From 279ae464bb170284563bb60b713a234713bd8e56 Mon Sep 17 00:00:00 2001 From: Pietro Lesci <61748653+pietrolesci@users.noreply.github.com> Date: Tue, 17 Oct 2023 13:50:33 +0100 Subject: [PATCH] update new features (#17) * enhance pool loader instantiation * enhance pool loader instantiation * add logger save_to_parquet method * add logger save_to_parquet method * enhance datastores -- enforce default column names * make BADGE an hybrid strategy * add warning when index is bigger than dataset * update lock file * make estimator call hooks defined on Self * rename target to labels * format clustering utils * rename target to labels * rename target to labels * format * lint * format and lint * condense pool-based steps * fix typo and lint * lint * fix seals * fix train loader when anchoral --- .../active_learning/clustering_utilities.py | 27 +- energizer/active_learning/datastores/base.py | 21 +- .../datastores/classification.py | 17 +- energizer/active_learning/strategies/base.py | 142 +- .../active_learning/strategies/diversity.py | 175 +- .../active_learning/strategies/hybrid.py | 145 +- .../active_learning/strategies/random.py | 4 +- .../active_learning/strategies/two_stage.py | 59 +- .../active_learning/strategies/uncertainty.py | 62 +- energizer/datastores/base.py | 40 +- energizer/datastores/classification.py | 150 +- energizer/datastores/language_modelling.py | 4 +- energizer/enums.py | 9 +- energizer/estimator.py | 6 + energizer/loggers/__init__.py | 2 +- energizer/loggers/tensorboard.py | 16 + energizer/loggers/wandb.py | 30 +- examples/active_estimators.ipynb | 2 +- examples/bert_badge.py | 2 +- examples/bert_entropy.py | 2 +- examples/bert_random.py | 2 +- examples/datastores.ipynb | 2 +- examples/estimators.ipynb | 2 +- poetry.lock | 2056 ++++++++--------- pyproject.toml | 77 +- 25 files changed, 1529 insertions(+), 1525 deletions(-) create mode 100644 energizer/loggers/tensorboard.py diff --git a/energizer/active_learning/clustering_utilities.py b/energizer/active_learning/clustering_utilities.py index 6867e4a..a379908 100644 --- a/energizer/active_learning/clustering_utilities.py +++ b/energizer/active_learning/clustering_utilities.py @@ -2,7 +2,8 @@ import numpy as np from numpy.random import RandomState -from scipy.spatial.distance import cdist + +# from scipy.spatial.distance import cdist from sklearn.cluster import KMeans, kmeans_plusplus from sklearn.metrics import silhouette_score from sklearn.preprocessing import StandardScaler @@ -81,6 +82,18 @@ def kmeans_silhouette_sampling(X: np.ndarray, num_clusters: int, rng: RandomStat return _kmeans(X, num_clusters, rng, True, normalize) +def kmeans_pp_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, *args, **kwargs) -> List[int]: + _, indices = kmeans_plusplus(X, num_clusters, random_state=rng) + + unique_ids = list(set(indices.tolist())) + + # can this generate duplicates? + if len(unique_ids) != len(indices): + print(f"Kmeans++ returned duplicates. {X.shape=} {num_clusters=}") + + return unique_ids + + # def kmeans_pp_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, normalize: bool = True) -> List[int]: # """kmeans++ seeding algorithm. @@ -111,15 +124,3 @@ def kmeans_silhouette_sampling(X: np.ndarray, num_clusters: int, rng: RandomStat # centers_ids.append(new_center_id) # return centers_ids - - -def kmeans_pp_sampling(X: np.ndarray, num_clusters: int, rng: RandomState, *args, **kwargs) -> List[int]: - _, indices = kmeans_plusplus(X, num_clusters, random_state=rng) - - unique_ids = list(set(indices.tolist())) - - # can this generate duplicates? - if len(unique_ids) != len(indices): - print(f"Kmeans++ returned duplicates. {X.shape=} {num_clusters=}") - - return unique_ids diff --git a/energizer/active_learning/datastores/base.py b/energizer/active_learning/datastores/base.py index e632753..d3c37e9 100644 --- a/energizer/active_learning/datastores/base.py +++ b/energizer/active_learning/datastores/base.py @@ -132,7 +132,7 @@ def pool_dataset(self, round: Optional[int] = None, with_indices: Optional[List[ if with_indices is not None: mask = mask & self._train_data[SpecialKeys.ID].isin(with_indices) return Dataset.from_pandas( - self._train_data.loc[mask, [i for i in self._train_data.columns if i != InputKeys.TARGET]] + self._train_data.loc[mask, [i for i in self._train_data.columns if i != InputKeys.LABELS]] ) def label( @@ -155,11 +155,11 @@ def label( # train-validation split if validation_perc is not None: n_val = floor(validation_perc * len(indices)) or 1 # at least add one - currentdata = self._train_data.loc[mask, [SpecialKeys.ID, InputKeys.TARGET]] + currentdata = self._train_data.loc[mask, [SpecialKeys.ID, InputKeys.LABELS]] val_indices = sample( indices=currentdata[SpecialKeys.ID].tolist(), size=n_val, - labels=currentdata[InputKeys.TARGET].tolist(), + labels=currentdata[InputKeys.LABELS].tolist(), mode=validation_sampling, random_state=self._rng, ) @@ -180,13 +180,13 @@ def sample_from_pool( mask = self._pool_mask(round) if with_indices: mask = mask & self._train_data[SpecialKeys.ID].isin(with_indices) - data = self._train_data.loc[mask, [SpecialKeys.ID, InputKeys.TARGET]] + data = self._train_data.loc[mask, [SpecialKeys.ID, InputKeys.LABELS]] return sample( indices=data[SpecialKeys.ID].tolist(), size=size, random_state=random_state or self._rng, - labels=data[InputKeys.TARGET].tolist(), + labels=data[InputKeys.LABELS].tolist(), **kwargs, ) @@ -200,19 +200,19 @@ def save_labelled_dataset(self, save_dir: Union[str, Path]) -> None: """ def _labelled_mask(self, round: Optional[int] = None) -> pd.Series: - mask = self._train_data[SpecialKeys.IS_LABELLED] == True + mask = self._train_data[SpecialKeys.IS_LABELLED] == True # noqa: E712 if round is not None: mask = mask & (self._train_data[SpecialKeys.LABELLING_ROUND] <= round) return mask def _train_mask(self, round: Optional[int] = None) -> pd.Series: - return self._labelled_mask(round) & (self._train_data[SpecialKeys.IS_VALIDATION] == False) + return self._labelled_mask(round) & (self._train_data[SpecialKeys.IS_VALIDATION] == False) # noqa: E712 def _validation_mask(self, round: Optional[int] = None) -> pd.Series: - return self._labelled_mask(round) & (self._train_data[SpecialKeys.IS_VALIDATION] == True) + return self._labelled_mask(round) & (self._train_data[SpecialKeys.IS_VALIDATION] == True) # noqa: E712 def _pool_mask(self, round: Optional[int] = None) -> pd.Series: - mask = self._train_data[SpecialKeys.IS_LABELLED] == False + mask = self._train_data[SpecialKeys.IS_LABELLED] == False # noqa: E712 if round is not None: mask = mask | (self._train_data[SpecialKeys.LABELLING_ROUND] > round) return mask @@ -233,7 +233,8 @@ def get_pool_embeddings(self, ids: List[int]) -> np.ndarray: def get_train_embeddings(self, ids: List[int]) -> np.ndarray: # check all the ids are training ids - assert len(set(self.get_train_ids()).intersection(set(ids))) == len(ids) # type: ignore + train_ids = self.get_train_ids() # type: ignore + assert all(i in train_ids for i in ids), set(train_ids).difference(set(ids)) # now that we are sure, let's unmask them and get the items self.unmask_ids_from_index(ids) diff --git a/energizer/active_learning/datastores/classification.py b/energizer/active_learning/datastores/classification.py index 920ceb9..cec50f9 100644 --- a/energizer/active_learning/datastores/classification.py +++ b/energizer/active_learning/datastores/classification.py @@ -1,4 +1,4 @@ -from typing import List, Optional, Union +from typing import List, Optional from datasets import Dataset from transformers import PreTrainedTokenizerBase @@ -13,21 +13,20 @@ class ActivePandasDataStoreForSequenceClassification(SequenceClassificationMixin @classmethod def from_datasets( cls, - input_names: Union[str, List[str]], - target_name: str, tokenizer: PreTrainedTokenizerBase, - train_dataset: Dataset, - validation_dataset: Optional[Dataset] = None, - test_dataset: Optional[Dataset] = None, uid_name: Optional[str] = None, on_cpu: Optional[List[str]] = None, seed: Optional[int] = 42, + train_dataset: Optional[Dataset] = None, + validation_dataset: Optional[Dataset] = None, + test_dataset: Optional[Dataset] = None, ) -> Self: - obj = cls(seed) + obj = cls(seed) # type: ignore obj = _from_datasets( obj=obj, - input_names=input_names, - target_name=target_name, + mandatory_input_names=cls.MANDATORY_INPUT_NAMES, + optional_input_names=cls.OPTIONAL_INPUT_NAMES, + mandatory_target_name=cls.MANDATORY_TARGET_NAME, tokenizer=tokenizer, uid_name=uid_name, on_cpu=on_cpu, diff --git a/energizer/active_learning/strategies/base.py b/energizer/active_learning/strategies/base.py index edeb24c..b18b998 100644 --- a/energizer/active_learning/strategies/base.py +++ b/energizer/active_learning/strategies/base.py @@ -1,16 +1,18 @@ from abc import ABC, abstractmethod from pathlib import Path -from typing import Any, Callable, Dict, List, Literal, Mapping, Optional, Tuple, Union +from typing import Any, Callable, Dict, List, Literal, Optional, Tuple, Union +import numpy as np import torch from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule, _FabricOptimizer from torch.optim.lr_scheduler import _LRScheduler from energizer.active_learning.datastores.base import ActiveDataStore from energizer.active_learning.trackers import ActiveProgressTracker -from energizer.enums import RunningStage +from energizer.enums import InputKeys, OutputKeys, RunningStage, SpecialKeys from energizer.estimator import Estimator, OptimizationArgs, SchedulerArgs from energizer.types import BATCH_OUTPUT, METRIC, ROUND_OUTPUT +from energizer.utilities import ld_to_dl class ActiveEstimator(Estimator): @@ -77,10 +79,6 @@ def active_fit( model_cache_dir, query_size=query_size, replay=False, - validation_sampling=validation_sampling, - validation_perc=validation_perc, - limit_test_batches=limit_test_batches, - limit_pool_batches=limit_pool_batches, fit_loop_kwargs=dict( max_epochs=max_epochs, min_epochs=min_epochs, @@ -98,6 +96,9 @@ def active_fit( scheduler=scheduler, scheduler_kwargs=scheduler_kwargs, ), + test_kwargs=dict(limit_test_batches=limit_test_batches), + query_kwargs=dict(limit_pool_batches=limit_pool_batches), + label_kwargs=dict(validation_perc=validation_perc, validation_sampling=validation_sampling), ) def run_active_fit( @@ -117,9 +118,11 @@ def run_active_fit( if reinit_model: self.load_state_dict(model_cache_dir) - out = self.round_start(datastore) + # === RUN ROUND === # self.callback("on_round_start", datastore=datastore) + out = self.run_round(datastore, **kwargs) + out = self.round_end(datastore, out) self.callback("on_round_end", datastore=datastore, output=out) @@ -127,6 +130,7 @@ def run_active_fit( self.tracker.increment_round() self.tracker.increment_budget() + # ================= # # check if not self.tracker.is_last_round: @@ -149,20 +153,18 @@ def run_round( datastore: ActiveDataStore, query_size: int, replay: bool, - validation_perc: Optional[float], - validation_sampling: Literal["uniform", "stratified"], - limit_test_batches: Optional[int], - limit_pool_batches: Optional[int], fit_loop_kwargs: Dict, fit_opt_kwargs: Dict, + test_kwargs: Dict, + query_kwargs: Dict, + label_kwargs: Dict, ) -> ROUND_OUTPUT: - model, optimizer, scheduler, train_loader, validation_loader, test_loader, pool_loader = self._setup_round( + model, optimizer, scheduler, train_loader, validation_loader, test_loader = self._setup_round( datastore, replay, fit_loop_kwargs, fit_opt_kwargs, - limit_test_batches, - limit_pool_batches, + test_kwargs, ) output = {} @@ -181,12 +183,8 @@ def run_round( if ( not replay # do not annotate in replay and not self.tracker.is_last_round # last round is used only to test - and pool_loader is not None - and len(pool_loader or []) > query_size # enough instances ): - n_labelled = self.run_annotation( - model, pool_loader, datastore, query_size, validation_perc, validation_sampling - ) + n_labelled = self.run_annotation(model, datastore, query_size, query_kwargs, label_kwargs) elif replay: n_labelled = datastore.query_size(self.tracker.global_round) @@ -198,16 +196,17 @@ def run_round( def run_annotation( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, - validation_perc: Optional[float], - validation_sampling: Optional[Literal["uniform", "stratified"]], + query_kwargs: Dict, + label_kwargs: Dict, ) -> int: - # query + + # === QUERY === # self.callback("on_query_start", model=model, datastore=datastore) - indices = self.run_query(model, loader, datastore, query_size) + # NOTE: run_query is in charge of defining the pool_loader and the relative tracker + indices = self.run_query(model, datastore, query_size, **query_kwargs) # prevent to query more than available budget if self.tracker.global_budget + len(indices) >= self.tracker.budget_tracker.max: # type: ignore @@ -216,35 +215,38 @@ def run_annotation( self.callback("on_query_end", model=model, datastore=datastore, indices=indices) - # label + # ============= # + + # if no indices are returned, no need to annotated + if len(indices) == 0: + return 0 + + # === LABEL === # self.callback("on_label_start", datastore=datastore) n_labelled = datastore.label( indices=indices, round=self.tracker.global_round + 1, # because the data will be used in the following round - validation_perc=validation_perc, - validation_sampling=validation_sampling, + **label_kwargs, ) self.callback("on_label_end", datastore=datastore) + # ============= # return n_labelled def run_query( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, + **kwargs, ) -> List[int]: raise NotImplementedError def active_fit_end(self, datastore: ActiveDataStore, output: List[ROUND_OUTPUT]) -> Any: return output - def round_start(self, datastore: ActiveDataStore) -> None: - ... - def round_end(self, datastore: ActiveDataStore, output: ROUND_OUTPUT) -> ROUND_OUTPUT: return output @@ -254,8 +256,7 @@ def _setup_round( replay: bool, fit_loop_kwargs: Dict, fit_opt_kwargs: Dict, - limit_test_batches: Optional[int], - limit_pool_batches: Optional[int], + test_kwargs: Dict, ) -> Tuple[ _FabricModule, _FabricOptimizer, @@ -263,9 +264,8 @@ def _setup_round( Optional[_FabricDataLoader], Optional[_FabricDataLoader], Optional[_FabricDataLoader], - Optional[_FabricDataLoader], ]: - # start progress tracking + """Start progress tracking.""" num_round = self.tracker.global_round if replay else None @@ -285,22 +285,44 @@ def _setup_round( # configuration test test_loader = datastore.test_loader() + limit_test_batches = test_kwargs.get("limit_test_batches", None) self.tracker.setup_eval(RunningStage.TEST, num_batches=len(test_loader or []), limit_batches=limit_test_batches) test_loader = self.configure_dataloader(test_loader) - # configuration pool - pool_loader = None - if not replay: - pool_loader = datastore.pool_loader(round=num_round) - self.tracker.setup_eval( - RunningStage.POOL, num_batches=len(pool_loader or []), limit_batches=limit_pool_batches - ) - pool_loader = self.configure_dataloader(pool_loader) + return model, optimizer, scheduler, train_loader, validation_loader, test_loader + - return model, optimizer, scheduler, train_loader, validation_loader, test_loader, pool_loader +class PoolBasedMixin(ABC): + """Allows strategy to use the pool and/or the training set during the query process.""" + POOL_OUTPUT_KEY: OutputKeys + + def run_pool_evaluation(self, model: _FabricModule, loader: _FabricDataLoader) -> Dict[str, np.ndarray]: + out: List[Dict] = self.run_evaluation(model, loader, RunningStage.POOL) # type: ignore + _out = ld_to_dl(out) + return {k: np.concatenate(v) for k, v in _out.items()} + + def evaluation_step( + self, + model: _FabricModule, + batch: Any, + batch_idx: int, + loss_fn: Optional[Union[torch.nn.Module, Callable]], + metrics: Optional[METRIC], + stage: Union[str, RunningStage], + ) -> Union[Dict, BATCH_OUTPUT]: + if stage != RunningStage.POOL: + return super().evaluation_step(model, batch, batch_idx, loss_fn, metrics, stage) # type: ignore + + # keep IDs here in case user messes up in the function definition + ids = batch[InputKeys.ON_CPU][SpecialKeys.ID] + pool_out = self.pool_step(model, batch, batch_idx, loss_fn, metrics) + + assert isinstance(pool_out, torch.Tensor), f"`{stage}_step` must return a tensor`." + + # enforce that we always return a dict here + return {self.POOL_OUTPUT_KEY: pool_out, SpecialKeys.ID: ids} -class PoolBasedStrategyMixin(ABC): @abstractmethod def pool_step( self, @@ -309,8 +331,36 @@ def pool_step( batch_idx: int, loss_fn: Optional[Union[torch.nn.Module, Callable]], metrics: Optional[METRIC] = None, - ) -> BATCH_OUTPUT: + ) -> torch.Tensor: ... def pool_epoch_end(self, output: List[Dict], metrics: Optional[METRIC]) -> List[Dict]: return output + + def get_pool_loader(self, datastore: ActiveDataStore, **kwargs) -> Optional[_FabricDataLoader]: + subpool_ids = kwargs.get("subpool_ids", None) + loader = datastore.pool_loader(with_indices=subpool_ids) if subpool_ids is not None else datastore.pool_loader() + + if loader is not None: + if subpool_ids is not None: + assert len(loader.dataset) == len(subpool_ids), "Problems subsetting pool" # type: ignore + pool_loader = self.configure_dataloader(loader) # type: ignore + self.tracker.setup_eval( # type: ignore + RunningStage.POOL, num_batches=len(pool_loader or []), limit_batches=kwargs.get("limit_pool_batches") + ) + return pool_loader + + def get_train_loader(self, datastore: ActiveDataStore, **kwargs) -> Optional[_FabricDataLoader]: + + # NOTE: hack -- load train dataloader with the evaluation batch size + batch_size = datastore.loading_params["batch_size"] + datastore._loading_params["batch_size"] = datastore.loading_params["eval_batch_size"] + loader = datastore.train_loader(**kwargs) + datastore._loading_params["batch_size"] = batch_size + + if loader is not None: + train_loader = self.configure_dataloader(loader) # type: ignore + self.tracker.setup_eval( # type: ignore + RunningStage.POOL, num_batches=len(train_loader or []), limit_batches=kwargs.get("limit_pool_batches") + ) + return train_loader diff --git a/energizer/active_learning/strategies/diversity.py b/energizer/active_learning/strategies/diversity.py index 27b8731..32381f5 100644 --- a/energizer/active_learning/strategies/diversity.py +++ b/energizer/active_learning/strategies/diversity.py @@ -1,37 +1,25 @@ from abc import ABC, abstractmethod -from typing import Any, Callable, Dict, List, Optional, Tuple, Union +from typing import Any, Callable, List, Literal, Optional, Tuple, Union import numpy as np import torch -from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule +from lightning.fabric.wrappers import _FabricModule from numpy.random import RandomState from sklearn.utils.validation import check_random_state -from torch.nn.functional import one_hot -from energizer.active_learning.clustering_utilities import kmeans_pp_sampling from energizer.active_learning.datastores.base import ActiveDataStore -from energizer.active_learning.strategies.base import ActiveEstimator, PoolBasedStrategyMixin -from energizer.enums import InputKeys, OutputKeys, RunningStage, SpecialKeys +from energizer.active_learning.registries import CLUSTERING_FUNCTIONS +from energizer.active_learning.strategies.base import ActiveEstimator, PoolBasedMixin +from energizer.enums import OutputKeys, SpecialKeys from energizer.types import METRIC -from energizer.utilities import ld_to_dl, move_to_cpu -class DiversitySamplingMixin(ABC): - def get_embeddings( - self, - model: _FabricModule, - loader: _FabricDataLoader, - datastore: ActiveDataStore, - **kwargs, - ) -> np.ndarray: - raise NotImplementedError +class DiversityBasedStrategy(ABC, ActiveEstimator): + """This does not run on pool. - @abstractmethod - def select_from_embeddings(self, embeddings: np.ndarray, **kwargs) -> List[int]: - ... + Here for now, but usually even diversity-based require running on the pool. + """ - -class DiversityBasedStrategy(DiversitySamplingMixin, ActiveEstimator): rng: RandomState def __init__(self, *args, seed: int = 42, **kwargs) -> None: @@ -42,93 +30,102 @@ def __init__(self, *args, seed: int = 42, **kwargs) -> None: def run_query( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, + **kwargs, ) -> List[int]: - embeddings = self.get_embeddings(model, loader, datastore, query_size=query_size) - return self.select_from_embeddings(embeddings, query_size=query_size) + embeddings_and_ids = self.get_embeddings_and_ids(model, datastore, query_size, **kwargs) + if embeddings_and_ids is None: + return [] + else: + embeddings, ids = embeddings_and_ids + return self.select_from_embeddings(model, datastore, query_size, embeddings, ids, **kwargs) - -class BADGE(PoolBasedStrategyMixin, DiversityBasedStrategy): - def evaluation_step( + @abstractmethod + def get_embeddings_and_ids( self, model: _FabricModule, - batch: Any, - batch_idx: int, - loss_fn: Optional[Union[torch.nn.Module, Callable]], - metrics: Optional[METRIC], - stage: Union[str, RunningStage], - ) -> Dict: - if stage != RunningStage.POOL: - return super().evaluation_step(model, batch, batch_idx, loss_fn, metrics, stage) # type: ignore - - # keep IDs here in case user messes up in the function definition - ids = batch[InputKeys.ON_CPU][SpecialKeys.ID] - pool_out = self.pool_step(model, batch, batch_idx, loss_fn, metrics) - - assert isinstance(pool_out, torch.Tensor), "`pool_step` must return the gradient tensor`." - return { - OutputKeys.GRAD: move_to_cpu(pool_out), - SpecialKeys.ID: ids, - } # enforce that we always return a dict here + datastore: ActiveDataStore, + query_size: int, + **kwargs, + ) -> Optional[Tuple[np.ndarray, np.ndarray]]: + # NOTE: Always need the ids because you might not return the entire pool + ... - def pool_step( + @abstractmethod + def select_from_embeddings( self, model: _FabricModule, - batch: Any, - batch_idx: int, - loss_fn: Optional[Union[torch.nn.Module, Callable]], - metrics: Optional[METRIC], - ) -> torch.Tensor: - r"""Return the loss gradient with respect to the penultimate layer of the model. - - Uses the analytical form from the paper - - $g(x)_i = ( f(x; \theta)_i - \mathbf{1}(\hat{y} = i) ) h(x; W)$ + datastore: ActiveDataStore, + query_size: int, + embeddings: np.ndarray, + ids: np.ndarray, + **kwargs, + ) -> List[int]: + ... - Refs for the implementation: - https://github.com/forest-snow/alps/blob/3c7ef2c98249fc975a897b27f275695f97d5b7a9/src/sample.py#L65 - """ - penultimate_layer_out = self.get_penultimate_layer_out(model, batch) - logits = self.get_logits_from_penultimate_layer_out(model, penultimate_layer_out) - batch_size, num_classes = logits.size() - # compute scales - probs = logits.softmax(dim=-1) - preds_oh = one_hot(probs.argmax(dim=-1), num_classes=num_classes) - scales = probs - preds_oh +class DiversityBasedStrategyWithPool(PoolBasedMixin, DiversityBasedStrategy): + POOL_OUTPUT_KEY: OutputKeys = OutputKeys.EMBEDDINGS - # multiply - grads_3d = torch.einsum("bi,bj->bij", scales, penultimate_layer_out) - return grads_3d.view(batch_size, -1) # (batch_size,) + def get_embeddings_and_ids( + self, + model: _FabricModule, + datastore: ActiveDataStore, + query_size: int, + **kwargs, + ) -> Optional[Tuple[np.ndarray, np.ndarray]]: + pool_loader = self.get_pool_loader(datastore, **kwargs) + if pool_loader is not None and len(pool_loader.dataset or []) > query_size: # type: ignore + # enough instances + out = self.run_pool_evaluation(model, pool_loader) + return out[self.POOL_OUTPUT_KEY], out[SpecialKeys.ID] - def get_embeddings( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStore, **kwargs - ) -> Tuple[np.ndarray, np.ndarray]: - # NOTE: this is similar to `UncertaintyBasedStrategy.compute_most_uncertain` - out: List[Dict] = self.run_evaluation(model, loader, RunningStage.POOL) # type: ignore - _out = ld_to_dl(out) +class ClusteringMixin: + def __init__( + self, + *args, + clustering_fn: Literal["kmeans_sampling", "kmeans_silhouette_sampling", "kmeans_pp_sampling"], + **kwargs, + ) -> None: + super().__init__(*args, **kwargs) + self.clustering_fn = CLUSTERING_FUNCTIONS[clustering_fn] - grads = np.concatenate(_out[OutputKeys.GRAD]) - uids = np.concatenate(_out[SpecialKeys.ID]) + def select_from_embeddings( + self, + model: _FabricModule, + datastore: ActiveDataStore, + query_size: int, + embeddings: np.ndarray, + ids: np.ndarray, + **kwargs, + ) -> List[int]: + center_ids = self.clustering_fn(embeddings, query_size, rng=self.rng) # type: ignore + return ids[center_ids].tolist() - return grads, uids # NOTE: we output a tuple here - def select_from_embeddings(self, grads_and_ids: Tuple[np.ndarray, np.ndarray], **kwargs) -> List[int]: - # NOTE: the first argument is a tuple here! - grads, uids = grads_and_ids - query_size = kwargs["query_size"] - center_ids = kmeans_pp_sampling(grads, query_size, rng=self.rng) - return uids[center_ids].tolist() +class EmbeddingClustering(ClusteringMixin, DiversityBasedStrategy): + ... - @abstractmethod - def get_penultimate_layer_out(self, model: _FabricModule, batch: Any) -> torch.Tensor: - ... +class PoolBasedEmbeddingClustering(ClusteringMixin, DiversityBasedStrategyWithPool): @abstractmethod - def get_logits_from_penultimate_layer_out( - self, model: _FabricModule, penultimate_layer_out: torch.Tensor + def pool_step( + self, + model: _FabricModule, + batch: Any, + batch_idx: int, + loss_fn: Optional[Union[torch.nn.Module, Callable]], + metrics: Optional[METRIC], ) -> torch.Tensor: + """This needs to return the embedded batch.""" ... + + +# class GreedyCoreset(DiversityBasedStrategyWithPool): +# def __init__(self, *args, distance_metric: Literal["euclidean", "cosine"], normalize: bool = True, batch_size: int = 100, **kwargs) -> None: +# super().__init__(*args, **kwargs) +# self.distance_metric = distance_metric +# self.normalize = normalize +# self.batch_size = batch_size diff --git a/energizer/active_learning/strategies/hybrid.py b/energizer/active_learning/strategies/hybrid.py index 997035e..9c46697 100644 --- a/energizer/active_learning/strategies/hybrid.py +++ b/energizer/active_learning/strategies/hybrid.py @@ -1,14 +1,18 @@ -from typing import Callable, Dict, List, Optional, Union +from typing import Any, Callable, Dict, List, Optional, Tuple, Union import numpy as np -from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule +import torch +from lightning.fabric.wrappers import _FabricModule from numpy.random import RandomState from sklearn.utils import check_random_state +from torch.nn.functional import one_hot -from energizer.active_learning.datastores.base import ActiveDataStoreWithIndex +from energizer.active_learning.clustering_utilities import kmeans_pp_sampling +from energizer.active_learning.datastores.base import ActiveDataStore, ActiveDataStoreWithIndex from energizer.active_learning.registries import CLUSTERING_FUNCTIONS -from energizer.active_learning.strategies.diversity import DiversityBasedStrategy +from energizer.active_learning.strategies.diversity import DiversityBasedStrategy, DiversityBasedStrategyWithPool from energizer.active_learning.strategies.uncertainty import UncertaintyBasedStrategy +from energizer.types import METRIC class Tyrogue(DiversityBasedStrategy, UncertaintyBasedStrategy): @@ -55,30 +59,125 @@ def r_factor(self) -> int: def clustering_fn(self) -> Callable: return self._clustering_fn - def select_pool_subset( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStoreWithIndex, **kwargs + def run_query( + self, + model: _FabricModule, + datastore: ActiveDataStoreWithIndex, + query_size: int, + **kwargs, + ) -> List[int]: + + # === DIVERSITY === # + embeddings_and_ids = self.get_embeddings_and_ids(model, datastore, query_size, **kwargs) + if embeddings_and_ids is None: + return [] + else: + embeddings, ids = embeddings_and_ids + + subpool_ids = self.select_from_embeddings(model, datastore, query_size, embeddings, ids, **kwargs) + + # === UNCERTAINTY === # + pool_loader = self.get_pool_loader(datastore, subpool_ids=subpool_ids, **kwargs) + if pool_loader is None or len(pool_loader.dataset or []) <= query_size: # type: ignore + # not enough instances + return [] + return self.compute_most_uncertain(model, pool_loader, query_size) + + def get_embeddings_and_ids( + self, + model: _FabricModule, + datastore: ActiveDataStoreWithIndex, + query_size: int, + **kwargs, + ) -> Optional[Tuple[np.ndarray, np.ndarray]]: + pool_ids = kwargs.get("subpool_ids", None) or datastore.get_pool_ids() + return datastore.get_pool_embeddings(pool_ids), np.array(pool_ids) + + def select_from_embeddings( + self, + model: _FabricModule, + datastore: ActiveDataStoreWithIndex, + query_size: int, + embeddings: np.ndarray, + ids: np.ndarray, + **kwargs, ) -> List[int]: - query_size: int = kwargs["query_size"] num_clusters = query_size * self.r_factor - embeddings = self.get_embeddings(datastore, **kwargs) - subpool_ids = self.select_from_embeddings(embeddings, num_clusters=num_clusters) - loader = self._get_subpool_loader_by_ids(datastore, subpool_ids) + centers_ids = self.clustering_fn(embeddings, num_clusters, rng=self.clustering_rng, **self.clustering_kwargs) + + return ids[centers_ids].tolist() + + +class BADGE(DiversityBasedStrategyWithPool): + def select_from_embeddings( + self, + model: _FabricModule, + datastore: ActiveDataStore, + query_size: int, + embeddings: np.ndarray, + ids: np.ndarray, + **kwargs, + ) -> List[int]: + # k-means++ sampling + center_ids = kmeans_pp_sampling(embeddings, query_size, rng=self.rng) + return ids[center_ids].tolist() + + def pool_step( + self, + model: _FabricModule, + batch: Any, + batch_idx: int, + loss_fn: Optional[Union[torch.nn.Module, Callable]], + metrics: Optional[METRIC], + ) -> torch.Tensor: + r"""Return the loss gradient with respect to the penultimate layer of the model. + + Uses the analytical form from the paper + + $g(x)_i = ( f(x; \theta)_i - \mathbf{1}(\hat{y} = i) ) h(x; W)$ + + Refs for the implementation: + https://github.com/forest-snow/alps/blob/3c7ef2c98249fc975a897b27f275695f97d5b7a9/src/sample.py#L65 + """ + penultimate_layer_out = self.get_penultimate_layer_out(model, batch) + logits = self.get_logits_from_penultimate_layer_out(model, penultimate_layer_out) + batch_size, num_classes = logits.size() + + # compute scales + probs = logits.softmax(dim=-1) + preds_oh = one_hot(probs.argmax(dim=-1), num_classes=num_classes) + scales = probs - preds_oh + + # multiply + grads_3d = torch.einsum("bi,bj->bij", scales, penultimate_layer_out) + return grads_3d.view(batch_size, -1) # (batch_size,) + + def get_penultimate_layer_out(self, model: _FabricModule, batch: Any) -> torch.Tensor: + raise NotImplementedError("Either implement `get_penultimate_layer_out` of `pool_step` directly.") + + def get_logits_from_penultimate_layer_out( + self, model: _FabricModule, penultimate_layer_out: torch.Tensor + ) -> torch.Tensor: + raise NotImplementedError("Either implement `get_logits_from_penultimate_layer_out` of `pool_step` directly.") + + +# class ContrastiveActiveLearning(DiversityBasedStrategy, UncertaintyBasedStrategy): - return self.compute_most_uncertain(model, loader, query_size) +# train_embeddings +# scores = [] +# for p in pool: +# ids = knn(p, train_embeddings) +# train_instances = train_embeddings[ids,:] - def select_from_embeddings(self, embeddings: np.ndarray, **kwargs) -> List[int]: - num_clusters: int = kwargs["num_clusters"] - return self.clustering_fn(embeddings, num_clusters, rng=self.clustering_rng, **self.clustering_kwargs) +# p_probs = model(p) +# kls = [] +# for batch in train_instances: +# probs = model(batch) +# kls += KL(p_probs, probs) - def get_embeddings(self, datastore: ActiveDataStoreWithIndex, **kwargs) -> np.ndarray: - pool_ids = kwargs.get("pool_ids", None) or datastore.get_pool_ids() - return datastore.get_pool_embeddings(pool_ids) +# scores.append(kls.mean()) - def _get_subpool_loader_by_ids( - self, datastore: ActiveDataStoreWithIndex, subpool_ids: List[int] - ) -> _FabricDataLoader: - pool_loader = self.configure_dataloader(datastore.pool_loader(with_indices=subpool_ids)) # type: ignore - self.tracker.pool_tracker.max = len(pool_loader) # type: ignore - return pool_loader # type: ignore +# topk_ids = scores.argmax() +# return pool[topk_ids] diff --git a/energizer/active_learning/strategies/random.py b/energizer/active_learning/strategies/random.py index ecfcd33..a04ae74 100644 --- a/energizer/active_learning/strategies/random.py +++ b/energizer/active_learning/strategies/random.py @@ -1,6 +1,6 @@ from typing import List -from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule +from lightning.fabric.wrappers import _FabricModule from numpy.random import RandomState # https://scikit-learn.org/stable/developers/develop.html#random-numbers @@ -21,8 +21,8 @@ def __init__(self, *args, seed: int = 42, **kwargs) -> None: def run_query( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, + **kwargs, ) -> List[int]: return datastore.sample_from_pool(size=query_size, random_state=self.rng) diff --git a/energizer/active_learning/strategies/two_stage.py b/energizer/active_learning/strategies/two_stage.py index 97c10c4..ee7bbd9 100644 --- a/energizer/active_learning/strategies/two_stage.py +++ b/energizer/active_learning/strategies/two_stage.py @@ -1,8 +1,9 @@ +from abc import ABC, abstractmethod from typing import Any, List, Optional import numpy as np import pandas as pd -from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule +from lightning.fabric.wrappers import _FabricModule from numpy.random import RandomState from sklearn.utils import check_random_state @@ -11,7 +12,7 @@ from energizer.enums import SpecialKeys -class BaseSubsetStrategy(ActiveEstimator): +class BaseSubsetStrategy(ABC, ActiveEstimator): """These strategies are applied in conjunction with a base query strategy. If the size of the pool falls below the given `k`, this implementation will not select a subset anymore and will just delegate to the base strategy instead. @@ -44,25 +45,21 @@ def base_strategy(self) -> ActiveEstimator: def run_query( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, + **kwargs, ) -> List[int]: if datastore.pool_size() > self.subpool_size: - subpool_ids = self.select_pool_subset(model, loader, datastore, query_size=query_size) - loader = self._get_subpool_loader_by_ids(datastore, subpool_ids) + subpool_ids = self.select_pool_subset(model, datastore, query_size, **kwargs) + kwargs["subpool_ids"] = subpool_ids - return self.base_strategy.run_query(model, loader, datastore, query_size) + return self.base_strategy.run_query(model, datastore, query_size, **kwargs) + @abstractmethod def select_pool_subset( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStore, **kwargs + self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs ) -> List[int]: - raise NotImplementedError - - def _get_subpool_loader_by_ids(self, datastore: ActiveDataStore, subpool_ids: List[int]) -> _FabricDataLoader: - pool_loader = self.configure_dataloader(datastore.pool_loader(with_indices=subpool_ids)) # type: ignore - self.tracker.pool_tracker.max = len(pool_loader) # type: ignore - return pool_loader # type: ignore + ... def __getattr__(self, attr: str) -> Any: if attr not in self.__dict__: @@ -72,7 +69,7 @@ def __getattr__(self, attr: str) -> Any: class RandomSubsetStrategy(BaseSubsetStrategy): def select_pool_subset( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStore, **kwargs + self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs ) -> List[int]: subpool_size = min(datastore.pool_size(), self.subpool_size) return datastore.sample_from_pool(size=subpool_size, random_state=self.rng) @@ -85,12 +82,11 @@ def __init__(self, *args, num_neighbours: int, max_search_size: Optional[int] = self.max_search_size = max_search_size def select_pool_subset( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStoreWithIndex, **kwargs + self, model: _FabricModule, datastore: ActiveDataStoreWithIndex, query_size: int, **kwargs ) -> List[int]: - query_size: int = kwargs["query_size"] # SELECT QUERIES - search_query_ids = self.select_search_query(model, loader, datastore, query_size=query_size) + search_query_ids = self.select_search_query(model, datastore, query_size, **kwargs) if len(search_query_ids) == 0: # if cold-starting there is no training embedding, fall-back to random sampling @@ -104,14 +100,6 @@ def select_pool_subset( # USE RESULTS TO SUBSET POOL return self.get_subpool_ids_from_search_results(candidate_df, datastore) - def select_search_query( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStoreWithIndex, **kwargs - ) -> List[int]: - raise NotImplementedError - - def get_query_embeddings(self, datastore: ActiveDataStoreWithIndex, search_query_ids: List[int]) -> np.ndarray: - raise NotImplementedError - def search_pool( self, datastore: ActiveDataStoreWithIndex, search_query_embeddings: np.ndarray, search_query_ids: List[int] ) -> pd.DataFrame: @@ -133,10 +121,21 @@ def search_pool( return candidate_df + @abstractmethod + def select_search_query( + self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs + ) -> List[int]: + ... + + @abstractmethod + def get_query_embeddings(self, datastore: ActiveDataStoreWithIndex, search_query_ids: List[int]) -> np.ndarray: + ... + + @abstractmethod def get_subpool_ids_from_search_results( self, candidate_df: pd.DataFrame, datastore: ActiveDataStoreWithIndex ) -> List[int]: - raise NotImplementedError + ... class SEALSStrategy(BaseSubsetWithSearchStrategy): @@ -148,21 +147,21 @@ class SEALSStrategy(BaseSubsetWithSearchStrategy): def run_query( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, + **kwargs, ) -> List[int]: - annotated_ids = super().run_query(model, loader, datastore, query_size) + annotated_ids = super().run_query(model, datastore, query_size, **kwargs) # in the next round we only need to search the newly labelled data - self.to_search += annotated_ids + self.to_search = annotated_ids # make sure to remove instances that might have been annotated self.subpool_ids = [i for i in self.subpool_ids if i not in annotated_ids] return annotated_ids def select_search_query( - self, model: _FabricModule, loader: _FabricDataLoader, datastore: ActiveDataStoreWithIndex, **kwargs + self, model: _FabricModule, datastore: ActiveDataStore, query_size: int, **kwargs ) -> List[int]: if len(self.to_search) < 1: return datastore.get_train_ids() diff --git a/energizer/active_learning/strategies/uncertainty.py b/energizer/active_learning/strategies/uncertainty.py index f9d1efe..33d76dd 100644 --- a/energizer/active_learning/strategies/uncertainty.py +++ b/energizer/active_learning/strategies/uncertainty.py @@ -1,22 +1,20 @@ -from typing import Any, Callable, Dict, List, Optional, Union +from typing import Callable, List, Union -import numpy as np -import torch from lightning.fabric.wrappers import _FabricDataLoader, _FabricModule from numpy.random import RandomState from sklearn.utils.validation import check_random_state from energizer.active_learning.datastores.base import ActiveDataStore from energizer.active_learning.registries import SCORING_FUNCTIONS -from energizer.active_learning.strategies.base import ActiveEstimator, PoolBasedStrategyMixin -from energizer.enums import InputKeys, OutputKeys, RunningStage, SpecialKeys -from energizer.types import BATCH_OUTPUT, METRIC -from energizer.utilities import ld_to_dl +from energizer.active_learning.strategies.base import ActiveEstimator, PoolBasedMixin +from energizer.enums import OutputKeys, SpecialKeys -class UncertaintyBasedStrategy(PoolBasedStrategyMixin, ActiveEstimator): +class UncertaintyBasedStrategy(PoolBasedMixin, ActiveEstimator): + # gets the ABC class from PoolBasedMixin rng: RandomState _score_fn: Callable + POOL_OUTPUT_KEY: OutputKeys = OutputKeys.SCORES def __init__(self, *args, score_fn: Union[str, Callable], seed: int = 42, **kwargs) -> None: super().__init__(*args, **kwargs) @@ -31,47 +29,23 @@ def score_fn(self) -> Callable: def run_query( self, model: _FabricModule, - loader: _FabricDataLoader, datastore: ActiveDataStore, query_size: int, + **kwargs, ) -> List[int]: - return self.compute_most_uncertain(model, loader, query_size) + pool_loader = self.get_pool_loader(datastore, **kwargs) + if pool_loader is None or len(pool_loader.dataset or []) <= query_size: # type: ignore + # not enough instances + return [] + return self.compute_most_uncertain(model, pool_loader, query_size) def compute_most_uncertain(self, model: _FabricModule, loader: _FabricDataLoader, query_size: int) -> List[int]: - # calls the pool_step and pool_epoch_end that we override - out: List[Dict] = self.run_evaluation(model, loader, RunningStage.POOL) # type: ignore - _out = ld_to_dl(out) - scores = np.concatenate(_out[OutputKeys.SCORES]) - ids = np.concatenate(_out[SpecialKeys.ID]) - # compute topk + # run evaluation + out = self.run_pool_evaluation(model, loader) + scores = out[self.POOL_OUTPUT_KEY] + ids = out[SpecialKeys.ID] + + # topk topk_ids = scores.argsort()[-query_size:] return ids[topk_ids].tolist() - - def evaluation_step( - self, - model: _FabricModule, - batch: Any, - batch_idx: int, - loss_fn: Optional[Union[torch.nn.Module, Callable]], - metrics: Optional[METRIC], - stage: Union[str, RunningStage], - ) -> Union[Dict, BATCH_OUTPUT]: - if stage != RunningStage.POOL: - return super().evaluation_step(model, batch, batch_idx, loss_fn, metrics, stage) # type: ignore - - # keep IDs here in case user messes up in the function definition - ids = batch[InputKeys.ON_CPU][SpecialKeys.ID] - pool_out = self.pool_step(model, batch, batch_idx, loss_fn, metrics) - - if isinstance(pool_out, torch.Tensor): - pool_out = {OutputKeys.SCORES: pool_out} - else: - assert isinstance(pool_out, dict) and OutputKeys.SCORES in pool_out, ( - "In `pool_step` you must return a Tensor with the scores per each element in the batch " - f"or a Dict with a '{OutputKeys.SCORES}' key and the Tensor of scores as the value." - ) - - pool_out[SpecialKeys.ID] = ids # type: ignore - - return pool_out # enforce that we always return a dict here diff --git a/energizer/datastores/base.py b/energizer/datastores/base.py index 736cf9f..ed727e9 100644 --- a/energizer/datastores/base.py +++ b/energizer/datastores/base.py @@ -12,6 +12,7 @@ import srsly import torch from datasets import Dataset +from lightning_utilities.core.rank_zero import rank_zero_warn from numpy.random import RandomState from sklearn.utils import check_random_state # type: ignore from torch.utils.data import DataLoader, RandomSampler, Sampler, SequentialSampler @@ -157,6 +158,7 @@ def show_batch(self, stage: Union[str, RunningStage] = RunningStage.TRAIN, *args return next(iter(loader)) def get_loader(self, stage: str, *args, **kwargs) -> Optional[DataLoader]: + # get dataset dataset = getattr(self, f"{stage}_dataset")(*args, **kwargs) if dataset is None: @@ -189,11 +191,6 @@ def _get_size(self, stage: RunningStage, *args, **kwargs) -> Optional[int]: if dataset is not None: return len(dataset) - def _get_num_batches(self, stage: RunningStage, *args, **kwargs) -> Optional[int]: - loader = self.get_loader(stage, *args, **kwargs) - if loader is not None: - return len(loader) - """Abstract methods implementation""" def train_loader(self, *args, **kwargs) -> Optional[DataLoader]: @@ -214,15 +211,6 @@ def validation_size(self, *args, **kwargs) -> Optional[int]: def test_size(self, *args, **kwargs) -> Optional[int]: return self._get_size(RunningStage.TEST, *args, **kwargs) - # def num_train_batches(self, *args, **kwargs) -> Optional[int]: - # return self._get_num_batches(RunningStage.TRAIN, *args, **kwargs) - - # def num_validation_batches(self, *args, **kwargs) -> Optional[int]: - # return self._get_num_batches(RunningStage.VALIDATION, *args, **kwargs) - - # def num_test_batches(self, *args, **kwargs) -> Optional[int]: - # return self._get_num_batches(RunningStage.VALIDATION, *args, **kwargs) - class PandasDataStore(Datastore): _train_data: Optional[pd.DataFrame] @@ -246,26 +234,6 @@ def get_by_ids(self, ids: List[int]) -> pd.DataFrame: return self._train_data.loc[self._train_data[SpecialKeys.ID].isin(ids)] # type: ignore -# class TorchDataStore(Datastore): -# _train_loader: Optional[DataLoader] -# _validation_loader: Optional[DataLoader] -# _test_loader: Optional[DataLoader] - -# def _get_dataset(self, stage: RunningStage, *args, **kwargs) -> Optional[DATASET]: -# loader = getattr(self, f"_{stage}_loader", None) -# if loader is not None: -# return loader.dataset - -# def train_dataset(self, *args, **kwargs) -> Optional[DATASET]: -# return self._get_dataset(RunningStage.TRAIN, *args, **kwargs) - -# def validation_dataset(self, *args, **kwargs) -> Optional[DATASET]: -# return self._get_dataset(RunningStage.VALIDATION, *args, **kwargs) - -# def test_dataset(self, *args, **kwargs) -> Optional[DATASET]: -# return self._get_dataset(RunningStage.TEST, *args, **kwargs) - - class IndexMixin: index: hb.Index = None embedding_name: str @@ -292,6 +260,10 @@ def load_index(self, index_path: Union[str, Path], metadata_path: Union[str, Pat # if dataset has been downsampled, mask the ids if len(index.get_ids_list()) > len(self._train_data[SpecialKeys.ID]): # type: ignore + rank_zero_warn( + "Index has more ids than dataset. Masking the missing ids from the index. " + "If this is expected (e.g., you downsampled your dataset), everything is fine." + ) missing_ids = set(index.get_ids_list()).difference(set(self._train_data[SpecialKeys.ID])) # type: ignore self.mask_ids_from_index(list(missing_ids)) diff --git a/energizer/datastores/classification.py b/energizer/datastores/classification.py index 1e72404..5db06a5 100644 --- a/energizer/datastores/classification.py +++ b/energizer/datastores/classification.py @@ -1,3 +1,4 @@ +from abc import ABC from collections import Counter from functools import partial from typing import Any, Callable, Dict, List, Optional, Union @@ -13,11 +14,14 @@ from energizer.utilities import _pad, ld_to_dl, sequential_numbers -class SequenceClassificationMixin: +class SequenceClassificationMixin(ABC): + MANDATORY_INPUT_NAMES: List[str] = [InputKeys.INPUT_IDS, InputKeys.ATT_MASK] + OPTIONAL_INPUT_NAMES: List[str] = [InputKeys.TOKEN_TYPE_IDS] + MANDATORY_TARGET_NAME: str = InputKeys.LABELS + _tokenizer: PreTrainedTokenizerBase _labels: List[str] _label_distribution: Dict[str, int] - input_names: List[str] on_cpu: List[str] @@ -34,7 +38,7 @@ def prepare_for_loading( replacement: bool = False, max_length: int = 512, ) -> None: - super().prepare_for_loading( + super().prepare_for_loading( # type: ignore batch_size, eval_batch_size, num_workers, @@ -45,7 +49,7 @@ def prepare_for_loading( seed, replacement, ) - self._loading_params["max_length"] = max_length + self._loading_params["max_length"] = max_length # type: ignore @property def tokenizer(self) -> PreTrainedTokenizerBase: @@ -72,8 +76,6 @@ def label_distribution(self, normalized: bool = False) -> Dict[str, Union[float, @classmethod def from_datasets( cls, - input_names: Union[str, List[str]], - target_name: str, tokenizer: PreTrainedTokenizerBase, uid_name: Optional[str] = None, on_cpu: Optional[List[str]] = None, @@ -82,11 +84,12 @@ def from_datasets( validation_dataset: Optional[Dataset] = None, test_dataset: Optional[Dataset] = None, ) -> Self: - obj = cls(seed) + obj = cls(seed) # type: ignore return _from_datasets( obj=obj, - input_names=input_names, - target_name=target_name, + mandatory_input_names=cls.MANDATORY_INPUT_NAMES, + optional_input_names=cls.OPTIONAL_INPUT_NAMES, + mandatory_target_name=cls.MANDATORY_TARGET_NAME, tokenizer=tokenizer, uid_name=uid_name, on_cpu=on_cpu, @@ -99,16 +102,12 @@ def from_datasets( def from_dataset_dict( cls, dataset_dict: DatasetDict, - input_names: Union[str, List[str]], - target_name: str, tokenizer: PreTrainedTokenizerBase, uid_name: Optional[str] = None, on_cpu: Optional[List[str]] = None, seed: Optional[int] = 42, ) -> Self: return cls.from_datasets( - input_names=input_names, - target_name=target_name, tokenizer=tokenizer, uid_name=uid_name, on_cpu=on_cpu, @@ -123,7 +122,7 @@ def get_collate_fn(self, stage: Optional[RunningStage] = None, show_batch: bool collate_fn, input_names=self.input_names, on_cpu=self.on_cpu, - max_length=None if show_batch else self.loading_params["max_length"], + max_length=None if show_batch else self.loading_params["max_length"], # type: ignore pad_token_id=self.tokenizer.pad_token_id, pad_fn=_pad, ) @@ -151,7 +150,7 @@ def collate_fn( # remove string columns that cannot be transfered on gpu values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch} - labels = new_batch.pop(InputKeys.TARGET, None) + labels = new_batch.pop(InputKeys.LABELS, None) # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad new_batch = { @@ -164,7 +163,7 @@ def collate_fn( } if labels is not None: - new_batch[InputKeys.TARGET] = torch.tensor(labels, dtype=torch.long) + new_batch[InputKeys.LABELS] = torch.tensor(labels, dtype=torch.long) # add things that need to remain on cpu if len(on_cpu) > 0: @@ -174,9 +173,10 @@ def collate_fn( def _from_datasets( - obj: Any, - input_names: Union[str, List[str]], - target_name: str, + obj, + mandatory_input_names: List[str], + optional_input_names: List[str], + mandatory_target_name: str, tokenizer: PreTrainedTokenizerBase, uid_name: Optional[str] = None, on_cpu: Optional[List[str]] = None, @@ -184,53 +184,85 @@ def _from_datasets( validation_dataset: Optional[Dataset] = None, test_dataset: Optional[Dataset] = None, ) -> Any: - obj._tokenizer = tokenizer - datasets = { + _datasets = { RunningStage.TRAIN: train_dataset, RunningStage.VALIDATION: validation_dataset, RunningStage.TEST: test_dataset, } - datasets = DatasetDict({k: v for k, v in datasets.items() if v is not None}) - - # label distribution - dataset = train_dataset or validation_dataset or test_dataset - if dataset is None: + datasets: Dict[RunningStage, Dataset] = {k: v for k, v in _datasets.items() if v is not None} + if len(datasets) < 1: raise ValueError("You need to pass at least one dataset.") - obj._labels = dataset.features[target_name].names - obj._label_distribution = Counter(dataset[target_name]) - - # column names - obj.input_names = [input_names] if isinstance(input_names, str) else input_names - assert all( - i in d.features for i in obj.input_names + [target_name] for d in datasets.values() - ), "Check input/target names passed." - datasets = datasets.rename_columns({target_name: InputKeys.TARGET}) - - obj.on_cpu = on_cpu or [] - for i in obj.on_cpu: - for d in datasets.values(): - if i not in d.features: - print(f"Some `on_cpu`={i} is not in dataset={d.features.keys()}") - obj.on_cpu += [SpecialKeys.ID] - - if datasets.get(RunningStage.TRAIN) is not None: + + # === INPUT NAMES === # + input_names = [] + for name in mandatory_input_names: + for split, dataset in datasets.items(): + if name in dataset.features: + input_names.append(name) + else: + raise ValueError(f"Mandatory column {name} not in dataset[{split}].") + + for name in optional_input_names: + for dataset in datasets.values(): + if name in dataset.features: + input_names.append(name) + + # === TARGET NAME === # + labels = [] + for split, dataset in datasets.items(): + assert ( + mandatory_target_name in dataset.features + ), f"Mandatory column {mandatory_target_name} not in dataset[{split}]." + labels.append(set(dataset.features[mandatory_target_name].names)) + + # check labels are consistent + assert all(s == labels[0] for s in labels), "Labels are inconsistent across splits" + + # === ON_CPU === # + if on_cpu is not None: + for name in on_cpu: + for split, dataset in datasets.items(): + assert name in dataset.features, f"{name=} not in dataset[{split}]={dataset.features.keys()}" + else: + on_cpu = [] + + # === UID NAME === # + uid_generator = sequential_numbers() + new_datasets = {} + for k, d in datasets.items(): if uid_name is None: - uid_generator = sequential_numbers() - datasets = datasets.map( - lambda ex: {SpecialKeys.ID: [next(uid_generator) for _ in range(len(ex[target_name]))]}, - batched=True, - ) + uids = [next(uid_generator) for _ in range(len(d))] + new_dataset = d.add_column(SpecialKeys.ID, uids) # type: ignore + print(f"UID column {SpecialKeys.ID} automatically created in dataset[{k}]") else: - # check - col = list(datasets[RunningStage.TRAIN][SpecialKeys.ID]) - assert len(set(col)) == len(col), f"`uid_column` {uid_name} is not unique." - datasets[RunningStage.TRAIN] = datasets[RunningStage.TRAIN].rename_columns({uid_name: SpecialKeys.ID}) - - # set data sources - datasets = datasets.select_columns(obj.input_names + obj.on_cpu + [InputKeys.TARGET]) # type: ignore - obj._train_data = datasets[RunningStage.TRAIN].to_pandas() # type: ignore - obj._validation_data = datasets.get(RunningStage.VALIDATION) - obj._test_data = datasets.get(RunningStage.TEST) + assert uid_name in d.features, f"{uid_name=} not in dataset[{k}]={d.features.keys()}" + ids = d[uid_name] + assert len(set(ids)) == len(ids), f"`uid_column` {uid_name} is not unique." + + new_dataset = d + if uid_name != SpecialKeys.ID: + new_dataset = new_dataset.rename_columns({uid_name: SpecialKeys.ID}) + print(f"UID column {uid_name} automatically renamed to {SpecialKeys.ID} in dataset[{k}]") + + new_datasets[k] = new_dataset + + on_cpu += [SpecialKeys.ID] + + # === FORMAT (KEEP ONLY USEFUL COLUMNS) === # + columns = input_names + on_cpu + [mandatory_target_name] + new_datasets = {k: v.with_format(columns=columns) for k, v in new_datasets.items()} + + # === SET ATTRIBUTES === # + if RunningStage.TRAIN in new_datasets: + obj._label_distribution = Counter(new_datasets[RunningStage.TRAIN][mandatory_target_name]) + + obj._labels = next(iter(new_datasets.values())).features[mandatory_target_name].names + obj.input_names = input_names + obj.on_cpu = on_cpu + obj._tokenizer = tokenizer + obj._train_data = new_datasets[RunningStage.TRAIN].to_pandas() # type: ignore + obj._validation_data = new_datasets.get(RunningStage.VALIDATION) # type: ignore + obj._test_data = new_datasets.get(RunningStage.TEST) # type: ignore return obj diff --git a/energizer/datastores/language_modelling.py b/energizer/datastores/language_modelling.py index c4e8b49..b2e926a 100644 --- a/energizer/datastores/language_modelling.py +++ b/energizer/datastores/language_modelling.py @@ -128,7 +128,7 @@ def collate_fn( # remove string columns that cannot be transfered on gpu values_on_cpu = {col: new_batch.pop(col, None) for col in on_cpu if col in new_batch} - labels = new_batch.pop(InputKeys.TARGET, None) + labels = new_batch.pop(InputKeys.LABELS, None) # input_ids and attention_mask to tensor: truncate -> convert to tensor -> pad new_batch = { @@ -144,7 +144,7 @@ def collate_fn( labels = new_batch[InputKeys.INPUT_IDS].clone() if pad_token_id is not None: labels[labels == pad_token_id] = -100 - new_batch[InputKeys.TARGET] = labels + new_batch[InputKeys.LABELS] = labels # add things that need to remain on cpu if len(on_cpu) > 0: diff --git a/energizer/enums.py b/energizer/enums.py index 103a1c8..aad60e7 100644 --- a/energizer/enums.py +++ b/energizer/enums.py @@ -53,7 +53,7 @@ class SpecialKeys(StrEnum): class InputKeys(StrEnum): - TARGET: str = "labels" + LABELS: str = "labels" INPUT_IDS: str = "input_ids" ATT_MASK: str = "attention_mask" TOKEN_TYPE_IDS: str = "token_type_ids" @@ -62,12 +62,13 @@ class InputKeys(StrEnum): class OutputKeys(StrEnum): - PRED: str = "y_hat" - TARGET: str = "y" + PREDS: str = "y_hat" + LABELS: str = "y" LOSS: str = "loss" LOGS: str = "logs" LOGITS: str = "logits" BATCH_SIZE: str = "batch_size" METRICS: str = "metrics" SCORES: str = "scores" - GRAD: str = "gradients" + GRADS: str = "gradients" + EMBEDDINGS: str = "embeddings" diff --git a/energizer/estimator.py b/energizer/estimator.py index 3b02352..f1c01bd 100644 --- a/energizer/estimator.py +++ b/energizer/estimator.py @@ -634,6 +634,12 @@ def load_state_dict(self, cache_dir: Union[str, Path], name: str = "state_dict.p self.model.load_state_dict(self.fabric.load(cache_dir / name)) def callback(self, hook: str, *args, **kwargs) -> Optional[Any]: + + # if estimator has the method + method = getattr(self, hook, None) + if method is not None and callable(method): + method(*args, **kwargs) + # passes self as first argument return self.fabric.call(hook, self, *args, **kwargs) diff --git a/energizer/loggers/__init__.py b/energizer/loggers/__init__.py index 54bd389..c5a1bcd 100644 --- a/energizer/loggers/__init__.py +++ b/energizer/loggers/__init__.py @@ -1,6 +1,6 @@ from lightning.fabric.loggers.csv_logs import CSVLogger -from lightning.fabric.loggers.tensorboard import TensorBoardLogger +from energizer.loggers.tensorboard import TensorBoardLogger from energizer.loggers.wandb import WandbLogger __all__ = ["TensorBoardLogger", "CSVLogger", "WandbLogger"] diff --git a/energizer/loggers/tensorboard.py b/energizer/loggers/tensorboard.py new file mode 100644 index 0000000..a7f4139 --- /dev/null +++ b/energizer/loggers/tensorboard.py @@ -0,0 +1,16 @@ +from pathlib import Path +from typing import Union + +from lightning.fabric.loggers.tensorboard import TensorBoardLogger as _TensorBoardLogger +from tbparse import SummaryReader + + +class TensorBoardLogger(_TensorBoardLogger): + LOGGER_NAME: str = "tensorboard" + + @property + def logger_name(self) -> str: + return self.LOGGER_NAME + + def save_to_parquet(self, path: Union[str, Path]) -> None: + SummaryReader(str(self.log_dir)).scalars.to_parquet(path) diff --git a/energizer/loggers/wandb.py b/energizer/loggers/wandb.py index 9f3ac72..0943642 100644 --- a/energizer/loggers/wandb.py +++ b/energizer/loggers/wandb.py @@ -1,7 +1,9 @@ import os from argparse import Namespace +from pathlib import Path from typing import Any, Dict, Mapping, Optional, Union +import pandas as pd import torch.nn as nn import wandb from lightning.fabric.loggers.logger import Logger, rank_zero_experiment @@ -14,6 +16,7 @@ class WandbLogger(Logger): _experiment: Optional[Union[Run, RunDisabled]] = None + LOGGER_NAME: str = "wandb" def __init__( self, @@ -51,6 +54,10 @@ def __getstate__(self) -> Dict[str, Any]: state["_experiment"] = None return state + @property + def logger_name(self) -> str: + return self.LOGGER_NAME + @property def name(self) -> Optional[str]: return self._wandb_init.get("name") @@ -59,10 +66,26 @@ def name(self) -> Optional[str]: def version(self) -> Optional[Union[int, str]]: return self._experiment.id if self._experiment else self._wandb_init.get("id") + @property + def entity(self) -> Optional[Union[int, str]]: + return self._experiment.entity if self._experiment else self._wandb_init.get("entity") + + @property + def project(self) -> Optional[Union[int, str]]: + return self._experiment.project if self._experiment else self._wandb_init.get("project") + @property def root_dir(self) -> Optional[str]: return self._wandb_init.get("dir") + @property + def run_id(self) -> Optional[Union[int, str]]: + return self.version + + @property + def run_path(self) -> str: + return f"{self.entity}/{self.project}/{self.run_id}" + @property @rank_zero_experiment def experiment(self) -> Union[Run, RunDisabled]: @@ -100,7 +123,7 @@ def watch(self, model: nn.Module, log: str = "gradients", log_freq: int = 100, l @rank_zero_only def finalize(self, status: str) -> None: - self.experiment.finalize(status) + self.experiment.finish() @rank_zero_only def log_hyperparams(self, params: Union[Dict[str, Any], Namespace]) -> None: @@ -113,6 +136,11 @@ def log_metrics(self, metrics: Mapping, step: int) -> None: assert rank_zero_only.rank == 0, "experiment tried to log from global_rank != 0" self.experiment.log(dict(metrics, **{"step": step})) + def save_to_parquet(self, path: Union[str, Path]) -> None: + run = wandb.Api().run(self.run_path) + df = pd.DataFrame(run.scan_history()) + df.to_parquet(path, index=False) + # @rank_zero_only # def log_table( # self, diff --git a/examples/active_estimators.ipynb b/examples/active_estimators.ipynb index 42da66d..740045b 100644 --- a/examples/active_estimators.ipynb +++ b/examples/active_estimators.ipynb @@ -72,7 +72,7 @@ " _ = batch.pop(InputKeys.ON_CPU, None)\n", "\n", " out = model(**batch)\n", - " out_metrics = metrics(out.logits, batch[InputKeys.TARGET])\n", + " out_metrics = metrics(out.logits, batch[InputKeys.LABELS])\n", "\n", " if stage == RunningStage.TRAIN:\n", " logs = {OutputKeys.LOSS: out.loss, **out_metrics}\n", diff --git a/examples/bert_badge.py b/examples/bert_badge.py index 9ed61c1..ae43e8d 100644 --- a/examples/bert_badge.py +++ b/examples/bert_badge.py @@ -40,7 +40,7 @@ def step( _ = batch.pop(InputKeys.ON_CPU, None) out = model(**batch) - out_metrics = metrics(out.logits, batch[InputKeys.TARGET]) + out_metrics = metrics(out.logits, batch[InputKeys.LABELS]) if stage == RunningStage.TRAIN: logs = {OutputKeys.LOSS: out.loss, **out_metrics} self.log_dict({f"{stage}/{k}": v for k, v in logs.items()}, step=self.tracker.global_batch) diff --git a/examples/bert_entropy.py b/examples/bert_entropy.py index 15c7d94..66529cc 100644 --- a/examples/bert_entropy.py +++ b/examples/bert_entropy.py @@ -34,7 +34,7 @@ def step( if stage == RunningStage.POOL: return self.score_fn(out.logits) - out_metrics = metrics(out.logits, batch[InputKeys.TARGET]) + out_metrics = metrics(out.logits, batch[InputKeys.LABELS]) if stage == RunningStage.TRAIN: logs = {OutputKeys.LOSS: out.loss, **out_metrics} self.log_dict({f"{stage}/{k}": v for k, v in logs.items()}, step=self.tracker.global_batch) diff --git a/examples/bert_random.py b/examples/bert_random.py index 666ef9b..9af74f7 100755 --- a/examples/bert_random.py +++ b/examples/bert_random.py @@ -29,7 +29,7 @@ def step( ) -> torch.Tensor: _ = batch.pop(InputKeys.ON_CPU, None) out = model(**batch) - out_metrics = metrics(out.logits, batch[InputKeys.TARGET]) + out_metrics = metrics(out.logits, batch[InputKeys.LABELS]) if stage == RunningStage.TRAIN: logs = {OutputKeys.LOSS: out.loss, **out_metrics} diff --git a/examples/datastores.ipynb b/examples/datastores.ipynb index a09a200..7012346 100644 --- a/examples/datastores.ipynb +++ b/examples/datastores.ipynb @@ -78,7 +78,7 @@ "{'input_ids': tensor([[ 101, 2470, 2003, 5791, 1999, 4367, 1996, 25935, 9949, 5080,\n", " 9338, 2003, 21366, 2000, 13467, 10908, 1012, 102]]),\n", " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\n", - " : tensor([1]),\n", + " : tensor([1]),\n", " : {: [122939]}}" ] }, diff --git a/examples/estimators.ipynb b/examples/estimators.ipynb index 4d6871c..f75d5d4 100644 --- a/examples/estimators.ipynb +++ b/examples/estimators.ipynb @@ -122,7 +122,7 @@ " if stage == RunningStage.POOL:\n", " return self.score_fn(out.logits)\n", "\n", - " out_metrics = metrics(out.logits, batch[InputKeys.TARGET])\n", + " out_metrics = metrics(out.logits, batch[InputKeys.LABELS])\n", "\n", " if stage == RunningStage.TRAIN:\n", " logs = {OutputKeys.LOSS: out.loss, **out_metrics}\n", diff --git a/poetry.lock b/poetry.lock index 678c48c..ac1580f 100644 --- a/poetry.lock +++ b/poetry.lock @@ -203,22 +203,23 @@ files = [ [[package]] name = "argon2-cffi" -version = "21.3.0" -description = "The secure Argon2 password hashing algorithm." +version = "23.1.0" +description = "Argon2 for Python" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "argon2-cffi-21.3.0.tar.gz", hash = "sha256:d384164d944190a7dd7ef22c6aa3ff197da12962bd04b17f64d4e93d934dba5b"}, - {file = "argon2_cffi-21.3.0-py3-none-any.whl", hash = "sha256:8c976986f2c5c0e5000919e6de187906cfd81fb1c72bf9d88c01177e77da7f80"}, + {file = "argon2_cffi-23.1.0-py3-none-any.whl", hash = "sha256:c670642b78ba29641818ab2e68bd4e6a78ba53b7eff7b4c3815ae16abf91c7ea"}, + {file = "argon2_cffi-23.1.0.tar.gz", hash = "sha256:879c3e79a2729ce768ebb7d36d4609e3a78a4ca2ec3a9f12286ca057e3d0db08"}, ] [package.dependencies] argon2-cffi-bindings = "*" [package.extras] -dev = ["cogapp", "coverage[toml] (>=5.0.2)", "furo", "hypothesis", "pre-commit", "pytest", "sphinx", "sphinx-notfound-page", "tomli"] -docs = ["furo", "sphinx", "sphinx-notfound-page"] -tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"] +dev = ["argon2-cffi[tests,typing]", "tox (>4)"] +docs = ["furo", "myst-parser", "sphinx", "sphinx-copybutton", "sphinx-notfound-page"] +tests = ["hypothesis", "pytest"] +typing = ["mypy"] [[package]] name = "argon2-cffi-bindings" @@ -273,17 +274,17 @@ python-dateutil = ">=2.7.0" [[package]] name = "asttokens" -version = "2.2.1" +version = "2.4.0" description = "Annotate AST trees with source code positions" optional = false python-versions = "*" files = [ - 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