| # Copyright The Lightning AI team. | |
| # | |
| # 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. | |
| """Enumerated utilities.""" | |
| from __future__ import annotations | |
| from lightning_utilities.core.enums import StrEnum as LightningEnum | |
| class GradClipAlgorithmType(LightningEnum): | |
| """Define gradient_clip_algorithm types - training-tricks. | |
| NORM type means "clipping gradients by norm". This computed over all model parameters together. | |
| VALUE type means "clipping gradients by value". This will clip the gradient value for each parameter. | |
| References: | |
| clip_by_norm: https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_norm_ | |
| clip_by_value: https://pytorch.org/docs/stable/nn.html#torch.nn.utils.clip_grad_value_ | |
| """ | |
| VALUE = "value" | |
| NORM = "norm" | |
| def supported_type(val: str) -> bool: | |
| return any(x.value == val for x in GradClipAlgorithmType) | |
| def supported_types() -> list[str]: | |
| return [x.value for x in GradClipAlgorithmType] | |