#
Trainer

Trainer クラスは、ほとんどの標準的なユースケースに対して、PyTorch で機能を完全にトレーニングするための API を提供します。これは、サンプル スクリプト のほとんどで使用されています。

Trainer をインスタンス化する前に、トレーニング中にカスタマイズのすべてのポイントにアクセスするために TrainingArguments を作成します。

この API は、複数の GPU/TPU での分散トレーニング、NVIDIA Apex および PyTorch のネイティブ AMP による混合精度をサポートします。

Trainer には、上記の機能をサポートする基本的なトレーニング ループが含まれています。カスタム動作を挿入するには、それらをサブクラス化し、次のメソッドをオーバーライドします。

- get_train_dataloader — トレーニング データローダーを作成します。
- get_eval_dataloader — 評価用データローダーを作成します。
- get_test_dataloader — テスト データローダーを作成します。
- log — トレーニングを監視しているさまざまなオブジェクトに関する情報をログに記録します。
- create_optimizer_and_scheduler — オプティマイザと学習率スケジューラが渡されなかった場合にセットアップします。 初期化。 `create_optimizer`メソッドと`create_scheduler`メソッドをサブクラス化またはオーバーライドすることもできることに注意してください。 別々に。
- create_optimizer — init で渡されなかった場合にオプティマイザーをセットアップします。
- create_scheduler — init で渡されなかった場合、学習率スケジューラを設定します。
- compute_loss - トレーニング入力のバッチの損失を計算します。
- training_step — トレーニング ステップを実行します。
- prediction_step — 評価/テスト ステップを実行します。
- evaluate — 評価ループを実行し、メトリクスを返します。
- predict — テスト セットの予測 (ラベルが使用可能な場合はメトリクスも含む) を返します。

Trainer クラスは 🤗 Transformers モデル用に最適化されており、驚くべき動作をする可能性があります 他の機種で使用する場合。独自のモデルで使用する場合は、次の点を確認してください。

- モデルは常に ModelOutput のタプルまたはサブクラスを返します。
- `labels` 引数が指定され、その損失が最初の値として返される場合、モデルは損失を計算できます。 タプルの要素 (モデルがタプルを返す場合)
- モデルは複数のラベル引数を受け入れることができます (TrainingArguments で `label_names` を使用して、その名前を Trainer に示します) が、それらのいずれにも `"label"` という名前を付ける必要はありません。

以下は、加重損失を使用するように Trainer をカスタマイズする方法の例です (不均衡なトレーニング セットがある場合に役立ちます)。

    from torch import nn
    from transformers import Trainer

    class CustomTrainer(Trainer):
        def compute_loss(self, model, inputs, return_outputs=False):
            labels = inputs.pop("labels")
            # forward pass
            outputs = model(**inputs)
            logits = outputs.get("logits")
            # compute custom loss (suppose one has 3 labels with different weights)
            loss_fct = nn.CrossEntropyLoss(weight=torch.tensor([1.0, 2.0, 3.0], device=model.device))
            loss = loss_fct(logits.view(-1, self.model.config.num_labels), labels.view(-1))
            return (loss, outputs) if return_outputs else loss

PyTorch Trainer のトレーニング ループの動作をカスタマイズするもう 1 つの方法は、トレーニング ループの状態を検査できる callbacks を使用することです (進行状況レポート、TensorBoard または他の ML プラットフォームでのログ記録など)。決定（早期停止など）。

##
Trainer

###
class transformers.Trainer

< source >

( model: Union = None args: TrainingArguments = None data_collator: Optional = None train_dataset: Union = None eval_dataset: Union = None tokenizer: Optional = None model_init: Optional = None compute_metrics: Optional = None callbacks: Optional = None optimizers: Tuple = (None, None) preprocess_logits_for_metrics: Optional = None )

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.

  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 (Union[`torch.utils.data.Dataset`, `torch.utils.data.IterableDataset`, `datasets.Dataset`], optional) — The dataset to use for training. If it is a `Dataset`, columns not accepted by the `model.forward()` method are automatically removed.

  Note that if it’s a `torch.utils.data.IterableDataset` with some randomization and you are training in a distributed fashion, your iterable dataset should either use a internal attribute `generator` that is a `torch.Generator` for the randomization that must be identical on all processes (and the Trainer will manually set the seed of this `generator` at each epoch) or have a `set_epoch()` method that internally sets the seed of the RNGs used.

- eval_dataset (Union[`torch.utils.data.Dataset`, Dict[str, `torch.utils.data.Dataset`, `datasets.Dataset`]), optional) — The dataset to use for evaluation. If it is a `Dataset`, columns not accepted by the `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each dataset prepending the dictionary key to the metric name.
- tokenizer (PreTrainedTokenizerBase, optional) — The tokenizer used to preprocess the data. If provided, will be used to automatically pad the inputs to 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/SigOpt 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, defaults to `(None, None)`) — 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`.
- preprocess_logits_for_metrics (`Callable[[torch.Tensor, torch.Tensor], torch.Tensor]`, optional) — A function that preprocess the logits right before caching them at each evaluation step. Must take two tensors, the logits and the labels, and return the logits once processed as desired. The modifications made by this function will be reflected in the predictions received by `compute_metrics`.

  Note that the labels (second parameter) will be `None` if the dataset does not have them.

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

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).
- place_model_on_device — Whether or not to automatically place the model on the device - it will be set to `False` if model parallel or deepspeed is used, or if the default `TrainingArguments.place_model_on_device` is overridden to return `False` .
- is_in_train — Whether or not a model is currently running `train` (e.g. when `evaluate` is called while in `train`)

####
add_callback

< source >

( callback )

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.

Add a callback to the current list of TrainerCallback.

####
autocast_smart_context_manager

< source >

( cache_enabled: Optional = True )

A helper wrapper that creates an appropriate context manager for `autocast` while feeding it the desired arguments, depending on the situation.

####
compute_loss

< source >

( model inputs return_outputs = False )

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

Subclass and override for custom behavior.

####
compute_loss_context_manager

< source >

( )

A helper wrapper to group together context managers.

####
create_model_card

< source >

( language: Optional = None license: Optional = None tags: Union = None model_name: Optional = None finetuned_from: Optional = None tasks: Union = None dataset_tags: Union = None dataset: Union = None dataset_args: Union = None )

Parameters

- language (`str`, optional) — The language of the model (if applicable)
- license (`str`, optional) — The license of the model. Will default to the license of the pretrained model used, if the original model given to the `Trainer` comes from a repo on the Hub.
- tags (`str` or `List[str]`, optional) — Some tags to be included in the metadata of the model card.
- model_name (`str`, optional) — The name of the model.
- finetuned_from (`str`, optional) — The name of the model used to fine-tune this one (if applicable). Will default to the name of the repo of the original model given to the `Trainer` (if it comes from the Hub).
- tasks (`str` or `List[str]`, optional) — One or several task identifiers, to be included in the metadata of the model card.
- dataset_tags (`str` or `List[str]`, optional) — One or several dataset tags, to be included in the metadata of the model card.
- dataset (`str` or `List[str]`, optional) — One or several dataset identifiers, to be included in the metadata of the model card.
- dataset_args (`str` or `List[str]`, optional) — One or several dataset arguments, to be included in the metadata of the model card.

Creates a draft of a model card using the information available to the `Trainer`.

####
create_optimizer

< source >

( )

Setup the optimizer.

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.

####
create_optimizer_and_scheduler

< source >

( num_training_steps: int )

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 (or `create_optimizer` and/or `create_scheduler`) in a subclass.

####
create_scheduler

< source >

( num_training_steps: int optimizer: Optimizer = None )

Parameters

- num_training_steps (int) — The number of training steps to do.

Setup the scheduler. The optimizer of the trainer must have been set up either before this method is called or passed as an argument.

####
evaluate

< source >

( eval_dataset: Union = None ignore_keys: Optional = None metric_key_prefix: str = 'eval' )

Parameters

- eval_dataset (Union[`Dataset`, Dict[str, `Dataset`]), optional) — Pass a dataset if you wish to override `self.eval_dataset`. If it is a `Dataset`, columns not accepted by the `model.forward()` method are automatically removed. If it is a dictionary, it will evaluate on each dataset, prepending the dictionary key to the metric name. Datasets must implement the `__len__` method.

  If you pass a dictionary with names of datasets as keys and datasets as values, evaluate will run separate evaluations on each dataset. This can be useful to monitor how training affects other datasets or simply to get a more fine-grained evaluation. When used with `load_best_model_at_end`, make sure `metric_for_best_model` references exactly one of the datasets. If you, for example, pass in `{"data1": data1, "data2": data2}` for two datasets `data1` and `data2`, you could specify `metric_for_best_model="eval_data1_loss"` for using the loss on `data1` and `metric_for_best_model="eval_data1_loss"` for the loss on `data2`.

- 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)

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.

####
evaluation_loop

< source >

( dataloader: DataLoader description: str prediction_loss_only: Optional = None ignore_keys: Optional = None metric_key_prefix: str = 'eval' )

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

Works both with or without labels.

####
floating_point_ops

< source >

( inputs: Dict ) →
`int`

Parameters

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

Returns

`int`

The number of floating-point operations.

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.

####
get_decay_parameter_names

< source >

( model )

Get all parameter names that weight decay will be applied to

Note that some models implement their own layernorm instead of calling nn.LayerNorm, weight decay could still apply to those modules since this function only filter out instance of nn.LayerNorm

####
get_eval_dataloader

< source >

( eval_dataset: Optional = None )

Parameters

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

Returns the evaluation `~torch.utils.data.DataLoader`.

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

####
get_learning_rates

< source >

( )

Returns the learning rate of each parameter from self.optimizer.

####
get_num_trainable_parameters

< source >

( )

Get the number of trainable parameters.

####
get_optimizer_cls_and_kwargs

< source >

( args: TrainingArguments model: Optional = None )

Parameters

- args (`transformers.training_args.TrainingArguments`) — The training arguments for the training session.

Returns the optimizer class and optimizer parameters based on the training arguments.

####
get_optimizer_group

< source >

( param: Union = None )

Parameters

- param (`str` or `torch.nn.parameter.Parameter`, optional) — The parameter for which optimizer group needs to be returned.

Returns optimizer group for a parameter if given, else returns all optimizer groups for params.

####
get_test_dataloader

< source >

( test_dataset: Dataset )

Parameters

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

Returns the test `~torch.utils.data.DataLoader`.

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

####
get_train_dataloader

< source >

( )

Returns the training `~torch.utils.data.DataLoader`.

Will use no sampler if `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.

####
hyperparameter_search

< source >

( hp_space: Optional = None compute_objective: Optional = None n_trials: int = 20 direction: Union = 'minimize' backend: Union = None hp_name: Optional = None **kwargs ) →
[`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]

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()` or `default_hp_space_sigopt()` 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` or `List[str]`, optional, defaults to `"minimize"`) — If it’s single objective optimization, direction is `str`, can be `"minimize"` or `"maximize"`, you should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics. If it’s multi objectives optimization, direction is `List[str]`, can be List of `"minimize"` and `"maximize"`, you should pick `"minimize"` when optimizing the validation loss, `"maximize"` when optimizing one or several metrics.
- backend (`str` or `~training_utils.HPSearchBackend`, optional) — The backend to use for hyperparameter search. Will default to optuna or Ray Tune or SigOpt, depending on which one is installed. If all are installed, will default to optuna.
- hp_name (`Callable[["optuna.Trial"], str]]`, optional) — A function that defines the trial/run name. Will default to None.
- kwargs (`Dict[str, Any]`, optional) — Additional keyword arguments passed along to `optuna.create_study` or `ray.tune.run`. For more information see:
  - the documentation of optuna.create_study
  - the documentation of tune.run
  - the documentation of sigopt

Returns

[`trainer_utils.BestRun` or `List[trainer_utils.BestRun]`]

All the information about the best run or best runs for multi-objective optimization. Experiment summary can be found in `run_summary` attribute for Ray backend.

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

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.

####
init_hf_repo

< source >

( token: Optional = None )

Initializes a git repo in `self.args.hub_model_id`.

####
is_local_process_zero

< 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

< 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

< source >

( logs: Dict )

Parameters

- logs (`Dict[str, float]`) — The values to log.

Log `logs` on the various objects watching training.

Subclass and override this method to inject custom behavior.

####
log_metrics

< source >

( split metrics )

Parameters

- split (`str`) — Mode/split name: one of `train`, `eval`, `test`
- metrics (`Dict[str, float]`) — The metrics returned from train/evaluate/predictmetrics: metrics dict

Log metrics in a specially formatted way

Under distributed environment this is done only for a process with rank 0.

Notes on memory reports:

In order to get memory usage report you need to install `psutil`. You can do that with `pip install psutil`.

Now when this method is run, you will see a report that will include: :

    init_mem_cpu_alloc_delta   =     1301MB
    init_mem_cpu_peaked_delta  =      154MB
    init_mem_gpu_alloc_delta   =      230MB
    init_mem_gpu_peaked_delta  =        0MB
    train_mem_cpu_alloc_delta  =     1345MB
    train_mem_cpu_peaked_delta =        0MB
    train_mem_gpu_alloc_delta  =      693MB
    train_mem_gpu_peaked_delta =        7MB

Understanding the reports:

- the first segment, e.g., `train__`, tells you which stage the metrics are for. Reports starting with `init_` will be added to the first stage that gets run. So that if only evaluation is run, the memory usage for the `__init__` will be reported along with the `eval_` metrics.
- the third segment, is either `cpu` or `gpu`, tells you whether it’s the general RAM or the gpu0 memory metric.
- `*_alloc_delta` - is the difference in the used/allocated memory counter between the end and the start of the stage - it can be negative if a function released more memory than it allocated.
- `*_peaked_delta` - is any extra memory that was consumed and then freed - relative to the current allocated memory counter - it is never negative. When you look at the metrics of any stage you add up `alloc_delta` + `peaked_delta` and you know how much memory was needed to complete that stage.

The reporting happens only for process of rank 0 and gpu 0 (if there is a gpu). Typically this is enough since the main process does the bulk of work, but it could be not quite so if model parallel is used and then other GPUs may use a different amount of gpu memory. This is also not the same under DataParallel where gpu0 may require much more memory than the rest since it stores the gradient and optimizer states for all participating GPUS. Perhaps in the future these reports will evolve to measure those too.

The CPU RAM metric measures RSS (Resident Set Size) includes both the memory which is unique to the process and the memory shared with other processes. It is important to note that it does not include swapped out memory, so the reports could be imprecise.

The CPU peak memory is measured using a sampling thread. Due to python’s GIL it may miss some of the peak memory if that thread didn’t get a chance to run when the highest memory was used. Therefore this report can be less than reality. Using `tracemalloc` would have reported the exact peak memory, but it doesn’t report memory allocations outside of python. So if some C++ CUDA extension allocated its own memory it won’t be reported. And therefore it was dropped in favor of the memory sampling approach, which reads the current process memory usage.

The GPU allocated and peak memory reporting is done with `torch.cuda.memory_allocated()` and `torch.cuda.max_memory_allocated()`. This metric reports only “deltas” for pytorch-specific allocations, as `torch.cuda` memory management system doesn’t track any memory allocated outside of pytorch. For example, the very first cuda call typically loads CUDA kernels, which may take from 0.5 to 2GB of GPU memory.

Note that this tracker doesn’t account for memory allocations outside of Trainer’s `__init__`, `train`, `evaluate` and `predict` calls.

Because `evaluation` calls may happen during `train`, we can’t handle nested invocations because `torch.cuda.max_memory_allocated` is a single counter, so if it gets reset by a nested eval call, `train`’s tracker will report incorrect info. If this pytorch issue gets resolved it will be possible to change this class to be re-entrant. Until then we will only track the outer level of `train`, `evaluate` and `predict` methods. Which means that if `eval` is called during `train`, it’s the latter that will account for its memory usage and that of the former.

This also means that if any other tool that is used along the Trainer calls `torch.cuda.reset_peak_memory_stats`, the gpu peak memory stats could be invalid. And the Trainer will disrupt the normal behavior of any such tools that rely on calling `torch.cuda.reset_peak_memory_stats` themselves.

For best performance you may want to consider turning the memory profiling off for production runs.

####
metrics_format

< source >

( metrics: Dict ) →
metrics (`Dict[str, float]`)

Parameters

- metrics (`Dict[str, float]`) — The metrics returned from train/evaluate/predict

Returns

metrics (`Dict[str, float]`)

The reformatted metrics

Reformat Trainer metrics values to a human-readable format

####
num_examples

< source >

( dataloader: DataLoader )

Helper to get number of samples in a `~torch.utils.data.DataLoader` by accessing its dataset. When dataloader.dataset does not exist or has no length, estimates as best it can

####
num_tokens

< source >

( train_dl: DataLoader max_steps: Optional = None )

Helper to get number of tokens in a `~torch.utils.data.DataLoader` by enumerating dataloader.

####
pop_callback

< source >

( callback ) →
TrainerCallback

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

TrainerCallback

The callback removed, if found.

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).

####
predict

< source >

( test_dataset: Dataset ignore_keys: Optional = None metric_key_prefix: str = 'test' )

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 `"test"`) — An optional prefix to be used as the metrics key prefix. For example the metrics “bleu” will be named “test_bleu” if the prefix is “test” (default)

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()`.

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

< source >

( dataloader: DataLoader description: str prediction_loss_only: Optional = None ignore_keys: Optional = None metric_key_prefix: str = 'eval' )

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

Works both with or without labels.

####
prediction_step

< source >

( model: Module inputs: Dict prediction_loss_only: bool ignore_keys: Optional = None ) →
Tuple[Optional[torch.Tensor], Optional[torch.Tensor], Optional[torch.Tensor]]

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 (`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.

Returns

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

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

Perform an evaluation step on `model` using `inputs`.

Subclass and override to inject custom behavior.

####
propagate_args_to_deepspeed

< source >

( auto_find_batch_size = False )

Sets values in the deepspeed plugin based on the Trainer args

####
push_to_hub

< source >

( commit_message: Optional = 'End of training' blocking: bool = True token: Optional = None **kwargs )

Parameters

- commit_message (`str`, optional, defaults to `"End of training"`) — Message to commit while pushing.
- blocking (`bool`, optional, defaults to `True`) — Whether the function should return only when the `git push` has finished.
- token (`str`, optional, defaults to `None`) — Token with write permission to overwrite Trainer’s original args.
- kwargs (`Dict[str, Any]`, optional) — Additional keyword arguments passed along to create_model_card().

Upload `self.model` and `self.tokenizer` to the 🤗 model hub on the repo `self.args.hub_model_id`.

####
remove_callback

< source >

( callback )

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.

Remove a callback from the current list of TrainerCallback.

####
save_metrics

< source >

( split metrics combined = True )

Parameters

- split (`str`) — Mode/split name: one of `train`, `eval`, `test`, `all`
- metrics (`Dict[str, float]`) — The metrics returned from train/evaluate/predict
- combined (`bool`, optional, defaults to `True`) — Creates combined metrics by updating `all_results.json` with metrics of this call

Save metrics into a json file for that split, e.g. `train_results.json`.

Under distributed environment this is done only for a process with rank 0.

To understand the metrics please read the docstring of log_metrics(). The only difference is that raw unformatted numbers are saved in the current method.

####
save_model

< source >

( output_dir: Optional = None _internal_call: bool = False )

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

Will only save from the main process.

####
save_state

< source >

( )

Saves the Trainer state, since Trainer.save_model saves only the tokenizer with the model

Under distributed environment this is done only for a process with rank 0.

####
train

< source >

( resume_from_checkpoint: Union = None trial: Union = None ignore_keys_for_eval: Optional = None **kwargs )

Parameters

- resume_from_checkpoint (`str` or `bool`, optional) — If a `str`, local path to a saved checkpoint as saved by a previous instance of Trainer. If a `bool` and equals `True`, load the last checkpoint in args.output_dir as saved by a previous instance of Trainer. If present, training will resume from the model/optimizer/scheduler states loaded here.
- trial (`optuna.Trial` or `Dict[str, Any]`, optional) — The trial run or the hyperparameter dictionary for hyperparameter search.
- ignore_keys_for_eval (`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 for evaluation during the training.
- kwargs (`Dict[str, Any]`, optional) — Additional keyword arguments used to hide deprecated arguments

Main training entry point.

####
training_step

< source >

( model: Module inputs: Dict ) →
`torch.Tensor`

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

`torch.Tensor`

The tensor with training loss on this batch.

Perform a training step on a batch of inputs.

Subclass and override to inject custom behavior.

##
Seq2SeqTrainer

###
class transformers.Seq2SeqTrainer

< source >

( model: Union = None args: TrainingArguments = None data_collator: Optional = None train_dataset: Optional = None eval_dataset: Union = None tokenizer: Optional = None model_init: Optional = None compute_metrics: Optional = None callbacks: Optional = None optimizers: Tuple = (None, None) preprocess_logits_for_metrics: Optional = None )

####
evaluate

< source >

( eval_dataset: Optional = None ignore_keys: Optional = None metric_key_prefix: str = 'eval' **gen_kwargs )

Parameters

- eval_dataset (`Dataset`, optional) — Pass a dataset if you wish to override `self.eval_dataset`. If it is an `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. gen_kwargs — Additional `generate` specific kwargs.

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.

####
predict

< source >

( test_dataset: Dataset ignore_keys: Optional = None metric_key_prefix: str = 'test' **gen_kwargs )

Parameters

- test_dataset (`Dataset`) — Dataset to run the predictions on. If it is a `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. gen_kwargs — Additional `generate` specific kwargs.

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()`.

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).

##
TrainingArguments

###
class transformers.TrainingArguments

< source >

( output_dir: str overwrite_output_dir: bool = False do_train: bool = False do_eval: bool = False do_predict: bool = False evaluation_strategy: Union = '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 = None per_gpu_eval_batch_size: Optional = None gradient_accumulation_steps: int = 1 eval_accumulation_steps: Optional = None eval_delay: Optional = 0 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: Union = 'linear' lr_scheduler_kwargs: Union = <factory> warmup_ratio: float = 0.0 warmup_steps: int = 0 log_level: Optional = 'passive' log_level_replica: Optional = 'warning' log_on_each_node: bool = True logging_dir: Optional = None logging_strategy: Union = 'steps' logging_first_step: bool = False logging_steps: float = 500 logging_nan_inf_filter: bool = True save_strategy: Union = 'steps' save_steps: float = 500 save_total_limit: Optional = None save_safetensors: Optional = True save_on_each_node: bool = False save_only_model: bool = False no_cuda: bool = False use_cpu: bool = False use_mps_device: bool = False seed: int = 42 data_seed: Optional = None jit_mode_eval: bool = False use_ipex: bool = False bf16: bool = False fp16: bool = False fp16_opt_level: str = 'O1' half_precision_backend: str = 'auto' bf16_full_eval: bool = False fp16_full_eval: bool = False tf32: Optional = None local_rank: int = -1 ddp_backend: Optional = None tpu_num_cores: Optional = None tpu_metrics_debug: bool = False debug: Union = '' dataloader_drop_last: bool = False eval_steps: Optional = None dataloader_num_workers: int = 0 dataloader_prefetch_factor: Optional = None past_index: int = -1 run_name: Optional = None disable_tqdm: Optional = None remove_unused_columns: Optional = True label_names: Optional = None load_best_model_at_end: Optional = False metric_for_best_model: Optional = None greater_is_better: Optional = None ignore_data_skip: bool = False fsdp: Union = '' fsdp_min_num_params: int = 0 fsdp_config: Union = None fsdp_transformer_layer_cls_to_wrap: Optional = None accelerator_config: Union = None deepspeed: Union = None label_smoothing_factor: float = 0.0 optim: Union = 'adamw_torch' optim_args: Optional = None adafactor: bool = False group_by_length: bool = False length_column_name: Optional = 'length' report_to: Union = None ddp_find_unused_parameters: Optional = None ddp_bucket_cap_mb: Optional = None ddp_broadcast_buffers: Optional = None dataloader_pin_memory: bool = True dataloader_persistent_workers: bool = False skip_memory_metrics: bool = True use_legacy_prediction_loop: bool = False push_to_hub: bool = False resume_from_checkpoint: Optional = None hub_model_id: Optional = None hub_strategy: Union = 'every_save' hub_token: Optional = None hub_private_repo: bool = False hub_always_push: bool = False gradient_checkpointing: bool = False gradient_checkpointing_kwargs: Union = None include_inputs_for_metrics: bool = False fp16_backend: str = 'auto' push_to_hub_model_id: Optional = None push_to_hub_organization: Optional = None push_to_hub_token: Optional = None mp_parameters: str = '' auto_find_batch_size: bool = False full_determinism: bool = False torchdynamo: Optional = None ray_scope: Optional = 'last' ddp_timeout: Optional = 1800 torch_compile: bool = False torch_compile_backend: Optional = None torch_compile_mode: Optional = None dispatch_batches: Optional = None split_batches: Optional = None include_tokens_per_second: Optional = False include_num_input_tokens_seen: Optional = False neftune_noise_alpha: Optional = None optim_target_modules: Union = None )

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 IntervalStrategy, 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/XPU/TPU/MPS/NPU core/CPU for training.
- per_device_eval_batch_size (`int`, optional, defaults to 8) — The batch size per GPU/XPU/TPU/MPS/NPU 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.

  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/NPU/TPU before being moved to the CPU (faster but requires more memory).
- eval_delay (`float`, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy.
- 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`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached.
- lr_scheduler_type (`str` or SchedulerType, optional, defaults to `"linear"`) — The scheduler type to use. See the documentation of SchedulerType for all possible values.
- lr_scheduler_kwargs (‘dict’, optional, defaults to {}) — The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
- warmup_ratio (`float`, optional, defaults to 0.0) — Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
- warmup_steps (`int`, optional, defaults to 0) — Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
- log_level (`str`, optional, defaults to `passive`) — Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’, ‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and keeps the current log level for the Transformers library (which will be `"warning"` by default).
- log_level_replica (`str`, optional, defaults to `"warning"`) — Logger log level to use on replicas. Same choices as `log_level`”
- log_on_each_node (`bool`, optional, defaults to `True`) — In multinode distributed training, whether to log using `log_level` once per node, or only on the main node.
- logging_dir (`str`, optional) — TensorBoard log directory. Will default to *output_dir/runs/CURRENT_DATETIME_HOSTNAME*.
- logging_strategy (`str` or IntervalStrategy, optional, defaults to `"steps"`) — The logging strategy to adopt during training. Possible values are:
  - `"no"`: No logging is done during training.
  - `"epoch"`: Logging is done at the end of each epoch.
  - `"steps"`: Logging is done every `logging_steps`.
- logging_first_step (`bool`, optional, defaults to `False`) — Whether to log the first `global_step` or not.
- logging_steps (`int` or `float`, optional, defaults to 500) — Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
- logging_nan_inf_filter (`bool`, optional, defaults to `True`) — Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead.

  `logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model.

- save_strategy (`str` or IntervalStrategy, optional, defaults to `"steps"`) — The checkpoint save strategy to adopt during training. Possible values are:
  - `"no"`: No save is done during training.
  - `"epoch"`: Save is done at the end of each epoch.
  - `"steps"`: Save is done every `save_steps`.
- save_steps (`int` or `float`, optional, defaults to 500) — Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
- save_total_limit (`int`, optional) — If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the “best” checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two checkpoints are saved: the last one and the best one (if they are different).
- save_safetensors (`bool`, optional, defaults to `True`) — Use safetensors saving and loading for state dicts instead of default `torch.load` and `torch.save`.
- save_on_each_node (`bool`, optional, defaults to `False`) — When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one.

  This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node.

- save_only_model (`bool`, optional, defaults to `False`) — When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state. Note that when this is true, you won’t be able to resume training from checkpoint. This enables you to save storage by not storing the optimizer, scheduler & rng state. You can only load the model using `from_pretrained` with this option set to `True`.
- use_cpu (`bool`, optional, defaults to `False`) — Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
- seed (`int`, optional, defaults to 42) — Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the `~Trainer.model_init` function to instantiate the model if it has some randomly initialized parameters.
- data_seed (`int`, optional) — Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed.
- jit_mode_eval (`bool`, optional, defaults to `False`) — Whether or not to use PyTorch jit trace for inference.
- use_ipex (`bool`, optional, defaults to `False`) — Use Intel extension for PyTorch when it is available. IPEX installation.
- bf16 (`bool`, optional, defaults to `False`) — Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
- fp16 (`bool`, optional, defaults to `False`) — Whether to use fp16 16-bit (mixed) precision training 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"`) — This argument is deprecated. Use `half_precision_backend` instead.
- half_precision_backend (`str`, optional, defaults to `"auto"`) — The backend to use for mixed precision training. Must be one of `"auto", "apex", "cpu_amp"`. `"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend.
- bf16_full_eval (`bool`, optional, defaults to `False`) — Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change.
- fp16_full_eval (`bool`, optional, defaults to `False`) — Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values.
- tf32 (`bool`, optional) — Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch’s version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to the TF32 documentation. This is an experimental API and it may change.
- local_rank (`int`, optional, defaults to -1) — Rank of the process during distributed training.
- ddp_backend (`str`, optional) — The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"ccl"`, `"gloo"`, `"hccl"`.
- tpu_num_cores (`int`, optional) — When training on TPU, the number of TPU cores (automatically passed by launcher script).
- 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` or `float`, optional) — Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
- 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 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 and mlflow logging.
- disable_tqdm (`bool`, optional) — Whether or not to disable the tqdm progress bars and table of metrics produced by `~notebook.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`) — Whether or not to automatically remove the columns unused by the model forward method.
- label_names (`List[str]`, optional) — The list of keys in your dictionary of inputs that correspond to the labels.

  Will eventually default to the list of argument names accepted by the model that contain the word “label”, except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the `["start_positions", "end_positions"]` keys.

- 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. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more.

  When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in the case it is “steps”, `save_steps` must be a round multiple of `eval_steps`.

- 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_data_skip (`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.
- fsdp (`bool`, `str` or list of `FSDPOption`, optional, defaults to `''`) — Use PyTorch Distributed Parallel Training (in distributed training only).

  A list of options along the following:

  - `"full_shard"`: Shard parameters, gradients and optimizer states.
  - `"shard_grad_op"`: Shard optimizer states and gradients.
  - `"hybrid_shard"`: Apply `FULL_SHARD` within a node, and replicate parameters across nodes.
  - `"hybrid_shard_zero2"`: Apply `SHARD_GRAD_OP` within a node, and replicate parameters across nodes.
  - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and `"shard_grad_op"`).
  - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`.
- fsdp_config (`str` or `dict`, optional) — Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.

  A List of config and its options:

  - min_num_params (`int`, optional, defaults to `0`): FSDP’s minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed).

  - transformer_layer_cls_to_wrap (`List[str]`, optional): List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` … (useful only when `fsdp` flag is passed).

  - backward_prefetch (`str`, optional) FSDP’s backward prefetch mode. Controls when to prefetch next set of parameters (useful only when `fsdp` field is passed).

    A list of options along the following:

    - `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter’s gradient computation.
    - `"backward_post"` : This prefetches the next set of parameters after the current set of parameter’s gradient computation.

  - forward_prefetch (`bool`, optional, defaults to `False`) FSDP’s forward prefetch mode (useful only when `fsdp` field is passed). If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass.

  - limit_all_gathers (`bool`, optional, defaults to `False`) FSDP’s limit_all_gathers (useful only when `fsdp` field is passed). If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers.

  - use_orig_params (`bool`, optional, defaults to `True`) If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019

  - sync_module_states (`bool`, optional, defaults to `True`) If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization

  - activation_checkpointing (`bool`, optional, defaults to `False`): If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage.

  - xla (`bool`, optional, defaults to `False`): Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature and its API may evolve in the future.

  - xla_fsdp_settings (`dict`, optional) The value is a dictionary which stores the XLA FSDP wrapping parameters.

    For a complete list of options, please see here.

  - xla_fsdp_grad_ckpt (`bool`, optional, defaults to `False`): Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.

- deepspeed (`str` or `dict`, optional) — Use Deepspeed. This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., `ds_config.json`) or an already loaded json file as a `dict`”
  If enabling any Zero-init, make sure that your model is not initialized until *after* initializing the `TrainingArguments`, else it will not be applied.
- accelerator_config (`str`, `dict`, or `AcceleratorConfig`, optional) — Config to be used with the internal `Accelerator` implementation. The value is either a location of accelerator json config file (e.g., `accelerator_config.json`), an already loaded json file as `dict`, or an instance of `AcceleratorConfig`.

  A list of config and its options:

  - split_batches (`bool`, optional, defaults to `False`): Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If `True` the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the `num_processes` you are using. If `False`, actual batch size used will be the one set in your script multiplied by the number of processes.
  - dispatch_batches (`bool`, optional): If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose underlying dataset is an `IterableDataset`, `False` otherwise.
  - even_batches (`bool`, optional, defaults to `True`): If set to `True`, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers.
  - use_seedable_sampler (`bool`, optional, defaults to `True`): Whether or not use a fully seedable random sampler (`accelerate.data_loader.SeedableRandomSampler`). Ensures training results are fully reproducable using a different sampling technique. While seed-to-seed results may differ, on average the differences are neglible when using multiple different seeds to compare. Should also be ran with `~utils.set_seed` for the best results.
- 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.
- debug (`str` or list of `DebugOption`, optional, defaults to `""`) — Enable one or more debug features. This is an experimental feature.

  Possible options are:

  - `"underflow_overflow"`: detects overflow in model’s input/outputs and reports the last frames that led to the event
  - `"tpu_metrics_debug"`: print debug metrics on TPU

  The options should be separated by whitespaces.

- optim (`str` or `training_args.OptimizerNames`, optional, defaults to `"adamw_torch"`) — The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or adafactor.
- optim_args (`str`, optional) — Optional arguments that are supplied to AnyPrecisionAdamW.
- group_by_length (`bool`, optional, defaults to `False`) — Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.
- length_column_name (`str`, optional, defaults to `"length"`) — Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an instance of `Dataset`.
- report_to (`str` or `List[str]`, optional, defaults to `"all"`) — The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"neptune"`, `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations.
- ddp_find_unused_parameters (`bool`, optional) — When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
- ddp_bucket_cap_mb (`int`, optional) — When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`.
- ddp_broadcast_buffers (`bool`, optional) — When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
- dataloader_pin_memory (`bool`, optional, defaults to `True`) — Whether you want to pin memory in data loaders or not. Will default to `True`.
- dataloader_persistent_workers (`bool`, optional, defaults to `False`) — If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to `False`.
- dataloader_prefetch_factor (`int`, optional) — Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers.
- skip_memory_metrics (`bool`, optional, defaults to `True`) — Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed.
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push the model to the Hub every time the model is saved. If this is activated, `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content will be pushed each time a save is triggered (depending on your `save_strategy`). Calling save_model() will also trigger a push.

  If `output_dir` exists, it needs to be a local clone of the repository to which the Trainer will be pushed.

- resume_from_checkpoint (`str`, optional) — The path to a folder with a valid checkpoint for your model. 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.
- hub_model_id (`str`, optional) — The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to `user_name/output_dir_name` with output_dir_name being the name of `output_dir`.

  Will default to the name of `output_dir`.

- hub_strategy (`str` or `HubStrategy`, optional, defaults to `"every_save"`) — Defines the scope of what is pushed to the Hub and when. Possible values are:
  - `"end"`: push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card when the save_model() method is called.
  - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training.
  - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`.
  - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository)
- hub_token (`str`, optional) — The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`.
- hub_private_repo (`bool`, optional, defaults to `False`) — If True, the Hub repo will be set to private.
- hub_always_push (`bool`, optional, defaults to `False`) — Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished.
- gradient_checkpointing (`bool`, optional, defaults to `False`) — If True, use gradient checkpointing to save memory at the expense of slower backward pass.
- gradient_checkpointing_kwargs (`dict`, optional, defaults to `None`) — Key word arguments to be passed to the `gradient_checkpointing_enable` method.
- include_inputs_for_metrics (`bool`, optional, defaults to `False`) — Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class.
- auto_find_batch_size (`bool`, optional, defaults to `False`) — Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
- full_determinism (`bool`, optional, defaults to `False`) — If `True`, enable_full_determinism() is called instead of set_seed() to ensure reproducible results in distributed training. Important: this will negatively impact the performance, so only use it for debugging.
- torchdynamo (`str`, optional) — If set, the backend compiler for TorchDynamo. Possible choices are `"eager"`, `"aot_eager"`, `"inductor"`, `"nvfuser"`, `"aot_nvfuser"`, `"aot_cudagraphs"`, `"ofi"`, `"fx2trt"`, `"onnxrt"` and `"ipex"`.
- ray_scope (`str`, optional, defaults to `"last"`) — The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation for more options.
- ddp_timeout (`int`, optional, defaults to 1800) — The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information.
- use_mps_device (`bool`, optional, defaults to `False`) — This argument is deprecated.`mps` device will be used if it is available similar to `cuda` device.
- torch_compile (`bool`, optional, defaults to `False`) — Whether or not to compile the model using PyTorch 2.0 `torch.compile`.

  This will use the best defaults for the `torch.compile` API. You can customize the defaults with the argument `torch_compile_backend` and `torch_compile_mode` but we don’t guarantee any of them will work as the support is progressively rolled in in PyTorch.

  This flag and the whole compile API is experimental and subject to change in future releases.

- torch_compile_backend (`str`, optional) — The backend to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

- torch_compile_mode (`str`, optional) — The mode to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

- split_batches (`bool`, optional) — Whether or not the accelerator should split the batches yielded by the dataloaders across the devices during distributed training. If

  set to `True`, the actual batch size used will be the same on any kind of distributed processes, but it must be a

  round multiple of the number of processes you are using (such as GPUs).

- include_tokens_per_second (`bool`, optional) — Whether or not to compute the number of tokens per second per device for training speed metrics.

  This will iterate over the entire training dataloader once beforehand,

  and will slow down the entire process.

- include_num_input_tokens_seen (`bool`, optional) — Whether or not to track the number of input tokens seen throughout training.

  May be slower in distributed training as gather operations must be called.

- neftune_noise_alpha (`Optional[float]`) — If not `None`, this will activate NEFTune noise embeddings. This can drastically improve model performance for instruction fine-tuning. Check out the original paper and the original code. Support transformers `PreTrainedModel` and also `PeftModel` from peft.
- optim_target_modules (`Union[str, List[str]]`, optional) — The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm https://arxiv.org/abs/2403.03507 See: https://github.com/jiaweizzhao/GaLore for more details. You need to make sure to pass a valid GaloRe optimizer, e.g. one of: “galore_adamw”, “galore_adamw_8bit”, “galore_adafactor” and make sure that the target modules are `nn.Linear` modules only.

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.

####
get_process_log_level

< source >

( )

Returns the log level to be used depending on whether this process is the main process of node 0, main process of node non-0, or a non-main process.

For the main process the log level defaults to the logging level set (`logging.WARNING` if you didn’t do anything) unless overridden by `log_level` argument.

For the replica processes the log level defaults to `logging.WARNING` unless overridden by `log_level_replica` argument.

The choice between the main and replica process settings is made according to the return value of `should_log`.

####
get_warmup_steps

< source >

( num_training_steps: int )

Get number of steps used for a linear warmup.

####
main_process_first

< source >

( local = True desc = 'work' )

Parameters

- local (`bool`, optional, defaults to `True`) — if `True` first means process of rank 0 of each node if `False` first means process of rank 0 of node rank 0 In multi-node environment with a shared filesystem you most likely will want to use `local=False` so that only the main process of the first node will do the processing. If however, the filesystem is not shared, then the main process of each node will need to do the processing, which is the default behavior.
- desc (`str`, optional, defaults to `"work"`) — a work description to be used in debug logs

A context manager for torch distributed environment where on needs to do something on the main process, while blocking replicas, and when it’s finished releasing the replicas.

One such use is for `datasets`’s `map` feature which to be efficient should be run once on the main process, which upon completion saves a cached version of results and which then automatically gets loaded by the replicas.

####
set_dataloader

< source >

( train_batch_size: int = 8 eval_batch_size: int = 8 drop_last: bool = False num_workers: int = 0 pin_memory: bool = True persistent_workers: bool = False prefetch_factor: Optional = None auto_find_batch_size: bool = False ignore_data_skip: bool = False sampler_seed: Optional = None )

Parameters

- 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.
- 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.
- pin_memory (`bool`, optional, defaults to `True`) — Whether you want to pin memory in data loaders or not. Will default to `True`.
- persistent_workers (`bool`, optional, defaults to `False`) — If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to `False`.
- prefetch_factor (`int`, optional) — Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers.
- auto_find_batch_size (`bool`, optional, defaults to `False`) — Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
- ignore_data_skip (`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.
- sampler_seed (`int`, optional) — Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `self.seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed.

A method that regroups all arguments linked to the dataloaders creation.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_dataloader(train_batch_size=16, eval_batch_size=64)
    >>> args.per_device_train_batch_size
    16

####
set_evaluate

< source >

( strategy: Union = 'no' steps: int = 500 batch_size: int = 8 accumulation_steps: Optional = None delay: Optional = None loss_only: bool = False jit_mode: bool = False )

Parameters

- strategy (`str` or IntervalStrategy, 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 `steps`.
  - `"epoch"`: Evaluation is done at the end of each epoch.

  Setting a `strategy` different from `"no"` will set `self.do_eval` to `True`.

- steps (`int`, optional, defaults to 500) — Number of update steps between two evaluations if `strategy="steps"`.
- batch_size (`int` optional, defaults to 8) — The batch size per device (GPU/TPU core/CPU…) used for evaluation.
- 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).
- delay (`float`, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy.
- loss_only (`bool`, optional, defaults to `False`) — Ignores all outputs except the loss.
- jit_mode (`bool`, optional) — Whether or not to use PyTorch jit trace for inference.

A method that regroups all arguments linked to evaluation.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_evaluate(strategy="steps", steps=100)
    >>> args.eval_steps
    100

####
set_logging

< source >

( strategy: Union = 'steps' steps: int = 500 report_to: Union = 'none' level: str = 'passive' first_step: bool = False nan_inf_filter: bool = False on_each_node: bool = False replica_level: str = 'passive' )

Parameters

- strategy (`str` or IntervalStrategy, optional, defaults to `"steps"`) — The logging strategy to adopt during training. Possible values are:
  - `"no"`: No logging is done during training.
  - `"epoch"`: Logging is done at the end of each epoch.
  - `"steps"`: Logging is done every `logging_steps`.
- steps (`int`, optional, defaults to 500) — Number of update steps between two logs if `strategy="steps"`.
- level (`str`, optional, defaults to `"passive"`) — Logger log level to use on the main process. Possible choices are the log levels as strings: `"debug"`, `"info"`, `"warning"`, `"error"` and `"critical"`, plus a `"passive"` level which doesn’t set anything and lets the application set the level.
- report_to (`str` or `List[str]`, optional, defaults to `"all"`) — The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"neptune"`, `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations.
- first_step (`bool`, optional, defaults to `False`) — Whether to log and evaluate the first `global_step` or not.
- nan_inf_filter (`bool`, optional, defaults to `True`) — Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead.

  `nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model.

- on_each_node (`bool`, optional, defaults to `True`) — In multinode distributed training, whether to log using `log_level` once per node, or only on the main node.
- replica_level (`str`, optional, defaults to `"passive"`) — Logger log level to use on replicas. Same choices as `log_level`

A method that regroups all arguments linked to logging.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_logging(strategy="steps", steps=100)
    >>> args.logging_steps
    100

####
set_lr_scheduler

< source >

( name: Union = 'linear' num_epochs: float = 3.0 max_steps: int = -1 warmup_ratio: float = 0 warmup_steps: int = 0 )

Parameters

- name (`str` or SchedulerType, optional, defaults to `"linear"`) — The scheduler type to use. See the documentation of SchedulerType for all possible values.
- num_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`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached.
- warmup_ratio (`float`, optional, defaults to 0.0) — Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
- warmup_steps (`int`, optional, defaults to 0) — Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.

A method that regroups all arguments linked to the learning rate scheduler and its hyperparameters.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_lr_scheduler(name="cosine", warmup_ratio=0.05)
    >>> args.warmup_ratio
    0.05

####
set_optimizer

< source >

( name: Union = 'adamw_torch' learning_rate: float = 5e-05 weight_decay: float = 0 beta1: float = 0.9 beta2: float = 0.999 epsilon: float = 1e-08 args: Optional = None )

Parameters

- name (`str` or `training_args.OptimizerNames`, optional, defaults to `"adamw_torch"`) — The optimizer to use: `"adamw_hf"`, `"adamw_torch"`, `"adamw_torch_fused"`, `"adamw_apex_fused"`, `"adamw_anyprecision"` or `"adafactor"`.
- learning_rate (`float`, optional, defaults to 5e-5) — The initial learning rate.
- weight_decay (`float`, optional, defaults to 0) — The weight decay to apply (if not zero) to all layers except all bias and LayerNorm weights.
- beta1 (`float`, optional, defaults to 0.9) — The beta1 hyperparameter for the adam optimizer or its variants.
- beta2 (`float`, optional, defaults to 0.999) — The beta2 hyperparameter for the adam optimizer or its variants.
- epsilon (`float`, optional, defaults to 1e-8) — The epsilon hyperparameter for the adam optimizer or its variants.
- args (`str`, optional) — Optional arguments that are supplied to AnyPrecisionAdamW (only useful when `optim="adamw_anyprecision"`).

A method that regroups all arguments linked to the optimizer and its hyperparameters.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_optimizer(name="adamw_torch", beta1=0.8)
    >>> args.optim
    'adamw_torch'

####
set_push_to_hub

< source >

( model_id: str strategy: Union = 'every_save' token: Optional = None private_repo: bool = False always_push: bool = False )

Parameters

- model_id (`str`) — The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`.
- strategy (`str` or `HubStrategy`, optional, defaults to `"every_save"`) — Defines the scope of what is pushed to the Hub and when. Possible values are:
  - `"end"`: push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card when the save_model() method is called.
  - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training.
  - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`.
  - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository)
- token (`str`, optional) — The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`.
- private_repo (`bool`, optional, defaults to `False`) — If True, the Hub repo will be set to private.
- always_push (`bool`, optional, defaults to `False`) — Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished.

A method that regroups all arguments linked to synchronizing checkpoints with the Hub.

Calling this method will set `self.push_to_hub` to `True`, which means the `output_dir` will begin a git directory synced with the repo (determined by `model_id`) and the content will be pushed each time a save is triggered (depending on`self.save_strategy`). Calling save_model() will also trigger a push.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_push_to_hub("me/awesome-model")
    >>> args.hub_model_id
    'me/awesome-model'

####
set_save

< source >

( strategy: Union = 'steps' steps: int = 500 total_limit: Optional = None on_each_node: bool = False )

Parameters

- strategy (`str` or IntervalStrategy, optional, defaults to `"steps"`) — The checkpoint save strategy to adopt during training. Possible values are:
  - `"no"`: No save is done during training.
  - `"epoch"`: Save is done at the end of each epoch.
  - `"steps"`: Save is done every `save_steps`.
- steps (`int`, optional, defaults to 500) — Number of updates steps before two checkpoint saves if `strategy="steps"`.
- total_limit (`int`, optional) — If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`.
- on_each_node (`bool`, optional, defaults to `False`) — When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one.

  This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node.

A method that regroups all arguments linked to checkpoint saving.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_save(strategy="steps", steps=100)
    >>> args.save_steps
    100

####
set_testing

< source >

( batch_size: int = 8 loss_only: bool = False jit_mode: bool = False )

Parameters

- batch_size (`int` optional, defaults to 8) — The batch size per device (GPU/TPU core/CPU…) used for testing.
- loss_only (`bool`, optional, defaults to `False`) — Ignores all outputs except the loss.
- jit_mode (`bool`, optional) — Whether or not to use PyTorch jit trace for inference.

A method that regroups all basic arguments linked to testing on a held-out dataset.

Calling this method will automatically set `self.do_predict` to `True`.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_testing(batch_size=32)
    >>> args.per_device_eval_batch_size
    32

####
set_training

< source >

( learning_rate: float = 5e-05 batch_size: int = 8 weight_decay: float = 0 num_epochs: float = 3 max_steps: int = -1 gradient_accumulation_steps: int = 1 seed: int = 42 gradient_checkpointing: bool = False )

Parameters

- learning_rate (`float`, optional, defaults to 5e-5) — The initial learning rate for the optimizer.
- batch_size (`int` optional, defaults to 8) — The batch size per device (GPU/TPU core/CPU…) used for training.
- 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 the optimizer.
- 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`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached.
- gradient_accumulation_steps (`int`, optional, defaults to 1) — Number of updates steps to accumulate the gradients for, before performing a backward/update pass.

  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.

- seed (`int`, optional, defaults to 42) — Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the `~Trainer.model_init` function to instantiate the model if it has some randomly initialized parameters.
- gradient_checkpointing (`bool`, optional, defaults to `False`) — If True, use gradient checkpointing to save memory at the expense of slower backward pass.

A method that regroups all basic arguments linked to the training.

Calling this method will automatically set `self.do_train` to `True`.

Example:

    >>> from transformers import TrainingArguments

    >>> args = TrainingArguments("working_dir")
    >>> args = args.set_training(learning_rate=1e-4, batch_size=32)
    >>> args.learning_rate
    1e-4

####
to_dict

< source >

( )

Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates the token values by removing their value.

####
to_json_string

< source >

( )

Serializes this instance to a JSON string.

####
to_sanitized_dict

< source >

( )

Sanitized serialization to use with TensorBoard’s hparams

##
Seq2SeqTrainingArguments

###
class transformers.Seq2SeqTrainingArguments

< source >

( output_dir: str overwrite_output_dir: bool = False do_train: bool = False do_eval: bool = False do_predict: bool = False evaluation_strategy: Union = '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 = None per_gpu_eval_batch_size: Optional = None gradient_accumulation_steps: int = 1 eval_accumulation_steps: Optional = None eval_delay: Optional = 0 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: Union = 'linear' lr_scheduler_kwargs: Union = <factory> warmup_ratio: float = 0.0 warmup_steps: int = 0 log_level: Optional = 'passive' log_level_replica: Optional = 'warning' log_on_each_node: bool = True logging_dir: Optional = None logging_strategy: Union = 'steps' logging_first_step: bool = False logging_steps: float = 500 logging_nan_inf_filter: bool = True save_strategy: Union = 'steps' save_steps: float = 500 save_total_limit: Optional = None save_safetensors: Optional = True save_on_each_node: bool = False save_only_model: bool = False no_cuda: bool = False use_cpu: bool = False use_mps_device: bool = False seed: int = 42 data_seed: Optional = None jit_mode_eval: bool = False use_ipex: bool = False bf16: bool = False fp16: bool = False fp16_opt_level: str = 'O1' half_precision_backend: str = 'auto' bf16_full_eval: bool = False fp16_full_eval: bool = False tf32: Optional = None local_rank: int = -1 ddp_backend: Optional = None tpu_num_cores: Optional = None tpu_metrics_debug: bool = False debug: Union = '' dataloader_drop_last: bool = False eval_steps: Optional = None dataloader_num_workers: int = 0 dataloader_prefetch_factor: Optional = None past_index: int = -1 run_name: Optional = None disable_tqdm: Optional = None remove_unused_columns: Optional = True label_names: Optional = None load_best_model_at_end: Optional = False metric_for_best_model: Optional = None greater_is_better: Optional = None ignore_data_skip: bool = False fsdp: Union = '' fsdp_min_num_params: int = 0 fsdp_config: Union = None fsdp_transformer_layer_cls_to_wrap: Optional = None accelerator_config: Union = None deepspeed: Union = None label_smoothing_factor: float = 0.0 optim: Union = 'adamw_torch' optim_args: Optional = None adafactor: bool = False group_by_length: bool = False length_column_name: Optional = 'length' report_to: Union = None ddp_find_unused_parameters: Optional = None ddp_bucket_cap_mb: Optional = None ddp_broadcast_buffers: Optional = None dataloader_pin_memory: bool = True dataloader_persistent_workers: bool = False skip_memory_metrics: bool = True use_legacy_prediction_loop: bool = False push_to_hub: bool = False resume_from_checkpoint: Optional = None hub_model_id: Optional = None hub_strategy: Union = 'every_save' hub_token: Optional = None hub_private_repo: bool = False hub_always_push: bool = False gradient_checkpointing: bool = False gradient_checkpointing_kwargs: Union = None include_inputs_for_metrics: bool = False fp16_backend: str = 'auto' push_to_hub_model_id: Optional = None push_to_hub_organization: Optional = None push_to_hub_token: Optional = None mp_parameters: str = '' auto_find_batch_size: bool = False full_determinism: bool = False torchdynamo: Optional = None ray_scope: Optional = 'last' ddp_timeout: Optional = 1800 torch_compile: bool = False torch_compile_backend: Optional = None torch_compile_mode: Optional = None dispatch_batches: Optional = None split_batches: Optional = None include_tokens_per_second: Optional = False include_num_input_tokens_seen: Optional = False neftune_noise_alpha: Optional = None optim_target_modules: Union = None sortish_sampler: bool = False predict_with_generate: bool = False generation_max_length: Optional = None generation_num_beams: Optional = None generation_config: Union = None )

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 IntervalStrategy, 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/XPU/TPU/MPS/NPU core/CPU for training.
- per_device_eval_batch_size (`int`, optional, defaults to 8) — The batch size per GPU/XPU/TPU/MPS/NPU 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.

  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/NPU/TPU before being moved to the CPU (faster but requires more memory).
- eval_delay (`float`, optional) — Number of epochs or steps to wait for before the first evaluation can be performed, depending on the evaluation_strategy.
- 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`. For a finite dataset, training is reiterated through the dataset (if all data is exhausted) until `max_steps` is reached.
- lr_scheduler_type (`str` or SchedulerType, optional, defaults to `"linear"`) — The scheduler type to use. See the documentation of SchedulerType for all possible values.
- lr_scheduler_kwargs (‘dict’, optional, defaults to {}) — The extra arguments for the lr_scheduler. See the documentation of each scheduler for possible values.
- warmup_ratio (`float`, optional, defaults to 0.0) — Ratio of total training steps used for a linear warmup from 0 to `learning_rate`.
- warmup_steps (`int`, optional, defaults to 0) — Number of steps used for a linear warmup from 0 to `learning_rate`. Overrides any effect of `warmup_ratio`.
- log_level (`str`, optional, defaults to `passive`) — Logger log level to use on the main process. Possible choices are the log levels as strings: ‘debug’, ‘info’, ‘warning’, ‘error’ and ‘critical’, plus a ‘passive’ level which doesn’t set anything and keeps the current log level for the Transformers library (which will be `"warning"` by default).
- log_level_replica (`str`, optional, defaults to `"warning"`) — Logger log level to use on replicas. Same choices as `log_level`”
- log_on_each_node (`bool`, optional, defaults to `True`) — In multinode distributed training, whether to log using `log_level` once per node, or only on the main node.
- logging_dir (`str`, optional) — TensorBoard log directory. Will default to *output_dir/runs/CURRENT_DATETIME_HOSTNAME*.
- logging_strategy (`str` or IntervalStrategy, optional, defaults to `"steps"`) — The logging strategy to adopt during training. Possible values are:
  - `"no"`: No logging is done during training.
  - `"epoch"`: Logging is done at the end of each epoch.
  - `"steps"`: Logging is done every `logging_steps`.
- logging_first_step (`bool`, optional, defaults to `False`) — Whether to log the first `global_step` or not.
- logging_steps (`int` or `float`, optional, defaults to 500) — Number of update steps between two logs if `logging_strategy="steps"`. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
- logging_nan_inf_filter (`bool`, optional, defaults to `True`) — Whether to filter `nan` and `inf` losses for logging. If set to `True` the loss of every step that is `nan` or `inf` is filtered and the average loss of the current logging window is taken instead.

  `logging_nan_inf_filter` only influences the logging of loss values, it does not change the behavior the gradient is computed or applied to the model.

- save_strategy (`str` or IntervalStrategy, optional, defaults to `"steps"`) — The checkpoint save strategy to adopt during training. Possible values are:
  - `"no"`: No save is done during training.
  - `"epoch"`: Save is done at the end of each epoch.
  - `"steps"`: Save is done every `save_steps`.
- save_steps (`int` or `float`, optional, defaults to 500) — Number of updates steps before two checkpoint saves if `save_strategy="steps"`. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
- save_total_limit (`int`, optional) — If a value is passed, will limit the total amount of checkpoints. Deletes the older checkpoints in `output_dir`. When `load_best_model_at_end` is enabled, the “best” checkpoint according to `metric_for_best_model` will always be retained in addition to the most recent ones. For example, for `save_total_limit=5` and `load_best_model_at_end`, the four last checkpoints will always be retained alongside the best model. When `save_total_limit=1` and `load_best_model_at_end`, it is possible that two checkpoints are saved: the last one and the best one (if they are different).
- save_safetensors (`bool`, optional, defaults to `True`) — Use safetensors saving and loading for state dicts instead of default `torch.load` and `torch.save`.
- save_on_each_node (`bool`, optional, defaults to `False`) — When doing multi-node distributed training, whether to save models and checkpoints on each node, or only on the main one.

  This should not be activated when the different nodes use the same storage as the files will be saved with the same names for each node.

- save_only_model (`bool`, optional, defaults to `False`) — When checkpointing, whether to only save the model, or also the optimizer, scheduler & rng state. Note that when this is true, you won’t be able to resume training from checkpoint. This enables you to save storage by not storing the optimizer, scheduler & rng state. You can only load the model using `from_pretrained` with this option set to `True`.
- use_cpu (`bool`, optional, defaults to `False`) — Whether or not to use cpu. If set to False, we will use cuda or mps device if available.
- seed (`int`, optional, defaults to 42) — Random seed that will be set at the beginning of training. To ensure reproducibility across runs, use the `~Trainer.model_init` function to instantiate the model if it has some randomly initialized parameters.
- data_seed (`int`, optional) — Random seed to be used with data samplers. If not set, random generators for data sampling will use the same seed as `seed`. This can be used to ensure reproducibility of data sampling, independent of the model seed.
- jit_mode_eval (`bool`, optional, defaults to `False`) — Whether or not to use PyTorch jit trace for inference.
- use_ipex (`bool`, optional, defaults to `False`) — Use Intel extension for PyTorch when it is available. IPEX installation.
- bf16 (`bool`, optional, defaults to `False`) — Whether to use bf16 16-bit (mixed) precision training instead of 32-bit training. Requires Ampere or higher NVIDIA architecture or using CPU (use_cpu) or Ascend NPU. This is an experimental API and it may change.
- fp16 (`bool`, optional, defaults to `False`) — Whether to use fp16 16-bit (mixed) precision training 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"`) — This argument is deprecated. Use `half_precision_backend` instead.
- half_precision_backend (`str`, optional, defaults to `"auto"`) — The backend to use for mixed precision training. Must be one of `"auto", "apex", "cpu_amp"`. `"auto"` will use CPU/CUDA AMP or APEX depending on the PyTorch version detected, while the other choices will force the requested backend.
- bf16_full_eval (`bool`, optional, defaults to `False`) — Whether to use full bfloat16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values. This is an experimental API and it may change.
- fp16_full_eval (`bool`, optional, defaults to `False`) — Whether to use full float16 evaluation instead of 32-bit. This will be faster and save memory but can harm metric values.
- tf32 (`bool`, optional) — Whether to enable the TF32 mode, available in Ampere and newer GPU architectures. The default value depends on PyTorch’s version default of `torch.backends.cuda.matmul.allow_tf32`. For more details please refer to the TF32 documentation. This is an experimental API and it may change.
- local_rank (`int`, optional, defaults to -1) — Rank of the process during distributed training.
- ddp_backend (`str`, optional) — The backend to use for distributed training. Must be one of `"nccl"`, `"mpi"`, `"ccl"`, `"gloo"`, `"hccl"`.
- tpu_num_cores (`int`, optional) — When training on TPU, the number of TPU cores (automatically passed by launcher script).
- 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` or `float`, optional) — Number of update steps between two evaluations if `evaluation_strategy="steps"`. Will default to the same value as `logging_steps` if not set. Should be an integer or a float in range `[0,1)`. If smaller than 1, will be interpreted as ratio of total training steps.
- 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 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 and mlflow logging.
- disable_tqdm (`bool`, optional) — Whether or not to disable the tqdm progress bars and table of metrics produced by `~notebook.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`) — Whether or not to automatically remove the columns unused by the model forward method.
- label_names (`List[str]`, optional) — The list of keys in your dictionary of inputs that correspond to the labels.

  Will eventually default to the list of argument names accepted by the model that contain the word “label”, except if the model used is one of the `XxxForQuestionAnswering` in which case it will also include the `["start_positions", "end_positions"]` keys.

- 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. When this option is enabled, the best checkpoint will always be saved. See `save_total_limit` for more.

  When set to `True`, the parameters `save_strategy` needs to be the same as `evaluation_strategy`, and in the case it is “steps”, `save_steps` must be a round multiple of `eval_steps`.

- 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_data_skip (`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.
- fsdp (`bool`, `str` or list of `FSDPOption`, optional, defaults to `''`) — Use PyTorch Distributed Parallel Training (in distributed training only).

  A list of options along the following:

  - `"full_shard"`: Shard parameters, gradients and optimizer states.
  - `"shard_grad_op"`: Shard optimizer states and gradients.
  - `"hybrid_shard"`: Apply `FULL_SHARD` within a node, and replicate parameters across nodes.
  - `"hybrid_shard_zero2"`: Apply `SHARD_GRAD_OP` within a node, and replicate parameters across nodes.
  - `"offload"`: Offload parameters and gradients to CPUs (only compatible with `"full_shard"` and `"shard_grad_op"`).
  - `"auto_wrap"`: Automatically recursively wrap layers with FSDP using `default_auto_wrap_policy`.
- fsdp_config (`str` or `dict`, optional) — Config to be used with fsdp (Pytorch Distributed Parallel Training). The value is either a location of fsdp json config file (e.g., `fsdp_config.json`) or an already loaded json file as `dict`.

  A List of config and its options:

  - min_num_params (`int`, optional, defaults to `0`): FSDP’s minimum number of parameters for Default Auto Wrapping. (useful only when `fsdp` field is passed).

  - transformer_layer_cls_to_wrap (`List[str]`, optional): List of transformer layer class names (case-sensitive) to wrap, e.g, `BertLayer`, `GPTJBlock`, `T5Block` … (useful only when `fsdp` flag is passed).

  - backward_prefetch (`str`, optional) FSDP’s backward prefetch mode. Controls when to prefetch next set of parameters (useful only when `fsdp` field is passed).

    A list of options along the following:

    - `"backward_pre"` : Prefetches the next set of parameters before the current set of parameter’s gradient computation.
    - `"backward_post"` : This prefetches the next set of parameters after the current set of parameter’s gradient computation.

  - forward_prefetch (`bool`, optional, defaults to `False`) FSDP’s forward prefetch mode (useful only when `fsdp` field is passed). If `"True"`, then FSDP explicitly prefetches the next upcoming all-gather while executing in the forward pass.

  - limit_all_gathers (`bool`, optional, defaults to `False`) FSDP’s limit_all_gathers (useful only when `fsdp` field is passed). If `"True"`, FSDP explicitly synchronizes the CPU thread to prevent too many in-flight all-gathers.

  - use_orig_params (`bool`, optional, defaults to `True`) If `"True"`, allows non-uniform `requires_grad` during init, which means support for interspersed frozen and trainable paramteres. Useful in cases such as parameter-efficient fine-tuning. Please refer this [blog](https://dev-discuss.pytorch.org/t/rethinking-pytorch-fully-sharded-data-parallel-fsdp-from-first-principles/1019

  - sync_module_states (`bool`, optional, defaults to `True`) If `"True"`, each individually wrapped FSDP unit will broadcast module parameters from rank 0 to ensure they are the same across all ranks after initialization

  - activation_checkpointing (`bool`, optional, defaults to `False`): If `"True"`, activation checkpointing is a technique to reduce memory usage by clearing activations of certain layers and recomputing them during a backward pass. Effectively, this trades extra computation time for reduced memory usage.

  - xla (`bool`, optional, defaults to `False`): Whether to use PyTorch/XLA Fully Sharded Data Parallel Training. This is an experimental feature and its API may evolve in the future.

  - xla_fsdp_settings (`dict`, optional) The value is a dictionary which stores the XLA FSDP wrapping parameters.

    For a complete list of options, please see here.

  - xla_fsdp_grad_ckpt (`bool`, optional, defaults to `False`): Will use gradient checkpointing over each nested XLA FSDP wrapped layer. This setting can only be used when the xla flag is set to true, and an auto wrapping policy is specified through fsdp_min_num_params or fsdp_transformer_layer_cls_to_wrap.

- deepspeed (`str` or `dict`, optional) — Use Deepspeed. This is an experimental feature and its API may evolve in the future. The value is either the location of DeepSpeed json config file (e.g., `ds_config.json`) or an already loaded json file as a `dict`”
  If enabling any Zero-init, make sure that your model is not initialized until *after* initializing the `TrainingArguments`, else it will not be applied.
- accelerator_config (`str`, `dict`, or `AcceleratorConfig`, optional) — Config to be used with the internal `Accelerator` implementation. The value is either a location of accelerator json config file (e.g., `accelerator_config.json`), an already loaded json file as `dict`, or an instance of `AcceleratorConfig`.

  A list of config and its options:

  - split_batches (`bool`, optional, defaults to `False`): Whether or not the accelerator should split the batches yielded by the dataloaders across the devices. If `True` the actual batch size used will be the same on any kind of distributed processes, but it must be a round multiple of the `num_processes` you are using. If `False`, actual batch size used will be the one set in your script multiplied by the number of processes.
  - dispatch_batches (`bool`, optional): If set to `True`, the dataloader prepared by the Accelerator is only iterated through on the main process and then the batches are split and broadcast to each process. Will default to `True` for `DataLoader` whose underlying dataset is an `IterableDataset`, `False` otherwise.
  - even_batches (`bool`, optional, defaults to `True`): If set to `True`, in cases where the total batch size across all processes does not exactly divide the dataset, samples at the start of the dataset will be duplicated so the batch can be divided equally among all workers.
  - use_seedable_sampler (`bool`, optional, defaults to `True`): Whether or not use a fully seedable random sampler (`accelerate.data_loader.SeedableRandomSampler`). Ensures training results are fully reproducable using a different sampling technique. While seed-to-seed results may differ, on average the differences are neglible when using multiple different seeds to compare. Should also be ran with `~utils.set_seed` for the best results.
- 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.
- debug (`str` or list of `DebugOption`, optional, defaults to `""`) — Enable one or more debug features. This is an experimental feature.

  Possible options are:

  - `"underflow_overflow"`: detects overflow in model’s input/outputs and reports the last frames that led to the event
  - `"tpu_metrics_debug"`: print debug metrics on TPU

  The options should be separated by whitespaces.

- optim (`str` or `training_args.OptimizerNames`, optional, defaults to `"adamw_torch"`) — The optimizer to use: adamw_hf, adamw_torch, adamw_torch_fused, adamw_apex_fused, adamw_anyprecision or adafactor.
- optim_args (`str`, optional) — Optional arguments that are supplied to AnyPrecisionAdamW.
- group_by_length (`bool`, optional, defaults to `False`) — Whether or not to group together samples of roughly the same length in the training dataset (to minimize padding applied and be more efficient). Only useful if applying dynamic padding.
- length_column_name (`str`, optional, defaults to `"length"`) — Column name for precomputed lengths. If the column exists, grouping by length will use these values rather than computing them on train startup. Ignored unless `group_by_length` is `True` and the dataset is an instance of `Dataset`.
- report_to (`str` or `List[str]`, optional, defaults to `"all"`) — The list of integrations to report the results and logs to. Supported platforms are `"azure_ml"`, `"clearml"`, `"codecarbon"`, `"comet_ml"`, `"dagshub"`, `"dvclive"`, `"flyte"`, `"mlflow"`, `"neptune"`, `"tensorboard"`, and `"wandb"`. Use `"all"` to report to all integrations installed, `"none"` for no integrations.
- ddp_find_unused_parameters (`bool`, optional) — When using distributed training, the value of the flag `find_unused_parameters` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
- ddp_bucket_cap_mb (`int`, optional) — When using distributed training, the value of the flag `bucket_cap_mb` passed to `DistributedDataParallel`.
- ddp_broadcast_buffers (`bool`, optional) — When using distributed training, the value of the flag `broadcast_buffers` passed to `DistributedDataParallel`. Will default to `False` if gradient checkpointing is used, `True` otherwise.
- dataloader_pin_memory (`bool`, optional, defaults to `True`) — Whether you want to pin memory in data loaders or not. Will default to `True`.
- dataloader_persistent_workers (`bool`, optional, defaults to `False`) — If True, the data loader will not shut down the worker processes after a dataset has been consumed once. This allows to maintain the workers Dataset instances alive. Can potentially speed up training, but will increase RAM usage. Will default to `False`.
- dataloader_prefetch_factor (`int`, optional) — Number of batches loaded in advance by each worker. 2 means there will be a total of 2 * num_workers batches prefetched across all workers.
- skip_memory_metrics (`bool`, optional, defaults to `True`) — Whether to skip adding of memory profiler reports to metrics. This is skipped by default because it slows down the training and evaluation speed.
- push_to_hub (`bool`, optional, defaults to `False`) — Whether or not to push the model to the Hub every time the model is saved. If this is activated, `output_dir` will begin a git directory synced with the repo (determined by `hub_model_id`) and the content will be pushed each time a save is triggered (depending on your `save_strategy`). Calling save_model() will also trigger a push.

  If `output_dir` exists, it needs to be a local clone of the repository to which the Trainer will be pushed.

- resume_from_checkpoint (`str`, optional) — The path to a folder with a valid checkpoint for your model. 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.
- hub_model_id (`str`, optional) — The name of the repository to keep in sync with the local output_dir. It can be a simple model ID in which case the model will be pushed in your namespace. Otherwise it should be the whole repository name, for instance `"user_name/model"`, which allows you to push to an organization you are a member of with `"organization_name/model"`. Will default to `user_name/output_dir_name` with output_dir_name being the name of `output_dir`.

  Will default to the name of `output_dir`.

- hub_strategy (`str` or `HubStrategy`, optional, defaults to `"every_save"`) — Defines the scope of what is pushed to the Hub and when. Possible values are:
  - `"end"`: push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card when the save_model() method is called.
  - `"every_save"`: push the model, its configuration, the tokenizer (if passed along to the Trainer) and a draft of a model card each time there is a model save. The pushes are asynchronous to not block training, and in case the save are very frequent, a new push is only attempted if the previous one is finished. A last push is made with the final model at the end of training.
  - `"checkpoint"`: like `"every_save"` but the latest checkpoint is also pushed in a subfolder named last-checkpoint, allowing you to resume training easily with `trainer.train(resume_from_checkpoint="last-checkpoint")`.
  - `"all_checkpoints"`: like `"checkpoint"` but all checkpoints are pushed like they appear in the output folder (so you will get one checkpoint folder per folder in your final repository)
- hub_token (`str`, optional) — The token to use to push the model to the Hub. Will default to the token in the cache folder obtained with `huggingface-cli login`.
- hub_private_repo (`bool`, optional, defaults to `False`) — If True, the Hub repo will be set to private.
- hub_always_push (`bool`, optional, defaults to `False`) — Unless this is `True`, the `Trainer` will skip pushing a checkpoint when the previous push is not finished.
- gradient_checkpointing (`bool`, optional, defaults to `False`) — If True, use gradient checkpointing to save memory at the expense of slower backward pass.
- gradient_checkpointing_kwargs (`dict`, optional, defaults to `None`) — Key word arguments to be passed to the `gradient_checkpointing_enable` method.
- include_inputs_for_metrics (`bool`, optional, defaults to `False`) — Whether or not the inputs will be passed to the `compute_metrics` function. This is intended for metrics that need inputs, predictions and references for scoring calculation in Metric class.
- auto_find_batch_size (`bool`, optional, defaults to `False`) — Whether to find a batch size that will fit into memory automatically through exponential decay, avoiding CUDA Out-of-Memory errors. Requires accelerate to be installed (`pip install accelerate`)
- full_determinism (`bool`, optional, defaults to `False`) — If `True`, enable_full_determinism() is called instead of set_seed() to ensure reproducible results in distributed training. Important: this will negatively impact the performance, so only use it for debugging.
- torchdynamo (`str`, optional) — If set, the backend compiler for TorchDynamo. Possible choices are `"eager"`, `"aot_eager"`, `"inductor"`, `"nvfuser"`, `"aot_nvfuser"`, `"aot_cudagraphs"`, `"ofi"`, `"fx2trt"`, `"onnxrt"` and `"ipex"`.
- ray_scope (`str`, optional, defaults to `"last"`) — The scope to use when doing hyperparameter search with Ray. By default, `"last"` will be used. Ray will then use the last checkpoint of all trials, compare those, and select the best one. However, other options are also available. See the Ray documentation for more options.
- ddp_timeout (`int`, optional, defaults to 1800) — The timeout for `torch.distributed.init_process_group` calls, used to avoid GPU socket timeouts when performing slow operations in distributed runnings. Please refer the [PyTorch documentation] (https://pytorch.org/docs/stable/distributed.html#torch.distributed.init_process_group) for more information.
- use_mps_device (`bool`, optional, defaults to `False`) — This argument is deprecated.`mps` device will be used if it is available similar to `cuda` device.
- torch_compile (`bool`, optional, defaults to `False`) — Whether or not to compile the model using PyTorch 2.0 `torch.compile`.

  This will use the best defaults for the `torch.compile` API. You can customize the defaults with the argument `torch_compile_backend` and `torch_compile_mode` but we don’t guarantee any of them will work as the support is progressively rolled in in PyTorch.

  This flag and the whole compile API is experimental and subject to change in future releases.

- torch_compile_backend (`str`, optional) — The backend to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

- torch_compile_mode (`str`, optional) — The mode to use in `torch.compile`. If set to any value, `torch_compile` will be set to `True`.

  Refer to the PyTorch doc for possible values and note that they may change across PyTorch versions.

  This flag is experimental and subject to change in future releases.

- split_batches (`bool`, optional) — Whether or not the accelerator should split the batches yielded by the dataloaders across the devices during distributed training. If

  set to `True`, the actual batch size used will be the same on any kind of distributed processes, but it must be a

  round multiple of the number of processes you are using (such as GPUs).

- include_tokens_per_second (`bool`, optional) — Whether or not to compute the number of tokens per second per device for training speed metrics.

  This will iterate over the entire training dataloader once beforehand,

  and will slow down the entire process.

- include_num_input_tokens_seen (`bool`, optional) — Whether or not to track the number of input tokens seen throughout training.

  May be slower in distributed training as gather operations must be called.

- neftune_noise_alpha (`Optional[float]`) — If not `None`, this will activate NEFTune noise embeddings. This can drastically improve model performance for instruction fine-tuning. Check out the original paper and the original code. Support transformers `PreTrainedModel` and also `PeftModel` from peft.
- optim_target_modules (`Union[str, List[str]]`, optional) — The target modules to optimize, i.e. the module names that you would like to train, right now this is used only for GaLore algorithm https://arxiv.org/abs/2403.03507 See: https://github.com/jiaweizzhao/GaLore for more details. You need to make sure to pass a valid GaloRe optimizer, e.g. one of: “galore_adamw”, “galore_adamw_8bit”, “galore_adafactor” and make sure that the target modules are `nn.Linear` modules only.
- 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).
- generation_max_length (`int`, optional) — The `max_length` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `max_length` value of the model configuration.
- generation_num_beams (`int`, optional) — The `num_beams` to use on each evaluation loop when `predict_with_generate=True`. Will default to the `num_beams` value of the model configuration.
- generation_config (`str` or `Path` or GenerationConfig, optional) — Allows to load a GenerationConfig from the `from_pretrained` method. This can be either:
  - a string, the model id of a pretrained model configuration hosted inside a model repo on huggingface.co.
  - a path to a directory containing a configuration file saved using the save_pretrained() method, e.g., `./my_model_directory/`.
  - a GenerationConfig object.

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.

####
to_dict

< source >

( )

Serializes this instance while replace `Enum` by their values and `GenerationConfig` by dictionaries (for JSON serialization support). It obfuscates the token values by removing their value.

##
Checkpoints

デフォルトでは、Trainer はすべてのチェックポイントを、 TrainingArguments を使用しています。これらは、xxx を含む`checkpoint-xxx`という名前のサブフォルダーに保存されます。 それはトレーニングの段階でした。

チェックポイントからトレーニングを再開するには、次のいずれかを使用して Trainer.train() を呼び出します。

- `resume_from_checkpoint=True` は最新のチェックポイントからトレーニングを再開します
- `resume_from_checkpoint=checkpoint_dir` ディレクトリ内の特定のチェックポイントからトレーニングを再開します 合格した。

さらに、`push_to_hub=True` を使用すると、モデル ハブにチェックポイントを簡単に保存できます。デフォルトでは、すべて 中間チェックポイントに保存されたモデルは別のコミットに保存されますが、オプティマイザーの状態は保存されません。適応できます TrainingArguments の `hub-strategy` 値を次のいずれかにします。

- `"checkpoint"`: 最新のチェックポイントも last-checkpoint という名前のサブフォルダーにプッシュされます。 `trainer.train(resume_from_checkpoint="output_dir/last-checkpoint")` を使用してトレーニングを簡単に再開します。
- `"all_checkpoints"`: すべてのチェックポイントは、出力フォルダーに表示されるようにプッシュされます (したがって、1 つのチェックポイントが得られます) 最終リポジトリ内のフォルダーごとのチェックポイント フォルダー)

##
Logging

デフォルトでは、Trainer はメインプロセスに `logging.INFO` を使用し、レプリカがある場合には `logging.WARNING` を使用します。

これらのデフォルトは、TrainingArguments の 5 つの `logging` レベルのいずれかを使用するようにオーバーライドできます。 引数:

- `log_level` - メインプロセス用
- `log_level_replica` - レプリカ用

さらに、TrainingArguments の `log_on_each_node` が `False` に設定されている場合、メイン ノードのみが メイン プロセスのログ レベル設定を使用すると、他のすべてのノードはレプリカのログ レベル設定を使用します。

Trainer は、`transformers` のログ レベルをノードごとに個別に設定することに注意してください。 `Trainer.__init__()`。したがって、他の機能を利用する場合は、これをより早く設定することをお勧めします (次の例を参照)。 Trainer オブジェクトを作成する前の `transformers` 機能。

これをアプリケーションで使用する方法の例を次に示します。

    [...]
    logger = logging.getLogger(__name__)

    # Setup logging
    logging.basicConfig(
        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
        datefmt="%m/%d/%Y %H:%M:%S",
        handlers=[logging.StreamHandler(sys.stdout)],
    )

    # set the main code and the modules it uses to the same log-level according to the node
    log_level = training_args.get_process_log_level()
    logger.setLevel(log_level)
    datasets.utils.logging.set_verbosity(log_level)
    transformers.utils.logging.set_verbosity(log_level)

    trainer = Trainer(...)

そして、メイン ノードと他のすべてのノードで重複する可能性が高いものを出力しないように警告するだけを表示したい場合は、 警告: 次のように実行できます。

    my_app.py ... --log_level warning --log_level_replica error

マルチノード環境で、各ノードのメインプロセスのログを繰り返したくない場合は、次のようにします。 上記を次のように変更します。

    my_app.py ... --log_level warning --log_level_replica error --log_on_each_node 0

その後、最初のノードのメイン プロセスのみが「警告」レベルでログに記録され、メイン ノード上の他のすべてのプロセスはログに記録されます。 ノードと他のノード上のすべてのプロセスは「エラー」レベルでログに記録されます。

アプリケーションをできるだけ静かにする必要がある場合は、次のようにします。

    my_app.py ... --log_level error --log_level_replica error --log_on_each_node 0

(マルチノード環境の場合は `--log_on_each_node 0` を追加します)

##
Randomness

Trainer によって生成されたチェックポイントから再開する場合、すべての努力がその状態を復元するために行われます。 python、numpy、および pytorch の RNG 状態は、そのチェックポイントを保存した時点と同じ状態になります。 これにより、「停止して再開」というスタイルのトレーニングが、ノンストップトレーニングに可能な限り近づけられるはずです。

ただし、さまざまなデフォルトの非決定的な pytorch 設定により、これは完全に機能しない可能性があります。フルをご希望の場合は 決定論については、ランダム性のソースの制御 を参照してください。ドキュメントで説明されているように、これらの設定の一部は 物事を決定論的にするもの (例: `torch.backends.cudnn.deterministic`) は物事を遅くする可能性があるため、これは デフォルトでは実行できませんが、必要に応じて自分で有効にすることができます。

##
Specific GPUs Selection

どの GPU をどのような順序で使用するかをプログラムに指示する方法について説明します。

`DistributedDataParallel` を使用して GPU のサブセットのみを使用する場合、使用する GPU の数を指定するだけです。 。たとえば、GPU が 4 つあるが、最初の 2 つを使用したい場合は、次のようにします。

    torchrun --nproc_per_node=2  trainer-program.py ...

`accelerate` または `deepspeed` がインストールされている場合は、次を使用して同じことを達成することもできます。の一つ：

    accelerate launch --num_processes 2 trainer-program.py ...

    deepspeed --num_gpus 2 trainer-program.py ...

これらのランチャーを使用するために、Accelerate または Deepspeed 統合 機能を使用する必要はありません。

これまでは、プログラムに使用する GPU の数を指示できました。次に、特定の GPU を選択し、その順序を制御する方法について説明します。

次の環境変数は、使用する GPU とその順序を制御するのに役立ちます。

`CUDA_VISIBLE_DEVICES`

複数の GPU があり、そのうちの 1 つまたはいくつかの GPU だけを使用したい場合は、環境変数 `CUDA_VISIBLE_DEVICES` を使用する GPU のリストに設定します。

たとえば、4 つの GPU (0、1、2、3) があるとします。物理 GPU 0 と 2 のみで実行するには、次のようにします。

    CUDA_VISIBLE_DEVICES=0,2 torchrun trainer-program.py ...

したがって、pytorch は 2 つの GPU のみを認識し、物理 GPU 0 と 2 はそれぞれ `cuda:0` と `cuda:1` にマッピングされます。

順序を変更することもできます。

    CUDA_VISIBLE_DEVICES=2,0 torchrun trainer-program.py ...

ここでは、物理 GPU 0 と 2 がそれぞれ`cuda:1`と`cuda:0`にマッピングされています。

上記の例はすべて `DistributedDataParallel` 使用パターンのものですが、同じ方法が `DataParallel` でも機能します。

    CUDA_VISIBLE_DEVICES=2,0 python trainer-program.py ...

GPU のない環境をエミュレートするには、次のようにこの環境変数を空の値に設定するだけです。

    CUDA_VISIBLE_DEVICES= python trainer-program.py ...

他の環境変数と同様に、これらをコマンド ラインに追加する代わりに、次のようにエクスポートすることもできます。

    export CUDA_VISIBLE_DEVICES=0,2
    torchrun trainer-program.py ...

ただし、この方法では、以前に環境変数を設定したことを忘れて、なぜ間違った GPU が使用されているのか理解できない可能性があるため、混乱を招く可能性があります。したがって、このセクションのほとんどの例で示されているように、同じコマンド ラインで特定の実行に対してのみ環境変数を設定するのが一般的です。

`CUDA_DEVICE_ORDER`

物理デバイスの順序を制御する追加の環境変数 `CUDA_DEVICE_ORDER` があります。選択肢は次の 2 つです。

1. PCIe バス ID 順 (`nvidia-smi` の順序と一致) - これがデフォルトです。

    export CUDA_DEVICE_ORDER=PCI_BUS_ID

2. GPU コンピューティング能力順に並べる

    export CUDA_DEVICE_ORDER=FASTEST_FIRST

ほとんどの場合、この環境変数を気にする必要はありませんが、古い GPU と新しい GPU が物理的に挿入されているため、遅い古いカードが遅くなっているように見えるような偏ったセットアップを行っている場合には、非常に役立ちます。初め。これを解決する 1 つの方法は、カードを交換することです。ただし、カードを交換できない場合 (デバイスの冷却が影響を受けた場合など)、`CUDA_DEVICE_ORDER=FASTEST_FIRST`を設定すると、常に新しい高速カードが最初に配置されます。ただし、`nvidia-smi`は依然として PCIe の順序でレポートするため、多少混乱するでしょう。

順序を入れ替えるもう 1 つの解決策は、以下を使用することです。

    export CUDA_VISIBLE_DEVICES=1,0

この例では 2 つの GPU だけを使用していますが、もちろん、コンピューターに搭載されている数の GPU にも同じことが当てはまります。

また、この環境変数を設定する場合は、`~/.bashrc` ファイルまたはその他の起動設定ファイルに設定して、忘れるのが最善です。

##
Trainer Integrations

Trainer は、トレーニングを劇的に改善する可能性のあるライブラリをサポートするように拡張されました。 時間とはるかに大きなモデルに適合します。

現在、サードパーティのソリューション DeepSpeed および PyTorch FSDP をサポートしています。論文 ZeRO: メモリの最適化兆パラメータ モデルのトレーニングに向けて、Samyam Rajbhandari、Jeff Rasley、Olatunji Ruwase、Yuxiong He 著。

この提供されるサポートは、この記事の執筆時点では新しくて実験的なものです。 DeepSpeed と PyTorch FSDP のサポートはアクティブであり、それに関する問題は歓迎しますが、FairScale 統合は PyTorch メインに統合されているため、もうサポートしていません (PyTorch FSDP 統合)

###
CUDA Extension Installation Notes

この記事の執筆時点では、Deepspeed を使用するには、CUDA C++ コードをコンパイルする必要があります。

すべてのインストールの問題は、Deepspeed の対応する GitHub の問題を通じて対処する必要がありますが、ビルド中に発生する可能性のある一般的な問題がいくつかあります。 CUDA 拡張機能を構築する必要がある PyTorch 拡張機能。

したがって、次の操作を実行中に CUDA 関連のビルドの問題が発生した場合は、次のとおりです。

    pip install deepspeed

まず次の注意事項をお読みください。

これらのノートでは、`pytorch` が CUDA `10.2` でビルドされた場合に何をすべきかの例を示します。あなたの状況が次のような場合 異なる場合は、バージョン番号を目的のバージョンに調整することを忘れないでください。

####
Possible problem #1

Pytorch には独自の CUDA ツールキットが付属していますが、これら 2 つのプロジェクトをビルドするには、同一バージョンの CUDA が必要です。 システム全体にインストールされます。

たとえば、Python 環境に `cudatoolkit==10.2` を指定して `pytorch` をインストールした場合は、次のものも必要です。 CUDA `10.2` がシステム全体にインストールされました。

正確な場所はシステムによって異なる場合がありますが、多くのシステムでは`/usr/local/cuda-10.2`が最も一般的な場所です。 Unix システム。 CUDA が正しく設定され、`PATH`環境変数に追加されると、 次のようにしてインストール場所を指定します。

    which nvcc

CUDA がシステム全体にインストールされていない場合は、最初にインストールしてください。お気に入りを使用して手順を見つけることができます 検索エンジン。たとえば、Ubuntu を使用している場合は、ubuntu cuda 10.2 install を検索するとよいでしょう。

####
Possible problem #2

もう 1 つの考えられる一般的な問題は、システム全体に複数の CUDA ツールキットがインストールされている可能性があることです。たとえばあなた がある可能性があり：

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

この状況では、`PATH` および `LD_LIBRARY_PATH` 環境変数に以下が含まれていることを確認する必要があります。 目的の CUDA バージョンへの正しいパス。通常、パッケージ インストーラーは、これらに、 最後のバージョンがインストールされました。適切なパッケージが見つからないためにパッケージのビルドが失敗するという問題が発生した場合は、 CUDA バージョンがシステム全体にインストールされているにもかかわらず、前述の 2 つを調整する必要があることを意味します 環境変数。

まず、その内容を見てみましょう。

    echo $PATH
    echo $LD_LIBRARY_PATH

それで、中に何が入っているかがわかります。

`LD_LIBRARY_PATH` が空である可能性があります。

`PATH` は実行可能ファイルが存在する場所をリストし、`LD_LIBRARY_PATH` は共有ライブラリの場所を示します。 探すことです。どちらの場合も、前のエントリが後のエントリより優先されます。 `:` は複数を区切るために使用されます エントリ。

ここで、ビルド プログラムに特定の CUDA ツールキットの場所を指示するには、最初にリストされる希望のパスを挿入します。 やっていること：

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

既存の値を上書きするのではなく、先頭に追加することに注意してください。

もちろん、必要に応じてバージョン番号やフルパスを調整します。割り当てたディレクトリが実際に機能することを確認してください 存在する。 `lib64` サブディレクトリは、`libcudart.so` などのさまざまな CUDA `.so` オブジェクトが存在する場所です。 システムでは別の名前が付けられますが、現実を反映するように調整してください。

####
Possible problem #3

一部の古い CUDA バージョンは、新しいコンパイラでのビルドを拒否する場合があります。たとえば、あなたは`gcc-9`を持っていますが、それが必要です `gcc-7`。

それにはさまざまな方法があります。

最新の CUDA ツールキットをインストールできる場合は、通常、新しいコンパイラがサポートされているはずです。

あるいは、既に所有しているコンパイラに加えて、下位バージョンのコンパイラをインストールすることもできます。 すでに存在しますが、デフォルトではないため、ビルドシステムはそれを認識できません。 「gcc-7」がインストールされているが、 ビルドシステムが見つからないというメッセージを表示する場合は、次の方法で解決できる可能性があります。

    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++

ここでは、`/usr/local/cuda-10.2/bin/gcc` から `gcc-7` へのシンボリックリンクを作成しています。 `/usr/local/cuda-10.2/bin/` は `PATH` 環境変数内にある必要があります (前の問題の解決策を参照)。 `gcc-7` (および `g++7`) が見つかるはずで、ビルドは成功します。

いつものように、状況に合わせて例のパスを編集してください。

###
PyTorch Fully Sharded Data parallel

より大きなバッチ サイズで巨大なモデルのトレーニングを高速化するには、完全にシャード化されたデータ並列モデルを使用できます。 このタイプのデータ並列パラダイムでは、オプティマイザーの状態、勾配、パラメーターをシャーディングすることで、より多くのデータと大規模なモデルをフィッティングできます。 この機能とその利点の詳細については、完全シャーディング データ並列ブログ をご覧ください。 最新の PyTorch の Fully Sharded Data Parallel (FSDP) トレーニング機能を統合しました。 必要なのは、設定を通じて有効にすることだけです。

FSDP サポートに必要な PyTorch バージョン: PyTorch Nightly (リリース後にこれを読んだ場合は 1.12.0) FSDP を有効にしたモデルの保存は、最近の修正でのみ利用できるためです。

使用法：

- 配布されたランチャーが追加されていることを確認してください まだ使用していない場合は、`-m torch.distributed.launch --nproc_per_node=NUMBER_OF_GPUS_YOU_HAVE`を使用します。

- シャーディング戦略:

  - FULL_SHARD : データ並列ワーカー/GPU にわたるシャード オプティマイザーの状態 + 勾配 + モデル パラメーター。 このためには、コマンドライン引数に`--fsdp full_shard`を追加します。
  - SHARD_GRAD_OP : シャード オプティマイザーの状態 + データ並列ワーカー/GPU 全体の勾配。 このためには、コマンドライン引数に`--fsdp shard_grad_op`を追加します。
  - NO_SHARD : シャーディングなし。このためには、コマンドライン引数に`--fsdp no_shard`を追加します。

- パラメータと勾配を CPU にオフロードするには、 コマンドライン引数に`--fsdp "full_shard offload"`または`--fsdp "shard_grad_op offload"`を追加します。

- `default_auto_wrap_policy` を使用して FSDP でレイヤーを自動的に再帰的にラップするには、 コマンドライン引数に`--fsdp "full_shard auto_wrap"`または`--fsdp "shard_grad_op auto_wrap"`を追加します。

- CPU オフロードと自動ラッピングの両方を有効にするには、 コマンドライン引数に`--fsdp "full_shard offload auto_wrap"`または`--fsdp "shard_grad_op offload auto_wrap"`を追加します。

- 残りの FSDP 構成は、`--fsdp_config <path_to_fsdp_config.json>`を介して渡されます。それは、次のいずれかの場所です。 FSDP json 構成ファイル (例: `fsdp_config.json`)、またはすでにロードされている json ファイルを `dict` として使用します。

  - 自動ラッピングが有効な場合は、トランスベースの自動ラップ ポリシーまたはサイズ ベースの自動ラップ ポリシーを使用できます。
    - トランスフォーマーベースの自動ラップポリシーの場合、構成ファイルで `fsdp_transformer_layer_cls_to_wrap` を指定することをお勧めします。指定しない場合、使用可能な場合、デフォルト値は `model._no_split_modules` になります。 これは、ラップするトランスフォーマー層クラス名のリスト (大文字と小文字を区別) を指定します (例: `BertLayer`、`GPTJBlock`、`T5Block` …)。 重みを共有するサブモジュール (埋め込み層など) が異なる FSDP ラップされたユニットにならないようにする必要があるため、これは重要です。 このポリシーを使用すると、マルチヘッド アテンションとそれに続くいくつかの MLP レイヤーを含むブロックごとにラッピングが発生します。 共有埋め込みを含む残りの層は、同じ最も外側の FSDP ユニットにラップされるのが便利です。 したがって、トランスベースのモデルにはこれを使用してください。
    - サイズベースの自動ラップポリシーの場合は、設定ファイルに`fsdp_min_num_params`を追加してください。 自動ラッピングのための FSDP のパラメータの最小数を指定します。
  - 設定ファイルで `fsdp_backward_prefetch` を指定できるようになりました。次のパラメータのセットをいつプリフェッチするかを制御します。 `backward_pre` と `backward_pos` が利用可能なオプションです。 詳細については、`torch.distributed.fsdp.full_sharded_data_Parallel.BackwardPrefetch`を参照してください。
  - 設定ファイルで `fsdp_forward_prefetch` を指定できるようになりました。次のパラメータのセットをいつプリフェッチするかを制御します。 `True`の場合、FSDP はフォワード パスでの実行中に、次に来るオールギャザーを明示的にプリフェッチします。
  - 設定ファイルで `limit_all_gathers` を指定できるようになりました。 `True`の場合、FSDP は CPU スレッドを明示的に同期して、実行中のオールギャザが多すぎるのを防ぎます。
  - `activation_checkpointing`を設定ファイルで指定できるようになりました。 `True`の場合、FSDP アクティベーション チェックポイントは、FSDP のアクティベーションをクリアすることでメモリ使用量を削減する手法です。 特定のレイヤーを処理し、バックワード パス中にそれらを再計算します。事実上、これは余分な計算時間を犠牲にします メモリ使用量を削減します。

注意すべき注意点がいくつかあります

- これは `generate` と互換性がないため、 `--predict_with_generate` とも互換性がありません すべての seq2seq/clm スクリプト (翻訳/要約/clm など)。 問題 #21667 を参照してください。

###
PyTorch/XLA Fully Sharded Data parallel

TPU ユーザーの皆様に朗報です。 PyTorch/XLA は FSDP をサポートするようになりました。 最新の Fully Sharded Data Parallel (FSDP) トレーニングがすべてサポートされています。 詳細については、FSDP を使用した Cloud TPU での PyTorch モデルのスケーリング および PyTorch/XLA 実装 を参照してください。 FSDP の 必要なのは、設定を通じて有効にすることだけです。

FSDP サポートに必要な PyTorch/XLA バージョン: >=2.0

使用法：

`--fsdp "full shard"` を、`--fsdp_config <path_to_fsdp_config.json>` に加えられる次の変更とともに渡します。

- PyTorch/XLA FSDP を有効にするには、`xla`を`True`に設定する必要があります。
- `xla_fsdp_settings` 値は、XLA FSDP ラッピング パラメータを格納する辞書です。 オプションの完全なリストについては、こちら。
- `xla_fsdp_grad_ckpt`。 `True`の場合、ネストされた XLA FSDP でラップされた各レイヤー上で勾配チェックポイントを使用します。 この設定は、xla フラグが true に設定されており、自動ラッピング ポリシーが指定されている場合にのみ使用できます。 `fsdp_min_num_params` または `fsdp_transformer_layer_cls_to_wrap`。
- トランスフォーマー ベースの自動ラップ ポリシーまたはサイズ ベースの自動ラップ ポリシーのいずれかを使用できます。
  - トランスフォーマーベースの自動ラップポリシーの場合、構成ファイルで `fsdp_transformer_layer_cls_to_wrap` を指定することをお勧めします。指定しない場合、使用可能な場合、デフォルト値は `model._no_split_modules` になります。 これは、ラップするトランスフォーマー層クラス名のリスト (大文字と小文字を区別) を指定します (例: `BertLayer`、`GPTJBlock`、`T5Block` …)。 重みを共有するサブモジュール (埋め込み層など) が異なる FSDP ラップされたユニットにならないようにする必要があるため、これは重要です。 このポリシーを使用すると、マルチヘッド アテンションとそれに続くいくつかの MLP レイヤーを含むブロックごとにラッピングが発生します。 共有埋め込みを含む残りの層は、同じ最も外側の FSDP ユニットにラップされるのが便利です。 したがって、トランスベースのモデルにはこれを使用してください。
  - サイズベースの自動ラップポリシーの場合は、設定ファイルに`fsdp_min_num_params`を追加してください。 自動ラッピングのための FSDP のパラメータの最小数を指定します。

###
Using Trainer for accelerated PyTorch Training on Mac

PyTorch v1.12 リリースにより、開発者と研究者は Apple シリコン GPU を利用してモデル トレーニングを大幅に高速化できます。 これにより、プロトタイピングや微調整などの機械学習ワークフローを Mac 上でローカルで実行できるようになります。 PyTorch のバックエンドとしての Apple の Metal Performance Shaders (MPS) はこれを可能にし、新しい `"mps"` デバイス経由で使用できます。 これにより、計算グラフとプリミティブが MPS Graph フレームワークと MPS によって提供される調整されたカーネルにマッピングされます。 詳細については、公式ドキュメント Mac での Accelerated PyTorch Training の紹介 を参照してください。 および MPS バックエンド。

MacOS マシンに PyTorch >= 1.13 (執筆時点ではナイトリー バージョン) をインストールすることを強くお勧めします。 トランスベースのモデルのモデルの正確性とパフォーマンスの向上に関連する主要な修正が行われています。 詳細については、https://github.com/pytorch/pytorch/issues/82707 を参照してください。

Apple Silicon チップを使用したトレーニングと推論の利点

1. ユーザーがローカルで大規模なネットワークやバッチ サイズをトレーニングできるようにします
2. ユニファイド メモリ アーキテクチャにより、データ取得の遅延が短縮され、GPU がメモリ ストア全体に直接アクセスできるようになります。 したがって、エンドツーエンドのパフォーマンスが向上します。
3. クラウドベースの開発に関連するコストや追加のローカル GPU の必要性を削減します。

前提条件: mps サポートを備えたトーチをインストールするには、 この素晴らしいメディア記事 GPU アクセラレーションが M1 Mac の PyTorch に登場 に従ってください。 。

使用法： `mps` デバイスは、`cuda` デバイスが使用される方法と同様に利用可能な場合、デフォルトで使用されます。 したがって、ユーザーによるアクションは必要ありません。 たとえば、以下のコマンドを使用して、Apple Silicon GPU を使用して公式の Glue テキスト分類タスクを (ルート フォルダーから) 実行できます。

    export TASK_NAME=mrpc

    python examples/pytorch/text-classification/run_glue.py \
      --model_name_or_path google-bert/bert-base-cased \
      --task_name $TASK_NAME \
      --do_train \
      --do_eval \
      --max_seq_length 128 \
      --per_device_train_batch_size 32 \
      --learning_rate 2e-5 \
      --num_train_epochs 3 \
      --output_dir /tmp/$TASK_NAME/ \
      --overwrite_output_dir

注意すべきいくつかの注意事項

1. 一部の PyTorch 操作は mps に実装されていないため、エラーがスローされます。 これを回避する 1 つの方法は、環境変数 `PYTORCH_ENABLE_MPS_FALLBACK=1` を設定することです。 これらの操作では CPU にフォールバックします。ただし、それでも UserWarning がスローされます。
2. 分散セットアップ`gloo`および`nccl`は、`mps`デバイスでは動作しません。 これは、現在「mps」デバイス タイプの単一 GPU のみを使用できることを意味します。

最後に、覚えておいてください。 🤗 `Trainer` は MPS バックエンドのみを統合するため、 MPS バックエンドの使用に関して問題や質問がある場合は、 PyTorch GitHub に問題を提出してください。

##
Using Accelerate Launcher with Trainer

加速してトレーナーにパワーを与えましょう。ユーザーが期待することに関しては、次のとおりです。

- トレーナー引数に対して FSDP、DeepSpeed などのトレーナー インテレーションを変更せずに使用し続けることができます。
- トレーナーで Accelerate Launcher を使用できるようになりました (推奨)。

トレーナーで Accelerate Launcher を使用する手順:

1. 🤗 Accelerate がインストールされていることを確認してください。Accelerate がないと `Trainer` を使用することはできません。そうでない場合は、`pip install accelerate`してください。 Accelerate のバージョンを更新する必要がある場合もあります: `pip install activate --upgrade`

2. `accelerate config`を実行し、アンケートに記入します。以下は加速設定の例です。 ａ． DDP マルチノード マルチ GPU 構成:

       compute_environment: LOCAL_MACHINE
       distributed_type: MULTI_GPU
       downcast_bf16: 'no'
       gpu_ids: all
       machine_rank: 0 #change rank as per the node
       main_process_ip: 192.168.20.1
       main_process_port: 9898
       main_training_function: main
       mixed_precision: fp16
       num_machines: 2
       num_processes: 8
       rdzv_backend: static
       same_network: true
       tpu_env: []
       tpu_use_cluster: false
       tpu_use_sudo: false
       use_cpu: false

   b. FSDP config:

       compute_environment: LOCAL_MACHINE
       distributed_type: FSDP
       downcast_bf16: 'no'
       fsdp_config:
         fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
         fsdp_backward_prefetch_policy: BACKWARD_PRE
         fsdp_forward_prefetch: true
         fsdp_offload_params: false
         fsdp_sharding_strategy: 1
         fsdp_state_dict_type: FULL_STATE_DICT
         fsdp_sync_module_states: true
         fsdp_transformer_layer_cls_to_wrap: BertLayer
         fsdp_use_orig_params: true
       machine_rank: 0
       main_training_function: main
       mixed_precision: bf16
       num_machines: 1
       num_processes: 2
       rdzv_backend: static
       same_network: true
       tpu_env: []
       tpu_use_cluster: false
       tpu_use_sudo: false
       use_cpu: false

   c.ファイルを指す DeepSpeed 構成:

       compute_environment: LOCAL_MACHINE
       deepspeed_config:
         deepspeed_config_file: /home/user/configs/ds_zero3_config.json
         zero3_init_flag: true
       distributed_type: DEEPSPEED
       downcast_bf16: 'no'
       machine_rank: 0
       main_training_function: main
       num_machines: 1
       num_processes: 4
       rdzv_backend: static
       same_network: true
       tpu_env: []
       tpu_use_cluster: false
       tpu_use_sudo: false
       use_cpu: false

   d.加速プラグインを使用した DeepSpeed 構成:

       compute_environment: LOCAL_MACHINE
       deepspeed_config:
         gradient_accumulation_steps: 1
         gradient_clipping: 0.7
         offload_optimizer_device: cpu
         offload_param_device: cpu
         zero3_init_flag: true
         zero_stage: 2
       distributed_type: DEEPSPEED
       downcast_bf16: 'no'
       machine_rank: 0
       main_training_function: main
       mixed_precision: bf16
       num_machines: 1
       num_processes: 4
       rdzv_backend: static
       same_network: true
       tpu_env: []
       tpu_use_cluster: false
       tpu_use_sudo: false
       use_cpu: false

3. 加速設定またはランチャー引数によって上記で処理された引数以外の引数を使用して、トレーナー スクリプトを実行します。 以下は、上記の FSDP 構成で`accelerate launcher`を使用して`run_glue.py`を実行する例です。

    cd transformers

    accelerate launch \
    ./examples/pytorch/text-classification/run_glue.py \
    --model_name_or_path google-bert/bert-base-cased \
    --task_name $TASK_NAME \
    --do_train \
    --do_eval \
    --max_seq_length 128 \
    --per_device_train_batch_size 16 \
    --learning_rate 5e-5 \
    --num_train_epochs 3 \
    --output_dir /tmp/$TASK_NAME/ \
    --overwrite_output_dir

4. `accelerate launch`するための cmd 引数を直接使用することもできます。上の例は次のようにマッピングされます。

    cd transformers

    accelerate launch --num_processes=2 \
    --use_fsdp \
    --mixed_precision=bf16 \
    --fsdp_auto_wrap_policy=TRANSFORMER_BASED_WRAP  \
    --fsdp_transformer_layer_cls_to_wrap="BertLayer" \
    --fsdp_sharding_strategy=1 \
    --fsdp_state_dict_type=FULL_STATE_DICT \
    ./examples/pytorch/text-classification/run_glue.py
    --model_name_or_path google-bert/bert-base-cased \
    --task_name $TASK_NAME \
    --do_train \
    --do_eval \
    --max_seq_length 128 \
    --per_device_train_batch_size 16 \
    --learning_rate 5e-5 \
    --num_train_epochs 3 \
    --output_dir /tmp/$TASK_NAME/ \
    --overwrite_output_dir

詳細については、🤗 Accelerate CLI ガイドを参照してください: 🤗 Accelerate スクリプトの起動。

移動されたセクション:

[ DeepSpeed | Installation | Deployment with multiple GPUs | Deployment with one GPU | Deployment in Notebooks | Configuration | Passing Configuration | Shared Configuration | ZeRO | ZeRO-2 Config | ZeRO-3 Config | NVMe Support | ZeRO-2 vs ZeRO-3 Performance | ZeRO-2 Example | ZeRO-3 Example | Optimizer | Scheduler | fp32 Precision | Automatic Mixed Precision | Batch Size | Gradient Accumulation | Gradient Clipping | Getting The Model Weights Out ]