first commit

docs/source/zh/hpo_train.md

@@ -0,0 +1,123 @@
+<!--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。