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# mypy: allow-untyped-defs
from typing import Optional, Union
from torch import Tensor
from .adam import Adam, adam
from .optimizer import (
_capturable_doc,
_differentiable_doc,
_foreach_doc,
_fused_doc,
_maximize_doc,
_params_doc,
ParamsT,
)
__all__ = ["AdamW", "adamw"]
class AdamW(Adam):
def __init__(
self,
params: ParamsT,
lr: Union[float, Tensor] = 1e-3,
betas: tuple[Union[float, Tensor], Union[float, Tensor]] = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 1e-2,
amsgrad: bool = False,
*,
maximize: bool = False,
foreach: Optional[bool] = None,
capturable: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
):
super().__init__(
params,
lr,
betas,
eps,
weight_decay,
amsgrad,
foreach=foreach,
maximize=maximize,
capturable=capturable,
differentiable=differentiable,
fused=fused,
decoupled_weight_decay=True,
)
# Preserve decoupled_weight_decay from AdamW for backwards compatibility. The following
# guarantees that decoupled_weight_decay will always be True for loading any state into
# AdamW
def __setstate__(self, state):
super().__setstate__(state)
for group in self.param_groups:
group["decoupled_weight_decay"] = True
AdamW.__doc__ = (
r"""Implements AdamW algorithm, where weight decay does not accumulate in the momentum nor variance.
.. math::
\begin{aligned}
&\rule{110mm}{0.4pt} \\
&\textbf{input} : \gamma \text{(lr)}, \: \beta_1, \beta_2
\text{(betas)}, \: \theta_0 \text{(params)}, \: f(\theta) \text{(objective)},
\: \epsilon \text{ (epsilon)} \\
&\hspace{13mm} \lambda \text{(weight decay)}, \: \textit{amsgrad},
\: \textit{maximize} \\
&\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0
\text{ ( second moment)}, \: v_0^{max}\leftarrow 0 \\[-1.ex]
&\rule{110mm}{0.4pt} \\
&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
&\hspace{5mm}\textbf{if} \: \textit{maximize}: \\
&\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm}\textbf{else} \\
&\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
&\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\
&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
&\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
&\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
&\hspace{5mm}\textbf{if} \: amsgrad \\
&\hspace{10mm} v_t^{max} \leftarrow \mathrm{max}(v_{t-1}^{max},v_t) \\
&\hspace{10mm}\widehat{v_t} \leftarrow v_t^{max}/\big(1-\beta_2^t \big) \\
&\hspace{5mm}\textbf{else} \\
&\hspace{10mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
&\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
&\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 `Decoupled Weight Decay Regularization`_.
"""
+ rf"""
Args:
{_params_doc}
lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
is not yet supported for all our implementations. Please use a float
LR if you are not also specifying fused=True or capturable=True.
betas (Tuple[float, float], optional): coefficients used for computing
running averages of gradient and its square (default: (0.9, 0.999))
eps (float, optional): term added to the denominator to improve
numerical stability (default: 1e-8)
weight_decay (float, optional): weight decay coefficient (default: 1e-2)
amsgrad (bool, optional): whether to use the AMSGrad variant of this
algorithm from the paper `On the Convergence of Adam and Beyond`_
(default: False)
{_maximize_doc}
{_foreach_doc}
{_capturable_doc}
{_differentiable_doc}
{_fused_doc}
.. Note::
A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`.
.. _Decoupled Weight Decay Regularization:
https://arxiv.org/abs/1711.05101
.. _On the Convergence of Adam and Beyond:
https://openreview.net/forum?id=ryQu7f-RZ
"""
)
# @_disable_dynamo_if_unsupported logic occurs in the decorator that's applied to F.adam
def adamw(
params: list[Tensor],
grads: list[Tensor],
exp_avgs: list[Tensor],
exp_avg_sqs: list[Tensor],
max_exp_avg_sqs: 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,
capturable: bool = False,
differentiable: bool = False,
fused: Optional[bool] = None,
grad_scale: Optional[Tensor] = None,
found_inf: Optional[Tensor] = None,
has_complex: bool = False,
*,
amsgrad: bool,
beta1: float,
beta2: float,
lr: Union[float, Tensor],
weight_decay: float,
eps: float,
maximize: bool,
):
r"""Functional API that performs AdamW algorithm computation.
See :class:`~torch.optim.AdamW` for details.
"""
adam(
params,
grads,
exp_avgs,
exp_avg_sqs,
max_exp_avg_sqs,
state_steps,
foreach=foreach,
capturable=capturable,
differentiable=differentiable,
fused=fused,
grad_scale=grad_scale,
found_inf=found_inf,
has_complex=has_complex,
amsgrad=amsgrad,
beta1=beta1,
beta2=beta2,
lr=lr,
weight_decay=weight_decay,
eps=eps,
maximize=maximize,
decoupled_weight_decay=True,
)
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