diff --git a/README.md b/README.md index 29ef6e41..5f2bf974 100644 --- a/README.md +++ b/README.md @@ -11,9 +11,9 @@ pip install clarinpl-embeddings Text-classification with polemo2 dataset and transformer-based embeddings ```python -from embeddings.pipeline.hugging_face_classification import HuggingFaceClassificationPipeline +from embeddings.pipeline.flair_classification import FlairClassificationPipeline -pipeline = HuggingFaceClassificationPipeline( +pipeline = FlairClassificationPipeline( dataset_name="clarin-pl/polemo2-official", embedding_name="allegro/herbert-base-cased", input_column_name="text", @@ -47,8 +47,8 @@ We share predefined pipelines for common NLP tasks with corresponding scripts. ```python from pathlib import Path -from embeddings.data.hugging_face_data_loader import HuggingFaceDataLoader -from embeddings.data.hugging_face_dataset import HuggingFaceDataset +from embeddings.data.data_loader import HuggingFaceDataLoader +from embeddings.data.dataset import HuggingFaceDataset from embeddings.embedding.auto_flair import AutoFlairDocumentEmbedding from embeddings.evaluator.text_classification_evaluator import TextClassificationEvaluator from embeddings.model.flair_model import FlairModel @@ -124,12 +124,12 @@ compatible with our pipeline. Model and training parameters can be controlled via `task_model_kwargs` and `task_train_kwargs` parameters. -## Example with `polemo2` dataset. +## Example with `polemo2` dataset. ```python -from embeddings.pipeline.hugging_face_classification import HuggingFaceClassificationPipeline +from embeddings.pipeline.flair_classification import FlairClassificationPipeline -pipeline = HuggingFaceClassificationPipeline( +pipeline = FlairClassificationPipeline( dataset_name="clarin-pl/polemo2-official", embedding_name="allegro/herbert-base-cased", input_column_name="text", @@ -231,9 +231,9 @@ df, metadata = pipeline.run() After the parameters search process we can train model with best parameters found. ```python -from embeddings.pipeline.hugging_face_classification import HuggingFaceClassificationPipeline +from embeddings.pipeline.flair_classification import FlairClassificationPipeline -pipeline = HuggingFaceClassificationPipeline(**metadata) +pipeline = FlairClassificationPipeline(**metadata) results = pipeline.run() ``` diff --git a/embeddings/data/datamodule.py b/embeddings/data/datamodule.py new file mode 100644 index 00000000..b4f4c102 --- /dev/null +++ b/embeddings/data/datamodule.py @@ -0,0 +1,173 @@ +import abc +from typing import Any, Dict, Generic, List, Optional, Sequence, TypeVar, Union + +import datasets +import pytorch_lightning as pl +from datasets import ClassLabel, DatasetDict +from torch.utils.data import DataLoader +from transformers import AutoTokenizer, BatchEncoding + +from embeddings.utils.loggers import get_logger + +Data = TypeVar("Data") +HuggingFaceDataset = TypeVar("HuggingFaceDataset") + +_logger = get_logger(__name__) + + +class BaseDataModule(abc.ABC, pl.LightningDataModule, Generic[Data]): + dataset: Data + + +class HuggingFaceDataModule(BaseDataModule[DatasetDict]): + LOADER_COLUMNS = [ + "datasets_idx", + "input_ids", + "token_type_ids", + "attention_mask", + "start_positions", + "end_positions", + "labels", + ] + DEFAULT_TOKENIZER_KWARGS = {"use_fast": True} + DEFAULT_BATCH_ENCODING_KWARGS = { + "padding": True, + "truncation": True, + } + + def __init__( + self, + tokenizer_name_or_path: str, + dataset_name: str, + target_field: str, + max_seq_length: Optional[int] = None, + train_batch_size: int = 32, + eval_batch_size: int = 32, + tokenizer_kwargs: Optional[Dict[str, Any]] = None, + batch_encoding_kwargs: Optional[Dict[str, Any]] = None, + load_dataset_kwargs: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> None: + # ignoring the type to avoid calling to untyped function "__init__" in typed context error + # caused by pl.LightningDataModule __init__ method not being typed + super().__init__() # type: ignore + self.tokenizer_name_or_path = tokenizer_name_or_path + self.dataset_name = dataset_name + self.target_field = target_field + self.max_seq_length = max_seq_length + self.train_batch_size = train_batch_size + self.eval_batch_size = eval_batch_size + self.tokenizer = AutoTokenizer.from_pretrained( + self.tokenizer_name_or_path, + **tokenizer_kwargs if tokenizer_kwargs else self.DEFAULT_TOKENIZER_KWARGS, + ) + self.batch_encoding_kwargs = ( + batch_encoding_kwargs if batch_encoding_kwargs else self.DEFAULT_BATCH_ENCODING_KWARGS + ) + self.load_dataset_kwargs = load_dataset_kwargs if load_dataset_kwargs else {} + + def load_dataset(self) -> DatasetDict: + return datasets.load_dataset(self.dataset_name, **self.load_dataset_kwargs) + + def get_num_classes(self) -> int: + assert isinstance(self.dataset, DatasetDict) + if not isinstance(self.dataset["train"].features[self.target_field], ClassLabel): + self.dataset.class_encode_column(self.target_field) + num_classes = self.dataset["train"].features[self.target_field].num_classes + assert isinstance(num_classes, int) + return num_classes + + def prepare_data(self) -> None: + datasets.load_dataset(self.dataset_name) + AutoTokenizer.from_pretrained(self.tokenizer_name_or_path) + + def setup(self, stage: Optional[str] = None) -> None: + self.dataset = self.load_dataset() + self.num_classes = self.get_num_classes() + self.process_data() + + def process_data(self) -> None: + columns = [c for c in self.dataset["train"].column_names if c not in self.LOADER_COLUMNS] + self.dataset = self.dataset.map( + self.convert_to_features, + batched=True, + remove_columns=columns, + ) + self.dataset.set_format(type="torch") + + def train_dataloader(self) -> DataLoader[HuggingFaceDataset]: + return DataLoader(self.dataset["train"], batch_size=self.train_batch_size) + + # Ignoring the type of val_dataloader method from supertype "DataHooks" allowing for None + # and training without validation dataset. + def val_dataloader(self) -> Optional[DataLoader[HuggingFaceDataset]]: # type: ignore + if "validation" in self.dataset: + return DataLoader(self.dataset["validation"], batch_size=self.eval_batch_size) + else: + return None + + def test_dataloader(self) -> DataLoader[HuggingFaceDataset]: + return DataLoader(self.dataset["test"], batch_size=self.eval_batch_size) + + @abc.abstractmethod + def convert_to_features( + self, example_batch: Dict[str, Any], indices: Optional[List[int]] = None + ) -> BatchEncoding: + pass + + +class TextClassificationDataModule(HuggingFaceDataModule): + def __init__( + self, + tokenizer_name_or_path: str, + dataset_name: str, + text_fields: Union[str, Sequence[str]], + target_field: str, + max_seq_length: Optional[int] = None, + train_batch_size: int = 32, + eval_batch_size: int = 32, + tokenizer_kwargs: Optional[Dict[str, Any]] = None, + batch_encoding_kwargs: Optional[Dict[str, Any]] = None, + load_dataset_kwargs: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ): + if isinstance(text_fields, str): + text_fields = [text_fields] + if len(text_fields) > 2: + raise ValueError("Too many fields given in text_fields attribute") + self.text_fields = text_fields + super().__init__( + tokenizer_name_or_path=tokenizer_name_or_path, + dataset_name=dataset_name, + target_field=target_field, + max_seq_length=max_seq_length, + train_batch_siz=train_batch_size, + eval_batch_size=eval_batch_size, + tokenizer_kwargs=tokenizer_kwargs, + batch_encoding_kwargs=batch_encoding_kwargs, + load_dataset_kwargs=load_dataset_kwargs, + **kwargs, + ) + + def convert_to_features( + self, example_batch: Dict[str, Any], indices: Optional[List[int]] = None + ) -> BatchEncoding: + """Encodes either single sentence or sentence pairs.""" + if len(self.text_fields) == 2: + texts_or_text_pairs = list( + zip(example_batch[self.text_fields[0]], example_batch[self.text_fields[1]]) + ) + elif len(self.text_fields) == 1: + texts_or_text_pairs = example_batch[self.text_fields[0]] + else: + raise ValueError("Inappropriate length of text_fields attribute") + + features = self.tokenizer( + texts_or_text_pairs, + max_length=self.max_seq_length, + **self.batch_encoding_kwargs, + ) + + features["labels"] = example_batch[self.target_field] + + return features diff --git a/embeddings/model/lightning_model.py b/embeddings/model/lightning_model.py new file mode 100644 index 00000000..649c72d0 --- /dev/null +++ b/embeddings/model/lightning_model.py @@ -0,0 +1,29 @@ +from typing import Any, Dict, Literal + +import pytorch_lightning as pl +from numpy import typing as nptyping +from torch.utils.data import DataLoader + +from embeddings.model.model import Model +from embeddings.task.lightning_task.lightning_task import HuggingFaceLightningTask + + +class LightningModel(Model[pl.LightningDataModule, Dict[str, nptyping.NDArray[Any]]]): + def __init__( + self, + trainer: pl.Trainer, + task: HuggingFaceLightningTask, + predict_subset: Literal["dev", "test"] = "test", + ) -> None: + super().__init__() + self.trainer = trainer + self.task = task + self.predict_subset = predict_subset + + def execute(self, data: pl.LightningDataModule) -> Dict[str, nptyping.NDArray[Any]]: + self.trainer.fit(self.task, data) + dataloader = ( + data.test_dataloader() if self.predict_subset == "test" else data.val_dataloader() + ) + assert isinstance(dataloader, DataLoader) + return self.task.predict(dataloader=dataloader) diff --git a/embeddings/pipeline/hugging_face_classification.py b/embeddings/pipeline/flair_classification.py similarity index 98% rename from embeddings/pipeline/hugging_face_classification.py rename to embeddings/pipeline/flair_classification.py index 0ea4a3e3..fe39d328 100644 --- a/embeddings/pipeline/hugging_face_classification.py +++ b/embeddings/pipeline/flair_classification.py @@ -22,7 +22,7 @@ from embeddings.transformation.transformation import Transformation -class HuggingFaceClassificationPipeline( +class FlairClassificationPipeline( StandardPipeline[ str, datasets.DatasetDict, Corpus, Dict[str, nptyping.NDArray[Any]], Dict[str, Any] ] diff --git a/embeddings/pipeline/hugging_face_pair_classification.py b/embeddings/pipeline/flair_pair_classification.py similarity index 98% rename from embeddings/pipeline/hugging_face_pair_classification.py rename to embeddings/pipeline/flair_pair_classification.py index 8b53235e..365e7f44 100644 --- a/embeddings/pipeline/hugging_face_pair_classification.py +++ b/embeddings/pipeline/flair_pair_classification.py @@ -22,7 +22,7 @@ from embeddings.transformation.transformation import Transformation -class HuggingFacePairClassificationPipeline( +class FlairPairClassificationPipeline( StandardPipeline[ str, datasets.DatasetDict, Corpus, Dict[str, nptyping.NDArray[Any]], Dict[str, Any] ] diff --git a/embeddings/pipeline/hugging_face_sequence_labeling.py b/embeddings/pipeline/flair_sequence_labeling.py similarity index 98% rename from embeddings/pipeline/hugging_face_sequence_labeling.py rename to embeddings/pipeline/flair_sequence_labeling.py index b87a3c44..de620a24 100644 --- a/embeddings/pipeline/hugging_face_sequence_labeling.py +++ b/embeddings/pipeline/flair_sequence_labeling.py @@ -21,7 +21,7 @@ from embeddings.transformation.transformation import Transformation -class HuggingFaceSequenceLabelingPipeline( +class FlairSequenceLabelingPipeline( StandardPipeline[ str, datasets.DatasetDict, Corpus, Dict[str, nptyping.NDArray[Any]], Dict[str, Any] ] diff --git a/embeddings/pipeline/lightning_classification.py b/embeddings/pipeline/lightning_classification.py new file mode 100644 index 00000000..fcffd9c6 --- /dev/null +++ b/embeddings/pipeline/lightning_classification.py @@ -0,0 +1,65 @@ +from typing import Any, Dict, Optional, Sequence, Union + +import datasets +import pytorch_lightning as pl +from numpy import typing as nptyping + +from embeddings.data.datamodule import TextClassificationDataModule +from embeddings.data.io import T_path +from embeddings.evaluator.text_classification_evaluator import TextClassificationEvaluator +from embeddings.model.lightning_model import LightningModel +from embeddings.pipeline.lightning_pipeline import LightningPipeline +from embeddings.task.lightning_task.text_classification import TextClassification + + +class LightningClassificationPipeline( + LightningPipeline[datasets.DatasetDict, Dict[str, nptyping.NDArray[Any]], Dict[str, Any]] +): + DEFAULT_TASK_TRAIN_KWARGS = {"devices": "auto", "accelerator": "auto"} + DEFAULT_TASK_MODEL_KWARGS = {"use_scheduler": True} + + def __init__( + self, + embedding_name: str, + dataset_name: str, + input_column_name: Union[str, Sequence[str]], + target_column_name: str, + output_path: T_path, + max_seq_length: Optional[int] = None, + train_batch_size: int = 32, + eval_batch_size: int = 32, + tokenizer_name: Optional[str] = None, + tokenizer_kwargs: Optional[Dict[str, Any]] = None, + batch_encoding_kwargs: Optional[Dict[str, Any]] = None, + load_dataset_kwargs: Optional[Dict[str, Any]] = None, + task_model_kwargs: Optional[Dict[str, Any]] = None, + task_train_kwargs: Optional[Dict[str, Any]] = None, + ): + datamodule = TextClassificationDataModule( + tokenizer_name_or_path=tokenizer_name if tokenizer_name else embedding_name, + dataset_name=dataset_name, + text_fields=input_column_name, + target_field=target_column_name, + max_seq_length=max_seq_length, + train_batch_size=train_batch_size, + eval_batch_size=eval_batch_size, + tokenizer_kwargs=tokenizer_kwargs, + batch_encoding_kwargs=batch_encoding_kwargs, + load_dataset_kwargs=load_dataset_kwargs, + ) + trainer = pl.Trainer( + default_root_dir=output_path, + **task_train_kwargs if task_train_kwargs else self.DEFAULT_TASK_TRAIN_KWARGS + ) + + task = TextClassification( + model_name_or_path=embedding_name, + train_batch_size=train_batch_size, + eval_batch_size=eval_batch_size, + task_model_kwargs=task_model_kwargs + if task_model_kwargs + else self.DEFAULT_TASK_MODEL_KWARGS, + ) + model = LightningModel(trainer=trainer, task=task, predict_subset="test") + evaluator = TextClassificationEvaluator() + super().__init__(datamodule, model, evaluator) diff --git a/embeddings/pipeline/lightning_pipeline.py b/embeddings/pipeline/lightning_pipeline.py new file mode 100644 index 00000000..6e3ae00e --- /dev/null +++ b/embeddings/pipeline/lightning_pipeline.py @@ -0,0 +1,28 @@ +from typing import Generic, TypeVar + +from embeddings.data.datamodule import BaseDataModule, Data +from embeddings.evaluator.evaluator import Evaluator +from embeddings.model.model import Model +from embeddings.pipeline.pipeline import Pipeline + +EvaluationResult = TypeVar("EvaluationResult") +ModelResult = TypeVar("ModelResult") + + +class LightningPipeline( + Pipeline[EvaluationResult], + Generic[Data, ModelResult, EvaluationResult], +): + def __init__( + self, + datamodule: BaseDataModule[Data], + model: Model[BaseDataModule[Data], ModelResult], + evaluator: Evaluator[ModelResult, EvaluationResult], + ) -> None: + self.datamodule = datamodule + self.model = model + self.evaluator = evaluator + + def run(self) -> EvaluationResult: + model_result = self.model.execute(data=self.datamodule) + return self.evaluator.evaluate(model_result) diff --git a/embeddings/task/lightning_task/__init__.py b/embeddings/task/lightning_task/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/embeddings/task/lightning_task/lightning_task.py b/embeddings/task/lightning_task/lightning_task.py new file mode 100644 index 00000000..b6b3a0d4 --- /dev/null +++ b/embeddings/task/lightning_task/lightning_task.py @@ -0,0 +1,189 @@ +import abc +import sys +from typing import Any, Dict, Generic, List, Optional, Tuple, Type, TypeVar + +import pytorch_lightning as pl +import torch +from numpy import typing as nptyping +from pytorch_lightning.utilities.types import STEP_OUTPUT +from torch.optim import AdamW, Optimizer +from torch.utils.data import DataLoader +from torchmetrics import MetricCollection +from transformers import AutoConfig, AutoModel, get_linear_schedule_with_warmup + +from embeddings.data.datamodule import HuggingFaceDataset + +Model = TypeVar("Model") + + +class LightningTask(pl.LightningModule, abc.ABC, Generic[Model]): + def __init__( + self, + metrics: Optional[MetricCollection] = None, + learning_rate: float = 1e-4, + adam_epsilon: float = 1e-8, + warmup_steps: int = 100, + weight_decay: float = 0.0, + train_batch_size: int = 32, + eval_batch_size: int = 32, + use_scheduler: bool = False, + **kwargs: Any, + ): + super().__init__() + self.save_hyperparameters(ignore=["downstream_model_type"]) # cannot pickle model type + self.metrics = metrics + + @abc.abstractmethod + def get_default_metrics(self) -> MetricCollection: + pass + + @abc.abstractmethod + def forward(self, *args: Any, **kwargs: Any) -> Any: + pass + + @abc.abstractmethod + def shared_step(self, **batch: Any) -> Tuple[torch.Tensor, torch.Tensor]: + pass + + @abc.abstractmethod + def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT: + pass + + @abc.abstractmethod + def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]: + pass + + @abc.abstractmethod + def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]: + pass + + @abc.abstractmethod + def predict( + self, dataloader: DataLoader[HuggingFaceDataset] + ) -> Dict[str, nptyping.NDArray[Any]]: + pass + + def configure_metrics(self) -> None: + if self.metrics is None: + self.metrics = self.get_default_metrics() + self.train_metrics = self.metrics.clone(prefix="train/") + self.val_metrics = self.metrics.clone(prefix="val/") + self.test_metrics = self.metrics.clone(prefix="test/") + + def training_epoch_end(self, outputs: List[Any]) -> None: + self._aggregate_and_log_metrics(self.train_metrics) + + def validation_epoch_end(self, outputs: List[Any]) -> None: + self._aggregate_and_log_metrics(self.val_metrics, prog_bar=True) + + def test_epoch_end(self, outputs: List[Any]) -> None: + self._aggregate_and_log_metrics(self.test_metrics) + + def _aggregate_and_log_metrics( + self, metrics: MetricCollection, prog_bar: bool = False + ) -> Dict[str, float]: + metric_values = metrics.compute() + metrics.reset() + self.log_dict(metric_values, prog_bar=prog_bar) + return metric_values + + def configure_optimizers(self) -> Tuple[List[Optimizer], List[Any]]: + """Prepare optimizer and schedule (linear warmup and decay)""" + no_decay = ["bias", "LayerNorm.weight"] + optimizer_grouped_parameters = [ + { + "params": [ + p for n, p in self.named_parameters() if not any(nd in n for nd in no_decay) + ], + "weight_decay": self.hparams.weight_decay, + }, + { + "params": [ + p for n, p in self.named_parameters() if any(nd in n for nd in no_decay) + ], + "weight_decay": 0.0, + }, + ] + optimizer = AdamW( + optimizer_grouped_parameters, + lr=self.hparams.learning_rate, + eps=self.hparams.adam_epsilon, + ) + + if self.hparams.use_scheduler: + lr_schedulers = self.configure_schedulers(optimizer=optimizer) + else: + lr_schedulers = [] + + return [optimizer], lr_schedulers + + def configure_schedulers(self, optimizer: Optimizer) -> List[Dict[str, Any]]: + scheduler = get_linear_schedule_with_warmup( + optimizer, + num_warmup_steps=self.hparams.warmup_steps, + num_training_steps=self.total_steps, + ) + return [{"scheduler": scheduler, "interval": "step", "frequency": 1}] + + +class HuggingFaceLightningTask(LightningTask[AutoModel], abc.ABC): + def __init__( + self, + model_name_or_path: str, + downstream_model_type: Type["AutoModel"], + finetune_last_n_layers: int = -1, + metrics: Optional[MetricCollection] = None, + config_kwargs: Optional[Dict[str, Any]] = None, + task_model_kwargs: Optional[Dict[str, Any]] = None, + **kwargs: Any, + ) -> None: + super().__init__( + metrics=metrics, **task_model_kwargs if task_model_kwargs else {}, **kwargs + ) + self.save_hyperparameters({"downstream_model_type": downstream_model_type.__name__}) + self.downstream_model_type = downstream_model_type + self.config_kwargs = config_kwargs if config_kwargs else {} + + def setup(self, stage: Optional[str] = None) -> None: + self.configure_model() + self.configure_metrics() + if self.hparams.use_scheduler: + assert self.trainer is not None + train_loader = self.trainer.datamodule.train_dataloader() + tb_size = self.hparams.train_batch_size * max(1, getattr(self.trainer, "gpus", 1)) + ab_size = tb_size * self.trainer.accumulate_grad_batches + self.total_steps: int = int( + (len(train_loader.dataset) / ab_size) * float(self.trainer.max_epochs) + ) + + def configure_model(self) -> None: + assert self.trainer is not None + self.config = AutoConfig.from_pretrained( + self.hparams.model_name_or_path, + num_labels=self.trainer.datamodule.num_classes, + **self.config_kwargs, + ) + self.model: AutoModel = self.downstream_model_type.from_pretrained( + self.hparams.model_name_or_path, config=self.config + ) + if self.hparams.finetune_last_n_layers > -1: + self.freeze_transformer(finetune_last_n_layers=self.hparams.finetune_last_n_layers) + + def freeze_transformer(self, finetune_last_n_layers: int) -> None: + if finetune_last_n_layers == 0: + for name, param in self.model.base_model.named_parameters(): + param.requires_grad = False + else: + no_layers = self.model.config.num_hidden_layers + for name, param in self.model.base_model.named_parameters(): + if name.startswith("embeddings"): + layer = 0 + elif name.startswith("encoder"): + layer = int(name.split(".")[2]) + elif name.startswith("pooler"): + layer = sys.maxsize + else: + raise ValueError("Parameter name not recognized when freezing transformer") + if layer >= (no_layers - finetune_last_n_layers): + break + param.requires_grad = False diff --git a/embeddings/task/lightning_task/text_classification.py b/embeddings/task/lightning_task/text_classification.py new file mode 100644 index 00000000..21026fad --- /dev/null +++ b/embeddings/task/lightning_task/text_classification.py @@ -0,0 +1,114 @@ +from collections import ChainMap +from typing import Any, Dict, Optional, Tuple + +import numpy as np +import torch +from numpy import typing as nptyping +from pytorch_lightning.utilities.types import STEP_OUTPUT +from torch.utils.data import DataLoader +from torchmetrics import F1, Accuracy, MetricCollection, Precision, Recall +from transformers import AutoModelForSequenceClassification + +from embeddings.data.datamodule import HuggingFaceDataset +from embeddings.task.lightning_task.lightning_task import HuggingFaceLightningTask +from embeddings.utils.loggers import get_logger + +_logger = get_logger(__name__) + + +class TextClassification(HuggingFaceLightningTask): + downstream_model_type = AutoModelForSequenceClassification + + def __init__( + self, + model_name_or_path: str, + unfreeze_transformer_from_layer: Optional[int] = None, + metrics: Optional[MetricCollection] = None, + config_kwargs: Optional[Dict[str, Any]] = None, + task_model_kwargs: Optional[Dict[str, Any]] = None, + **kwargs: Any + ) -> None: + super().__init__( + model_name_or_path=model_name_or_path, + downstream_model_type=self.downstream_model_type, + unfreeze_transformer_from_layer=unfreeze_transformer_from_layer, + metrics=metrics, + config_kwargs=config_kwargs, + task_model_kwargs=task_model_kwargs, + **kwargs + ) + + def get_default_metrics(self) -> MetricCollection: + assert self.trainer is not None + num_classes = self.trainer.datamodule.num_classes + if num_classes > 2: + metrics = MetricCollection( + [ + Accuracy(num_classes=num_classes), + Precision(num_classes=num_classes, average="macro"), + Recall(num_classes=num_classes, average="macro"), + F1(num_classes=num_classes, average="macro"), + ] + ) + else: + metrics = MetricCollection( + [ + Accuracy(num_classes=num_classes), + Precision(num_classes=num_classes), + Recall(num_classes=num_classes), + F1(num_classes=num_classes), + ] + ) + return metrics + + def forward(self, *args: Any, **kwargs: Any) -> Any: + assert (not (args and kwargs)) and (args or kwargs) + inputs = kwargs if kwargs else args + if isinstance(inputs, tuple): + inputs = dict(ChainMap(*inputs)) + return self.model(**inputs) + + def shared_step(self, **batch: Any) -> Tuple[torch.Tensor, torch.Tensor]: + outputs = self.forward(**batch) + loss, logits = outputs[:2] + return loss, logits + + def training_step(self, *args: Any, **kwargs: Any) -> STEP_OUTPUT: + batch, batch_idx = args + loss, preds = self.shared_step(**batch) + self.train_metrics(preds, batch["labels"]) + self.log("train/Loss", loss) + if self.hparams.use_scheduler: + assert self.trainer is not None + last_lr = self.trainer.lr_schedulers[0]["scheduler"].get_last_lr() + self.log("train/BaseLR", last_lr[0], prog_bar=True) + self.log("train/LambdaLR", last_lr[1], prog_bar=True) + return {"loss": loss} + + def validation_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]: + batch, batch_idx = args + loss, preds = self.shared_step(**batch) + self.val_metrics(preds, batch["labels"]) + self.log("val/Loss", loss, on_epoch=True) + return None + + def test_step(self, *args: Any, **kwargs: Any) -> Optional[STEP_OUTPUT]: + batch, batch_idx = args + loss, preds = self.shared_step(**batch) + if -1 not in batch["labels"]: + self.test_metrics(preds, batch["labels"]) + self.log("test/Loss", loss, on_epoch=True) + else: + _logger.warning("Missing labels for the test data") + return None + + def predict( + self, dataloader: DataLoader[HuggingFaceDataset] + ) -> Dict[str, nptyping.NDArray[Any]]: + predictions = torch.argmax( + torch.cat([self.forward(**batch).logits for batch in dataloader]), dim=1 + ).numpy() + assert isinstance(predictions, np.ndarray) + ground_truth = torch.cat([x["labels"] for x in dataloader]).numpy() + assert isinstance(ground_truth, np.ndarray) + return {"y_pred": predictions, "y_true": ground_truth} diff --git a/examples/evaluate_document_classification.py b/examples/evaluate_document_classification.py index 9c2d81d4..d7df7e8a 100644 --- a/examples/evaluate_document_classification.py +++ b/examples/evaluate_document_classification.py @@ -4,7 +4,7 @@ import typer from embeddings.defaults import RESULTS_PATH -from embeddings.pipeline.hugging_face_classification import HuggingFaceClassificationPipeline +from embeddings.pipeline.flair_classification import FlairClassificationPipeline app = typer.Typer() @@ -29,7 +29,7 @@ def run( output_path = Path(root, embedding_name, dataset_name) output_path.mkdir(parents=True, exist_ok=True) - pipeline = HuggingFaceClassificationPipeline( + pipeline = FlairClassificationPipeline( embedding_name, dataset_name, input_column_name, target_column_name, output_path ) result = pipeline.run() diff --git a/examples/evaluate_document_pair_classification.py b/examples/evaluate_document_pair_classification.py index 63c43c79..b96c929a 100644 --- a/examples/evaluate_document_pair_classification.py +++ b/examples/evaluate_document_pair_classification.py @@ -5,9 +5,7 @@ import typer from embeddings.defaults import RESULTS_PATH -from embeddings.pipeline.hugging_face_pair_classification import ( - HuggingFacePairClassificationPipeline, -) +from embeddings.pipeline.flair_pair_classification import FlairPairClassificationPipeline app = typer.Typer() @@ -32,7 +30,7 @@ def run( output_path = Path(root, embedding_name, dataset_name) output_path.mkdir(parents=True, exist_ok=True) - pipeline = HuggingFacePairClassificationPipeline( + pipeline = FlairPairClassificationPipeline( embedding_name, dataset_name, input_columns_names_pair, target_column_name, output_path ) result = pipeline.run() diff --git a/examples/evaluate_lightning_document_classification.py b/examples/evaluate_lightning_document_classification.py new file mode 100644 index 00000000..7e0eccd7 --- /dev/null +++ b/examples/evaluate_lightning_document_classification.py @@ -0,0 +1,42 @@ +import pprint +from pathlib import Path + +import typer + +from embeddings.defaults import RESULTS_PATH +from embeddings.pipeline.lightning_classification import LightningClassificationPipeline + + +def run( + embedding_name: str = typer.Option( + "allegro/herbert-base-cased", help="Hugging Face embedding model name or path." + ), + dataset_name: str = typer.Option( + "clarin-pl/polemo2-official", help="Hugging Face dataset name or path." + ), + input_columns_name: str = typer.Option( + "text", help="Pair of column names that contain texts to classify." + ), + target_column_name: str = typer.Option( + "target", help="Column name that contains label for classification." + ), + root: str = typer.Option(RESULTS_PATH.joinpath("lightning_sequence_classification")), +) -> None: + typer.echo(pprint.pformat(locals())) + + output_path = Path(root, embedding_name, dataset_name) + output_path.mkdir(parents=True, exist_ok=True) + + pipeline = LightningClassificationPipeline( + embedding_name=embedding_name, + dataset_name=dataset_name, + input_column_name=input_columns_name, + target_column_name=target_column_name, + output_path=root, + ) + + result = pipeline.run() + typer.echo(pprint.pformat(result)) + + +typer.run(run) diff --git a/examples/evaluate_sequence_labelling.py b/examples/evaluate_sequence_labelling.py index f4826b17..18777b0d 100644 --- a/examples/evaluate_sequence_labelling.py +++ b/examples/evaluate_sequence_labelling.py @@ -5,7 +5,7 @@ import typer from embeddings.defaults import RESULTS_PATH -from embeddings.pipeline.hugging_face_sequence_labeling import HuggingFaceSequenceLabelingPipeline +from embeddings.pipeline.flair_sequence_labeling import FlairSequenceLabelingPipeline app = typer.Typer() @@ -37,7 +37,7 @@ def run( output_path = Path(root, embedding_name, dataset_name) output_path.mkdir(parents=True, exist_ok=True) - pipeline = HuggingFaceSequenceLabelingPipeline( + pipeline = FlairSequenceLabelingPipeline( embedding_name, dataset_name, input_column_name, diff --git a/examples/preprocess_evaluate_sequence_labeling.py b/examples/preprocess_evaluate_sequence_labeling.py new file mode 100644 index 00000000..8c490db8 --- /dev/null +++ b/examples/preprocess_evaluate_sequence_labeling.py @@ -0,0 +1,68 @@ +import pprint +from pathlib import Path +from typing import Optional + +import typer + +from embeddings.defaults import DATASET_PATH, RESULTS_PATH +from embeddings.pipeline.evaluation_pipeline import FlairSequenceLabelingEvaluationPipeline +from embeddings.pipeline.preprocessing_pipeline import FlairSequenceLabelingPreprocessingPipeline + +app = typer.Typer() + + +def run( + embedding_name: str = typer.Option( + "allegro/herbert-base-cased", help="Hugging Face embedding model name or path." + ), + dataset_name: str = typer.Option( + "clarin-pl/kpwr-ner", help="Hugging Face dataset name or path." + ), + input_column_name: str = typer.Option( + "tokens", help="Column name that contains text to classify." + ), + target_column_name: str = typer.Option( + "ner", help="Column name that contains tag labels for POS tagging." + ), + hidden_size: int = typer.Option(32, help="Number of hidden states in RNN."), + evaluation_mode: str = typer.Option( + "conll", help="Evaluation mode. Supported modes: [unit, conll, strict]." + ), + tagging_scheme: Optional[str] = typer.Option( + None, help="Tagging scheme. Supported schemes: [IOB1, IOB2, IOE1, IOE2, IOBES, BILOU]" + ), +) -> None: + typer.echo(pprint.pformat(locals())) + dataset_path = Path(DATASET_PATH, embedding_name, dataset_name) + dataset_path.mkdir(parents=True, exist_ok=True) + + preprocessing_pipeline = FlairSequenceLabelingPreprocessingPipeline( + dataset_name=dataset_name, + input_column_name=input_column_name, + target_column_name=target_column_name, + persist_path=str(dataset_path), + sample_missing_splits=(0.1, 0.1), + ignore_test_subset=False, + ) + dataset = preprocessing_pipeline.run() + + output_path = Path(RESULTS_PATH, embedding_name, dataset_name) + output_path.mkdir(parents=True, exist_ok=True) + persist_out_path = Path(output_path, f"{embedding_name}.json") + persist_out_path.parent.mkdir(parents=True, exist_ok=True) + + evaluation_pipeline = FlairSequenceLabelingEvaluationPipeline( + dataset_path=str(dataset_path), + embedding_name=embedding_name, + output_path=str(output_path), + hidden_size=hidden_size, + persist_path=str(persist_out_path), + predict_subset="test", + task_train_kwargs={"max_epochs": 1}, + ) + + result = evaluation_pipeline.run() + typer.echo(pprint.pformat(result)) + + +typer.run(run) diff --git a/poetry.lock b/poetry.lock index 3478bc14..5e72f188 100644 --- a/poetry.lock +++ b/poetry.lock @@ -9,6 +9,37 @@ python-versions = ">=3.6" [package.dependencies] six = "*" +[[package]] +name = "aiohttp" +version = "3.8.1" +description = "Async http client/server framework (asyncio)" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +aiosignal = ">=1.1.2" +async-timeout = ">=4.0.0a3,<5.0" +attrs = ">=17.3.0" +charset-normalizer = ">=2.0,<3.0" +frozenlist = ">=1.1.1" +multidict = ">=4.5,<7.0" +yarl = ">=1.0,<2.0" + +[package.extras] +speedups = ["aiodns", "brotli", "cchardet"] + +[[package]] +name = "aiosignal" +version = "1.2.0" +description = "aiosignal: a list of registered asynchronous callbacks" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +frozenlist = ">=1.1.0" + [[package]] name = "alembic" version = "1.7.5" @@ -40,6 +71,17 @@ category = "dev" optional = false python-versions = "*" +[[package]] +name = "async-timeout" +version = "4.0.1" +description = "Timeout context manager for asyncio programs" +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +typing-extensions = ">=3.6.5" + [[package]] name = "atomicwrites" version = "1.4.0" @@ -139,6 +181,17 @@ category = "main" optional = false python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*" +[[package]] +name = "charset-normalizer" +version = "2.0.9" +description = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet." +category = "main" +optional = false +python-versions = ">=3.5.0" + +[package.extras] +unicode_backport = ["unicodedata2"] + [[package]] name = "click" version = "7.1.2" @@ -306,6 +359,17 @@ python-versions = ">=2.7, !=3.0.*" [package.extras] graph = ["objgraph (>=1.7.2)"] +[[package]] +name = "fasteners" +version = "0.16.3" +description = "A python package that provides useful locks." +category = "main" +optional = true +python-versions = "*" + +[package.dependencies] +six = "*" + [[package]] name = "filelock" version = "3.4.0" @@ -375,6 +439,14 @@ ufo = ["fs (>=2.2.0,<3)"] unicode = ["unicodedata2 (>=13.0.0)"] woff = ["zopfli (>=0.1.4)", "brotlicffi (>=0.8.0)", "brotli (>=1.0.1)"] +[[package]] +name = "frozenlist" +version = "1.2.0" +description = "A list-like structure which implements collections.abc.MutableSequence" +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "fsspec" version = "2021.11.1" @@ -383,6 +455,10 @@ category = "main" optional = false python-versions = ">=3.6" +[package.dependencies] +aiohttp = {version = "*", optional = true, markers = "extra == \"http\""} +requests = {version = "*", optional = true, markers = "extra == \"http\""} + [package.extras] abfs = ["adlfs"] adl = ["adlfs"] @@ -521,6 +597,17 @@ six = ">=1.5.2" [package.extras] protobuf = ["grpcio-tools (>=1.42.0)"] +[[package]] +name = "h5py" +version = "3.6.0" +description = "Read and write HDF5 files from Python" +category = "main" +optional = true +python-versions = ">=3.7" + +[package.dependencies] +numpy = ">=1.14.5" + [[package]] name = "huggingface-hub" version = "0.0.12" @@ -772,6 +859,14 @@ category = "main" optional = false python-versions = "*" +[[package]] +name = "multidict" +version = "5.2.0" +description = "multidict implementation" +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "multiprocess" version = "0.70.12.2" @@ -1053,6 +1148,14 @@ typing-extensions = ">=3.7.4.3" dotenv = ["python-dotenv (>=0.10.4)"] email = ["email-validator (>=1.0.3)"] +[[package]] +name = "pydeprecate" +version = "0.3.1" +description = "Deprecation tooling" +category = "main" +optional = false +python-versions = ">=3.6" + [[package]] name = "pyflakes" version = "2.3.1" @@ -1069,6 +1172,15 @@ category = "main" optional = true python-versions = "*" +[package.dependencies] +annoy = ">=1.11.4" +fasteners = ">=0.14.1" +h5py = ">=2.8.0" +lz4 = ">=1.0.0" +numpy = ">=1.14.0" +torch = "*" +xxhash = ">=1.0.1" + [[package]] name = "pyparsing" version = "3.0.6" @@ -1136,6 +1248,37 @@ python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,>=2.7" [package.dependencies] six = ">=1.5" +[[package]] +name = "pytorch-lightning" +version = "1.5.4" +description = "PyTorch Lightning is the lightweight PyTorch wrapper for ML researchers. Scale your models. Write less boilerplate." +category = "main" +optional = false +python-versions = ">=3.6" + +[package.dependencies] +fsspec = {version = ">=2021.05.0,<2021.06.0 || >2021.06.0", extras = ["http"]} +future = ">=0.17.1" +numpy = ">=1.17.2" +packaging = ">=17.0" +pyDeprecate = "0.3.1" +PyYAML = ">=5.1" +tensorboard = ">=2.2.0" +torch = ">=1.7" +torchmetrics = ">=0.4.1" +tqdm = ">=4.41.0" +typing-extensions = "*" + +[package.extras] +all = ["matplotlib (>3.1)", "horovod (>=0.21.2)", "torchtext (>=0.8)", "omegaconf (>=2.0.5)", "hydra-core (>=1.0.5)", "jsonargparse[signatures] (>=4.0.4)", "gcsfs (>=2021.5.0)", "rich (>=10.2.2)", "neptune-client (>=0.10.0)", "comet-ml (>=3.1.12)", "mlflow (>=1.0.0)", "test-tube (>=0.7.5)", "wandb (>=0.8.21)", "coverage (>5.2.0)", "codecov (>=2.1)", "pytest (>=6.0)", "pytest-rerunfailures (>=10.2)", "twine (==3.2)", "mypy (>=0.900)", "flake8 (>=3.9.2)", "pre-commit (>=1.0)", "cloudpickle (>=1.3)", "scikit-learn (>0.22.1)", "onnxruntime", "pandas", "torchvision 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