from __future__ import annotations import json import logging import os import shutil from collections.abc import Iterable from pathlib import Path from typing import TYPE_CHECKING, Any, Callable import numpy as np import torch import transformers from packaging import version from torch import Tensor, nn from torch.optim import Optimizer from torch.optim.lr_scheduler import LambdaLR from torch.utils.data import DataLoader from tqdm.autonotebook import trange from transformers import TrainerCallback, TrainerControl, TrainerState from sentence_transformers.datasets.NoDuplicatesDataLoader import NoDuplicatesDataLoader from sentence_transformers.datasets.SentenceLabelDataset import SentenceLabelDataset from sentence_transformers.training_args import ( BatchSamplers, MultiDatasetBatchSamplers, SentenceTransformerTrainingArguments, ) from sentence_transformers.util import batch_to_device, fullname, is_datasets_available from .evaluation import SentenceEvaluator from .model_card_templates import ModelCardTemplate if is_datasets_available(): from datasets import Dataset, DatasetDict logger = logging.getLogger(__name__) if TYPE_CHECKING: from sentence_transformers.readers.InputExample import InputExample from sentence_transformers.SentenceTransformer import SentenceTransformer class SaveModelCallback(TrainerCallback): """A Callback to save the model to the `output_dir`. There are two cases: 1. save_best_model is True and evaluator is defined: We save on evaluate, but only if the new model is better than the currently saved one according to the evaluator. 2. If evaluator is not defined: We save after the model has been trained. """ def __init__(self, output_dir: str, evaluator: SentenceEvaluator | None, save_best_model: bool) -> None: super().__init__() self.output_dir = output_dir self.evaluator = evaluator self.save_best_model = save_best_model self.best_metric = None def is_better(self, new_metric: float) -> bool: if getattr(self.evaluator, "greater_is_better", True): return new_metric > self.best_metric return new_metric < self.best_metric def on_evaluate( self, args: SentenceTransformerTrainingArguments, state: TrainerState, control: TrainerControl, metrics: dict[str, Any], model: SentenceTransformer, **kwargs, ) -> None: if self.evaluator is not None and self.save_best_model: metric_key = getattr(self.evaluator, "primary_metric", "evaluator") for key, value in metrics.items(): if key.endswith(metric_key): if self.best_metric is None or self.is_better(value): self.best_metric = value model.save(self.output_dir) def on_train_end( self, args: SentenceTransformerTrainingArguments, state: TrainerState, control: TrainerControl, model: SentenceTransformer, **kwargs, ) -> None: if self.evaluator is None: model.save(self.output_dir) class EvaluatorCallback(TrainerCallback): """The SentenceTransformers.fit method always ran the evaluator on every epoch, in addition to every "evaluation_steps". This callback is responsible for that. The `.trainer` must be provided after the trainer has been created. """ def __init__(self, evaluator: SentenceEvaluator) -> None: super().__init__() self.evaluator = evaluator self.metric_key_prefix = "eval" self.trainer = None def on_epoch_end( self, args: SentenceTransformerTrainingArguments, state: TrainerState, control: TrainerControl, model: SentenceTransformer, **kwargs, ) -> None: evaluator_metrics = self.evaluator(model, epoch=state.epoch) 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"{self.metric_key_prefix}_"): evaluator_metrics[f"{self.metric_key_prefix}_{key}"] = evaluator_metrics.pop(key) if self.trainer is not None: self.trainer.callback_handler.on_evaluate(args, state, control, metrics=evaluator_metrics) class OriginalCallback(TrainerCallback): """A Callback to invoke the original callback function that was provided to SentenceTransformer.fit() This callback has the following signature: `(score: float, epoch: int, steps: int) -> None` """ def __init__(self, callback: Callable[[float, int, int], None], evaluator: SentenceEvaluator) -> None: super().__init__() self.callback = callback self.evaluator = evaluator def on_evaluate( self, args: transformers.TrainingArguments, state: TrainerState, control: TrainerControl, metrics: dict[str, Any], **kwargs, ) -> None: metric_key = getattr(self.evaluator, "primary_metric", "evaluator") for key, value in metrics.items(): if key.endswith(metric_key): return self.callback(value, state.epoch, state.global_step) class FitMixin: """Mixin class for injecting the `fit` method into Sentence Transformers""" def fit( self, train_objectives: Iterable[tuple[DataLoader, nn.Module]], evaluator: SentenceEvaluator = None, epochs: int = 1, steps_per_epoch=None, scheduler: str = "WarmupLinear", warmup_steps: int = 10000, optimizer_class: type[Optimizer] = torch.optim.AdamW, optimizer_params: dict[str, object] = {"lr": 2e-5}, weight_decay: float = 0.01, evaluation_steps: int = 0, output_path: str = None, save_best_model: bool = True, max_grad_norm: float = 1, use_amp: bool = False, callback: Callable[[float, int, int], None] = None, show_progress_bar: bool = True, checkpoint_path: str = None, checkpoint_save_steps: int = 500, checkpoint_save_total_limit: int = 0, ) -> None: """ Deprecated training method from before Sentence Transformers v3.0, it is recommended to use :class:`~sentence_transformers.trainer.SentenceTransformerTrainer` instead. This method uses :class:`~sentence_transformers.trainer.SentenceTransformerTrainer` behind the scenes, but does not provide as much flexibility as the Trainer itself. This training approach uses a list of DataLoaders and Loss functions to train the model. Each DataLoader is sampled in turn for one batch. We sample only as many batches from each DataLoader as there are in the smallest one to make sure of equal training with each dataset, i.e. round robin sampling. This method should produce equivalent results in v3.0+ as before v3.0, but if you encounter any issues with your existing training scripts, then you may wish to use :meth:`SentenceTransformer.old_fit ` instead. That uses the old training method from before v3.0. Args: train_objectives: Tuples of (DataLoader, LossFunction). Pass more than one for multi-task learning evaluator: An evaluator (sentence_transformers.evaluation) evaluates the model performance during training on held- out dev data. It is used to determine the best model that is saved to disc. epochs: Number of epochs for training steps_per_epoch: Number of training steps per epoch. If set to None (default), one epoch is equal the DataLoader size from train_objectives. scheduler: Learning rate scheduler. Available schedulers: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts warmup_steps: Behavior depends on the scheduler. For WarmupLinear (default), the learning rate is increased from o up to the maximal learning rate. After these many training steps, the learning rate is decreased linearly back to zero. optimizer_class: Optimizer optimizer_params: Optimizer parameters weight_decay: Weight decay for model parameters evaluation_steps: If > 0, evaluate the model using evaluator after each number of training steps output_path: Storage path for the model and evaluation files save_best_model: If true, the best model (according to evaluator) is stored at output_path max_grad_norm: Used for gradient normalization. use_amp: Use Automatic Mixed Precision (AMP). Only for Pytorch >= 1.6.0 callback: Callback function that is invoked after each evaluation. It must accept the following three parameters in this order: `score`, `epoch`, `steps` show_progress_bar: If True, output a tqdm progress bar checkpoint_path: Folder to save checkpoints during training checkpoint_save_steps: Will save a checkpoint after so many steps checkpoint_save_total_limit: Total number of checkpoints to store """ if not is_datasets_available(): raise ImportError("Please install `datasets` to use this function: `pip install datasets`.") # Delayed import to counter the SentenceTransformers -> FitMixin -> SentenceTransformerTrainer -> SentenceTransformers circular import from sentence_transformers.trainer import SentenceTransformerTrainer data_loaders, loss_fns = zip(*train_objectives) # Clear the dataloaders from collate functions as we just want raw InputExamples def identity(batch): return batch for data_loader in data_loaders: data_loader.collate_fn = identity batch_size = 8 batch_sampler = BatchSamplers.BATCH_SAMPLER # Convert dataloaders into a DatasetDict # TODO: This is rather inefficient, as we load all data into memory. We might benefit from a more efficient solution train_dataset_dict = {} for loader_idx, data_loader in enumerate(data_loaders, start=1): if isinstance(data_loader, NoDuplicatesDataLoader): batch_sampler = BatchSamplers.NO_DUPLICATES elif hasattr(data_loader, "dataset") and isinstance(data_loader.dataset, SentenceLabelDataset): batch_sampler = BatchSamplers.GROUP_BY_LABEL batch_size = getattr(data_loader, "batch_size", batch_size) texts = [] labels = [] for batch in data_loader: batch_texts, batch_labels = zip(*[(example.texts, example.label) for example in batch]) texts += batch_texts labels += batch_labels dataset = Dataset.from_dict({f"sentence_{idx}": text for idx, text in enumerate(zip(*texts))}) # Add label column, unless all labels are 0 (the default value for `labels` in InputExample) add_label_column = True try: if set(labels) == {0}: add_label_column = False except TypeError: pass if add_label_column: dataset = dataset.add_column("label", labels) train_dataset_dict[f"_dataset_{loader_idx}"] = dataset train_dataset_dict = DatasetDict(train_dataset_dict) def _default_checkpoint_dir() -> str: dir_name = "checkpoints/model" idx = 1 while Path(dir_name).exists() and len(list(Path(dir_name).iterdir())) != 0: dir_name = f"checkpoints/model_{idx}" idx += 1 return dir_name # Convert loss_fns into a dict with `dataset_{idx}` keys loss_fn_dict = {f"_dataset_{idx}": loss_fn for idx, loss_fn in enumerate(loss_fns, start=1)} # Use steps_per_epoch to perhaps set max_steps max_steps = -1 if steps_per_epoch is not None and steps_per_epoch > 0: if epochs == 1: max_steps = steps_per_epoch else: logger.warning( "Setting `steps_per_epoch` alongside `epochs` > 1 no longer works. " "We will train with the full datasets per epoch." ) steps_per_epoch = None # Transformers renamed `evaluation_strategy` to `eval_strategy` in v4.41.0 eval_strategy_key = ( "eval_strategy" if version.parse(transformers.__version__) >= version.parse("4.41.0") else "evaluation_strategy" ) args = SentenceTransformerTrainingArguments( output_dir=checkpoint_path or _default_checkpoint_dir(), batch_sampler=batch_sampler, multi_dataset_batch_sampler=MultiDatasetBatchSamplers.ROUND_ROBIN, per_device_train_batch_size=batch_size, per_device_eval_batch_size=batch_size, num_train_epochs=epochs, max_steps=max_steps, **{ eval_strategy_key: "steps" if evaluation_steps is not None and evaluation_steps > 0 else "no", }, eval_steps=evaluation_steps, # load_best_model_at_end=save_best_model, # <- TODO: Look into a good solution for save_best_model max_grad_norm=max_grad_norm, fp16=use_amp, disable_tqdm=not show_progress_bar, save_strategy="steps" if checkpoint_path is not None else "no", save_steps=checkpoint_save_steps, save_total_limit=checkpoint_save_total_limit, ) if steps_per_epoch is None or steps_per_epoch == 0: steps_per_epoch = min([len(train_dataset) // batch_size for train_dataset in train_dataset_dict.values()]) num_train_steps = int(steps_per_epoch * epochs) # Prepare optimizer & scheduler param_optimizer = list(self.named_parameters()) no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], "weight_decay": weight_decay, }, {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = optimizer_class(optimizer_grouped_parameters, **optimizer_params) scheduler_obj = self._get_scheduler( optimizer, scheduler=scheduler, warmup_steps=warmup_steps, t_total=num_train_steps ) # Create callbacks callbacks = [] if evaluator is not None: callbacks.append(EvaluatorCallback(evaluator)) if callback is not None: callbacks.append(OriginalCallback(callback, evaluator)) trainer = SentenceTransformerTrainer( model=self, args=args, train_dataset=train_dataset_dict, eval_dataset=None, loss=loss_fn_dict, evaluator=evaluator, optimizers=(optimizer, scheduler_obj), callbacks=callbacks, ) # Set the trainer on the EvaluatorCallback, required for logging the metrics for callback in trainer.callback_handler.callbacks: if isinstance(callback, EvaluatorCallback): callback.trainer = trainer if output_path is not None: trainer.add_callback(SaveModelCallback(output_path, evaluator, save_best_model)) trainer.train() @staticmethod def _get_scheduler(optimizer, scheduler: str, warmup_steps: int, t_total: int) -> LambdaLR: """ Returns the correct learning rate scheduler. Available scheduler: - constantlr, - warmupconstant, - warmuplinear, - warmupcosine, - warmupcosinewithhardrestarts """ scheduler = scheduler.lower() if scheduler == "constantlr": return transformers.get_constant_schedule(optimizer) elif scheduler == "warmupconstant": return transformers.get_constant_schedule_with_warmup(optimizer, num_warmup_steps=warmup_steps) elif scheduler == "warmuplinear": return transformers.get_linear_schedule_with_warmup( optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total ) elif scheduler == "warmupcosine": return transformers.get_cosine_schedule_with_warmup( optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total ) elif scheduler == "warmupcosinewithhardrestarts": return transformers.get_cosine_with_hard_restarts_schedule_with_warmup( optimizer, num_warmup_steps=warmup_steps, num_training_steps=t_total ) else: raise ValueError(f"Unknown scheduler {scheduler}") def smart_batching_collate(self, batch: list[InputExample]) -> tuple[list[dict[str, Tensor]], Tensor]: """ Transforms a batch from a SmartBatchingDataset to a batch of tensors for the model Here, batch is a list of InputExample instances: [InputExample(...), ...] Args: batch: a batch from a SmartBatchingDataset Returns: a batch of tensors for the model """ texts = [example.texts for example in batch] sentence_features = [self.tokenize(sentence) for sentence in zip(*texts)] labels = [example.label for example in batch] # Use torch.from_numpy to convert the numpy array directly to a tensor, # which is the recommended approach for converting numpy arrays to tensors if labels and isinstance(labels[0], np.ndarray): labels_tensor = torch.from_numpy(np.stack(labels)) else: labels_tensor = torch.tensor(labels) return sentence_features, labels_tensor """ Temporary methods that will be removed when this refactor is complete: """ def old_fit( self, train_objectives: Iterable[tuple[DataLoader, nn.Module]], evaluator: SentenceEvaluator = None, epochs: int = 1, steps_per_epoch=None, scheduler: str = "WarmupLinear", warmup_steps: int = 10000, optimizer_class: type[Optimizer] = torch.optim.AdamW, optimizer_params: dict[str, object] = {"lr": 2e-5}, weight_decay: float = 0.01, evaluation_steps: int = 0, output_path: str = None, save_best_model: bool = True, max_grad_norm: float = 1, use_amp: bool = False, callback: Callable[[float, int, int], None] = None, show_progress_bar: bool = True, checkpoint_path: str = None, checkpoint_save_steps: int = 500, checkpoint_save_total_limit: int = 0, ) -> None: """ Deprecated training method from before Sentence Transformers v3.0, it is recommended to use :class:`sentence_transformers.trainer.SentenceTransformerTrainer` instead. This method should only be used if you encounter issues with your existing training scripts after upgrading to v3.0+. This training approach uses a list of DataLoaders and Loss functions to train the model. Each DataLoader is sampled in turn for one batch. We sample only as many batches from each DataLoader as there are in the smallest one to make sure of equal training with each dataset, i.e. round robin sampling. Args: train_objectives: Tuples of (DataLoader, LossFunction). Pass more than one for multi-task learning evaluator: An evaluator (sentence_transformers.evaluation) evaluates the model performance during training on held- out dev data. It is used to determine the best model that is saved to disc. epochs: Number of epochs for training steps_per_epoch: Number of training steps per epoch. If set to None (default), one epoch is equal the DataLoader size from train_objectives. scheduler: Learning rate scheduler. Available schedulers: constantlr, warmupconstant, warmuplinear, warmupcosine, warmupcosinewithhardrestarts warmup_steps: Behavior depends on the scheduler. For WarmupLinear (default), the learning rate is increased from o up to the maximal learning rate. After these many training steps, the learning rate is decreased linearly back to zero. optimizer_class: Optimizer optimizer_params: Optimizer parameters weight_decay: Weight decay for model parameters evaluation_steps: If > 0, evaluate the model using evaluator after each number of training steps output_path: Storage path for the model and evaluation files save_best_model: If true, the best model (according to evaluator) is stored at output_path max_grad_norm: Used for gradient normalization. use_amp: Use Automatic Mixed Precision (AMP). Only for Pytorch >= 1.6.0 callback: Callback function that is invoked after each evaluation. It must accept the following three parameters in this order: `score`, `epoch`, `steps` show_progress_bar: If True, output a tqdm progress bar checkpoint_path: Folder to save checkpoints during training checkpoint_save_steps: Will save a checkpoint after so many steps checkpoint_save_total_limit: Total number of checkpoints to store """ ##Add info to model card # info_loss_functions = "\n".join(["- {} with {} training examples".format(str(loss), len(dataloader)) for dataloader, loss in train_objectives]) info_loss_functions = [] for dataloader, loss in train_objectives: info_loss_functions.extend(ModelCardTemplate.get_train_objective_info(dataloader, loss)) info_loss_functions = "\n\n".join([text for text in info_loss_functions]) info_fit_parameters = json.dumps( { "evaluator": fullname(evaluator), "epochs": epochs, "steps_per_epoch": steps_per_epoch, "scheduler": scheduler, "warmup_steps": warmup_steps, "optimizer_class": str(optimizer_class), "optimizer_params": optimizer_params, "weight_decay": weight_decay, "evaluation_steps": evaluation_steps, "max_grad_norm": max_grad_norm, }, indent=4, sort_keys=True, ) self._model_card_text = None self._model_card_vars["{TRAINING_SECTION}"] = ModelCardTemplate.__TRAINING_SECTION__.replace( "{LOSS_FUNCTIONS}", info_loss_functions ).replace("{FIT_PARAMETERS}", info_fit_parameters) if use_amp: from torch.cuda.amp import autocast scaler = torch.cuda.amp.GradScaler() self.to(self.device) dataloaders = [dataloader for dataloader, _ in train_objectives] # Use smart batching for dataloader in dataloaders: dataloader.collate_fn = self.smart_batching_collate loss_models = [loss for _, loss in train_objectives] for loss_model in loss_models: loss_model.to(self.device) self.best_score = -9999999 if steps_per_epoch is None or steps_per_epoch == 0: steps_per_epoch = min([len(dataloader) for dataloader in dataloaders]) num_train_steps = int(steps_per_epoch * epochs) # Prepare optimizers optimizers = [] schedulers = [] for loss_model in loss_models: param_optimizer = list(loss_model.named_parameters()) no_decay = ["bias", "LayerNorm.bias", "LayerNorm.weight"] optimizer_grouped_parameters = [ { "params": [p for n, p in param_optimizer if not any(nd in n for nd in no_decay)], "weight_decay": weight_decay, }, {"params": [p for n, p in param_optimizer if any(nd in n for nd in no_decay)], "weight_decay": 0.0}, ] optimizer = optimizer_class(optimizer_grouped_parameters, **optimizer_params) scheduler_obj = self._get_scheduler( optimizer, scheduler=scheduler, warmup_steps=warmup_steps, t_total=num_train_steps ) optimizers.append(optimizer) schedulers.append(scheduler_obj) global_step = 0 data_iterators = [iter(dataloader) for dataloader in dataloaders] num_train_objectives = len(train_objectives) skip_scheduler = False for epoch in trange(epochs, desc="Epoch", disable=not show_progress_bar): training_steps = 0 for loss_model in loss_models: loss_model.zero_grad() loss_model.train() for _ in trange(steps_per_epoch, desc="Iteration", smoothing=0.05, disable=not show_progress_bar): for train_idx in range(num_train_objectives): loss_model = loss_models[train_idx] optimizer = optimizers[train_idx] scheduler = schedulers[train_idx] data_iterator = data_iterators[train_idx] try: data = next(data_iterator) except StopIteration: data_iterator = iter(dataloaders[train_idx]) data_iterators[train_idx] = data_iterator data = next(data_iterator) features, labels = data labels = labels.to(self.device) features = list(map(lambda batch: batch_to_device(batch, self.device), features)) if use_amp: with autocast(): loss_value = loss_model(features, labels) scale_before_step = scaler.get_scale() scaler.scale(loss_value).backward() scaler.unscale_(optimizer) torch.nn.utils.clip_grad_norm_(loss_model.parameters(), max_grad_norm) scaler.step(optimizer) scaler.update() skip_scheduler = scaler.get_scale() != scale_before_step else: loss_value = loss_model(features, labels) loss_value.backward() torch.nn.utils.clip_grad_norm_(loss_model.parameters(), max_grad_norm) optimizer.step() optimizer.zero_grad() if not skip_scheduler: scheduler.step() training_steps += 1 global_step += 1 if evaluation_steps > 0 and training_steps % evaluation_steps == 0: self._eval_during_training( evaluator, output_path, save_best_model, epoch, training_steps, callback ) for loss_model in loss_models: loss_model.zero_grad() loss_model.train() if ( checkpoint_path is not None and checkpoint_save_steps is not None and checkpoint_save_steps > 0 and global_step % checkpoint_save_steps == 0 ): self._save_checkpoint(checkpoint_path, checkpoint_save_total_limit, global_step) self._eval_during_training(evaluator, output_path, save_best_model, epoch, -1, callback) if evaluator is None and output_path is not None: # No evaluator, but output path: save final model version self.save(output_path) if checkpoint_path is not None: self._save_checkpoint(checkpoint_path, checkpoint_save_total_limit, global_step) def _eval_during_training(self, evaluator, output_path, save_best_model, epoch, steps, callback) -> None: """Runs evaluation during the training""" eval_path = output_path if output_path is not None: os.makedirs(output_path, exist_ok=True) eval_path = os.path.join(output_path, "eval") os.makedirs(eval_path, exist_ok=True) if evaluator is not None: score = evaluator(self, output_path=eval_path, epoch=epoch, steps=steps) if callback is not None: callback(score, epoch, steps) if score > self.best_score: self.best_score = score if save_best_model: self.save(output_path) def _save_checkpoint(self, checkpoint_path, checkpoint_save_total_limit, step) -> None: # Store new checkpoint self.save(os.path.join(checkpoint_path, str(step))) # Delete old checkpoints if checkpoint_save_total_limit is not None and checkpoint_save_total_limit > 0: old_checkpoints = [] for subdir in os.listdir(checkpoint_path): if subdir.isdigit(): old_checkpoints.append({"step": int(subdir), "path": os.path.join(checkpoint_path, subdir)}) if len(old_checkpoints) > checkpoint_save_total_limit: old_checkpoints = sorted(old_checkpoints, key=lambda x: x["step"]) shutil.rmtree(old_checkpoints[0]["path"])