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| # coding=utf-8 | |
| # Copyright 2023-present the HuggingFace Inc. 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. | |
| from .integrations import ( | |
| is_optuna_available, | |
| is_ray_available, | |
| is_sigopt_available, | |
| is_wandb_available, | |
| run_hp_search_optuna, | |
| run_hp_search_ray, | |
| run_hp_search_sigopt, | |
| run_hp_search_wandb, | |
| ) | |
| from .trainer_utils import ( | |
| HPSearchBackend, | |
| default_hp_space_optuna, | |
| default_hp_space_ray, | |
| default_hp_space_sigopt, | |
| default_hp_space_wandb, | |
| ) | |
| from .utils import logging | |
| logger = logging.get_logger(__name__) | |
| class HyperParamSearchBackendBase: | |
| name: str | |
| pip_package: str = None | |
| def is_available(): | |
| raise NotImplementedError | |
| def run(self, trainer, n_trials: int, direction: str, **kwargs): | |
| raise NotImplementedError | |
| def default_hp_space(self, trial): | |
| raise NotImplementedError | |
| def ensure_available(self): | |
| if not self.is_available(): | |
| raise RuntimeError( | |
| f"You picked the {self.name} backend, but it is not installed. Run {self.pip_install()}." | |
| ) | |
| def pip_install(cls): | |
| return f"`pip install {cls.pip_package or cls.name}`" | |
| class OptunaBackend(HyperParamSearchBackendBase): | |
| name = "optuna" | |
| def is_available(): | |
| return is_optuna_available() | |
| def run(self, trainer, n_trials: int, direction: str, **kwargs): | |
| return run_hp_search_optuna(trainer, n_trials, direction, **kwargs) | |
| def default_hp_space(self, trial): | |
| return default_hp_space_optuna(trial) | |
| class RayTuneBackend(HyperParamSearchBackendBase): | |
| name = "ray" | |
| pip_package = "'ray[tune]'" | |
| def is_available(): | |
| return is_ray_available() | |
| def run(self, trainer, n_trials: int, direction: str, **kwargs): | |
| return run_hp_search_ray(trainer, n_trials, direction, **kwargs) | |
| def default_hp_space(self, trial): | |
| return default_hp_space_ray(trial) | |
| class SigOptBackend(HyperParamSearchBackendBase): | |
| name = "sigopt" | |
| def is_available(): | |
| return is_sigopt_available() | |
| def run(self, trainer, n_trials: int, direction: str, **kwargs): | |
| return run_hp_search_sigopt(trainer, n_trials, direction, **kwargs) | |
| def default_hp_space(self, trial): | |
| return default_hp_space_sigopt(trial) | |
| class WandbBackend(HyperParamSearchBackendBase): | |
| name = "wandb" | |
| def is_available(): | |
| return is_wandb_available() | |
| def run(self, trainer, n_trials: int, direction: str, **kwargs): | |
| return run_hp_search_wandb(trainer, n_trials, direction, **kwargs) | |
| def default_hp_space(self, trial): | |
| return default_hp_space_wandb(trial) | |
| ALL_HYPERPARAMETER_SEARCH_BACKENDS = { | |
| HPSearchBackend(backend.name): backend for backend in [OptunaBackend, RayTuneBackend, SigOptBackend, WandbBackend] | |
| } | |
| def default_hp_search_backend() -> str: | |
| available_backends = [backend for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() if backend.is_available()] | |
| if len(available_backends) > 0: | |
| name = available_backends[0].name | |
| if len(available_backends) > 1: | |
| logger.info( | |
| f"{len(available_backends)} hyperparameter search backends available. Using {name} as the default." | |
| ) | |
| return name | |
| raise RuntimeError( | |
| "No hyperparameter search backend available.\n" | |
| + "\n".join( | |
| f" - To install {backend.name} run {backend.pip_install()}" | |
| for backend in ALL_HYPERPARAMETER_SEARCH_BACKENDS.values() | |
| ) | |
| ) | |