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Python

# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import warnings
from typing import TYPE_CHECKING
from paddle.optimizer import AdamW as PaddleAdamW
if TYPE_CHECKING:
from collections.abc import Sequence
from paddle import Tensor
from paddle.optimizer.adam import _AdamParameterConfig
class AdamW(PaddleAdamW):
def __init__(
self,
params: Sequence[Tensor] | Sequence[_AdamParameterConfig] | None,
lr: float | Tensor = 1e-3,
betas: tuple[float | Tensor, float | Tensor] = (0.9, 0.999),
eps: float = 1e-8,
weight_decay: float = 1e-2,
amsgrad: bool = False,
*,
maximize: bool = False,
foreach: bool | None = None,
capturable: bool = False,
differentiable: bool = False,
fused: bool | None = None,
) -> None:
if (
foreach is not None
or capturable is True
or differentiable is True
or fused is not None
):
warnings.warn(
"foreach, capturable, differentiable, fused are currently not supported in AdamW and will be ignored. "
"The parameters are reserved for future implementation."
)
super().__init__(
learning_rate=lr,
beta1=betas[0],
beta2=betas[1],
epsilon=eps,
parameters=params,
weight_decay=weight_decay,
amsgrad=amsgrad,
maximize=maximize,
)