Source code for torch.optim.adadelta
import torch
from torch import Tensor
from .optimizer import (Optimizer, _use_grad_for_differentiable, _default_to_fused_or_foreach,
_differentiable_doc, _foreach_doc, _maximize_doc)
from typing import List, Optional
__all__ = ["Adadelta", "adadelta"]
[docs]class Adadelta(Optimizer):
def __init__(
self,
params,
lr=1.0,
rho=0.9,
eps=1e-6,
weight_decay=0,
foreach: Optional[bool] = None,
*,
maximize: bool = False,
differentiable: bool = False,
):
if not 0.0 <= lr:
raise ValueError(f"Invalid learning rate: {lr}")
if not 0.0 <= rho <= 1.0:
raise ValueError(f"Invalid rho value: {rho}")
if not 0.0 <= eps:
raise ValueError(f"Invalid epsilon value: {eps}")
if not 0.0 <= weight_decay:
raise ValueError(f"Invalid weight_decay value: {weight_decay}")
defaults = dict(
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
foreach=foreach,
differentiable=differentiable,
)
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)
def _init_group(self, group, params_with_grad, grads, square_avgs, acc_deltas):
for p in group["params"]:
if p.grad is None:
continue
params_with_grad.append(p)
if p.grad.is_sparse:
raise RuntimeError("Adadelta does not support sparse gradients")
grads.append(p.grad)
state = self.state[p]
# Lazy state initialization
if len(state) == 0:
state["step"] = 0
state["square_avg"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
state["acc_delta"] = torch.zeros_like(
p, memory_format=torch.preserve_format
)
square_avgs.append(state["square_avg"])
acc_deltas.append(state["acc_delta"])
state["step"] += 1
[docs] @_use_grad_for_differentiable
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 = []
square_avgs = []
acc_deltas = []
lr, rho, eps, weight_decay, foreach, maximize, differentiable = (
group["lr"],
group["rho"],
group["eps"],
group["weight_decay"],
group["foreach"],
group["maximize"],
group["differentiable"],
)
self._init_group(group, params_with_grad, grads, square_avgs, acc_deltas)
adadelta(
params_with_grad,
grads,
square_avgs,
acc_deltas,
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
foreach=foreach,
maximize=maximize,
differentiable=differentiable,
)
return loss
Adadelta.__doc__ = r"""Implements Adadelta algorithm.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{ (lr)}, \: \theta_0 \text{ (params)},
\: f(\theta) \text{ (objective)}, \: \rho \text{ (decay)},
\: \lambda \text{ (weight decay)} \\
&\textbf{initialize} : v_0 \leftarrow 0 \: \text{ (square avg)},
\: u_0 \leftarrow 0 \: \text{ (accumulate variables)} \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}if \: \lambda \neq 0 \\
&\hspace{10mm} g_t \leftarrow g_t + \lambda \theta_{t-1} \\
&\hspace{5mm} v_t \leftarrow v_{t-1} \rho + g^2_t (1 - \rho) \\
&\hspace{5mm}\Delta x_t \leftarrow \frac{\sqrt{u_{t-1} +
\epsilon }}{ \sqrt{v_t + \epsilon} }g_t \hspace{21mm} \\
&\hspace{5mm} u_t \leftarrow u_{t-1} \rho +
\Delta x^2_t (1 - \rho) \\
&\hspace{5mm}\theta_t \leftarrow \theta_{t-1} - \gamma \Delta x_t \\
&\rule{110mm}{0.4pt} \\[-1.ex]
&\bf{return} \: \theta_t \\[-1.ex]
&\rule{110mm}{0.4pt} \\[-1.ex]
\end{aligned}
For further details regarding the algorithm we refer to `ADADELTA: An Adaptive Learning Rate Method`_.
""" + fr"""
Args:
params (iterable): iterable of parameters to optimize or dicts defining
parameter groups
rho (float, optional): coefficient used for computing a running average
of squared gradients (default: 0.9)
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-6)
lr (float, optional): coefficient that scale delta before it is applied
to the parameters (default: 1.0)
weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
{_foreach_doc}
{_maximize_doc}
{_differentiable_doc}
.. _ADADELTA\: An Adaptive Learning Rate Method:
https://arxiv.org/abs/1212.5701
"""
def adadelta(
params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: 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,
differentiable: bool = False,
*,
lr: float,
rho: float,
eps: float,
weight_decay: float,
maximize: bool,
):
r"""Functional API that performs Adadelta algorithm computation.
See :class:`~torch.optim.Adadelta` for details.
"""
# We still respect when the user inputs False for foreach.
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_adadelta
else:
func = _single_tensor_adadelta
func(
params,
grads,
square_avgs,
acc_deltas,
lr=lr,
rho=rho,
eps=eps,
weight_decay=weight_decay,
maximize=maximize,
differentiable=differentiable,
)
def _single_tensor_adadelta(
params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: List[Tensor],
*,
lr: float,
rho: float,
eps: float,
weight_decay: float,
maximize: bool,
differentiable: bool,
):
for (param, grad, square_avg, acc_delta) in zip(
params, grads, square_avgs, acc_deltas
):
grad = grad if not maximize else -grad
if weight_decay != 0:
grad = grad.add(param, alpha=weight_decay)
if torch.is_complex(param):
square_avg = torch.view_as_real(square_avg)
acc_delta = torch.view_as_real(acc_delta)
grad = torch.view_as_real(grad)
square_avg.mul_(rho).addcmul_(grad, grad, value=1 - rho)
std = square_avg.add(eps).sqrt_()
delta = acc_delta.add(eps).sqrt_()
if differentiable:
delta = delta.clone()
delta.div_(std).mul_(grad)
acc_delta.mul_(rho).addcmul_(delta, delta, value=1 - rho)
if torch.is_complex(param):
delta = torch.view_as_complex(delta)
param.add_(delta, alpha=-lr)
def _multi_tensor_adadelta(
params: List[Tensor],
grads: List[Tensor],
square_avgs: List[Tensor],
acc_deltas: List[Tensor],
*,
lr: float,
weight_decay: float,
rho: float,
eps: float,
maximize: bool,
differentiable: bool,
):
assert not differentiable, "_foreach ops don't support autograd"
if len(params) == 0:
return
grouped_tensors = Optimizer._group_tensors_by_device_and_dtype([params, grads, square_avgs, acc_deltas])
for ((device_params, device_grads, device_square_avgs, device_acc_deltas), _) in grouped_tensors.values():
if maximize:
device_grads = torch._foreach_neg(device_grads)
if weight_decay != 0:
# Re-use the intermediate memory (device_grads) already allocated for maximize
if maximize:
torch._foreach_add_(device_grads, device_params, alpha=weight_decay)
else:
device_grads = torch._foreach_add(device_grads, device_params, alpha=weight_decay)
torch._foreach_mul_(device_square_avgs, rho)
torch._foreach_addcmul_(device_square_avgs, device_grads, device_grads, value=1 - rho)
std = torch._foreach_add(device_square_avgs, eps)
torch._foreach_sqrt_(std)
deltas = torch._foreach_add(device_acc_deltas, eps)
torch._foreach_sqrt_(deltas)
torch._foreach_div_(deltas, std)
torch._foreach_mul_(deltas, device_grads)
torch._foreach_add_(device_params, deltas, alpha=-lr)
torch._foreach_mul_(device_acc_deltas, rho)
torch._foreach_addcmul_(device_acc_deltas, deltas, deltas, value=1 - rho)
