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]),
... }Optuna provides multi-objective HPO. You can pass direction in hyperparameter_search and define your own compute_objective to return multiple objective values. The Pareto Front (List[BestRun]) will be returned in hyperparameter_search, you should refer to the test case TrainerHyperParameterMultiObjectOptunaIntegrationTest in test_trainer. It’s like following
>>> best_trials = trainer.hyperparameter_search(
... direction=["minimize", "maximize"],
... backend="optuna",
... hp_space=optuna_hp_space,
... n_trials=20,
... compute_objective=compute_objective,
... )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,
... 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.
< > Update on GitHub