
Hyperparameter Search using Trainer API
🤗 Transformers provides a [Trainer] class optimized for training 🤗 Transformers models, making it easier to start training without manually writing your own training loop. The [Trainer] provides API for hyperparameter search. This doc shows how to enable it in example. 
Hyperparameter Search backend
[Trainer] supports four hyperparameter search backends currently:
optuna, sigopt, raytune and wandb.
you should install them before using them as the hyperparameter search backend

pip install optuna/sigopt/wandb/ray[tune]
How to enable Hyperparameter search in example
Define the hyperparameter search space, different backends need different format.
For sigopt, see sigopt object_parameter, it's like following:

def sigopt_hp_space(trial):
     return [
         {"bounds": {"min": 1e-6, "max": 1e-4}, "name": "learning_rate", "type": "double"},
         {
             "categorical_values": ["16", "32", "64", "128"],
             "name": "per_device_train_batch_size",
             "type": "categorical",
         },
     ]

For optuna, see optuna object_parameter, it's like following:

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]),
     }

For raytune, see raytune object_parameter, it's like following:

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]),
     }

For wandb, see wandb object_parameter, it's like following:

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]},
         },
     }

Define a model_init function and pass it to the [Trainer], as an example:

def model_init(trial):
     return AutoModelForSequenceClassification.from_pretrained(
         model_args.model_name_or_path,
         from_tf=bool(".ckpt" in model_args.model_name_or_path),
         config=config,
         cache_dir=model_args.cache_dir,
         revision=model_args.model_revision,
         use_auth_token=True if model_args.use_auth_token else None,
     )

Create a [Trainer] with your model_init function, training arguments, training and test datasets, and evaluation function:

trainer = Trainer(
     model=None,
     args=training_args,
     train_dataset=small_train_dataset,
     eval_dataset=small_eval_dataset,
     compute_metrics=compute_metrics,
     tokenizer=tokenizer,
     model_init=model_init,
     data_collator=data_collator,
 )

Call hyperparameter search, get the best trial parameters, backend could be "optuna"/"sigopt"/"wandb"/"ray". direction can be"minimize" or "maximize", which indicates whether to optimize greater or lower objective.
You could define your own compute_objective function, if not defined, the default compute_objective will be called, and the sum of eval metric like f1 is returned as objective value.

best_trial = trainer.hyperparameter_search(
     direction="maximize",
     backend="optuna",
     hp_space=optuna_hp_space,
     n_trials=20,
     compute_objective=compute_objective,
 )

Hyperparameter search For DDP finetune
Currently, Hyperparameter search for DDP is enabled for optuna and sigopt. Only the rank-zero process will generate the search trial and pass the argument to other ranks.