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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, _view_as_real) | |
from typing import List, Optional | |
__all__ = ["Adadelta", "adadelta"] | |
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): | |
has_complex = False | |
for p in group["params"]: | |
if p.grad is None: | |
continue | |
has_complex |= torch.is_complex(p) | |
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 | |
return has_complex | |
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 = [] | |
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"], | |
) | |
has_complex = 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, | |
has_complex=has_complex, | |
) | |
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). A higher value of `rho` will | |
result in a slower average, which can be helpful for preventing | |
oscillations in the learning process. | |
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, | |
has_complex: 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, | |
has_complex=has_complex, | |
) | |
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, | |
has_complex: 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, | |
has_complex: 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 has_complex: | |
_view_as_real(device_params, device_grads, device_square_avgs, device_acc_deltas) | |
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) | |