wehub-resource-sync
e06fe8e8c6
Secret Leaks / trufflehog (push) Failing after 1s
Build documentation / build (push) Failing after 1s
Build documentation / build_other_lang (push) Failing after 0s
CodeQL Security Analysis / CodeQL Analysis (push) Failing after 0s
PR CI / pr-ci (push) Failing after 1s
Slow tests on important models (on Push - A10) / Get all modified files (push) Failing after 1s
Slow tests on important models (on Push - A10) / Model CI (push) Has been skipped
Self-hosted runner (benchmark) / Benchmark (aws-g5-4xlarge-cache) (push) Has been cancelled
New model PR merged notification / Notify new model (push) Has been cancelled
Update Transformers metadata / build_and_package (push) Has been cancelled
124 lines
4.9 KiB
Markdown
124 lines
4.9 KiB
Markdown
<!--Copyright 2022 The HuggingFace Team. 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
|
||
|
||
⚠️ Note that this file is in Markdown but contain specific syntax for our doc-builder (similar to MDX) that may not be
|
||
rendered properly in your Markdown viewer.
|
||
|
||
-->
|
||
|
||
# 使用Trainer API进行超参数搜索
|
||
|
||
🤗 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。
|
||
|
||
|
||
## 超参数搜索后端
|
||
|
||
[`Trainer`] 目前支持四种超参数搜索后端：[optuna](https://optuna.org/)，[raytune](https://docs.ray.io/en/latest/tune/index.html)，[wandb](https://wandb.ai/site/sweeps)
|
||
|
||
在使用它们之前，您应该先安装它们作为超参数搜索后端。
|
||
|
||
```bash
|
||
pip install optuna/wandb/ray[tune]
|
||
```
|
||
|
||
## 如何在示例中启用超参数搜索
|
||
|
||
定义超参数搜索空间，不同的后端需要不同的格式。
|
||
|
||
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：
|
||
|
||
```py
|
||
>>> def optuna_hp_space(trial):
|
||
... return {
|
||
... "learning_rate": trial.suggest_float("learning_rate", 1e-6, 1e-4, log=True),
|
||
... "per_device_train_batch_size": trial.suggest_categorical("per_device_train_batch_size", [16, 32, 64, 128]),
|
||
... }
|
||
```
|
||
|
||
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：
|
||
|
||
```py
|
||
>>> best_trials = trainer.hyperparameter_search(
|
||
... direction=["minimize", "maximize"],
|
||
... backend="optuna",
|
||
... hp_space=optuna_hp_space,
|
||
... n_trials=20,
|
||
... compute_objective=compute_objective,
|
||
... )
|
||
```
|
||
|
||
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：
|
||
|
||
```py
|
||
>>> def ray_hp_space(trial):
|
||
... return {
|
||
... "learning_rate": tune.loguniform(1e-6, 1e-4),
|
||
... "per_device_train_batch_size": tune.choice([16, 32, 64, 128]),
|
||
... }
|
||
```
|
||
|
||
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：
|
||
|
||
```py
|
||
>>> def wandb_hp_space(trial):
|
||
... return {
|
||
... "method": "random",
|
||
... "metric": {"name": "objective", "goal": "minimize"},
|
||
... "parameters": {
|
||
... "learning_rate": {"distribution": "uniform", "min": 1e-6, "max": 1e-4},
|
||
... "per_device_train_batch_size": {"values": [16, 32, 64, 128]},
|
||
... },
|
||
... }
|
||
```
|
||
|
||
定义一个`model_init`函数并将其传递给[Trainer]，作为示例：
|
||
|
||
```py
|
||
>>> def model_init(trial):
|
||
... return AutoModelForSequenceClassification.from_pretrained(
|
||
... model_args.model_name_or_path,
|
||
... config=config,
|
||
... cache_dir=model_args.cache_dir,
|
||
... revision=model_args.model_revision,
|
||
... )
|
||
```
|
||
|
||
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`]。
|
||
|
||
```py
|
||
>>> trainer = Trainer(
|
||
... model=None,
|
||
... args=training_args,
|
||
... train_dataset=small_train_dataset,
|
||
... eval_dataset=small_eval_dataset,
|
||
... compute_metrics=compute_metrics,
|
||
... processing_class=tokenizer,
|
||
... model_init=model_init,
|
||
... data_collator=data_collator,
|
||
... )
|
||
```
|
||
|
||
调用超参数搜索，获取最佳试验参数，后端可以是`"optuna"`/`"wandb"`/`"ray"`。方向可以是`"minimize"`或`"maximize"`，表示是否优化更大或更低的目标。
|
||
|
||
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。
|
||
|
||
```py
|
||
>>> best_trial = trainer.hyperparameter_search(
|
||
... direction="maximize",
|
||
... backend="optuna",
|
||
... hp_space=optuna_hp_space,
|
||
... n_trials=20,
|
||
... compute_objective=compute_objective,
|
||
... )
|
||
```
|
||
|
||
## 针对DDP微调的超参数搜索
|
||
目前，Optuna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。
|