Trainer

The Trainer and TFTrainer classes provide an API for feature-complete training in most standard use cases. It’s used in most of the example scripts.

Before instantiating your Trainer/TFTrainer, create a TrainingArguments/TFTrainingArguments to access all the points of customization during training.

The API supports distributed training on multiple GPUs/TPUs, mixed precision through NVIDIA Apex for PyTorch and tf.keras.mixed_precision for TensorFlow.

Both Trainer and TFTrainer contain the basic training loop supporting the previous features. To inject custom behavior you can subclass them and override the following methods:

  • get_train_dataloader/get_train_tfdataset – Creates the training DataLoader (PyTorch) or TF Dataset.

  • get_eval_dataloader/get_eval_tfdataset – Creates the evaluation DataLoader (PyTorch) or TF Dataset.

  • get_test_dataloader/get_test_tfdataset – Creates the test DataLoader (PyTorch) or TF Dataset.

  • log – Logs information on the various objects watching training.

  • create_optimizer_and_scheduler – Setups the optimizer and learning rate scheduler if they were not passed at init.

  • compute_loss - Computes the loss on a batch of training inputs.

  • training_step – Performs a training step.

  • prediction_step – Performs an evaluation/test step.

  • run_model (TensorFlow only) – Basic pass through the model.

  • evaluate – Runs an evaluation loop and returns metrics.

  • predict – Returns predictions (with metrics if labels are available) on a test set.

Here is an example of how to customize Trainer using a custom loss function:

from transformers import Trainer
class MyTrainer(Trainer):
    def compute_loss(self, model, inputs):
        labels = inputs.pop("labels")
        outputs = model(**inputs)
        logits = outputs[0]
        return my_custom_loss(logits, labels)

Another way to customize the training loop behavior for the PyTorch Trainer is to use callbacks that can inspect the training loop state (for progress reporting, logging on TensorBoard or other ML platforms…) and take decisions (like early stopping).

Trainer

class transformers.Trainer(model: torch.nn.modules.module.Module = None, args: transformers.training_args.TrainingArguments = None, data_collator: Optional[NewType.<locals>.new_type] = None, train_dataset: Optional[torch.utils.data.dataset.Dataset] = None, eval_dataset: Optional[torch.utils.data.dataset.Dataset] = None, tokenizer: Optional[PreTrainedTokenizerBase] = None, model_init: Callable[transformers.modeling_utils.PreTrainedModel] = None, compute_metrics: Optional[Callable[transformers.trainer_utils.EvalPrediction, Dict]] = None, callbacks: Optional[List[transformers.trainer_callback.TrainerCallback]] = None, optimizers: Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None))[source]

Trainer is a simple but feature-complete training and eval loop for PyTorch, optimized for 🤗 Transformers.

Parameters
  • model (PreTrainedModel or torch.nn.Module, optional) –

    The model to train, evaluate or use for predictions. If not provided, a model_init must be passed.

    Note

    Trainer is optimized to work with the PreTrainedModel provided by the library. You can still use your own models defined as torch.nn.Module as long as they work the same way as the 🤗 Transformers models.

  • args (TrainingArguments, optional) – The arguments to tweak for training. Will default to a basic instance of TrainingArguments with the output_dir set to a directory named tmp_trainer in the current directory if not provided.

  • data_collator (DataCollator, optional) – The function to use to form a batch from a list of elements of train_dataset or eval_dataset. Will default to default_data_collator() if no tokenizer is provided, an instance of DataCollatorWithPadding() otherwise.

  • train_dataset (torch.utils.data.dataset.Dataset, optional) – The dataset to use for training. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed.

  • eval_dataset (torch.utils.data.dataset.Dataset, optional) – The dataset to use for evaluation. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed.

  • tokenizer (PreTrainedTokenizerBase, optional) – The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs the maximum length when batching inputs, and it will be saved along the model to make it easier to rerun an interrupted training or reuse the fine-tuned model.

  • model_init (Callable[[], PreTrainedModel], optional) –

    A function that instantiates the model to be used. If provided, each call to train() will start from a new instance of the model as given by this function.

    The function may have zero argument, or a single one containing the optuna/Ray Tune trial object, to be able to choose different architectures according to hyper parameters (such as layer count, sizes of inner layers, dropout probabilities etc).

  • compute_metrics (Callable[[EvalPrediction], Dict], optional) – The function that will be used to compute metrics at evaluation. Must take a EvalPrediction and return a dictionary string to metric values.

  • callbacks (List of TrainerCallback, optional) –

    A list of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in here.

    If you want to remove one of the default callbacks used, use the Trainer.remove_callback() method.

  • optimizers (Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR, optional) – A tuple containing the optimizer and the scheduler to use. Will default to an instance of AdamW on your model and a scheduler given by get_linear_schedule_with_warmup() controlled by args.

Important attributes:

  • model – Always points to the core model. If using a transformers model, it will be a PreTrainedModel subclass.

  • model_wrapped – Always points to the most external model in case one or more other modules wrap the original model. This is the model that should be used for the forward pass. For example, under DeepSpeed, the inner model is wrapped in DeepSpeed and then again in torch.nn.DistributedDataParallel. If the inner model hasn’t been wrapped, then self.model_wrapped is the same as self.model.

  • is_model_parallel – Whether or not a model has been switched to a model parallel mode (different from data parallelism, this means some of the model layers are split on different GPUs).

add_callback(callback)[source]

Add a callback to the current list of TrainerCallback.

Parameters

callback (type or TrainerCallback) – A TrainerCallback class or an instance of a TrainerCallback. In the first case, will instantiate a member of that class.

compute_loss(model, inputs)[source]

How the loss is computed by Trainer. By default, all models return the loss in the first element.

Subclass and override for custom behavior.

create_optimizer_and_scheduler(num_training_steps: int)[source]

Setup the optimizer and the learning rate scheduler.

We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the Trainer’s init through optimizers, or subclass and override this method in a subclass.

evaluate(eval_dataset: Optional[torch.utils.data.dataset.Dataset] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval') → Dict[str, float][source]

Run evaluation and returns metrics.

The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init compute_metrics argument).

You can also subclass and override this method to inject custom behavior.

Parameters
  • eval_dataset (Dataset, optional) – Pass a dataset if you wish to override self.eval_dataset. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement the __len__ method.

  • ignore_keys (Lst[str], optional) – A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

  • metric_key_prefix (str, optional, defaults to "eval") – An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named “eval_bleu” if the prefix is “eval” (default)

Returns

A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The dictionary also contains the epoch number which comes from the training state.

floating_point_ops(inputs: Dict[str, Union[torch.Tensor, Any]])[source]

For models that inherit from PreTrainedModel, uses that method to compute the number of floating point operations for every backward + forward pass. If using another model, either implement such a method in the model or subclass and override this method.

Parameters

inputs (Dict[str, Union[torch.Tensor, Any]]) – The inputs and targets of the model.

Returns

The number of floating-point operations.

Return type

int

get_eval_dataloader(eval_dataset: Optional[torch.utils.data.dataset.Dataset] = None) → torch.utils.data.dataloader.DataLoader[source]

Returns the evaluation DataLoader.

Subclass and override this method if you want to inject some custom behavior.

Parameters

eval_dataset (torch.utils.data.dataset.Dataset, optional) – If provided, will override self.eval_dataset. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement __len__.

get_test_dataloader(test_dataset: torch.utils.data.dataset.Dataset) → torch.utils.data.dataloader.DataLoader[source]

Returns the test DataLoader.

Subclass and override this method if you want to inject some custom behavior.

Parameters

test_dataset (torch.utils.data.dataset.Dataset, optional) – The test dataset to use. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement __len__.

get_train_dataloader() → torch.utils.data.dataloader.DataLoader[source]

Returns the training DataLoader.

Will use no sampler if self.train_dataset does not implement __len__, a random sampler (adapted to distributed training if necessary) otherwise.

Subclass and override this method if you want to inject some custom behavior.

Launch an hyperparameter search using optuna or Ray Tune. The optimized quantity is determined by compute_objectie, which defaults to a function returning the evaluation loss when no metric is provided, the sum of all metrics otherwise.

Warning

To use this method, you need to have provided a model_init when initializing your Trainer: we need to reinitialize the model at each new run. This is incompatible with the optimizers argument, so you need to subclass Trainer and override the method create_optimizer_and_scheduler() for custom optimizer/scheduler.

Parameters
  • hp_space (Callable[["optuna.Trial"], Dict[str, float]], optional) – A function that defines the hyperparameter search space. Will default to default_hp_space_optuna() or default_hp_space_ray() depending on your backend.

  • compute_objective (Callable[[Dict[str, float]], float], optional) – A function computing the objective to minimize or maximize from the metrics returned by the evaluate method. Will default to default_compute_objective().

  • n_trials (int, optional, defaults to 100) – The number of trial runs to test.

  • direction (str, optional, defaults to "minimize") – Whether to optimize greater or lower objects. Can be "minimize" or "maximize", you should pick "minimize" when optimizing the validation loss, "maximize" when optimizing one or several metrics.

  • backend (str or HPSearchBackend, optional) – The backend to use for hyperparameter search. Will default to optuna or Ray Tune, depending on which one is installed. If both are installed, will default to optuna.

  • kwargs

    Additional keyword arguments passed along to optuna.create_study or ray.tune.run. For more information see:

Returns

All the information about the best run.

Return type

transformers.trainer_utils.BestRun

is_local_process_zero() → bool[source]

Whether or not this process is the local (e.g., on one machine if training in a distributed fashion on several machines) main process.

is_world_process_zero() → bool[source]

Whether or not this process is the global main process (when training in a distributed fashion on several machines, this is only going to be True for one process).

log(logs: Dict[str, float]) → None[source]

Log logs on the various objects watching training.

Subclass and override this method to inject custom behavior.

Parameters

logs (Dict[str, float]) – The values to log.

num_examples(dataloader: torch.utils.data.dataloader.DataLoader) → int[source]

Helper to get number of samples in a DataLoader by accessing its dataset.

Will raise an exception if the underlying dataset dese not implement method __len__

pop_callback(callback)[source]

Remove a callback from the current list of TrainerCallback and returns it.

If the callback is not found, returns None (and no error is raised).

Parameters

callback (type or TrainerCallback) – A TrainerCallback class or an instance of a TrainerCallback. In the first case, will pop the first member of that class found in the list of callbacks.

Returns

The callback removed, if found.

Return type

TrainerCallback

predict(test_dataset: torch.utils.data.dataset.Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval') → transformers.trainer_utils.PredictionOutput[source]

Run prediction and returns predictions and potential metrics.

Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in evaluate().

Parameters
  • test_dataset (Dataset) – Dataset to run the predictions on. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. Has to implement the method __len__

  • ignore_keys (Lst[str], optional) – A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

  • metric_key_prefix (str, optional, defaults to "eval") – An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named “eval_bleu” if the prefix is “eval” (default)

Note

If your predictions or labels have different sequence length (for instance because you’re doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100.

Returns: NamedTuple A namedtuple with the following keys:

  • predictions (np.ndarray): The predictions on test_dataset.

  • label_ids (np.ndarray, optional): The labels (if the dataset contained some).

  • metrics (Dict[str, float], optional): The potential dictionary of metrics (if the dataset contained labels).

prediction_loop(dataloader: torch.utils.data.dataloader.DataLoader, description: str, prediction_loss_only: Optional[bool] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval') → transformers.trainer_utils.PredictionOutput[source]

Prediction/evaluation loop, shared by Trainer.evaluate() and Trainer.predict().

Works both with or without labels.

prediction_step(model: torch.nn.modules.module.Module, inputs: Dict[str, Union[torch.Tensor, Any]], prediction_loss_only: bool, ignore_keys: Optional[List[str]] = None) → Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]][source]

Perform an evaluation step on model using obj:inputs.

Subclass and override to inject custom behavior.

Parameters
  • model (nn.Module) – The model to evaluate.

  • inputs (Dict[str, Union[torch.Tensor, Any]]) –

    The inputs and targets of the model.

    The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument labels. Check your model’s documentation for all accepted arguments.

  • prediction_loss_only (bool) – Whether or not to return the loss only.

  • ignore_keys (Lst[str], optional) – A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

Returns

A tuple with the loss, logits and labels (each being optional).

Return type

Tuple[Optional[float], Optional[torch.Tensor], Optional[torch.Tensor]]

remove_callback(callback)[source]

Remove a callback from the current list of TrainerCallback.

Parameters

callback (type or TrainerCallback) – A TrainerCallback class or an instance of a TrainerCallback. In the first case, will remove the first member of that class found in the list of callbacks.

save_model(output_dir: Optional[str] = None)[source]

Will save the model, so you can reload it using from_pretrained().

Will only save from the world_master process (unless in TPUs).

train(model_path: Optional[str] = None, trial: Union[optuna.Trial, Dict[str, Any]] = None)[source]

Main training entry point.

Parameters
  • model_path (str, optional) – Local path to the model if the model to train has been instantiated from a local path. If present, training will resume from the optimizer/scheduler states loaded here.

  • trial (optuna.Trial or Dict[str, Any], optional) – The trial run or the hyperparameter dictionary for hyperparameter search.

training_step(model: torch.nn.modules.module.Module, inputs: Dict[str, Union[torch.Tensor, Any]]) → torch.Tensor[source]

Perform a training step on a batch of inputs.

Subclass and override to inject custom behavior.

Parameters
  • model (nn.Module) – The model to train.

  • inputs (Dict[str, Union[torch.Tensor, Any]]) –

    The inputs and targets of the model.

    The dictionary will be unpacked before being fed to the model. Most models expect the targets under the argument labels. Check your model’s documentation for all accepted arguments.

Returns

The tensor with training loss on this batch.

Return type

torch.Tensor

Seq2SeqTrainer

class transformers.Seq2SeqTrainer(model: torch.nn.modules.module.Module = None, args: transformers.training_args.TrainingArguments = None, data_collator: Optional[NewType.<locals>.new_type] = None, train_dataset: Optional[torch.utils.data.dataset.Dataset] = None, eval_dataset: Optional[torch.utils.data.dataset.Dataset] = None, tokenizer: Optional[PreTrainedTokenizerBase] = None, model_init: Callable[transformers.modeling_utils.PreTrainedModel] = None, compute_metrics: Optional[Callable[transformers.trainer_utils.EvalPrediction, Dict]] = None, callbacks: Optional[List[transformers.trainer_callback.TrainerCallback]] = None, optimizers: Tuple[torch.optim.optimizer.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None))[source]
evaluate(eval_dataset: Optional[torch.utils.data.dataset.Dataset] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval', max_length: Optional[int] = None, num_beams: Optional[int] = None) → Dict[str, float][source]

Run evaluation and returns metrics.

The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init compute_metrics argument).

You can also subclass and override this method to inject custom behavior.

Parameters
  • eval_dataset (Dataset, optional) – Pass a dataset if you wish to override self.eval_dataset. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. It must implement the __len__ method.

  • ignore_keys (List[str], optional) – A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

  • metric_key_prefix (str, optional, defaults to "eval") – An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named “eval_bleu” if the prefix is "eval" (default)

  • max_length (int, optional) – The maximum target length to use when predicting with the generate method.

  • num_beams (int, optional) – Number of beams for beam search that will be used when predicting with the generate method. 1 means no beam search.

Returns

A dictionary containing the evaluation loss and the potential metrics computed from the predictions. The dictionary also contains the epoch number which comes from the training state.

predict(test_dataset: torch.utils.data.dataset.Dataset, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = 'eval', max_length: Optional[int] = None, num_beams: Optional[int] = None) → transformers.trainer_utils.PredictionOutput[source]

Run prediction and returns predictions and potential metrics.

Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in evaluate().

Parameters
  • test_dataset (Dataset) – Dataset to run the predictions on. If it is an datasets.Dataset, columns not accepted by the model.forward() method are automatically removed. Has to implement the method __len__

  • ignore_keys (List[str], optional) – A list of keys in the output of your model (if it is a dictionary) that should be ignored when gathering predictions.

  • metric_key_prefix (str, optional, defaults to "eval") – An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named “eval_bleu” if the prefix is "eval" (default)

  • max_length (int, optional) – The maximum target length to use when predicting with the generate method.

  • num_beams (int, optional) – Number of beams for beam search that will be used when predicting with the generate method. 1 means no beam search.

Note

If your predictions or labels have different sequence lengths (for instance because you’re doing dynamic padding in a token classification task) the predictions will be padded (on the right) to allow for concatenation into one array. The padding index is -100.

Returns: NamedTuple A namedtuple with the following keys:

  • predictions (np.ndarray): The predictions on test_dataset.

  • label_ids (np.ndarray, optional): The labels (if the dataset contained some).

  • metrics (Dict[str, float], optional): The potential dictionary of metrics (if the dataset contained labels).

TFTrainer

class transformers.TFTrainer(model: transformers.modeling_tf_utils.TFPreTrainedModel, args: transformers.training_args_tf.TFTrainingArguments, train_dataset: Optional[tensorflow.python.data.ops.dataset_ops.DatasetV2] = None, eval_dataset: Optional[tensorflow.python.data.ops.dataset_ops.DatasetV2] = None, compute_metrics: Optional[Callable[transformers.trainer_utils.EvalPrediction, Dict]] = None, tb_writer: Optional[tensorflow.python.ops.summary_ops_v2.SummaryWriter] = None, optimizers: Tuple[tensorflow.python.keras.optimizer_v2.optimizer_v2.OptimizerV2, tensorflow.python.keras.optimizer_v2.learning_rate_schedule.LearningRateSchedule] = None, None)[source]

TFTrainer is a simple but feature-complete training and eval loop for TensorFlow, optimized for 🤗 Transformers.

Parameters
  • model (TFPreTrainedModel) – The model to train, evaluate or use for predictions.

  • args (TFTrainingArguments) – The arguments to tweak training.

  • train_dataset (Dataset, optional) – The dataset to use for training. The dataset should yield tuples of (features, labels) where features is a dict of input features and labels is the labels. If labels is a tensor, the loss is calculated by the model by calling model(features, labels=labels). If labels is a dict, such as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling model(features, **labels).

  • eval_dataset (Dataset, optional) – The dataset to use for evaluation. The dataset should yield tuples of (features, labels) where features is a dict of input features and labels is the labels. If labels is a tensor, the loss is calculated by the model by calling model(features, labels=labels). If labels is a dict, such as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling model(features, **labels).

  • compute_metrics (Callable[[EvalPrediction], Dict], optional) – The function that will be used to compute metrics at evaluation. Must take a EvalPrediction and return a dictionary string to metric values.

  • tb_writer (tf.summary.SummaryWriter, optional) – Object to write to TensorBoard.

  • optimizers (Tuple[tf.keras.optimizers.Optimizer, tf.keras.optimizers.schedules.LearningRateSchedule], optional) – A tuple containing the optimizer and the scheduler to use. The optimizer default to an instance of tf.keras.optimizers.Adam if args.weight_decay_rate is 0 else an instance of AdamWeightDecay. The scheduler will default to an instance of tf.keras.optimizers.schedules.PolynomialDecay if args.num_warmup_steps is 0 else an instance of WarmUp.

create_optimizer_and_scheduler(num_training_steps: int)[source]

Setup the optimizer and the learning rate scheduler.

We provide a reasonable default that works well. If you want to use something else, you can pass a tuple in the TFTrainer’s init through optimizers, or subclass and override this method.

evaluate(eval_dataset: Optional[tensorflow.python.data.ops.dataset_ops.DatasetV2] = None) → Dict[str, float][source]

Run evaluation and returns metrics.

The calling script will be responsible for providing a method to compute metrics, as they are task-dependent (pass it to the init compute_metrics argument).

Parameters

eval_dataset (Dataset, optional) – Pass a dataset if you wish to override self.eval_dataset. The dataset should yield tuples of (features, labels) where features is a dict of input features and labels is the labels. If labels is a tensor, the loss is calculated by the model by calling model(features, labels=labels). If labels is a dict, such as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling model(features, **labels).

Returns

A dictionary containing the evaluation loss and the potential metrics computed from the predictions.

get_eval_tfdataset(eval_dataset: Optional[tensorflow.python.data.ops.dataset_ops.DatasetV2] = None) → tensorflow.python.data.ops.dataset_ops.DatasetV2[source]

Returns the evaluation Dataset.

Parameters

eval_dataset (Dataset, optional) – If provided, will override self.eval_dataset. The dataset should yield tuples of (features, labels) where features is a dict of input features and labels is the labels. If labels is a tensor, the loss is calculated by the model by calling model(features, labels=labels). If labels is a dict, such as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling model(features, **labels).

Subclass and override this method if you want to inject some custom behavior.

get_test_tfdataset(test_dataset: tensorflow.python.data.ops.dataset_ops.DatasetV2) → tensorflow.python.data.ops.dataset_ops.DatasetV2[source]

Returns a test Dataset.

Parameters

test_dataset (Dataset) – The dataset to use. The dataset should yield tuples of (features, labels) where features is a dict of input features and labels is the labels. If labels is a tensor, the loss is calculated by the model by calling model(features, labels=labels). If labels is a dict, such as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling model(features, **labels).

Subclass and override this method if you want to inject some custom behavior.

get_train_tfdataset() → tensorflow.python.data.ops.dataset_ops.DatasetV2[source]

Returns the training Dataset.

Subclass and override this method if you want to inject some custom behavior.

log(logs: Dict[str, float]) → None[source]

Log logs on the various objects watching training.

Subclass and override this method to inject custom behavior.

Parameters

logs (Dict[str, float]) – The values to log.

predict(test_dataset: tensorflow.python.data.ops.dataset_ops.DatasetV2) → transformers.trainer_utils.PredictionOutput[source]

Run prediction and returns predictions and potential metrics.

Depending on the dataset and your use case, your test dataset may contain labels. In that case, this method will also return metrics, like in evaluate().

Parameters

test_dataset (Dataset) – Dataset to run the predictions on. The dataset should yield tuples of (features, labels) where features is a dict of input features and labels is the labels. If labels is a tensor, the loss is calculated by the model by calling model(features, labels=labels). If labels is a dict, such as when using a QuestionAnswering head model with multiple targets, the loss is instead calculated by calling model(features, **labels)

Returns: NamedTuple A namedtuple with the following keys:

  • predictions (np.ndarray): The predictions on test_dataset.

  • label_ids (np.ndarray, optional): The labels (if the dataset contained some).

  • metrics (Dict[str, float], optional): The potential dictionary of metrics (if the dataset contained labels).

prediction_loop(dataset: tensorflow.python.data.ops.dataset_ops.DatasetV2, steps: int, num_examples: int, description: str, prediction_loss_only: Optional[bool] = None) → transformers.trainer_utils.PredictionOutput[source]

Prediction/evaluation loop, shared by evaluate() and predict().

Works both with or without labels.

prediction_step(features: tensorflow.python.framework.ops.Tensor, labels: tensorflow.python.framework.ops.Tensor, nb_instances_in_global_batch: tensorflow.python.framework.ops.Tensor) → tensorflow.python.framework.ops.Tensor[source]

Compute the prediction on features and update the loss with labels.

Subclass and override to inject some custom behavior.

run_model(features, labels, training)[source]

Computes the loss of the given features and labels pair.

Subclass and override this method if you want to inject some custom behavior.

Parameters
  • features (tf.Tensor) – A batch of input features.

  • labels (tf.Tensor) – A batch of labels.

  • training (bool) – Whether or not to run the model in training mode.

Returns

The loss and logits.

Return type

A tuple of two tf.Tensor

save_model(output_dir: Optional[str] = None)[source]

Will save the model, so you can reload it using from_pretrained().

setup_comet()[source]

Setup the optional Comet.ml integration.

Environment:
COMET_MODE:

(Optional): str - “OFFLINE”, “ONLINE”, or “DISABLED”

COMET_PROJECT_NAME:

(Optional): str - Comet.ml project name for experiments

COMET_OFFLINE_DIRECTORY:

(Optional): str - folder to use for saving offline experiments when COMET_MODE is “OFFLINE”

For a number of configurable items in the environment, see here

setup_wandb()[source]

Setup the optional Weights & Biases (wandb) integration.

One can subclass and override this method to customize the setup if needed. Find more information here. You can also override the following environment variables:

Environment:
WANDB_PROJECT:

(Optional): str - “huggingface” by default, set this to a custom string to store results in a different project.

WANDB_DISABLED:

(Optional): boolean - defaults to false, set to “true” to disable wandb entirely.

train() → None[source]

Train method to train the model.

training_step(features, labels, nb_instances_in_global_batch)[source]

Perform a training step on features and labels.

Subclass and override to inject some custom behavior.

TrainingArguments

class transformers.TrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = None, do_predict: bool = False, evaluation_strategy: transformers.trainer_utils.EvaluationStrategy = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = -1, lr_scheduler_type: transformers.trainer_utils.SchedulerType = 'linear', warmup_steps: int = 0, logging_dir: Optional[str] = <factory>, logging_first_step: bool = False, logging_steps: int = 500, save_steps: int = 500, save_total_limit: Optional[int] = None, no_cuda: bool = False, seed: int = 42, fp16: bool = False, fp16_opt_level: str = 'O1', fp16_backend: str = 'auto', local_rank: int = -1, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: bool = False, dataloader_drop_last: bool = False, eval_steps: int = None, dataloader_num_workers: int = 0, past_index: int = -1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: bool = False, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, adafactor: bool = False, group_by_length: bool = False)[source]

TrainingArguments is the subset of the arguments we use in our example scripts which relate to the training loop itself.

Using HfArgumentParser we can turn this class into argparse arguments that can be specified on the command line.

Parameters
  • output_dir (str) – The output directory where the model predictions and checkpoints will be written.

  • overwrite_output_dir (bool, optional, defaults to False) – If True, overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory.

  • do_train (bool, optional, defaults to False) – Whether to run training or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • do_eval (bool, optional) – Whether to run evaluation on the validation set or not. Will be set to True if evaluation_strategy is different from "no". This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • do_predict (bool, optional, defaults to False) – Whether to run predictions on the test set or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • evaluation_strategy (str or EvaluationStrategy, optional, defaults to "no") –

    The evaluation strategy to adopt during training. Possible values are:

    • "no": No evaluation is done during training.

    • "steps": Evaluation is done (and logged) every eval_steps.

    • "epoch": Evaluation is done at the end of each epoch.

  • prediction_loss_only (bool, optional, defaults to False) – When performing evaluation and generating predictions, only returns the loss.

  • per_device_train_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for training.

  • per_device_eval_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for evaluation.

  • gradient_accumulation_steps (int, optional, defaults to 1) –

    Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

    Warning

    When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training examples.

  • eval_accumulation_steps (int, optional) – Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory).

  • learning_rate (float, optional, defaults to 5e-5) – The initial learning rate for AdamW optimizer.

  • weight_decay (float, optional, defaults to 0) – The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer.

  • adam_beta1 (float, optional, defaults to 0.9) – The beta1 hyperparameter for the AdamW optimizer.

  • adam_beta2 (float, optional, defaults to 0.999) – The beta2 hyperparameter for the AdamW optimizer.

  • adam_epsilon (float, optional, defaults to 1e-8) – The epsilon hyperparameter for the AdamW optimizer.

  • max_grad_norm (float, optional, defaults to 1.0) – Maximum gradient norm (for gradient clipping).

  • num_train_epochs (float, optional, defaults to 3.0) – Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).

  • max_steps (int, optional, defaults to -1) – If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs.

  • lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") – The scheduler type to use. See the documentation of SchedulerType for all possible values.

  • warmup_steps (int, optional, defaults to 0) – Number of steps used for a linear warmup from 0 to learning_rate.

  • logging_dir (str, optional) – TensorBoard log directory. Will default to runs/**CURRENT_DATETIME_HOSTNAME**.

  • logging_first_step (bool, optional, defaults to False) – Whether to log and evaluate the first global_step or not.

  • logging_steps (int, optional, defaults to 500) – Number of update steps between two logs.

  • save_steps (int, optional, defaults to 500) – Number of updates steps before two checkpoint saves.

  • save_total_limit (int, optional) – If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir.

  • no_cuda (bool, optional, defaults to False) – Whether to not use CUDA even when it is available or not.

  • seed (int, optional, defaults to 42) – Random seed for initialization.

  • fp16 (bool, optional, defaults to False) – Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.

  • fp16_opt_level (str, optional, defaults to ‘O1’) – For fp16 training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on the Apex documentation.

  • fp16_backend (str, optional, defaults to "auto") – The backend to use for mixed precision training. Must be one of "auto", "amp" or "apex". "auto" will use AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend.

  • local_rank (int, optional, defaults to -1) – Rank of the process during distributed training.

  • tpu_num_cores (int, optional) – When training on TPU, the number of TPU cores (automatically passed by launcher script).

  • debug (bool, optional, defaults to False) – When training on TPU, whether to print debug metrics or not.

  • dataloader_drop_last (bool, optional, defaults to False) – Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not.

  • eval_steps (int, optional) – Number of update steps between two evaluations if evaluation_strategy="steps". Will default to the same value as logging_steps if not set.

  • dataloader_num_workers (int, optional, defaults to 0) – Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.

  • past_index (int, optional, defaults to -1) – Some models like TransformerXL or :doc`XLNet <../model_doc/xlnet>` can make use of the past hidden states for their predictions. If this argument is set to a positive int, the Trainer will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument mems.

  • run_name (str, optional) – A descriptor for the run. Typically used for wandb logging.

  • disable_tqdm (bool, optional) – Whether or not to disable the tqdm progress bars and table of metrics produced by NotebookTrainingTracker in Jupyter Notebooks. Will default to True if the logging level is set to warn or lower (default), False otherwise.

  • remove_unused_columns (bool, optional, defaults to True) –

    If using datasets.Dataset datasets, whether or not to automatically remove the columns unused by the model forward method.

    (Note that this behavior is not implemented for TFTrainer yet.)

  • label_names (List[str], optional) –

    The list of keys in your dictionary of inputs that correspond to the labels.

    Will eventually default to ["labels"] except if the model used is one of the XxxForQuestionAnswering in which case it will default to ["start_positions", "end_positions"].

  • load_best_model_at_end (bool, optional, defaults to False) –

    Whether or not to load the best model found during training at the end of training.

    Note

    When set to True, the parameters save_steps will be ignored and the model will be saved after each evaluation.

  • metric_for_best_model (str, optional) –

    Use in conjunction with load_best_model_at_end to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix "eval_". Will default to "loss" if unspecified and load_best_model_at_end=True (to use the evaluation loss).

    If you set this value, greater_is_better will default to True. Don’t forget to set it to False if your metric is better when lower.

  • greater_is_better (bool, optional) –

    Use in conjunction with load_best_model_at_end and metric_for_best_model to specify if better models should have a greater metric or not. Will default to:

    • True if metric_for_best_model is set to a value that isn’t "loss" or "eval_loss".

    • False if metric_for_best_model is not set, or set to "loss" or "eval_loss".

  • ignore_skip_data (bool, optional, defaults to False) – When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to True, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have.

  • sharded_ddp (bool, optional, defaults to False) – Use Sharded DDP training from FairScale (in distributed training only). This is an experimental feature.

  • deepspeed (str, optional) – Use Deepspeed. This is an experimental feature and its API may evolve in the future. The value is the location of its json config file (usually ds_config.json).

  • label_smoothing_factor (float, optional, defaults to 0.0) – The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - label_smoothing_factor + label_smoothing_factor/num_labels respectively.

  • adafactor (bool, optional, defaults to False) – Whether or not to use the Adafactor optimizer instead of AdamW.

  • group_by_length (bool, optional, defaults to False) – Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.

property device

The device used by this process.

property eval_batch_size

The actual batch size for evaluation (may differ from per_gpu_eval_batch_size in distributed training).

property n_gpu

The number of GPUs used by this process.

Note

This will only be greater than one when you have multiple GPUs available but are not using distributed training. For distributed training, it will always be 1.

property parallel_mode

The current mode used for parallelism if multiple GPUs/TPU cores are available. One of:

  • ParallelMode.NOT_PARALLEL: no parallelism (CPU or one GPU).

  • ParallelMode.NOT_DISTRIBUTED: several GPUs in one single process (uses torch.nn.DataParallel).

  • ParallelMode.DISTRIBUTED: several GPUs, each ahving its own process (uses torch.nn.DistributedDataParallel).

  • ParallelMode.TPU: several TPU cores.

to_dict()[source]

Serializes this instance while replace Enum by their values (for JSON serialization support).

to_json_string()[source]

Serializes this instance to a JSON string.

to_sanitized_dict() → Dict[str, Any][source]

Sanitized serialization to use with TensorBoard’s hparams

property train_batch_size

The actual batch size for training (may differ from per_gpu_train_batch_size in distributed training).

Seq2SeqTrainingArguments

class transformers.Seq2SeqTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = None, do_predict: bool = False, evaluation_strategy: transformers.trainer_utils.EvaluationStrategy = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = -1, lr_scheduler_type: transformers.trainer_utils.SchedulerType = 'linear', warmup_steps: int = 0, logging_dir: Optional[str] = <factory>, logging_first_step: bool = False, logging_steps: int = 500, save_steps: int = 500, save_total_limit: Optional[int] = None, no_cuda: bool = False, seed: int = 42, fp16: bool = False, fp16_opt_level: str = 'O1', fp16_backend: str = 'auto', local_rank: int = -1, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: bool = False, dataloader_drop_last: bool = False, eval_steps: int = None, dataloader_num_workers: int = 0, past_index: int = -1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: bool = False, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, adafactor: bool = False, group_by_length: bool = False, sortish_sampler: bool = False, predict_with_generate: bool = False)[source]

TrainingArguments is the subset of the arguments we use in our example scripts which relate to the training loop itself.

Using HfArgumentParser we can turn this class into argparse arguments that can be specified on the command line.

Parameters
  • output_dir (str) – The output directory where the model predictions and checkpoints will be written.

  • overwrite_output_dir (bool, optional, defaults to False) – If True, overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory.

  • do_train (bool, optional, defaults to False) – Whether to run training or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • do_eval (bool, optional) – Whether to run evaluation on the validation set or not. Will be set to True if evaluation_strategy is different from "no". This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • do_predict (bool, optional, defaults to False) – Whether to run predictions on the test set or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • evaluation_strategy (str or EvaluationStrategy, optional, defaults to "no") –

    The evaluation strategy to adopt during training. Possible values are:

    • "no": No evaluation is done during training.

    • "steps": Evaluation is done (and logged) every eval_steps.

    • "epoch": Evaluation is done at the end of each epoch.

  • prediction_loss_only (bool, optional, defaults to False) – When performing evaluation and generating predictions, only returns the loss.

  • per_device_train_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for training.

  • per_device_eval_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for evaluation.

  • gradient_accumulation_steps (int, optional, defaults to 1) –

    Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

    Warning

    When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training examples.

  • eval_accumulation_steps (int, optional) – Number of predictions steps to accumulate the output tensors for, before moving the results to the CPU. If left unset, the whole predictions are accumulated on GPU/TPU before being moved to the CPU (faster but requires more memory).

  • learning_rate (float, optional, defaults to 5e-5) – The initial learning rate for AdamW optimizer.

  • weight_decay (float, optional, defaults to 0) – The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights in AdamW optimizer.

  • adam_beta1 (float, optional, defaults to 0.9) – The beta1 hyperparameter for the AdamW optimizer.

  • adam_beta2 (float, optional, defaults to 0.999) – The beta2 hyperparameter for the AdamW optimizer.

  • adam_epsilon (float, optional, defaults to 1e-8) – The epsilon hyperparameter for the AdamW optimizer.

  • max_grad_norm (float, optional, defaults to 1.0) – Maximum gradient norm (for gradient clipping).

  • num_train_epochs (float, optional, defaults to 3.0) – Total number of training epochs to perform (if not an integer, will perform the decimal part percents of the last epoch before stopping training).

  • max_steps (int, optional, defaults to -1) – If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs.

  • lr_scheduler_type (str or SchedulerType, optional, defaults to "linear") – The scheduler type to use. See the documentation of SchedulerType for all possible values.

  • warmup_steps (int, optional, defaults to 0) – Number of steps used for a linear warmup from 0 to learning_rate.

  • logging_dir (str, optional) – TensorBoard log directory. Will default to runs/**CURRENT_DATETIME_HOSTNAME**.

  • logging_first_step (bool, optional, defaults to False) – Whether to log and evaluate the first global_step or not.

  • logging_steps (int, optional, defaults to 500) – Number of update steps between two logs.

  • save_steps (int, optional, defaults to 500) – Number of updates steps before two checkpoint saves.

  • save_total_limit (int, optional) – If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir.

  • no_cuda (bool, optional, defaults to False) – Whether to not use CUDA even when it is available or not.

  • seed (int, optional, defaults to 42) – Random seed for initialization.

  • fp16 (bool, optional, defaults to False) – Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.

  • fp16_opt_level (str, optional, defaults to ‘O1’) – For fp16 training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on the Apex documentation.

  • fp16_backend (str, optional, defaults to "auto") – The backend to use for mixed precision training. Must be one of "auto", "amp" or "apex". "auto" will use AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend.

  • local_rank (int, optional, defaults to -1) – Rank of the process during distributed training.

  • tpu_num_cores (int, optional) – When training on TPU, the number of TPU cores (automatically passed by launcher script).

  • debug (bool, optional, defaults to False) – When training on TPU, whether to print debug metrics or not.

  • dataloader_drop_last (bool, optional, defaults to False) – Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not.

  • eval_steps (int, optional) – Number of update steps between two evaluations if evaluation_strategy="steps". Will default to the same value as logging_steps if not set.

  • dataloader_num_workers (int, optional, defaults to 0) – Number of subprocesses to use for data loading (PyTorch only). 0 means that the data will be loaded in the main process.

  • past_index (int, optional, defaults to -1) – Some models like TransformerXL or :doc`XLNet <../model_doc/xlnet>` can make use of the past hidden states for their predictions. If this argument is set to a positive int, the Trainer will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument mems.

  • run_name (str, optional) –

    A descriptor for the run. Typically used for wandb logging.

  • disable_tqdm (bool, optional) – Whether or not to disable the tqdm progress bars and table of metrics produced by NotebookTrainingTracker in Jupyter Notebooks. Will default to True if the logging level is set to warn or lower (default), False otherwise.

  • remove_unused_columns (bool, optional, defaults to True) –

    If using datasets.Dataset datasets, whether or not to automatically remove the columns unused by the model forward method.

    (Note that this behavior is not implemented for TFTrainer yet.)

  • label_names (List[str], optional) –

    The list of keys in your dictionary of inputs that correspond to the labels.

    Will eventually default to ["labels"] except if the model used is one of the XxxForQuestionAnswering in which case it will default to ["start_positions", "end_positions"].

  • load_best_model_at_end (bool, optional, defaults to False) –

    Whether or not to load the best model found during training at the end of training.

    Note

    When set to True, the parameters save_steps will be ignored and the model will be saved after each evaluation.

  • metric_for_best_model (str, optional) –

    Use in conjunction with load_best_model_at_end to specify the metric to use to compare two different models. Must be the name of a metric returned by the evaluation with or without the prefix "eval_". Will default to "loss" if unspecified and load_best_model_at_end=True (to use the evaluation loss).

    If you set this value, greater_is_better will default to True. Don’t forget to set it to False if your metric is better when lower.

  • greater_is_better (bool, optional) –

    Use in conjunction with load_best_model_at_end and metric_for_best_model to specify if better models should have a greater metric or not. Will default to:

    • True if metric_for_best_model is set to a value that isn’t "loss" or "eval_loss".

    • False if metric_for_best_model is not set, or set to "loss" or "eval_loss".

  • ignore_skip_data (bool, optional, defaults to False) – When resuming training, whether or not to skip the epochs and batches to get the data loading at the same stage as in the previous training. If set to True, the training will begin faster (as that skipping step can take a long time) but will not yield the same results as the interrupted training would have.

  • sharded_ddp (bool, optional, defaults to False) – Use Sharded DDP training from FairScale (in distributed training only). This is an experimental feature.

  • deepspeed (str, optional) – Use Deepspeed. This is an experimental feature and its API may evolve in the future. The value is the location of its json config file (usually ds_config.json).

  • label_smoothing_factor (float, optional, defaults to 0.0) – The label smoothing factor to use. Zero means no label smoothing, otherwise the underlying onehot-encoded labels are changed from 0s and 1s to label_smoothing_factor/num_labels and 1 - label_smoothing_factor + label_smoothing_factor/num_labels respectively.

  • adafactor (bool, optional, defaults to False) – Whether or not to use the Adafactor optimizer instead of AdamW.

  • group_by_length (bool, optional, defaults to False) – Whether or not to group together samples of roughly the same legnth in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.

sortish_sampler (bool, optional, defaults to False):

Whether to use a sortish sampler or not. Only possible if the underlying datasets are Seq2SeqDataset for now but will become generally available in the near future.

It sorts the inputs according to lengths in order to minimize the padding size, with a bit of randomness for the training set.

predict_with_generate (bool, optional, defaults to False):

Whether to use generate to calculate generative metrics (ROUGE, BLEU).

TFTrainingArguments

class transformers.TFTrainingArguments(output_dir: str, overwrite_output_dir: bool = False, do_train: bool = False, do_eval: bool = None, do_predict: bool = False, evaluation_strategy: transformers.trainer_utils.EvaluationStrategy = 'no', prediction_loss_only: bool = False, per_device_train_batch_size: int = 8, per_device_eval_batch_size: int = 8, per_gpu_train_batch_size: Optional[int] = None, per_gpu_eval_batch_size: Optional[int] = None, gradient_accumulation_steps: int = 1, eval_accumulation_steps: Optional[int] = None, learning_rate: float = 5e-05, weight_decay: float = 0.0, adam_beta1: float = 0.9, adam_beta2: float = 0.999, adam_epsilon: float = 1e-08, max_grad_norm: float = 1.0, num_train_epochs: float = 3.0, max_steps: int = -1, lr_scheduler_type: transformers.trainer_utils.SchedulerType = 'linear', warmup_steps: int = 0, logging_dir: Optional[str] = <factory>, logging_first_step: bool = False, logging_steps: int = 500, save_steps: int = 500, save_total_limit: Optional[int] = None, no_cuda: bool = False, seed: int = 42, fp16: bool = False, fp16_opt_level: str = 'O1', fp16_backend: str = 'auto', local_rank: int = -1, tpu_num_cores: Optional[int] = None, tpu_metrics_debug: bool = False, debug: bool = False, dataloader_drop_last: bool = False, eval_steps: int = None, dataloader_num_workers: int = 0, past_index: int = -1, run_name: Optional[str] = None, disable_tqdm: Optional[bool] = None, remove_unused_columns: Optional[bool] = True, label_names: Optional[List[str]] = None, load_best_model_at_end: Optional[bool] = False, metric_for_best_model: Optional[str] = None, greater_is_better: Optional[bool] = None, ignore_data_skip: bool = False, sharded_ddp: bool = False, deepspeed: Optional[str] = None, label_smoothing_factor: float = 0.0, adafactor: bool = False, group_by_length: bool = False, tpu_name: str = None, poly_power: float = 1.0, xla: bool = False)[source]

TrainingArguments is the subset of the arguments we use in our example scripts which relate to the training loop itself.

Using HfArgumentParser we can turn this class into argparse arguments that can be specified on the command line.

Parameters
  • output_dir (str) – The output directory where the model predictions and checkpoints will be written.

  • overwrite_output_dir (bool, optional, defaults to False) – If True, overwrite the content of the output directory. Use this to continue training if output_dir points to a checkpoint directory.

  • do_train (bool, optional, defaults to False) – Whether to run training or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • do_eval (bool, optional) – Whether to run evaluation on the validation set or not. Will be set to True if evaluation_strategy is different from "no". This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • do_predict (bool, optional, defaults to False) – Whether to run predictions on the test set or not. This argument is not directly used by Trainer, it’s intended to be used by your training/evaluation scripts instead. See the example scripts for more details.

  • evaluation_strategy (str or EvaluationStrategy, optional, defaults to "no") –

    The evaluation strategy to adopt during training. Possible values are:

    • "no": No evaluation is done during training.

    • "steps": Evaluation is done (and logged) every eval_steps.

    • "epoch": Evaluation is done at the end of each epoch.

  • per_device_train_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for training.

  • per_device_eval_batch_size (int, optional, defaults to 8) – The batch size per GPU/TPU core/CPU for evaluation.

  • gradient_accumulation_steps

    (int, optional, defaults to 1): Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

    Warning

    When using gradient accumulation, one step is counted as one step with backward pass. Therefore, logging, evaluation, save will be conducted every gradient_accumulation_steps * xxx_step training examples.

  • learning_rate (float, optional, defaults to 5e-5) – The initial learning rate for Adam.

  • weight_decay (float, optional, defaults to 0) – The weight decay to apply (if not zero).

  • adam_beta1 (float, optional, defaults to 0.9) – The beta1 hyperparameter for the Adam optimizer.

  • adam_beta2 (float, optional, defaults to 0.999) – The beta2 hyperparameter for the Adam optimizer.

  • adam_epsilon (float, optional, defaults to 1e-8) – The epsilon hyperparameter for the Adam optimizer.

  • max_grad_norm (float, optional, defaults to 1.0) – Maximum gradient norm (for gradient clipping).

  • num_train_epochs (float, optional, defaults to 3.0) – Total number of training epochs to perform.

  • max_steps (int, optional, defaults to -1) – If set to a positive number, the total number of training steps to perform. Overrides num_train_epochs.

  • warmup_steps (int, optional, defaults to 0) – Number of steps used for a linear warmup from 0 to learning_rate.

  • logging_dir (str, optional) – TensorBoard log directory. Will default to runs/**CURRENT_DATETIME_HOSTNAME**.

  • logging_first_step (bool, optional, defaults to False) – Whether to log and evaluate the first global_step or not.

  • logging_steps (int, optional, defaults to 500) – Number of update steps between two logs.

  • save_steps (int, optional, defaults to 500) – Number of updates steps before two checkpoint saves.

  • save_total_limit (int, optional) – If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in output_dir.

  • no_cuda (bool, optional, defaults to False) – Whether to not use CUDA even when it is available or not.

  • seed (int, optional, defaults to 42) – Random seed for initialization.

  • fp16 (bool, optional, defaults to False) – Whether to use 16-bit (mixed) precision training (through NVIDIA Apex) instead of 32-bit training.

  • fp16_opt_level (str, optional, defaults to ‘O1’) – For fp16 training, Apex AMP optimization level selected in [‘O0’, ‘O1’, ‘O2’, and ‘O3’]. See details on the Apex documentation.

  • local_rank (int, optional, defaults to -1) – During distributed training, the rank of the process.

  • tpu_num_cores (int, optional) – When training on TPU, the number of TPU cores (automatically passed by launcher script).

  • debug (bool, optional, defaults to False) – Whether to activate the trace to record computation graphs and profiling information or not.

  • dataloader_drop_last (bool, optional, defaults to False) – Whether to drop the last incomplete batch (if the length of the dataset is not divisible by the batch size) or not.

  • eval_steps (int, optional, defaults to 1000) – Number of update steps before two evaluations.

  • past_index (int, optional, defaults to -1) – Some models like TransformerXL or :doc`XLNet <../model_doc/xlnet>` can make use of the past hidden states for their predictions. If this argument is set to a positive int, the Trainer will use the corresponding output (usually index 2) as the past state and feed it to the model at the next training step under the keyword argument mems.

  • tpu_name (str, optional) – The name of the TPU the process is running on.

  • run_name (str, optional) – A descriptor for the run. Notably used for wandb logging.

  • xla (bool, optional) – Whether to activate the XLA compilation or not.

property eval_batch_size

The actual batch size for evaluation (may differ from per_gpu_eval_batch_size in distributed training).

property n_gpu

The number of replicas (CPUs, GPUs or TPU cores) used in this training.

property n_replicas

The number of replicas (CPUs, GPUs or TPU cores) used in this training.

property strategy

The strategy used for distributed training.

property train_batch_size

The actual batch size for training (may differ from per_gpu_train_batch_size in distributed training).

Trainer Integrations

The Trainer has been extended to support libraries that may dramatically improve your training time and fit much bigger models.

Currently it supports third party solutions, DeepSpeed and FairScale, which implement parts of the paper ZeRO: Memory Optimizations Toward Training Trillion Parameter Models, by Samyam Rajbhandari, Jeff Rasley, Olatunji Ruwase, Yuxiong He.

This provided support is new and experimental as of this writing.

Installation Notes

As of this writing, both FairScale and Deepspeed require compilation of CUDA C++ code, before they can be used.

While all installation issues should be dealt with through the corresponding GitHub Issues of FairScale and Deepspeed, there are a few common issues that one may encounter while building any PyTorch extension that needs to build CUDA extensions.

Therefore, if you encounter a CUDA-related build issue while doing one of the following or both:

pip install fairscale
pip install deepspeed

please, read the following notes first.

In these notes we give examples for what to do when pytorch has been built with CUDA 10.2. If your situation is different remember to adjust the version number to the one you are after.

Possible problem #1:

While, Pytorch comes with its own CUDA toolkit, to build these two projects you must have an identical version of CUDA installed system-wide.

For example, if you installed pytorch with cudatoolkit==10.2 in the Python environment, you also need to have CUDA 10.2 installed system-wide.

The exact location may vary from system to system, but /usr/local/cuda-10.2 is the most common location on many Unix systems. When CUDA is correctly set up and added to the PATH environment variable, one can find the installation location by doing:

which nvcc

If you don’t have CUDA installed system-wide, install it first. You will find the instructions by using your favorite search engine. For example, if you’re on Ubuntu you may want to search for: ubuntu cuda 10.2 install.

Possible problem #2:

Another possible common problem is that you may have more than one CUDA toolkit installed system-wide. For example you may have:

/usr/local/cuda-10.2
/usr/local/cuda-11.0

Now, in this situation you need to make sure that your PATH and LD_LIBRARY_PATH environment variables contain the correct paths to the desired CUDA version. Typically, package installers will set these to contain whatever the last version was installed. If you encounter the problem, where the package build fails because it can’t find the right CUDA version despite you having it installed system-wide, it means that you need to adjust the 2 aforementioned environment variables.

First, you may look at their contents:

echo $PATH
echo $LD_LIBRARY_PATH

so you get an idea of what is inside.

It’s possible that LD_LIBRARY_PATH is empty.

PATH lists the locations of where executables can be found and LD_LIBRARY_PATH is for where shared libraries are to looked for. In both cases, earlier entries have priority over the later ones. : is used to separate multiple entries.

Now, to tell the build program where to find the specific CUDA toolkit, insert the desired paths to be listed first by doing:

export PATH=/usr/local/cuda-10.2/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-10.2/lib64:$LD_LIBRARY_PATH

Note that we aren’t overwriting the existing values, but prepending instead.

Of course, adjust the version number, the full path if need be. Check that the directories you assign actually do exist. lib64 sub-directory is where the various CUDA .so objects, like libcudart.so reside, it’s unlikely that your system will have it named differently, but if it is adjust it to reflect your reality.

Possible problem #3:

Some older CUDA versions may refuse to build with newer compilers. For example, you my have gcc-9 but it wants gcc-7.

There are various ways to go about it.

If you can install the latest CUDA toolkit it typically should support the newer compiler.

Alternatively, you could install the lower version of the compiler in addition to the one you already have, or you may already have it but it’s not the default one, so the build system can’t see it. If you have gcc-7 installed but the build system complains it can’t find it, the following might do the trick:

sudo ln -s /usr/bin/gcc-7  /usr/local/cuda-10.2/bin/gcc
sudo ln -s /usr/bin/g++-7  /usr/local/cuda-10.2/bin/g++

Here, we are making a symlink to gcc-7 from /usr/local/cuda-10.2/bin/gcc and since /usr/local/cuda-10.2/bin/ should be in the PATH environment variable (see the previous problem’s solution), it should find gcc-7 (and g++7) and then the build will succeed.

As always make sure to edit the paths in the example to match your situation.

If still unsuccessful:

If after addressing these you still encounter build issues, please, proceed with the GitHub Issue of FairScale and Deepspeed, depending on the project you have the problem with.

FairScale

By integrating FairScale the Trainer provides support for the following features from the ZeRO paper:

  1. Optimizer State Sharding

  2. Gradient Sharding

You will need at least two GPUs to use this feature.

To deploy this feature:

  1. Install the library via pypi:

    pip install fairscale
    

    or find more details on the FairScale’s GitHub page.

  2. Add --sharded_ddp to the command line arguments, and make sure you have added the distributed launcher -m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE if you haven’t been using it already.

For example here is how you could use it for finetune_trainer.py with 2 GPUs:

cd examples/seq2seq
python -m torch.distributed.launch --nproc_per_node=2 ./finetune_trainer.py \
--model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
--output_dir output_dir --overwrite_output_dir \
--do_train --n_train 500 --num_train_epochs 1 \
--per_device_train_batch_size 1  --freeze_embeds \
--src_lang en_XX --tgt_lang ro_RO --task translation \
--fp16 --sharded_ddp

Notes:

  • This feature requires distributed training (so multiple GPUs).

  • It is not implemented for TPUs.

  • It works with --fp16 too, to make things even faster.

  • One of the main benefits of enabling --sharded_ddp is that it uses a lot less GPU memory, so you should be able to use significantly larger batch sizes using the same hardware (e.g. 3x and even bigger) which should lead to significantly shorter training time.

DeepSpeed

DeepSpeed implements everything described in the ZeRO paper, except ZeRO’s stage 3. “Parameter Partitioning (Pos+g+p)”. Currently it provides full support for:

  1. Optimizer State Partitioning (ZeRO stage 1)

  2. Add Gradient Partitioning (ZeRO stage 2)

Installation

Install the library via pypi:

pip install deepspeed

or find more details on the DeepSpeed’s GitHub page.

Deployment with multiple GPUs

To deploy this feature with multiple GPUs adjust the Trainer command line arguments as following:

  1. replace python -m torch.distributed.launch with deepspeed.

  2. add a new argument --deepspeed ds_config.json, where ds_config.json is the DeepSpeed configuration file as documented here. The file naming is up to you.

Therefore, if your original command line looked as following:

python -m torch.distributed.launch --nproc_per_node=2 your_program.py <normal cl args>

Now it should be:

deepspeed --num_gpus=2 your_program.py <normal cl args> --deepspeed ds_config.json

Unlike, torch.distributed.launch where you have to specify how many GPUs to use with --nproc_per_node, with the deepspeed launcher you don’t have to use the corresponding --num_gpus if you want all of your GPUs used. The full details on how to configure various nodes and GPUs can be found here.

Here is an example of running finetune_trainer.py under DeepSpeed deploying all available GPUs:

cd examples/seq2seq
deepspeed ./finetune_trainer.py --deepspeed ds_config.json \
--model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
--output_dir output_dir --overwrite_output_dir \
--do_train --n_train 500 --num_train_epochs 1 \
--per_device_train_batch_size 1  --freeze_embeds \
--src_lang en_XX --tgt_lang ro_RO --task translation

Note that in the DeepSpeed documentation you are likely to see --deepspeed --deepspeed_config ds_config.json - i.e. two DeepSpeed-related arguments, but for the sake of simplicity, and since there are already so many arguments to deal with, we combined the two into a single argument.

For some practical usage examples, please, see this post.

Deployment with one GPU

To deploy DeepSpeed with one GPU adjust the Trainer command line arguments as following:

cd examples/seq2seq
deepspeed --num_gpus=1 ./finetune_trainer.py --deepspeed ds_config.json \
--model_name_or_path sshleifer/distill-mbart-en-ro-12-4 --data_dir wmt_en_ro \
--output_dir output_dir --overwrite_output_dir \
--do_train --n_train 500 --num_train_epochs 1 \
--per_device_train_batch_size 1  --freeze_embeds \
--src_lang en_XX --tgt_lang ro_RO --task translation

This is almost the same as with multiple-GPUs, but here we tell DeepSpeed explicitly to use just one GPU. By default, DeepSpeed deploys all GPUs it can see. If you have only 1 GPU to start with, then you don’t need this argument. The following documentation discusses the launcher options.

Why would you want to use DeepSpeed with just one GPU?

  1. It has a ZeRO-offload feature which can delegate some computations and memory to the host’s CPU and RAM, and thus leave more GPU resources for model’s needs - e.g. larger batch size, or enabling a fitting of a very big model which normally won’t fit.

  2. It provides a smart GPU memory management system, that minimizes memory fragmentation, which again allows you to fit bigger models and data batches.

While we are going to discuss the configuration in details next, the key to getting a huge improvement on a single GPU with DeepSpeed is to have at least the following configuration in the configuration file:

{
  "zero_optimization": {
     "stage": 2,
     "allgather_partitions": true,
     "allgather_bucket_size": 2e8,
     "reduce_scatter": true,
     "reduce_bucket_size": 2e8,
     "overlap_comm": true,
     "contiguous_gradients": true,
     "cpu_offload": true
  },
}

which enables cpu_offload and some other important features. You may experiment with the buffer sizes, you will find more details in the discussion below.

For a practical usage example of this type of deployment, please, see this post.

Configuration

For the complete guide to the DeepSpeed configuration options that can be used in its configuration file please refer to the following documentation.

While you always have to supply the DeepSpeed configuration file, you can configure the DeepSpeed integration in several ways:

  1. Supply most of the configuration inside the file, and just use a few required command line arguments. This is the recommended way as it puts most of the configuration params in one place.

  2. Supply just the ZeRO configuration params inside the file, and configure the rest using the normal Trainer command line arguments.

  3. Any variation of the first two ways.

To get an idea of what DeepSpeed configuration file looks like, here is one that activates ZeRO stage 2 features, enables FP16, uses AdamW optimizer and WarmupLR scheduler:

{
    "fp16": {
        "enabled": true,
        "loss_scale": 0,
        "loss_scale_window": 1000,
        "hysteresis": 2,
        "min_loss_scale": 1
    },

   "zero_optimization": {
       "stage": 2,
       "allgather_partitions": true,
       "allgather_bucket_size": 5e8,
       "overlap_comm": true,
       "reduce_scatter": true,
       "reduce_bucket_size": 5e8,
       "contiguous_gradients": true,
       "cpu_offload": true
   },

   "optimizer": {
     "type": "AdamW",
     "params": {
       "lr": 3e-5,
       "betas": [ 0.8, 0.999 ],
       "eps": 1e-8,
       "weight_decay": 3e-7
     }
   },
   "zero_allow_untested_optimizer": true,

   "scheduler": {
     "type": "WarmupLR",
     "params": {
       "warmup_min_lr": 0,
       "warmup_max_lr": 3e-5,
       "warmup_num_steps": 500
     }
   }
}

If you already have a command line that you have been using with transformers.Trainer args, you can continue using those and the Trainer will automatically convert them into the corresponding DeepSpeed configuration at run time. For example, you could use the following configuration file:

{
   "zero_optimization": {
       "stage": 2,
       "allgather_partitions": true,
       "allgather_bucket_size": 5e8,
       "overlap_comm": true,
       "reduce_scatter": true,
       "reduce_bucket_size": 5e8,
       "contiguous_gradients": true,
       "cpu_offload": true
   }
}

and the following command line arguments:

--learning_rate 3e-5 --warmup_steps 500 --adam_beta1 0.8 --adam_beta2 0.999 --adam_epsilon 1e-8 \
--weight_decay 3e-7 --lr_scheduler_type constant_with_warmup --fp16 --fp16_backend amp

to achieve the same configuration as provided by the longer json file in the first example.

When you execute the program, DeepSpeed will log the configuration it received from the Trainer to the console, so you can see exactly what the final configuration was passed to it.

Shared Configuration

Some configuration information is required by both the Trainer and DeepSpeed to function correctly, therefore, to prevent conflicting definitions, which could lead to hard to detect errors, we chose to configure those via the Trainer command line arguments.

Therefore, the following DeepSpeed configuration params shouldn’t be used with the Trainer:

  • train_batch_size

  • train_micro_batch_size_per_gpu

  • gradient_accumulation_steps

as these will be automatically derived from the run time environment and the following 2 command line arguments:

--per_device_train_batch_size 8 --gradient_accumulation_steps 2

which are always required to be supplied.

Of course, you will need to adjust the values in this example to your situation.

ZeRO

The zero_optimization section of the configuration file is the most important part (docs), since that is where you define which ZeRO stages you want to enable and how to configure them.

{
   "zero_optimization": {
       "stage": 2,
       "allgather_partitions": true,
       "allgather_bucket_size": 5e8,
       "overlap_comm": true,
       "reduce_scatter": true,
       "reduce_bucket_size": 5e8,
       "contiguous_gradients": true,
       "cpu_offload": true
   }
}

Notes:

  • enabling cpu_offload should reduce GPU RAM usage (it requires "stage": 2)

  • "overlap_comm": true trades off increased GPU RAM usage to lower all-reduce latency. overlap_comm uses 4.5x the allgather_bucket_size and reduce_bucket_size values. So if they are set to 5e8, this requires a 9GB footprint (5e8 x 2Bytes x 2 x 4.5). Therefore, if you have a GPU with 8GB or less RAM, to avoid getting OOM-errors you will need to reduce those parameters to about 2e8, which would require 3.6GB.

This section has to be configured exclusively via DeepSpeed configuration - the Trainer provides no equivalent command line arguments.

Optimizer

DeepSpeed’s main optimizers are Adam, OneBitAdam, and Lamb. These have been thoroughly tested with ZeRO and are thus recommended to be used. It, however, can import other optimizers from torch. The full documentation is here.

If you don’t configure the optimizer entry in the configuration file, the Trainer will automatically set it to AdamW and will use the supplied values or the defaults for the following command line arguments: --learning_rate, --adam_beta1, --adam_beta2, --adam_epsilon and --weight_decay.

Here is an example of the pre-configured optimizer entry for AdamW:

{
   "zero_allow_untested_optimizer": true,
   "optimizer": {
       "type": "AdamW",
       "params": {
         "lr": 0.001,
         "betas": [0.8, 0.999],
         "eps": 1e-8,
         "weight_decay": 3e-7
       }
     }
}

Since AdamW isn’t on the list of tested with DeepSpeed/ZeRO optimizers, we have to add zero_allow_untested_optimizer flag.

If you want to use one of the officially supported optimizers, configure them explicitly in the configuration file, and make sure to adjust the values. e.g. if use Adam you will want weight_decay around 0.01.

Scheduler

DeepSpeed supports LRRangeTest, OneCycle, WarmupLR and WarmupDecayLR LR schedulers. The full documentation is here.

If you don’t configure the scheduler entry in the configuration file, the Trainer will use the value of --lr_scheduler_type to configure it. Currently the Trainer supports only 2 LR schedulers that are also supported by DeepSpeed:

  • WarmupLR via --lr_scheduler_type constant_with_warmup

  • WarmupDecayLR via --lr_scheduler_type linear. This is also the default value for --lr_scheduler_type, therefore, if you don’t configure the scheduler this is scheduler that will get configured by default.

In either case, the values of --learning_rate and --warmup_steps will be used for the configuration.

In other words, if you don’t use the configuration file to set the scheduler entry, provide either:

--lr_scheduler_type constant_with_warmup --learning_rate 3e-5 --warmup_steps 500

or

--lr_scheduler_type linear --learning_rate 3e-5 --warmup_steps 500

with the desired values. If you don’t pass these arguments, reasonable default values will be used instead.

In the case of WarmupDecayLR total_num_steps gets set either via the --max_steps command line argument, or if it is not provided, derived automatically at run time based on the environment and the size of the dataset and other command line arguments.

Here is an example of the pre-configured scheduler entry for WarmupLR (constant_with_warmup in the Trainer API):

{
   "scheduler": {
         "type": "WarmupLR",
         "params": {
             "warmup_min_lr": 0,
             "warmup_max_lr": 0.001,
             "warmup_num_steps": 1000
         }
     }
}

Automatic Mixed Precision

You can work with FP16 in one of the following ways:

  1. Pytorch native amp, as documented here.

  2. NVIDIA’s apex, as documented here.

If you want to use an equivalent of the Pytorch native amp, you can either configure the fp16 entry in the configuration file, or use the following command line arguments: --fp16 --fp16_backend amp.

Here is an example of the fp16 configuration:

{
    "fp16": {
        "enabled": true,
        "loss_scale": 0,
        "loss_scale_window": 1000,
        "hysteresis": 2,
        "min_loss_scale": 1
    },
}

If you want to use NVIDIA’s apex instead, you can can either configure the amp entry in the configuration file, or use the following command line arguments: --fp16 --fp16_backend apex --fp16_opt_level 01.

Here is an example of the amp configuration:

{
    "amp": {
        "enabled": true,
        "opt_level": "O1"
    }
}

Gradient Clipping

If you don’t configure the gradient_clipping entry in the configuration file, the Trainer will use the value of the --max_grad_norm command line argument to set it.

Here is an example of the gradient_clipping configuration:

{
    "gradient_clipping": 1.0,
}

Notes

  • DeepSpeed works with the PyTorch Trainer but not TF TFTrainer.

  • While DeepSpeed has a pip installable PyPI package, it is highly recommended that it gets installed from source to best match your hardware and also if you need to enable certain features, like 1-bit Adam, which aren’t available in the pypi distribution.

  • You don’t have to use the Trainer to use DeepSpeed with HuggingFace transformers - you can use any model with your own trainer, and you will have to adapt the latter according to the DeepSpeed integration instructions.

Main DeepSpeed Resources

Finally, please, remember that, HuggingFace Trainer only integrates DeepSpeed, therefore if you have any problems or questions with regards to DeepSpeed usage, please, file an issue with DeepSpeed GitHub.