(grad, alpha=-eta.item())
# averaging
if mu.item() != 1:
ax.add_(param.sub(ax).mul(mu))
else:
ax.copy_(param)
new_eta = torch.tensor(lr / math.pow((1 + lambd * lr * step), alpha))
eta.copy_(new_eta)
new_mu = torch.tensor(1 / max(1, step - t0))
mu.copy_(new_mu)
def _multi_tensor_asgd(params: List[Tensor],
grads: List[Tensor],
axs: List[Tensor],
mus: List[Tensor],
etas: List[Tensor],
state_steps: List[Tensor],
*,
lambd: float,
lr: float,
t0: float,
alpha: float,
weight_decay: float,
maximize: bool):
if len(params) == 0:
return
if maximize:
grads = torch._foreach_neg(grads)
def _view_complex_as_real(tensor_list):
return [torch.view_as_real(t) if torch.is_complex(t) else t for t in tensor_list]
grads = _view_complex_as_real(grads)
params = _view_complex_as_real(params)
axs = _view_complex_as_real(axs)
# update step
torch._foreach_add_(state_steps, 1)
if weight_decay != 0:
grads = torch._foreach_add(grads, params, alpha=weight_decay)
# decay term
eta = etas[0].item()
torch._foreach_mul_(params, 1 - lambd * eta)
# update parameter
torch._foreach_add_(params, grads, alpha=-eta)
# averaging
for i in range(len(axs)):
if mus[i].item() != 1:
axs[i].add_(params[i].sub(axs[i]).mul(mus[i]))
else:
axs[i].copy_(params[i])
# update eta and mu
for i in range(len(mus)):
new_eta = torch.tensor(lr / math.pow((1 + lambd * lr * state_steps[i].item()), alpha))
etas[i].copy_(new_eta)
new_mu = torch.tensor(1 / max(1, state_steps[i].item() - t0))
mus[i].copy_(new_mu)