torch.optim.asgd — PyTorch 2.7 documentation (original) (raw)
mypy: allow-untyped-defs
from typing import cast, Optional, Union
import torch from torch import Tensor
from .optimizer import ( _capturable_doc, _default_to_fused_or_foreach, _differentiable_doc, _disable_dynamo_if_unsupported, _foreach_doc, _get_capturable_supported_devices, _get_scalar_dtype, _get_value, _maximize_doc, _params_doc, _use_grad_for_differentiable, _view_as_real, Optimizer, ParamsT, )
all = ["ASGD", "asgd"]
[docs]class ASGD(Optimizer): def init( self, params: ParamsT, lr: Union[float, Tensor] = 1e-2, lambd: float = 1e-4, alpha: float = 0.75, t0: float = 1e6, weight_decay: float = 0, foreach: Optional[bool] = None, maximize: bool = False, differentiable: bool = False, capturable: bool = False, ): if isinstance(lr, Tensor) and lr.numel() != 1: raise ValueError("Tensor lr must be 1-element") 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}")
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)
for p in group["params"]:
p_state = self.state.get(p, [])
if len(p_state) != 0:
if not torch.is_tensor(p_state["step"]):
step_val = float(p_state["step"])
p_state["step"] = torch.tensor(
step_val, dtype=_get_scalar_dtype(), device=p.device
)
if not torch.is_tensor(p_state["eta"]):
p_state["eta"] = torch.tensor(
p_state["eta"], dtype=_get_scalar_dtype(), device=p.device
)
if not torch.is_tensor(p_state["mu"]):
p_state["mu"] = torch.tensor(
p_state["mu"], dtype=_get_scalar_dtype(), device=p.device
)
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=_get_scalar_dtype()
)
state["eta"] = (
torch.as_tensor(
group["lr"], device=p.device, dtype=_get_scalar_dtype()
)
.clone()
.detach()
)
state["mu"] = torch.ones(
(), device=p.device, dtype=_get_scalar_dtype()
)
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[docs] @_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.
"""
self._cuda_graph_capture_health_check()
loss = None
if closure is not None:
with torch.enable_grad():
loss = closure()
for group in self.param_groups:
params_with_grad: list[Tensor] = []
grads: list[Tensor] = []
mus: list[Tensor] = []
axs: list[Tensor] = []
etas: list[Tensor] = []
state_steps: list[Tensor] = []
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 lossASGD.doc = rf"""Implements Averaged Stochastic Gradient Descent.
It has been proposed in `Acceleration of stochastic approximation by
averaging`_.
Args:
{_params_doc}
lr (float, Tensor, 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}
.. _Acceleration of stochastic approximation by averaging:
https://dl.acm.org/citation.cfm?id=131098
"""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 compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch.compiler.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices()
assert (
param.device.type
== mu.device.type
== eta.device.type
== step_t.device.type
and param.device.type in capturable_supported_devices
), (
f"If capturable=True, params, mus, etas, and state_steps must be "
f"on supported devices: {capturable_supported_devices}."
)
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
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
if capturable:
param.mul_(1 - lambd * eta)
param.addcmul_(grad, eta, value=-1) # update parameter
else:
eta_value = _get_value(eta)
param.mul_(1 - lambd * eta_value) # decay term
param.add_(grad, alpha=-eta_value) # update parameter
# averaging
if capturable or mu.item() != 1:
ax.add_(param.sub(ax).mul_(mu))
else:
ax.copy_(param)
if capturable:
eta.copy_(lr / ((1 + lambd * lr * step_t) ** alpha))
mu.copy_(1 / torch.maximum(step_t - t0, torch.ones_like(step_t)))
else:
step = _get_value(step_t)
new_eta = torch.as_tensor(lr / ((1 + lambd * lr * step) ** alpha))
eta.copy_(new_eta)
new_mu = torch.as_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"
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
if not torch.compiler.is_compiling() and capturable:
capturable_supported_devices = _get_capturable_supported_devices(
supports_xla=False
)
assert all(
p.device.type == mu.device.type == eta.device.type == step.device.type
and p.device.type in capturable_supported_devices
for p, mu, eta, step in zip(params, mus, etas, state_steps)
), f"If capturable=True, params, mus, etas, and state_steps must be on supported devices: {capturable_supported_devices}."
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype(
[params, grads, axs, mus, etas, state_steps] # type: ignore[list-item]
)
for (device, _), (
(
grouped_params_,
grouped_grads_,
grouped_axs_,
grouped_mus_,
grouped_etas_,
grouped_state_steps_,
),
_,
) in grouped_tensors.items():
grouped_params = cast(list[Tensor], grouped_params_)
grouped_grads = cast(list[Tensor], grouped_grads_)
grouped_axs = cast(list[Tensor], grouped_axs_)
grouped_mus = cast(list[Tensor], grouped_mus_)
grouped_etas = cast(list[Tensor], grouped_etas_)
grouped_state_steps = cast(list[Tensor], grouped_state_steps_)
if has_complex:
_view_as_real(grouped_params, grouped_grads, grouped_axs)
if maximize:
grouped_grads = torch._foreach_neg(grouped_grads) # type: ignore[assignment]
# 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 not torch.compiler.is_compiling() and 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
intermediate: Union[tuple[Tensor, ...], list[Tensor]]
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
new_etas: Union[tuple[Tensor, ...], list[Tensor]]
new_mus: Union[tuple[Tensor, ...], list[Tensor]]
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_mul(grouped_state_steps, lambd)
torch._foreach_mul_(new_etas, lr)
torch._foreach_add_(new_etas, 1)
torch._foreach_pow_(new_etas, alpha)
torch._foreach_reciprocal_(new_etas)
torch._foreach_mul_(new_etas, lr)
torch._foreach_copy_(grouped_etas, new_etas)
else:
new_etas = [
torch.as_tensor(lr / ((1 + lambd * lr * step) ** alpha), device=device)
for step in grouped_state_steps
]
new_mus = [
torch.as_tensor(1 / max(1, _get_value(step) - t0), device=device)
for step in grouped_state_steps
]
torch._foreach_copy_(grouped_etas, new_etas)
torch._foreach_copy_(grouped_mus, new_mus)@_disable_dynamo_if_unsupported(single_tensor_fn=_single_tensor_asgd) 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:
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,
)