venv/lib/python3.11/site-packages/torch/optim/adamw.py

# 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,
    )