397 lines
14 KiB
Python
397 lines
14 KiB
Python
|
import torch
|
||
|
|
from torch import Tensor
|
||
|
|
|
||
|
|
from .optimizer import (Optimizer, _use_grad_for_differentiable, _get_value, _default_to_fused_or_foreach,
|
||
|
|
_get_scalar_dtype, _view_as_real, _differentiable_doc, _foreach_doc, _maximize_doc,
|
||
|
|
_capturable_doc)
|
||
|
|
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}")
|
||
|
|
|
||
|
|
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.tensor(group["lr"], device=p.device, dtype=_get_scalar_dtype())
|
||
|
|
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
|
||
|
|
|
||
|
|
@_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 = []
|
||
|
|
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}
|
||
|
|
|
||
|
|
.. _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:
|
||
|
|
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 compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
|
||
|
|
if not torch._utils.is_compiling() and capturable:
|
||
|
|
assert (param.is_cuda and mu.is_cuda and eta.is_cuda and step_t.is_cuda) or (
|
||
|
|
param.is_xla and mu.is_xla and eta.is_xla and step_t.is_xla
|
||
|
|
), "If capturable=True, params, mus, etas, and state_steps must be CUDA or XLA tensors."
|
||
|
|
|
||
|
|
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 = _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"
|
||
|
|
|
||
|
|
# If compiling, the compiler will handle cudagraph checks, see note [torch.compile x capturable]
|
||
|
|
if not torch._utils.is_compiling() and capturable:
|
||
|
|
assert all(p.is_cuda and mu.is_cuda and eta.is_cuda and step.is_cuda
|
||
|
|
for p, mu, eta, step in zip(params, mus, etas, state_steps)), \
|
||
|
|
"If capturable=True, params, mus, etas, and state_steps must be CUDA tensors."
|
||
|
|
|
||
|
|
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 has_complex:
|
||
|
|
_view_as_real(grouped_params, grouped_grads, grouped_axs)
|
||
|
|
|
||
|
|
if maximize:
|
||
|
|
grouped_grads = torch._foreach_neg(grouped_grads)
|
||
|
|
|
||
|
|
# 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)
|