import logging import os import warnings from contextlib import nullcontext from typing import TYPE_CHECKING, Any, Callable, Dict, List, Optional, Tuple, Union import torch from torch import nn from torch.utils.data import BatchSampler, ConcatDataset, DataLoader, SubsetRandomSampler from transformers import EvalPrediction, PreTrainedTokenizerBase, Trainer, TrainerCallback from transformers.data.data_collator import DataCollator from transformers.integrations import WandbCallback from transformers.trainer import TRAINING_ARGS_NAME from transformers.trainer_utils import EvalLoopOutput from transformers.training_args import ParallelMode from sentence_transformers.data_collator import SentenceTransformerDataCollator from sentence_transformers.evaluation import SentenceEvaluator, SequentialEvaluator from sentence_transformers.losses.CoSENTLoss import CoSENTLoss from sentence_transformers.model_card import ModelCardCallback from sentence_transformers.models.Transformer import Transformer from sentence_transformers.sampler import ( DefaultBatchSampler, GroupByLabelBatchSampler, NoDuplicatesBatchSampler, ProportionalBatchSampler, RoundRobinBatchSampler, ) from sentence_transformers.training_args import ( BatchSamplers, MultiDatasetBatchSamplers, SentenceTransformerTrainingArguments, ) from sentence_transformers.util import disable_logging, is_datasets_available, is_training_available if is_datasets_available(): from datasets import Dataset, DatasetDict logger = logging.getLogger(__name__) if TYPE_CHECKING: from sentence_transformers.SentenceTransformer import SentenceTransformer class SentenceTransformerTrainer(Trainer): """ SentenceTransformerTrainer is a simple but feature-complete training and eval loop for PyTorch based on the 🤗 Transformers :class:`~transformers.Trainer`. This trainer integrates support for various :class:`transformers.TrainerCallback` subclasses, such as: - :class:`~transformers.integrations.WandbCallback` to automatically log training metrics to W&B if `wandb` is installed - :class:`~transformers.integrations.TensorBoardCallback` to log training metrics to TensorBoard if `tensorboard` is accessible. - :class:`~transformers.integrations.CodeCarbonCallback` to track the carbon emissions of your model during training if `codecarbon` is installed. - Note: These carbon emissions will be included in your automatically generated model card. See the Transformers `Callbacks `_ documentation for more information on the integrated callbacks and how to write your own callbacks. Args: model (:class:`~sentence_transformers.SentenceTransformer`, *optional*): The model to train, evaluate or use for predictions. If not provided, a `model_init` must be passed. args (:class:`~sentence_transformers.training_args.SentenceTransformerTrainingArguments`, *optional*): The arguments to tweak for training. Will default to a basic instance of :class:`~sentence_transformers.training_args.SentenceTransformerTrainingArguments` with the `output_dir` set to a directory named *tmp_trainer* in the current directory if not provided. train_dataset (Union[:class:`datasets.Dataset`, :class:`datasets.DatasetDict`, Dict[str, :class:`datasets.Dataset`]], *optional*): The dataset to use for training. Must have a format accepted by your loss function, see `Training Overview > Dataset Format <../../../docs/sentence_transformer/training_overview.html#dataset-format>`_. eval_dataset (Union[:class:`datasets.Dataset`, :class:`datasets.DatasetDict`, Dict[str, :class:`datasets.Dataset`]], *optional*): The dataset to use for evaluation. Must have a format accepted by your loss function, see `Training Overview > Dataset Format <../../../docs/sentence_transformer/training_overview.html#dataset-format>`_. loss (Optional[Union[:class:`torch.nn.Module`, Dict[str, :class:`torch.nn.Module`],\ Callable[[:class:`~sentence_transformers.SentenceTransformer`], :class:`torch.nn.Module`],\ Dict[str, Callable[[:class:`~sentence_transformers.SentenceTransformer`]]]], *optional*): The loss function to use for training. Can either be a loss class instance, a dictionary mapping dataset names to loss class instances, a function that returns a loss class instance given a model, or a dictionary mapping dataset names to functions that return a loss class instance given a model. In practice, the latter two are primarily used for hyper-parameter optimization. Will default to :class:`~sentence_transformers.losses.CoSENTLoss` if no ``loss`` is provided. evaluator (Union[:class:`~sentence_transformers.evaluation.SentenceEvaluator`,\ List[:class:`~sentence_transformers.evaluation.SentenceEvaluator`]], *optional*): The evaluator instance for useful evaluation metrics during training. You can use an ``evaluator`` with or without an ``eval_dataset``, and vice versa. Generally, the metrics that an ``evaluator`` returns are more useful than the loss value returned from the ``eval_dataset``. A list of evaluators will be wrapped in a :class:`~sentence_transformers.evaluation.SequentialEvaluator` to run them sequentially. callbacks (List of [:class:`transformers.TrainerCallback`], *optional*): A list of callbacks to customize the training loop. Will add those to the list of default callbacks detailed in [here](callback). If you want to remove one of the default callbacks used, use the [`Trainer.remove_callback`] method. optimizers (`Tuple[:class:`torch.optim.Optimizer`, :class:`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 :class:`torch.optim.AdamW` on your model and a scheduler given by :func:`transformers.get_linear_schedule_with_warmup` controlled by `args`. Important attributes: - **model** -- Always points to the core model. If using a transformers model, it will be a [`PreTrainedModel`] subclass. - **model_wrapped** -- Always points to the most external model in case one or more other modules wrap the original model. This is the model that should be used for the forward pass. For example, under `DeepSpeed`, the inner model is wrapped in `DeepSpeed` and then again in `torch.nn.DistributedDataParallel`. If the inner model hasn't been wrapped, then `self.model_wrapped` is the same as `self.model`. - **is_model_parallel** -- Whether or not a model has been switched to a model parallel mode (different from data parallelism, this means some of the model layers are split on different GPUs). - **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`) """ def __init__( self, model: Optional["SentenceTransformer"] = None, args: SentenceTransformerTrainingArguments = None, train_dataset: Optional[Union["Dataset", "DatasetDict", Dict[str, "Dataset"]]] = None, eval_dataset: Optional[Union["Dataset", "DatasetDict", Dict[str, "Dataset"]]] = None, loss: Optional[ Union[ nn.Module, Dict[str, nn.Module], Callable[["SentenceTransformer"], torch.nn.Module], Dict[str, Callable[["SentenceTransformer"], torch.nn.Module]], ] ] = None, evaluator: Optional[Union[SentenceEvaluator, List[SentenceEvaluator]]] = None, data_collator: Optional[DataCollator] = None, tokenizer: Optional[Union[PreTrainedTokenizerBase, Callable]] = None, model_init: Optional[Callable[[], "SentenceTransformer"]] = None, compute_metrics: Optional[Callable[[EvalPrediction], Dict]] = None, callbacks: Optional[List[TrainerCallback]] = None, optimizers: Tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), preprocess_logits_for_metrics: Optional[Callable[[torch.Tensor, torch.Tensor], torch.Tensor]] = None, ) -> None: if not is_training_available(): raise RuntimeError( "To train a SentenceTransformer model, you need to install the `accelerate` and `datasets` modules. " "You can do so with the `train` extra:\n" 'pip install -U "sentence-transformers[train]"' ) if args is None: output_dir = "tmp_trainer" logger.info(f"No `TrainingArguments` passed, using `output_dir={output_dir}`.") args = SentenceTransformerTrainingArguments(output_dir=output_dir) elif not isinstance(args, SentenceTransformerTrainingArguments): raise ValueError("Please use `TrainingArguments` imported from `sentence_transformers`.") if model is None: if model_init is not None: self.model_init = model_init model = self.call_model_init() else: raise RuntimeError("`Trainer` requires either a `model` or `model_init` argument") else: if model_init is not None: warnings.warn( "`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will" " overwrite your model when calling the `train` method. This will become a fatal error in the next" " release.", FutureWarning, ) self.model_init = model_init # Get a dictionary of the default training arguments, so we can determine which arguments have been changed # for the model card default_args_dict = SentenceTransformerTrainingArguments(output_dir="unused").to_dict() # If the model ID is set via the SentenceTransformerTrainingArguments, but not via the SentenceTransformerModelCardData, # then we can set it here for the model card regardless if args.hub_model_id and not model.model_card_data.model_id: model.model_card_data.set_model_id(args.hub_model_id) if tokenizer is None and isinstance(model.tokenizer, PreTrainedTokenizerBase): tokenizer = model.tokenizer if data_collator is None: data_collator = SentenceTransformerDataCollator(tokenize_fn=model.tokenize) if isinstance(train_dataset, dict) and not isinstance(train_dataset, DatasetDict): train_dataset = DatasetDict(train_dataset) if isinstance(eval_dataset, dict) and not isinstance(eval_dataset, Dataset): eval_dataset = DatasetDict(eval_dataset) super().__init__( model=None if self.model_init else model, args=args, data_collator=data_collator, train_dataset=train_dataset, eval_dataset=eval_dataset, tokenizer=tokenizer, model_init=model_init, compute_metrics=compute_metrics, callbacks=callbacks, optimizers=optimizers, preprocess_logits_for_metrics=preprocess_logits_for_metrics, ) # Every Sentence Transformer model can always return a loss, so we set this to True # to avoid having to specify it in the data collator or model's forward self.can_return_loss = True self.model: SentenceTransformer self.args: SentenceTransformerTrainingArguments self.data_collator: SentenceTransformerDataCollator # Set the W&B project via environment variables if it's not already set if any([isinstance(callback, WandbCallback) for callback in self.callback_handler.callbacks]): os.environ.setdefault("WANDB_PROJECT", "sentence-transformers") if loss is None: logger.info("No `loss` passed, using `losses.CoSENTLoss` as a default option.") loss = CoSENTLoss(self.model) if isinstance(loss, dict): self.loss = {dataset_name: self.prepare_loss(loss_fn, model) for dataset_name, loss_fn in loss.items()} for dataset_name, dataset in zip(["train", "eval"], [train_dataset, eval_dataset]): if dataset is None: continue if not isinstance(dataset, dict): raise ValueError( f"If the provided `loss` is a dict, then the `{dataset_name}_dataset` must be a `DatasetDict`." ) if missing := set(dataset.keys()) - set(loss.keys()): raise ValueError( f"If the provided `loss` is a dict, then all keys from the `{dataset_name}_dataset` dictionary must occur in `loss` also. " f"Currently, {sorted(missing)} occur{'s' if len(missing) == 1 else ''} in `{dataset_name}_dataset` but not in `loss`." ) else: self.loss = self.prepare_loss(loss, model) # If evaluator is a list, we wrap it in a SequentialEvaluator if evaluator is not None and not isinstance(evaluator, SentenceEvaluator): evaluator = SequentialEvaluator(evaluator) self.evaluator = evaluator # Add a callback responsible for automatically tracking data required for the automatic model card generation model_card_callback = ModelCardCallback(self, default_args_dict) self.add_callback(model_card_callback) model_card_callback.on_init_end(self.args, self.state, self.control, self.model) def call_model_init(self, trial=None) -> "SentenceTransformer": model = super().call_model_init(trial=trial) # If the Trainer already has a loss, then we'll want to override the model in the loss function if not hasattr(self, "loss"): return model # Multi-loss training: if isinstance(self.loss, dict): for key, loss_fn in self.loss.items(): # If a loss function is not yet initialized, we initialize it here if not isinstance(loss_fn, torch.nn.Module): self.loss[key] = loss_fn(model) # Otherwise, we override the original model with the updated model in the loss function elif hasattr(loss_fn, "model"): self.loss = self.override_model_in_loss(self.loss, model) # Loss is a function accepting a model as an argument elif not isinstance(self.loss, torch.nn.Module): self.loss = self.loss(model) # Loss is an initialized torch.nn.Module elif hasattr(self.loss, "model"): self.loss = self.override_model_in_loss(self.loss, model) return model def override_model_in_loss(self, loss: torch.nn.Module, model: "SentenceTransformer") -> torch.nn.Module: from sentence_transformers import SentenceTransformer for name, child in loss.named_children(): if name == "model" and isinstance(child, SentenceTransformer): loss.model = model elif isinstance(child, torch.nn.Module): setattr(loss, name, self.override_model_in_loss(child, model)) return loss def prepare_loss( self, loss: Union[Callable[["SentenceTransformer"], torch.nn.Module], torch.nn.Module], model: "SentenceTransformer", ) -> torch.nn.Module: if isinstance(loss, torch.nn.Module): return loss.to(model.device) return loss(model).to(model.device) def add_dataset_name_column(self, dataset_dict: "DatasetDict") -> "DatasetDict": for key, dataset in dataset_dict.items(): if "dataset_name" not in dataset.column_names: dataset_dict[key] = dataset.add_column("dataset_name", [key] * len(dataset)) return dataset_dict def compute_loss( self, model: "SentenceTransformer", inputs: Dict[str, Union[torch.Tensor, Any]], return_outputs: bool = False, ) -> Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, Any]]]: """ Computes the loss for the SentenceTransformer model. It uses ``self.loss`` to compute the loss, which can be a single loss function or a dictionary of loss functions for different datasets. If the loss is a dictionary, the dataset name is expected to be passed in the inputs under the key "dataset_name". This is done automatically in the ``add_dataset_name_column`` method. Note that even if ``return_outputs = True``, the outputs will be empty, as the SentenceTransformers losses do not return outputs. Args: model (SentenceTransformer): The SentenceTransformer model. inputs (Dict[str, Union[torch.Tensor, Any]]): The input data for the model. return_outputs (bool, optional): Whether to return the outputs along with the loss. Defaults to False. Returns: Union[torch.Tensor, Tuple[torch.Tensor, Dict[str, Any]]]: The computed loss. If `return_outputs` is True, returns a tuple of loss and outputs. Otherwise, returns only the loss. """ dataset_name = inputs.pop("dataset_name", None) features, labels = self.collect_features(inputs) loss_fn = self.loss if isinstance(loss_fn, dict) and dataset_name: loss_fn = loss_fn[dataset_name] # Hackishly insert the distributed model into the loss function, if the loss stores the model # Only called once per process if ( self.args.parallel_mode != ParallelMode.NOT_PARALLEL and hasattr(model, "module") and hasattr(loss_fn, "model") ): loss_fn = self.override_model_in_loss(loss_fn, model) loss = loss_fn(features, labels) if return_outputs: # During prediction/evaluation, `compute_loss` will be called with `return_outputs=True`. # However, Sentence Transformer losses do not return outputs, so we return an empty dictionary. # This does not result in any problems, as the SentenceTransformerTrainingArguments sets # `prediction_loss_only=True` which means that the output is not used. return loss, {} return loss def collect_features( self, inputs: Dict[str, Union[torch.Tensor, Any]] ) -> Tuple[List[Dict[str, torch.Tensor]], Optional[torch.Tensor]]: """Turn the inputs from the dataloader into the separate model inputs & the labels. Example:: >>> list(inputs.keys()) ['return_loss', 'label', 'sentence_0_input_ids', 'sentence_0_token_type_ids', 'sentence_0_attention_mask', 'sentence_1_input_ids', 'sentence_1_token_type_ids', 'sentence_1_attention_mask'] >>> features, labels = self.collect_features(inputs) >>> len(features) 2 >>> list(features[0].keys()) ['input_ids', 'token_type_ids', 'attention_mask'] >>> list(features[1].keys()) ['input_ids', 'token_type_ids', 'attention_mask'] >>> torch.equal(labels, inputs["label"]) True """ # All inputs ending with `_input_ids` (Transformers), `_sentence_embedding` (BoW), `_pixel_values` (CLIPModel) # are considered to correspond to a feature features = [] for column in inputs: if column.endswith("_input_ids"): prefix = column[: -len("input_ids")] elif column.endswith("_sentence_embedding"): prefix = column[: -len("sentence_embedding")] elif column.endswith("_pixel_values"): prefix = column[: -len("pixel_values")] else: continue features.append({key[len(prefix) :]: value for key, value in inputs.items() if key.startswith(prefix)}) labels = inputs.get("label", None) return features, labels def evaluate( self, eval_dataset: Optional[Union["Dataset", Dict[str, "Dataset"]]] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "eval", ) -> Dict[str, float]: eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset if isinstance(eval_dataset, DatasetDict) and isinstance(self.loss, dict): eval_dataset = self.add_dataset_name_column(eval_dataset) return super().evaluate(eval_dataset, ignore_keys, metric_key_prefix) def evaluation_loop( self, dataloader: DataLoader, description: str, prediction_loss_only: Optional[bool] = None, ignore_keys: Optional[List[str]] = None, metric_key_prefix: str = "eval", ) -> EvalLoopOutput: output = super().evaluation_loop( dataloader=dataloader, description=description, prediction_loss_only=prediction_loss_only, ignore_keys=ignore_keys, metric_key_prefix=metric_key_prefix, ) # If the evaluator is not defined, we can just return the output if self.evaluator is None: return output # If we are training and eval_dataset is a DatasetDict, then we should # 1) only run the evaluator for the first dataset # 2) prefix that only run as "eval", rather than e.g. "eval_multi_nli" if self.is_in_train and isinstance(self.eval_dataset, dict) and metric_key_prefix.startswith("eval_"): if metric_key_prefix[5:] == list(self.eval_dataset.keys())[0]: metric_key_prefix = "eval" else: return output with nullcontext() if self.is_local_process_zero() else disable_logging(logging.INFO): evaluator_metrics = self.evaluator(self.model) if not isinstance(evaluator_metrics, dict): evaluator_metrics = {"evaluator": evaluator_metrics} # Prefix all keys with metric_key_prefix + '_' for key in list(evaluator_metrics.keys()): if not key.startswith(f"{metric_key_prefix}_"): evaluator_metrics[f"{metric_key_prefix}_{key}"] = evaluator_metrics.pop(key) output.metrics.update(evaluator_metrics) return output def _load_best_model(self) -> None: # We want to ensure that this does not fail, and it may change if transformers updates how checkpoints are saved # Loading the best model is only supported for `transformers`-based models if not isinstance(self.model[0], Transformer): logger.info("Could not load best model, as the model is not a `transformers`-based model.") return try: if checkpoint := self.state.best_model_checkpoint: step = checkpoint.rsplit("-", 1)[-1] self.model.model_card_data.set_best_model_step(int(step)) except Exception: pass # Override the model with the `tranformers`-based auto_model, and restore the original SentenceTransformers # model with the loaded `transformers` model full_model = self.model self.model = self.model[0].auto_model try: return super()._load_best_model() finally: loaded_auto_model = self.model self.model = full_model self.model[0].auto_model = loaded_auto_model def validate_column_names(self, dataset: "Dataset", dataset_name: Optional[str] = None) -> bool: if overlap := set(dataset.column_names) & {"return_loss", "dataset_name"}: raise ValueError( f"The following column names are invalid in your {dataset_name + ' ' if dataset_name else ''}dataset: {list(overlap)}." " Avoid using these column names, as they are reserved for internal use." ) def get_batch_sampler( self, dataset: "Dataset", batch_size: int, drop_last: bool, valid_label_columns: Optional[List[str]] = None, generator: Optional[torch.Generator] = None, ) -> BatchSampler: if self.args.batch_sampler == BatchSamplers.NO_DUPLICATES: return NoDuplicatesBatchSampler( dataset=dataset, batch_size=batch_size, drop_last=drop_last, valid_label_columns=valid_label_columns, generator=generator, ) if self.args.batch_sampler == BatchSamplers.GROUP_BY_LABEL: return GroupByLabelBatchSampler( dataset=dataset, batch_size=batch_size, drop_last=drop_last, valid_label_columns=valid_label_columns, ) if self.args.batch_sampler == BatchSamplers.BATCH_SAMPLER: return DefaultBatchSampler( SubsetRandomSampler(range(len(dataset)), generator=generator), batch_size=batch_size, drop_last=drop_last, ) def get_multi_dataset_batch_sampler( self, dataset: ConcatDataset, batch_samplers: List[BatchSampler], generator: Optional[torch.Generator] = None, seed: Optional[int] = 0, ) -> BatchSampler: if self.args.multi_dataset_batch_sampler == MultiDatasetBatchSamplers.ROUND_ROBIN: return RoundRobinBatchSampler( dataset=dataset, batch_samplers=batch_samplers, generator=generator, seed=seed, ) if self.args.multi_dataset_batch_sampler == MultiDatasetBatchSamplers.PROPORTIONAL: return ProportionalBatchSampler( dataset=dataset, batch_samplers=batch_samplers, generator=generator, seed=seed, ) def get_train_dataloader(self) -> DataLoader: """ 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. """ if self.train_dataset is None: raise ValueError("Trainer: training requires a train_dataset.") train_dataset = self.train_dataset data_collator = self.data_collator generator = torch.Generator() if self.args.seed: generator.manual_seed(self.args.seed) if isinstance(train_dataset, DatasetDict): for dataset_name, dataset in train_dataset.items(): self.validate_column_names(dataset, dataset_name=dataset_name) if isinstance(self.loss, dict): train_dataset = self.add_dataset_name_column(train_dataset) batch_samplers = [ self.get_batch_sampler( dataset, batch_size=self.args.per_device_train_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) for dataset in train_dataset.values() ] train_dataset = ConcatDataset(train_dataset.values()) batch_sampler = self.get_multi_dataset_batch_sampler( dataset=train_dataset, batch_samplers=batch_samplers, generator=generator, seed=self.args.seed, ) else: self.validate_column_names(train_dataset) batch_sampler = self.get_batch_sampler( train_dataset, batch_size=self.args.train_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) dataloader_params = { "collate_fn": data_collator, "num_workers": self.args.dataloader_num_workers, "pin_memory": self.args.dataloader_pin_memory, "persistent_workers": self.args.dataloader_persistent_workers, "prefetch_factor": self.args.dataloader_prefetch_factor, "batch_sampler": batch_sampler, } # If 'even_batches' is True, it will use the initial few samples to pad out the last sample. This can # cause issues with multi-dataset training, so we want to set this to False. # For evaluation, setting 'even_batches' to False results in hanging, so we keep it as True there. self.accelerator.even_batches = False self._train_dataloader = self.accelerator.prepare(DataLoader(train_dataset, **dataloader_params)) return self._train_dataloader def get_eval_dataloader(self, eval_dataset: Union["Dataset", None] = None) -> DataLoader: """ Returns the evaluation [`~torch.utils.data.DataLoader`]. Subclass and override this method if you want to inject some custom behavior. Args: eval_dataset (`torch.utils.data.Dataset`, *optional*): If provided, will override `self.eval_dataset`. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. It must implement `__len__`. """ if eval_dataset is None and self.eval_dataset is None: # Prevent errors if the evaluator is set but no eval_dataset is provided if self.evaluator is not None: return DataLoader([]) raise ValueError("Trainer: evaluation requires an eval_dataset.") eval_dataset = eval_dataset if eval_dataset is not None else self.eval_dataset data_collator = self.data_collator generator = torch.Generator() if self.args.seed: generator.manual_seed(self.args.seed) # TODO: Correctly validate the column names for the eval_dataset if isinstance(eval_dataset, DatasetDict): if isinstance(self.loss, dict): eval_dataset = self.add_dataset_name_column(eval_dataset) batch_samplers = [ self.get_batch_sampler( dataset, batch_size=self.args.per_device_eval_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) for dataset in eval_dataset.values() ] eval_dataset = ConcatDataset(eval_dataset.values()) batch_sampler = self.get_multi_dataset_batch_sampler( dataset=eval_dataset, batch_samplers=batch_samplers, generator=generator, seed=self.args.seed, ) else: batch_sampler = self.get_batch_sampler( eval_dataset, batch_size=self.args.train_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) dataloader_params = { "collate_fn": data_collator, "num_workers": self.args.dataloader_num_workers, "pin_memory": self.args.dataloader_pin_memory, "persistent_workers": self.args.dataloader_persistent_workers, "prefetch_factor": self.args.dataloader_prefetch_factor, "batch_sampler": batch_sampler, } # If 'even_batches' is True, it will use the initial few samples to pad out the last sample. This can # cause issues with multi-dataset training, so we want to set this to False during training. # For evaluation, setting 'even_batches' to False results in hanging, so we keep it as True here. self.accelerator.even_batches = True return self.accelerator.prepare(DataLoader(eval_dataset, **dataloader_params)) def get_test_dataloader(self, test_dataset: "Dataset") -> DataLoader: """ Returns the training [`~torch.utils.data.DataLoader`]. Subclass and override this method if you want to inject some custom behavior. Args: test_dataset (`torch.utils.data.Dataset`, *optional*): The test dataset to use. If it is a [`~datasets.Dataset`], columns not accepted by the `model.forward()` method are automatically removed. It must implement `__len__`. """ data_collator = self.data_collator generator = torch.Generator() if self.args.seed: generator.manual_seed(self.args.seed) if isinstance(test_dataset, DatasetDict): for dataset_name, dataset in test_dataset.items(): self.validate_column_names(dataset, dataset_name=dataset_name) if isinstance(self.loss, dict): test_dataset = self.add_dataset_name_column(test_dataset) batch_samplers = [ self.get_batch_sampler( dataset, batch_size=self.args.per_device_train_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) for dataset in test_dataset.values() ] test_dataset = ConcatDataset(test_dataset.values()) batch_sampler = self.get_multi_dataset_batch_sampler( dataset=test_dataset, batch_samplers=batch_samplers, generator=generator, seed=self.args.seed, ) else: self.validate_column_names(test_dataset) batch_sampler = self.get_batch_sampler( test_dataset, batch_size=self.args.train_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) dataloader_params = { "collate_fn": data_collator, "num_workers": self.args.dataloader_num_workers, "pin_memory": self.args.dataloader_pin_memory, "persistent_workers": self.args.dataloader_persistent_workers, "prefetch_factor": self.args.dataloader_prefetch_factor, "batch_sampler": batch_sampler, } # If 'even_batches' is True, it will use the initial few samples to pad out the last sample. This can # cause issues with multi-dataset training, so we want to set this to False. # For evaluation, setting 'even_batches' to False results in hanging, so we keep it as True there. self.accelerator.even_batches = False self._train_dataloader = self.accelerator.prepare(DataLoader(test_dataset, **dataloader_params)) return self._train_dataloader def _save(self, output_dir: Optional[str] = None, state_dict=None) -> None: # If we are executing this function, we are the process zero, so we don't check for that. output_dir = output_dir if output_dir is not None else self.args.output_dir os.makedirs(output_dir, exist_ok=True) logger.info(f"Saving model checkpoint to {output_dir}") self.model.save_pretrained(output_dir, safe_serialization=self.args.save_safetensors) if self.tokenizer is not None: self.tokenizer.save_pretrained(output_dir) # Good practice: save your training arguments together with the trained model torch.save(self.args, os.path.join(output_dir, TRAINING_ARGS_NAME)) def _load_from_checkpoint(self, checkpoint_path: str) -> None: from sentence_transformers import SentenceTransformer loaded_model = SentenceTransformer(checkpoint_path) self.model.load_state_dict(loaded_model.state_dict()) def create_model_card( self, language: Optional[str] = None, license: Optional[str] = None, tags: Union[str, List[str], None] = None, model_name: Optional[str] = None, finetuned_from: Optional[str] = None, tasks: Union[str, List[str], None] = None, dataset_tags: Union[str, List[str], None] = None, dataset: Union[str, List[str], None] = None, dataset_args: Union[str, List[str], None] = None, **kwargs, ) -> None: if not self.is_world_process_zero(): return if language: self.model.model_card_data.set_language(language) if license: self.model.model_card_data.set_license(license) if tags: self.model.model_card_data.add_tags(tags) self.model._create_model_card(self.args.output_dir, model_name=model_name)