from __future__ import annotations import logging import os from collections import OrderedDict from contextlib import nullcontext from functools import partial from typing import TYPE_CHECKING, Any, Callable import torch from packaging.version import parse as parse_version from torch import nn from torch.utils.data import BatchSampler, ConcatDataset, DataLoader, SubsetRandomSampler from transformers import EvalPrediction, PreTrainedTokenizerBase, Trainer, TrainerCallback from transformers import __version__ as transformers_version 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 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 import Pooling 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, IterableDataset, IterableDatasetDict, Value 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`, :class:`datasets.IterableDataset`, 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`, :class:`datasets.IterableDataset`, 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: SentenceTransformer | None = None, args: SentenceTransformerTrainingArguments = None, train_dataset: Dataset | DatasetDict | IterableDataset | dict[str, Dataset] | None = None, eval_dataset: Dataset | DatasetDict | IterableDataset | dict[str, Dataset] | None = None, loss: nn.Module | dict[str, nn.Module] | Callable[[SentenceTransformer], torch.nn.Module] | dict[str, Callable[[SentenceTransformer], torch.nn.Module]] | None = None, evaluator: SentenceEvaluator | list[SentenceEvaluator] | None = None, data_collator: DataCollator | None = None, tokenizer: PreTrainedTokenizerBase | Callable | None = None, model_init: Callable[[], SentenceTransformer] | None = None, compute_metrics: Callable[[EvalPrediction], dict] | None = None, callbacks: list[TrainerCallback] | None = None, optimizers: tuple[torch.optim.Optimizer, torch.optim.lr_scheduler.LambdaLR] = (None, None), preprocess_logits_for_metrics: Callable[[torch.Tensor, torch.Tensor], torch.Tensor] | None = 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: logger.warning( "`Trainer` requires either a `model` or `model_init` argument, but not both. `model_init` will" " overwrite your model when calling the `train` method." ) self.model_init = model_init if compute_metrics is not None: logger.warning( "`compute_metrics` is currently not compatible with the SentenceTransformerTrainer. Please use the " "`evaluator` argument instead for detailed evaluation metrics, or the `eval_dataset` argument for " "the evaluation loss." ) # 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) for dataset_name, dataset in zip(["train", "eval"], [train_dataset, eval_dataset]): if isinstance(dataset, IterableDataset) and dataset.column_names is None: sample = next(iter(dataset)) naive_type_mapping = {str: "string", int: "int64", float: "float32", bool: "bool"} example_features = { key: Value(naive_type_mapping.get(type(value), "null")) for key, value in sample.items() } raise ValueError( f"The provided `{dataset_name}_dataset` must have Features. Specify them with e.g.:\n" f"{dataset_name}_dataset = {dataset_name}_dataset.cast(Features({example_features}))\n" "or by providing the Features to the IterableDataset initialization method. See the Datasets " "documentation for more information on dataset Features: " "https://huggingface.co/docs/datasets/en/about_dataset_features" ) 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, DatasetDict): eval_dataset = DatasetDict(eval_dataset) super_kwargs = { "model": None if self.model_init else model, "args": args, "data_collator": data_collator, "train_dataset": train_dataset, "eval_dataset": eval_dataset if eval_dataset is not None or evaluator is None else "dummy", "model_init": model_init, "compute_metrics": compute_metrics, "callbacks": callbacks, "optimizers": optimizers, "preprocess_logits_for_metrics": preprocess_logits_for_metrics, } # Transformers v4.46.0 changed the `tokenizer` argument to a more general `processing_class` argument if parse_version(transformers_version) >= parse_version("4.46.0"): super_kwargs["processing_class"] = tokenizer else: super_kwargs["tokenizer"] = tokenizer super().__init__(**super_kwargs) # Transformers v4.46.0 introduced a ValueError if `eval_dataset` is None while eval_strategy is not "no", # but in Sentence Transformers you can also evaluate without an eval_dataset via an evaluator, so we set # it to "dummy" in that case to avoid the ValueError if self.eval_dataset == "dummy": self.eval_dataset = None # 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._prompt_length_mapping = {} 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 if self.train_dataset is not None: self.train_dataset = self.maybe_add_prompts_or_dataset_name_column( train_dataset, args.prompts, dataset_name="train" ) if self.eval_dataset is not None: self.eval_dataset = self.maybe_add_prompts_or_dataset_name_column( eval_dataset, args.prompts, dataset_name="eval" ) self.add_model_card_callback(default_args_dict) def add_model_card_callback(self, default_args_dict: dict[str, Any]) -> None: """ Add a callback responsible for automatically tracking data required for the automatic model card generation This method is called in the ``__init__`` method of the :class:`~sentence_transformers.trainer.SentenceTransformerTrainer` class. Args: default_args_dict (Dict[str, Any]): A dictionary of the default training arguments, so we can determine which arguments have been changed for the model card. .. note:: This method can be overriden by subclassing the trainer to remove/customize this callback in custom uses cases """ 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: 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, torch.Tensor | Any], return_outputs: bool = False, num_items_in_batch=None, ) -> 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. num_items_in_batch (int, optional): The number of items in the batch. Defaults to None. Unused, but required by the transformers Trainer. 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] # Insert the wrapped (e.g. distributed or compiled) model into the loss function, # if the loss stores the model. Only called once per process if ( model == self.model_wrapped and model != self.model # Only if the model is wrapped and hasattr(loss_fn, "model") # Only if the loss stores the model and loss_fn.model != model # Only if the wrapped model is not already stored ): 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, torch.Tensor | Any] ) -> tuple[list[dict[str, torch.Tensor]], torch.Tensor | None]: """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: Dataset | dict[str, Dataset] | None = None, ignore_keys: list[str] | None = None, metric_key_prefix: str = "eval", ) -> dict[str, float]: if eval_dataset: eval_dataset = self.maybe_add_prompts_or_dataset_name_column( eval_dataset, self.args.prompts, dataset_name="eval" ) else: eval_dataset = self.eval_dataset return super().evaluate(eval_dataset, ignore_keys, metric_key_prefix) def evaluation_loop( self, dataloader: DataLoader, description: str, prediction_loss_only: bool | None = None, ignore_keys: list[str] | None = 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 `transformers`-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: str | None = None) -> None: if isinstance(dataset, dict): for dataset_name, dataset in dataset.items(): self.validate_column_names(dataset, dataset_name=dataset_name) return 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: list[str] | None = None, generator: torch.Generator | None = None, ) -> BatchSampler | None: """ Returns the appropriate batch sampler based on the ``batch_sampler`` argument in ``self.args``. This batch sampler class supports ``__len__`` and ``__iter__`` methods, and is used as the ``batch_sampler`` to create the :class:`torch.utils.data.DataLoader`. .. note:: Override this method to provide a custom batch sampler. Args: dataset (Dataset): The dataset to sample from. batch_size (int): Number of samples per batch. drop_last (bool): If True, drop the last incomplete batch if the dataset size is not divisible by the batch size. valid_label_columns (List[str]): List of column names to check for labels. The first column name from ``valid_label_columns`` found in the dataset will be used as the label column. generator (torch.Generator, optional): Optional random number generator for shuffling the indices. """ if isinstance(dataset, IterableDataset): if self.args.batch_sampler != BatchSamplers.BATCH_SAMPLER: logger.warning("When using an IterableDataset, you cannot specify a batch sampler.") return None 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: torch.Generator | None = None, seed: int | None = 0, ) -> BatchSampler: """ Returns the appropriate multi-dataset batch sampler based on the ``multi_dataset_batch_sampler`` argument in ``self.args``. This batch sampler class supports ``__len__`` and ``__iter__`` methods, and is used as the ``batch_sampler`` to create the :class:`torch.utils.data.DataLoader`. .. note:: Override this method to provide a custom multi-dataset batch sampler. Args: dataset (ConcatDataset): The concatenation of all datasets. batch_samplers (List[BatchSampler]): List of batch samplers for each dataset in the concatenated dataset. generator (torch.Generator, optional): Optional random number generator for shuffling the indices. seed (int, optional): Optional seed for the random number generator """ 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("Training requires specifying a train_dataset to the SentenceTransformerTrainer.") train_dataset = self.train_dataset data_collator = self.data_collator generator = torch.Generator() if self.args.seed: generator.manual_seed(self.args.seed) 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, } if isinstance(train_dataset, IterableDataset): dataloader_params.update( { "batch_size": self.args.train_batch_size, "drop_last": self.args.dataloader_drop_last, } ) if self.args.batch_sampler != BatchSamplers.BATCH_SAMPLER: logger.warning("When using an IterableDataset, you cannot specify a batch sampler.") elif isinstance(train_dataset, IterableDatasetDict): raise ValueError( "Sentence Transformers is not compatible with IterableDatasetDict. Please use a DatasetDict instead." ) elif isinstance(train_dataset, DatasetDict): for dataset in train_dataset.values(): if isinstance(dataset, IterableDataset): raise ValueError( "Sentence Transformers is not compatible with a DatasetDict containing an IterableDataset." ) batch_samplers = [ self.get_batch_sampler( 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, ) 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, ) dataloader_params["batch_sampler"] = batch_sampler elif isinstance(train_dataset, 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["batch_sampler"] = batch_sampler else: raise ValueError( "Unsupported `train_dataset` type. Use a Dataset, DatasetDict, or IterableDataset for training." ) # 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: Dataset | DatasetDict | IterableDataset | 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("Evaluation requires specifying an eval_dataset to the SentenceTransformerTrainer.") 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) 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, } if isinstance(eval_dataset, IterableDataset): dataloader_params.update( { "batch_size": self.args.eval_batch_size, "drop_last": self.args.dataloader_drop_last, } ) elif isinstance(eval_dataset, IterableDatasetDict): raise ValueError( "Sentence Transformers is not compatible with IterableDatasetDict. Please use a DatasetDict instead." ) elif isinstance(eval_dataset, DatasetDict): for dataset in eval_dataset.values(): if isinstance(dataset, IterableDataset): raise ValueError( "Sentence Transformers is not compatible with a DatasetDict containing an IterableDataset." ) batch_samplers = [ self.get_batch_sampler( dataset, batch_size=self.args.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, ) dataloader_params["batch_sampler"] = batch_sampler elif isinstance(eval_dataset, Dataset): batch_sampler = self.get_batch_sampler( eval_dataset, batch_size=self.args.eval_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) dataloader_params["batch_sampler"] = batch_sampler else: raise ValueError( "Unsupported `eval_dataset` type. Use a Dataset, DatasetDict, or IterableDataset for evaluation." ) # 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 | DatasetDict | IterableDataset) -> 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) 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, } if isinstance(test_dataset, IterableDataset): dataloader_params.update( { "batch_size": self.args.eval_batch_size, "drop_last": self.args.dataloader_drop_last, } ) elif isinstance(test_dataset, IterableDatasetDict): raise ValueError( "Sentence Transformers is not compatible with IterableDatasetDict. Please use a DatasetDict instead." ) elif isinstance(test_dataset, DatasetDict): for dataset in test_dataset.values(): if isinstance(dataset, IterableDataset): raise ValueError( "Sentence Transformers is not compatible with a DatasetDict containing an IterableDataset." ) batch_samplers = [ self.get_batch_sampler( dataset, batch_size=self.args.eval_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, ) dataloader_params["batch_sampler"] = batch_sampler elif isinstance(test_dataset, Dataset): batch_sampler = self.get_batch_sampler( test_dataset, batch_size=self.args.eval_batch_size, drop_last=self.args.dataloader_drop_last, valid_label_columns=data_collator.valid_label_columns, generator=generator, ) dataloader_params["batch_sampler"] = batch_sampler else: raise ValueError( "Unsupported `test_dataset` type. Use a Dataset, DatasetDict, or IterableDataset for testing." ) # 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(test_dataset, **dataloader_params)) def _save(self, output_dir: str | None = 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) # Transformers v4.46.0 changed the `tokenizer` attribute to a more general `processing_class` attribute if parse_version(transformers_version) >= parse_version("4.46.0"): if self.processing_class is not None: self.processing_class.save_pretrained(output_dir) else: 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, trust_remote_code=self.model.trust_remote_code) self.model.load_state_dict(loaded_model.state_dict()) def _get_prompt_length(self, prompt: str) -> int: try: return self._prompt_length_mapping[prompt] except KeyError: prompt_length = self.model.tokenize([prompt])["input_ids"].shape[-1] - 1 self._prompt_length_mapping[prompt] = prompt_length return prompt_length def _include_prompt_length(self) -> bool: """ Return whether the prompt length should be passed to the model's forward method. True if the model does not include the prompt in the pooling layer. Can be overridden by the user if it's useful to include the prompt length. """ for module in self.model: if isinstance(module, Pooling): return not module.include_prompt return False @staticmethod def add_prompts_or_dataset_name_transform( batch: dict[str, list[Any]], prompts: dict[str, str] | str | None = None, prompt_lengths: dict[str, int] | int | None = None, dataset_name: str | None = None, transform: Callable[[dict[str, list[Any]]], dict[str, list[Any]]] = None, **kwargs, ) -> dict[str, list[Any]]: """A transform/map function that adds prompts or dataset names to the batch. Args: batch (dict[str, list[Any]]): The batch of data, where each key is a column name and each value is a list of values. prompts (dict[str, str] | str | None, optional): An optional mapping of column names to string prompts, or a string prompt for all columns. Defaults to None. prompt_lengths (dict[str, int] | int | None, optional): An optional mapping of prompts names to prompt token length, or a prompt token length if the prompt is a string. Defaults to None. dataset_name (str | None, optional): The name of this dataset, only if there are multiple datasets that use a different loss. Defaults to None. transform (Callable[[dict[str, list[Any]]], dict[str, list[Any]]], optional): An optional transform function to apply on the batch before adding prompts, etc. Defaults to None. Returns: dict[str, list[Any]]: The "just-in-time" transformed batch with prompts and/or dataset names added. """ # If the dataset is a Dataset(Dict), then we use set_transform and we want to also apply any # previous transform if it exists if transform: batch = transform(batch) # Return if the batch has no columns... if not batch: return batch # ... or if it's empty first_column = list(batch.keys())[0] if not batch[first_column]: return batch # Apply one prompt to all columns... if isinstance(prompts, str): for column_name, column in list(batch.items()): if isinstance(column[0], str): batch[column_name] = [prompts + value for value in column] if prompt_lengths is not None: batch[f"{column_name}_prompt_length"] = [prompt_lengths] * len(column) # ... or a column-specific prompt if isinstance(prompts, dict): for column_name, prompt in prompts.items(): if column_name in batch: batch[column_name] = [prompt + value for value in batch[column_name]] if prompt_lengths: batch[f"{column_name}_prompt_length"] = [prompt_lengths[prompt]] * len(batch[column_name]) # If we have multiple losses, then we need to add the dataset name to the batch if dataset_name: batch["dataset_name"] = [dataset_name] * len(batch[first_column]) return batch def maybe_add_prompts_or_dataset_name_column( self, dataset_dict: DatasetDict | Dataset | None, prompts: dict[str, dict[str, str]] | dict[str, str] | str | None = None, dataset_name: str | None = None, ) -> DatasetDict | Dataset | None: """ Maybe add prompts or dataset names to the dataset. We add the dataset_name column to the dataset if: 1. The loss is a dictionary and the dataset is a DatasetDict, or 2. The prompts contain a mapping to dataset names. There are 4 cases for the prompts: 1. `str`: One prompt for all datasets and columns. 2. `dict[str, str]`: A column to prompt mapping. 3. `dict[str, str]`: A dataset to prompt mapping. 4. `dict[str, dict[str, str]]`: A dataset to column to prompt mapping. And 2 cases for the dataset: A. `Dataset`: A single dataset. B. `DatasetDict`: A dictionary of datasets. 3A is not allowed, and 2A doesn't make sense. Args: dataset_dict (DatasetDict | Dataset | None): The dataset to add prompts or dataset names to. Returns: DatasetDict | Dataset | None: The dataset with prompts or dataset names added. """ if dataset_dict is None: return None include_dataset_name = isinstance(self.loss, dict) # If we've already added the transform to this (iterable) dataset, don't add it again if hasattr(dataset_dict, "_sentence_transformers_preprocessed"): return dataset_dict # Ensure that there's no "dataset_name"/"return_loss" columns in the unprocessed datasets self.validate_column_names(dataset_dict, dataset_name=dataset_name) # Only add if 1) we have prompts or 2) we need the dataset name for the loss dictionary if prompts or include_dataset_name: include_prompt_lengths = self._include_prompt_length() dataset_dict = self.add_prompts_or_dataset_name_column( dataset_dict, prompts=prompts, include_prompt_lengths=include_prompt_lengths, include_dataset_name=include_dataset_name, ) return dataset_dict def add_prompts_or_dataset_name_column( self, dataset_dict: DatasetDict | IterableDatasetDict | Dataset | IterableDataset, prompts: dict[str, str] | str | None = None, dataset_name: str | None = None, include_prompt_lengths: bool = False, include_dataset_name: bool = False, ) -> DatasetDict | Dataset | None: # If we have DatasetDict, recurse if isinstance(dataset_dict, (IterableDatasetDict, DatasetDict)): for dataset_name, dataset in dataset_dict.items(): # If prompts is a dictionary that matches the dataset names, then take the nested prompts nested_prompts = prompts.get(dataset_name, prompts) if isinstance(prompts, dict) else prompts dataset_dict[dataset_name] = self.add_prompts_or_dataset_name_column( dataset_dict=dataset, prompts=nested_prompts, dataset_name=dataset_name if include_dataset_name else None, include_prompt_lengths=include_prompt_lengths, include_dataset_name=include_dataset_name, ) return dataset_dict # Get the prompt lengths if needed for the pooling layer prompt_lengths = None if prompts: if isinstance(prompts, str): if include_prompt_lengths: prompt_lengths = self._get_prompt_length(prompts) elif isinstance(prompts, dict): first_key = list(prompts.keys())[0] if isinstance(prompts[first_key], dict): raise ValueError( "The prompts provided to the trainer are a nested dictionary. In this setting, the first " "level of the dictionary should map to dataset names and the second level to column names. " "However, as the provided dataset is a not a DatasetDict, no dataset names can be inferred. " f"The keys to the provided prompts dictionary are {list(prompts.keys())!r}" ) if include_prompt_lengths: # If prompt columns exist, add the prompt length column prompt_lengths = { prompt: self._get_prompt_length(prompt) for column_name, prompt in prompts.items() if column_name in dataset_dict.column_names } # If we have a Dataset, we can set the transform directly... if isinstance(dataset_dict, Dataset): dataset_dict.set_transform( partial( self.add_prompts_or_dataset_name_transform, prompts=prompts, prompt_lengths=prompt_lengths, dataset_name=dataset_name, **dataset_dict._format_kwargs, ) ) # ... otherwise, we have an IterableDataset and we need to map it, which performs the same operation as above elif isinstance(dataset_dict, IterableDataset): # Update the features to include the new columns features = dataset_dict.features if dataset_name: features["dataset_name"] = Value("string") if prompt_lengths: if isinstance(prompts, str): for column_name in dataset_dict.column_names: feature = features[column_name] if isinstance(feature, Value) and feature.dtype in ("string", "large_string"): features[f"{column_name}_prompt_length"] = Value("int16") elif isinstance(prompts, dict): for column_name, prompt in prompts.items(): feature = features[column_name] if ( prompt in prompt_lengths and isinstance(feature, Value) and feature.dtype in ("string", "large_string") ): features[f"{column_name}_prompt_length"] = Value("int16") dataset_dict = dataset_dict.map( partial( self.add_prompts_or_dataset_name_transform, prompts=prompts, prompt_lengths=prompt_lengths, dataset_name=dataset_name, ), batched=True, features=features, ) else: raise ValueError("Unsupported dataset type.") # Add a tag to the dataset to indicate that it has been preprocessed, to ensure that we don't apply the map or # transform multiple times. dataset_dict._sentence_transformers_preprocessed = True return dataset_dict def create_model_card( self, language: str | None = None, license: str | None = None, tags: str | list[str] | None = None, model_name: str | None = None, finetuned_from: str | None = None, tasks: str | list[str] | None = None, dataset_tags: str | list[str] | None = None, dataset: str | list[str] | None = None, dataset_args: 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) def get_optimizer_cls_and_kwargs( self, args: SentenceTransformerTrainingArguments, model: SentenceTransformer | None = None ) -> tuple[Any, Any]: """ We have to override the optimizer_grouped_parameters because the Trainer superclass bases it on the `model` itself, but the SentenceTransformer losses can have weights that should be updated as well, e.g. SoftmaxLoss (see #2872). This method requires `transformers` >= 4.43.0. """ if isinstance(self.loss, dict): loss_model = nn.Sequential(OrderedDict(self.loss)) else: loss_model = self.loss optimizer_cls, optimizer_kwargs = super().get_optimizer_cls_and_kwargs(args, loss_model) # If the kwargs were not overridden by the super() call, then we should override them here so that the potential # weights in the loss(es) can also be updated. if not {"params", "model", "optimizer_dict"} & set(optimizer_kwargs.keys()): decay_parameters = self.get_decay_parameter_names(loss_model) optimizer_kwargs["optimizer_dict"] = [ { "params": [ p for n, p in loss_model.named_parameters() if (n in decay_parameters and p.requires_grad) ], "weight_decay": self.args.weight_decay, }, { "params": [ p for n, p in loss_model.named_parameters() if (n not in decay_parameters and p.requires_grad) ], "weight_decay": 0.0, }, ] return optimizer_cls, optimizer_kwargs