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53 lines
1.7 KiB
Python
53 lines
1.7 KiB
Python
# Copyright (c) 2026 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING, Any
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from paddle.optimizer import Optimizer as PaddleOptimizer
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if TYPE_CHECKING:
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from collections.abc import Sequence
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from paddle import Tensor
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from paddle.optimizer.optimizer import _ParameterConfig
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class Optimizer(PaddleOptimizer):
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def __init__(
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self,
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params: Sequence[Tensor] | Sequence[_ParameterConfig] | None,
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defaults: dict[str, Any],
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) -> None:
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lr = defaults.pop('lr', None)
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learning_rate = defaults.pop('learning_rate', None)
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if lr is not None and learning_rate is not None:
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raise ValueError(
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"Cannot specify both 'lr' and 'learning_rate' in defaults."
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)
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lr = lr if lr is not None else learning_rate
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weight_decay = defaults.pop('weight_decay', None)
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grad_clip = defaults.pop('grad_clip', None)
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maximize = defaults.pop('maximize', False)
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super().__init__(
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learning_rate=lr,
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parameters=params,
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weight_decay=weight_decay,
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grad_clip=grad_clip,
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maximize=maximize,
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)
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