import torch
from torch import Tensor
from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _default_to_fused_or_foreach,
_differentiable_doc, _foreach_doc, _maximize_doc, _capturable_doc, _view_as_real)
from torch._utils import is_compiling
from typing import List, Optional
__all__ = ["ASGD", "asgd"]
def _to_tensor(x, device=None):
if not isinstance(x, torch.Tensor):
return torch.tensor(x, device=device)
return x
class ASGD(Optimizer):
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,
differentiable: bool = False,
capturable: bool = False,
):
if not 0.0 <= lr:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
if foreach is False and capturable:
raise ValueError("Capturable not supported with single tensor ASGD")
defaults = dict(
lr=lr,
lambd=lambd,
alpha=alpha,
t0=t0,
weight_decay=weight_decay,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
capturable=capturable,
)
super().__init__(params, defaults)
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group.setdefault("foreach", None)
group.setdefault("maximize", False)
group.setdefault("differentiable", False)
group.setdefault("capturable", 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"]), dtype=torch.float32)
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"], dtype=torch.float32)
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"]), dtype=torch.float32)
def _init_group(self, group, params_with_grad, grads, mus, axs, etas, state_steps):
has_complex = False
for p in group["params"]:
if p.grad is not None:
has_complex |= torch.is_complex(p)
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.zeros((), device=p.device, dtype=torch.float32)
state["eta"] = torch.tensor(group["lr"], device=p.device, dtype=torch.float32)
state["mu"] = torch.ones((), device=p.device, dtype=torch.float32)
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"])
return has_complex
@_use_grad_for_differentiable
def step(self, closure=None):
"""Perform 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 = []
has_complex = self._init_group(group, params_with_grad, grads, mus, axs, etas, state_steps)
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"],
differentiable=group["differentiable"],
capturable=group["capturable"],
has_complex=has_complex,
)
return loss
ASGD.__doc__ = fr"""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_doc}
{_maximize_doc}
{_differentiable_doc}
{_capturable_doc} For ASGD, capturable is only supported when foreach is True.
.. _Acceleration of stochastic approximation by averaging:
https://dl.acm.org/citation.cfm?id=131098
"""
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: Optional[bool] = None,
maximize: bool = False,
differentiable: bool = False,
capturable: bool = False,
has_complex: 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:
_, foreach = _default_to_fused_or_foreach(params, differentiable, use_fused=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:
if capturable and not is_compiling():
raise RuntimeError("Capturable not supported with single tensor ASGD")
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,
differentiable=differentiable,
capturable=capturable,
has_complex=has_complex,
)
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,
differentiable: bool,
capturable: bool,
has_complex: 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 = _get_value(step_t)
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
eta_value = _get_value(eta)
# decay term
param.mul_(1 - lambd * eta_value)
# update parameter
param.add_(grad, alpha=-eta_value)
# averaging
if is_compiling() or mu.item() != 1:
ax.add_(param.sub(ax).mul(mu))
else:
ax.copy_(param)
new_eta = _to_tensor(lr / ((1 + lambd * lr * step) ** alpha))
eta.copy_(new_eta)
new_mu = _to_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,
differentiable: bool,
capturable: bool,
has_complex: bool,
):
if len(params) == 0:
return
assert not differentiable, "_foreach ops don't support autograd"
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, axs, mus, etas, state_steps])
for ((device, _), ((grouped_params, grouped_grads, grouped_axs, grouped_mus,
grouped_etas, grouped_state_steps), _)) in grouped_tensors.items():
if maximize:
grouped_grads = torch._foreach_neg(grouped_grads)
grouped_grads = list(grouped_grads)
if has_complex:
_view_as_real(grouped_params, grouped_grads, grouped_axs)
# Update steps
# If steps are on CPU, foreach will fall back to the slow path, which is a for-loop calling t.add(1) over
# and over. 1 will then be wrapped into a Tensor over and over again, which is slower than if we just
# wrapped it once now. The alpha is required to assure we go to the right overload.
if grouped_state_steps[0].is_cpu:
torch._foreach_add_(grouped_state_steps, torch.tensor(1.0, device='cpu'), alpha=1.0)
else:
torch._foreach_add_(grouped_state_steps, 1)
# intermediate = grad + param * lambd
if weight_decay != 0:
if maximize:
torch._foreach_add_(grouped_grads, grouped_params, alpha=weight_decay)
intermediate = grouped_grads
else:
intermediate = torch._foreach_add(grouped_grads, grouped_params, alpha=weight_decay)
torch._foreach_add_(intermediate, grouped_params, alpha=lambd)
else:
intermediate = torch._foreach_add(grouped_grads, grouped_params, alpha=lambd)
# update param
# param * (1 - lambd * eta) - eta * grad
# => param - param * lambd * eta - eta * grad
# => param - eta * intermediate
torch._foreach_addcmul_(grouped_params, intermediate, grouped_etas, value=-1)
del intermediate
# update grouped_axs
# averaging: ax = ax + mu * (param - ax)
# Note (mlazos): We can't use lerp here since it requires weight to be float64
# and our grouping code requires dtypes to match for all tensors in a group (and it should, since
# we use the mus in other places)
# all dtypes need to match, so we could introduce a cast in a loop
# but since this only adds one additional kernel launch, this looks like the cleaner
# and faster solution
intermediate = torch._foreach_sub(grouped_params, grouped_axs)
torch._foreach_addcmul_(grouped_axs, intermediate, grouped_mus)
del intermediate
if capturable:
# update grouped_mus
new_mus = torch._foreach_sub(grouped_state_steps, t0)
torch._foreach_maximum_(new_mus, 1.0)
torch._foreach_reciprocal_(new_mus)
torch._foreach_copy_(grouped_mus, new_mus)
del new_mus
# update eta = lr / (1 + lambd * lr * step^alpha)
new_etas = torch._foreach_pow(grouped_state_steps, alpha)
torch._foreach_mul_(new_etas, lambd)
torch._foreach_mul_(new_etas, lr)
torch._foreach_add_(new_etas, 1)
torch._foreach_reciprocal_(new_etas)
torch._foreach_mul_(new_etas, lr)
torch._foreach_copy_(grouped_etas, new_etas)
else:
step = grouped_state_steps[0].item()
new_etas = []
new_mus = []
for i in range(len(grouped_mus)):
new_eta = _to_tensor(
lr / (1 + lambd * lr * step ** alpha), device=device
)
new_etas.append(new_eta)
new_mu = _to_tensor(1 / max(1, step - t0), device=device)
new_mus.append(new_mu)
torch._foreach_copy_(grouped_etas, new_etas)
torch._foreach_copy_(grouped_mus, new_mus)