139 lines
5.6 KiB
Markdown
139 lines
5.6 KiB
Markdown
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# 使用Trainer API进行超参数搜索
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🤗 Transformers库提供了一个优化过的[`Trainer`]类，用于训练🤗 Transformers模型，相比于手动编写自己的训练循环，这更容易开始训练。[`Trainer`]提供了超参数搜索的API。本文档展示了如何在示例中启用它。
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## 超参数搜索后端
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[`Trainer`] 目前支持四种超参数搜索后端：[optuna](https://optuna.org/)，[sigopt](https://sigopt.com/)，[raytune](https://docs.ray.io/en/latest/tune/index.html)，[wandb](https://wandb.ai/site/sweeps)
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在使用它们之前，您应该先安装它们作为超参数搜索后端。
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```bash
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pip install optuna/sigopt/wandb/ray[tune]
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```
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## 如何在示例中启用超参数搜索
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定义超参数搜索空间，不同的后端需要不同的格式。
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对于sigopt，请参阅sigopt [object_parameter](https://docs.sigopt.com/ai-module-api-references/api_reference/objects/object_parameter)，它类似于以下内容：
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```py
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>>> def sigopt_hp_space(trial):
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... return [
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... {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
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... {
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... "categorical_values": ["16", "32", "64", "128"],
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... "name": "per_device_train_batch_size",
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... "type": "categorical",
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... },
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... ]
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```
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对于optuna，请参阅optuna [object_parameter](https://optuna.readthedocs.io/en/stable/tutorial/10_key_features/002_configurations.html#sphx-glr-tutorial-10-key-features-002-configurations-py)，它类似于以下内容：
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```py
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>>> def optuna_hp_space(trial):
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... return {
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... "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
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... "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]),
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... }
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```
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Optuna提供了多目标HPO。您可以在`hyperparameter_search`中传递`direction`参数，并定义自己的`compute_objective`以返回多个目标值。在`hyperparameter_search`中将返回Pareto Front（`List[BestRun]`），您应该参考[test_trainer](https://github.com/huggingface/transformers/blob/main/tests/trainer/test_trainer.py)中的测试用例`TrainerHyperParameterMultiObjectOptunaIntegrationTest`。它类似于以下内容：
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```py
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>>> best_trials = trainer.hyperparameter_search(
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... direction=["minimize", "maximize"],
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... backend="optuna",
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... hp_space=optuna_hp_space,
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... n_trials=20,
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... compute_objective=compute_objective,
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... )
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```
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对于raytune，可以参考raytune的[object_parameter](https://docs.ray.io/en/latest/tune/api/search_space.html)，它类似于以下内容：
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```py
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>>> def ray_hp_space(trial):
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... return {
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... "learning_rate": tune.loguniform(1e-6, 1e-4),
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... "per_device_train_batch_size": tune.choice([16, 32, 64, 128]),
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... }
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```
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对于wandb，可以参考wandb的[object_parameter](https://docs.wandb.ai/guides/sweeps/configuration)，它类似于以下内容：
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```py
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>>> def wandb_hp_space(trial):
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... return {
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... "method": "random",
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... "metric": {"name": "objective", "goal": "minimize"},
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... "parameters": {
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... "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
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... "per_device_train_batch_size": {"values": [16, 32, 64, 128]},
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... },
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... }
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```
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定义一个`model_init`函数并将其传递给[Trainer]，作为示例：
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```py
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>>> def model_init(trial):
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... return AutoModelForSequenceClassification.from_pretrained(
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... model_args.model_name_or_path,
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... from_tf=bool(".ckpt" in model_args.model_name_or_path),
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... config=config,
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... cache_dir=model_args.cache_dir,
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... revision=model_args.model_revision,
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... use_auth_token=True if model_args.use_auth_token else None,
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... )
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```
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使用你的`model_init`函数、训练参数、训练和测试数据集以及评估函数创建一个[`Trainer`]。
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```py
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>>> trainer = Trainer(
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... model=None,
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... args=training_args,
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... train_dataset=small_train_dataset,
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... eval_dataset=small_eval_dataset,
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... compute_metrics=compute_metrics,
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... tokenizer=tokenizer,
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... model_init=model_init,
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... data_collator=data_collator,
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... )
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```
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调用超参数搜索，获取最佳试验参数，后端可以是`"optuna"`/`"sigopt"`/`"wandb"`/`"ray"`。方向可以是`"minimize"`或`"maximize"`，表示是否优化更大或更低的目标。
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您可以定义自己的compute_objective函数，如果没有定义，将调用默认的compute_objective，并将评估指标（如f1）之和作为目标值返回。
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```py
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>>> best_trial = trainer.hyperparameter_search(
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... direction="maximize",
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... backend="optuna",
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... hp_space=optuna_hp_space,
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... n_trials=20,
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... compute_objective=compute_objective,
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... )
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```
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## 针对DDP微调的超参数搜索
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目前，Optuna和Sigopt已启用针对DDP的超参数搜索。只有rank-zero进程会进行超参数搜索并将参数传递给其他进程。 |