181 lines
7.2 KiB
Python
181 lines
7.2 KiB
Python
# mypy: allow-untyped-defs
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from typing import Optional, Union
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from torch import Tensor
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from .adam import Adam, adam
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from .optimizer import (
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_capturable_doc,
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_differentiable_doc,
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_foreach_doc,
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_fused_doc,
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_maximize_doc,
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_params_doc,
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ParamsT,
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)
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__all__ = ["AdamW", "adamw"]
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class AdamW(Adam):
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def __init__(
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self,
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params: ParamsT,
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lr: Union[float, Tensor] = 1e-3,
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betas: tuple[Union[float, Tensor], Union[float, Tensor]] = (0.9, 0.999),
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eps: float = 1e-8,
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weight_decay: float = 1e-2,
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amsgrad: bool = False,
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*,
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maximize: bool = False,
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foreach: Optional[bool] = None,
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capturable: bool = False,
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differentiable: bool = False,
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fused: Optional[bool] = None,
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):
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super().__init__(
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params,
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lr,
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betas,
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eps,
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weight_decay,
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amsgrad,
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foreach=foreach,
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maximize=maximize,
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capturable=capturable,
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differentiable=differentiable,
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fused=fused,
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decoupled_weight_decay=True,
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)
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# Preserve decoupled_weight_decay from AdamW for backwards compatibility. The following
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# guarantees that decoupled_weight_decay will always be True for loading any state into
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# AdamW
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def __setstate__(self, state):
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super().__setstate__(state)
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for group in self.param_groups:
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group["decoupled_weight_decay"] = True
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AdamW.__doc__ = (
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r"""Implements AdamW algorithm, where weight decay does not accumulate in the momentum nor variance.
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.. math::
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\begin{aligned}
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&\rule{110mm}{0.4pt} \\
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&\textbf{input} : \gamma \text{(lr)}, \: \beta_1, \beta_2
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\text{(betas)}, \: \theta_0 \text{(params)}, \: f(\theta) \text{(objective)},
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\: \epsilon \text{ (epsilon)} \\
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&\hspace{13mm} \lambda \text{(weight decay)}, \: \textit{amsgrad},
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\: \textit{maximize} \\
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&\textbf{initialize} : m_0 \leftarrow 0 \text{ (first moment)}, v_0 \leftarrow 0
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\text{ ( second moment)}, \: v_0^{max}\leftarrow 0 \\[-1.ex]
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&\rule{110mm}{0.4pt} \\
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&\textbf{for} \: t=1 \: \textbf{to} \: \ldots \: \textbf{do} \\
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&\hspace{5mm}\textbf{if} \: \textit{maximize}: \\
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&\hspace{10mm}g_t \leftarrow -\nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm}\textbf{else} \\
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&\hspace{10mm}g_t \leftarrow \nabla_{\theta} f_t (\theta_{t-1}) \\
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&\hspace{5mm} \theta_t \leftarrow \theta_{t-1} - \gamma \lambda \theta_{t-1} \\
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&\hspace{5mm}m_t \leftarrow \beta_1 m_{t-1} + (1 - \beta_1) g_t \\
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&\hspace{5mm}v_t \leftarrow \beta_2 v_{t-1} + (1-\beta_2) g^2_t \\
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&\hspace{5mm}\widehat{m_t} \leftarrow m_t/\big(1-\beta_1^t \big) \\
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&\hspace{5mm}\textbf{if} \: amsgrad \\
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&\hspace{10mm} v_t^{max} \leftarrow \mathrm{max}(v_{t-1}^{max},v_t) \\
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&\hspace{10mm}\widehat{v_t} \leftarrow v_t^{max}/\big(1-\beta_2^t \big) \\
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&\hspace{5mm}\textbf{else} \\
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&\hspace{10mm}\widehat{v_t} \leftarrow v_t/\big(1-\beta_2^t \big) \\
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&\hspace{5mm}\theta_t \leftarrow \theta_t - \gamma \widehat{m_t}/
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\big(\sqrt{\widehat{v_t}} + \epsilon \big) \\
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&\rule{110mm}{0.4pt} \\[-1.ex]
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&\bf{return} \: \theta_t \\[-1.ex]
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&\rule{110mm}{0.4pt} \\[-1.ex]
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\end{aligned}
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For further details regarding the algorithm we refer to `Decoupled Weight Decay Regularization`_.
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"""
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+ rf"""
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Args:
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{_params_doc}
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lr (float, Tensor, optional): learning rate (default: 1e-3). A tensor LR
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is not yet supported for all our implementations. Please use a float
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LR if you are not also specifying fused=True or capturable=True.
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betas (Tuple[float, float], optional): coefficients used for computing
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running averages of gradient and its square (default: (0.9, 0.999))
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eps (float, optional): term added to the denominator to improve
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numerical stability (default: 1e-8)
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weight_decay (float, optional): weight decay coefficient (default: 1e-2)
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amsgrad (bool, optional): whether to use the AMSGrad variant of this
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algorithm from the paper `On the Convergence of Adam and Beyond`_
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(default: False)
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{_maximize_doc}
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{_foreach_doc}
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{_capturable_doc}
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{_differentiable_doc}
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{_fused_doc}
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.. Note::
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A prototype implementation of Adam and AdamW for MPS supports `torch.float32` and `torch.float16`.
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.. _Decoupled Weight Decay Regularization:
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https://arxiv.org/abs/1711.05101
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.. _On the Convergence of Adam and Beyond:
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https://openreview.net/forum?id=ryQu7f-RZ
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"""
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)
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# @_disable_dynamo_if_unsupported logic occurs in the decorator that's applied to F.adam
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def adamw(
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params: list[Tensor],
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grads: list[Tensor],
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exp_avgs: list[Tensor],
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exp_avg_sqs: list[Tensor],
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max_exp_avg_sqs: list[Tensor],
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state_steps: list[Tensor],
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# kwonly args with defaults are not supported by functions compiled with torchscript issue #70627
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# setting this as kwarg for now as functional API is compiled by torch/distributed/optim
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foreach: Optional[bool] = None,
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capturable: bool = False,
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differentiable: bool = False,
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fused: Optional[bool] = None,
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grad_scale: Optional[Tensor] = None,
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found_inf: Optional[Tensor] = None,
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has_complex: bool = False,
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*,
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amsgrad: bool,
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beta1: float,
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beta2: float,
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lr: Union[float, Tensor],
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weight_decay: float,
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eps: float,
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maximize: bool,
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):
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r"""Functional API that performs AdamW algorithm computation.
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See :class:`~torch.optim.AdamW` for details.
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"""
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adam(
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params,
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grads,
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exp_avgs,
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exp_avg_sqs,
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max_exp_avg_sqs,
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state_steps,
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foreach=foreach,
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capturable=capturable,
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differentiable=differentiable,
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fused=fused,
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grad_scale=grad_scale,
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found_inf=found_inf,
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has_complex=has_complex,
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amsgrad=amsgrad,
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beta1=beta1,
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beta2=beta2,
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lr=lr,
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weight_decay=weight_decay,
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eps=eps,
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maximize=maximize,
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decoupled_weight_decay=True,
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)
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