import logging import random import time from copy import deepcopy from pathlib import Path from typing import List, Optional, Set, Union import lightning as pl import torch from lightning.pytorch.trainer.states import RunningStage from omegaconf import DictConfig from torch.utils.data import DataLoader from tqdm import tqdm from relik.common.log import get_console_logger, get_logger from relik.retriever.callbacks.base import PredictionCallback from relik.retriever.common.model_inputs import ModelInputs from relik.retriever.data.base.datasets import BaseDataset from relik.retriever.data.datasets import GoldenRetrieverDataset from relik.retriever.data.utils import HardNegativesManager from relik.retriever.indexers.base import BaseDocumentIndex from relik.retriever.pytorch_modules.model import GoldenRetriever console_logger = get_console_logger() logger = get_logger(__name__, level=logging.INFO) class GoldenRetrieverPredictionCallback(PredictionCallback): def __init__( self, k: Optional[int] = None, batch_size: int = 32, num_workers: int = 8, document_index: Optional[BaseDocumentIndex] = None, precision: Union[str, int] = 32, force_reindex: bool = True, retriever_dir: Optional[Path] = None, stages: Optional[Set[Union[str, RunningStage]]] = None, other_callbacks: Optional[List[DictConfig]] = None, dataset: Optional[Union[DictConfig, BaseDataset]] = None, dataloader: Optional[DataLoader] = None, *args, **kwargs, ): super().__init__(batch_size, stages, other_callbacks, dataset, dataloader) self.k = k self.num_workers = num_workers self.document_index = document_index self.precision = precision self.force_reindex = force_reindex self.retriever_dir = retriever_dir @torch.no_grad() def __call__( self, trainer: pl.Trainer, pl_module: pl.LightningModule, datasets: Optional[ Union[DictConfig, BaseDataset, List[DictConfig], List[BaseDataset]] ] = None, dataloaders: Optional[Union[DataLoader, List[DataLoader]]] = None, *args, **kwargs, ) -> dict: stage = trainer.state.stage logger.info(f"Computing predictions for stage {stage.value}") if stage not in self.stages: raise ValueError( f"Stage `{stage}` not supported, only {self.stages} are supported" ) self.datasets, self.dataloaders = self._get_datasets_and_dataloaders( datasets, dataloaders, trainer, dataloader_kwargs=dict( batch_size=self.batch_size, num_workers=self.num_workers, pin_memory=True, shuffle=False, ), ) # set the model to eval mode pl_module.eval() # get the retriever retriever: GoldenRetriever = pl_module.model # here we will store the samples with predictions for each dataloader dataloader_predictions = {} # compute the passage embeddings index for each dataloader for dataloader_idx, dataloader in enumerate(self.dataloaders): current_dataset: GoldenRetrieverDataset = self.datasets[dataloader_idx] logger.info( f"Computing passage embeddings for dataset {current_dataset.name}" ) # passages = self._get_passages_dataloader(current_dataset, trainer) tokenizer = current_dataset.tokenizer def collate_fn(x): return ModelInputs( tokenizer( x, truncation=True, padding=True, max_length=current_dataset.max_passage_length, return_tensors="pt", ) ) # check if we need to reindex the passages and # also if we need to load the retriever from disk if (self.retriever_dir is not None and trainer.current_epoch == 0) or ( self.retriever_dir is not None and stage == RunningStage.TESTING ): force_reindex = False else: force_reindex = self.force_reindex if ( not force_reindex and self.retriever_dir is not None and stage == RunningStage.TESTING ): retriever = retriever.from_pretrained(self.retriever_dir) # set the retriever to eval mode if we are loading it from disk # you never know :) retriever.eval() retriever.index( batch_size=self.batch_size, num_workers=self.num_workers, max_length=current_dataset.max_passage_length, collate_fn=collate_fn, precision=self.precision, compute_on_cpu=False, force_reindex=force_reindex, ) # pl_module_original_device = pl_module.device # if ( # and pl_module.device.type == "cuda" # ): # pl_module.to("cpu") # now compute the question embeddings and compute the top-k accuracy predictions = [] start = time.time() for batch in tqdm( dataloader, desc=f"Computing predictions for dataset {current_dataset.name}", ): batch = batch.to(pl_module.device) # get the top-k indices retriever_output = retriever.retrieve( **batch.questions, k=self.k, precision=self.precision ) # compute recall at k for batch_idx, retrieved_samples in enumerate(retriever_output): # get the positive passages gold_passages = batch["positives"][batch_idx] # get the index of the gold passages in the retrieved passages gold_passage_indices = [ retriever.get_index_from_passage(passage) for passage in gold_passages ] retrieved_indices = [r.index for r in retrieved_samples] retrieved_passages = [r.label for r in retrieved_samples] retrieved_scores = [r.score for r in retrieved_samples] # correct predictions are the passages that are in the top-k and are gold correct_indices = set(gold_passage_indices) & set(retrieved_indices) # wrong predictions are the passages that are in the top-k and are not gold wrong_indices = set(retrieved_indices) - set(gold_passage_indices) # add the predictions to the list prediction_output = dict( sample_idx=batch.sample_idx[batch_idx], gold=gold_passages, predictions=retrieved_passages, scores=retrieved_scores, correct=[ retriever.get_passage_from_index(i) for i in correct_indices ], wrong=[ retriever.get_passage_from_index(i) for i in wrong_indices ], ) predictions.append(prediction_output) end = time.time() logger.info(f"Time to retrieve: {str(end - start)}") dataloader_predictions[dataloader_idx] = predictions # if pl_module_original_device != pl_module.device: # pl_module.to(pl_module_original_device) # return the predictions return dataloader_predictions # @staticmethod # def _get_passages_dataloader( # indexer: Optional[BaseIndexer] = None, # dataset: Optional[GoldenRetrieverDataset] = None, # trainer: Optional[pl.Trainer] = None, # ): # if indexer is None: # logger.info( # f"Indexer is None, creating indexer from passages not found in dataset {dataset.name}, computing them from the dataloaders" # ) # # get the passages from the all the dataloader passage ids # passages = set() # set to avoid duplicates # for batch in trainer.train_dataloader: # passages.update( # [ # " ".join(map(str, [c for c in passage_ids.tolist() if c != 0])) # for passage_ids in batch["passages"]["input_ids"] # ] # ) # for d in trainer.val_dataloaders: # for batch in d: # passages.update( # [ # " ".join( # map(str, [c for c in passage_ids.tolist() if c != 0]) # ) # for passage_ids in batch["passages"]["input_ids"] # ] # ) # for d in trainer.test_dataloaders: # for batch in d: # passages.update( # [ # " ".join( # map(str, [c for c in passage_ids.tolist() if c != 0]) # ) # for passage_ids in batch["passages"]["input_ids"] # ] # ) # passages = list(passages) # else: # passages = dataset.passages # return passages class NegativeAugmentationCallback(GoldenRetrieverPredictionCallback): """ Callback that computes the predictions of a retriever model on a dataset and computes the negative examples for the training set. Args: k (:obj:`int`, `optional`, defaults to 100): The number of top-k retrieved passages to consider for the evaluation. batch_size (:obj:`int`, `optional`, defaults to 32): The batch size to use for the evaluation. num_workers (:obj:`int`, `optional`, defaults to 0): The number of workers to use for the evaluation. force_reindex (:obj:`bool`, `optional`, defaults to :obj:`False`): Whether to force the reindexing of the dataset. retriever_dir (:obj:`Path`, `optional`): The path to the retriever directory. If not specified, the retriever will be initialized from scratch. stages (:obj:`Set[str]`, `optional`): The stages to run the callback on. If not specified, the callback will be run on train, validation and test. other_callbacks (:obj:`List[DictConfig]`, `optional`): A list of other callbacks to run on the same stages. dataset (:obj:`Union[DictConfig, BaseDataset]`, `optional`): The dataset to use for the evaluation. If not specified, the dataset will be initialized from scratch. metrics_to_monitor (:obj:`List[str]`, `optional`): The metrics to monitor for the evaluation. threshold (:obj:`float`, `optional`, defaults to 0.8): The threshold to consider. If the recall score of the retriever is above the threshold, the negative examples will be added to the training set. max_negatives (:obj:`int`, `optional`, defaults to 5): The maximum number of negative examples to add to the training set. add_with_probability (:obj:`float`, `optional`, defaults to 1.0): The probability with which to add the negative examples to the training set. refresh_every_n_epochs (:obj:`int`, `optional`, defaults to 1): The number of epochs after which to refresh the index. """ def __init__( self, k: int = 100, batch_size: int = 32, num_workers: int = 0, force_reindex: bool = False, retriever_dir: Optional[Path] = None, stages: Set[Union[str, RunningStage]] = None, other_callbacks: Optional[List[DictConfig]] = None, dataset: Optional[Union[DictConfig, BaseDataset]] = None, metrics_to_monitor: List[str] = None, threshold: float = 0.8, max_negatives: int = 5, add_with_probability: float = 1.0, refresh_every_n_epochs: int = 1, *args, **kwargs, ): super().__init__( k=k, batch_size=batch_size, num_workers=num_workers, force_reindex=force_reindex, retriever_dir=retriever_dir, stages=stages, other_callbacks=other_callbacks, dataset=dataset, *args, **kwargs, ) if metrics_to_monitor is None: metrics_to_monitor = ["val_loss"] self.metrics_to_monitor = metrics_to_monitor self.threshold = threshold self.max_negatives = max_negatives self.add_with_probability = add_with_probability self.refresh_every_n_epochs = refresh_every_n_epochs @torch.no_grad() def __call__( self, trainer: pl.Trainer, pl_module: pl.LightningModule, *args, **kwargs, ) -> dict: """ Computes the predictions of a retriever model on a dataset and computes the negative examples for the training set. Args: trainer (:obj:`pl.Trainer`): The trainer object. pl_module (:obj:`pl.LightningModule`): The lightning module. Returns: A dictionary containing the negative examples. """ stage = trainer.state.stage if stage not in self.stages: return {} if self.metrics_to_monitor not in trainer.logged_metrics: raise ValueError( f"Metric `{self.metrics_to_monitor}` not found in trainer.logged_metrics" f"Available metrics: {trainer.logged_metrics.keys()}" ) if trainer.logged_metrics[self.metrics_to_monitor] < self.threshold: return {} if trainer.current_epoch % self.refresh_every_n_epochs != 0: return {} # if all( # [ # trainer.logged_metrics.get(metric) is None # for metric in self.metrics_to_monitor # ] # ): # raise ValueError( # f"No metric from {self.metrics_to_monitor} not found in trainer.logged_metrics" # f"Available metrics: {trainer.logged_metrics.keys()}" # ) # if all( # [ # trainer.logged_metrics.get(metric) < self.threshold # for metric in self.metrics_to_monitor # if trainer.logged_metrics.get(metric) is not None # ] # ): # return {} if trainer.current_epoch % self.refresh_every_n_epochs != 0: return {} logger.info( f"At least one metric from {self.metrics_to_monitor} is above threshold " f"{self.threshold}. Computing hard negatives." ) # make a copy of the dataset to avoid modifying the original one trainer.datamodule.train_dataset.hn_manager = None dataset_copy = deepcopy(trainer.datamodule.train_dataset) predictions = super().__call__( trainer, pl_module, datasets=dataset_copy, dataloaders=DataLoader( dataset_copy.to_torch_dataset(), shuffle=False, batch_size=None, num_workers=self.num_workers, pin_memory=True, collate_fn=lambda x: x, ), *args, **kwargs, ) logger.info(f"Computing hard negatives for epoch {trainer.current_epoch}") # predictions is a dict with the dataloader index as key and the predictions as value # since we only have one dataloader, we can get the predictions directly predictions = list(predictions.values())[0] # store the predictions in a dictionary for faster access based on the sample index hard_negatives_list = {} for prediction in tqdm(predictions, desc="Collecting hard negatives"): if random.random() < 1 - self.add_with_probability: continue top_k_passages = prediction["predictions"] gold_passages = prediction["gold"] # get the ids of the max_negatives wrong passages with the highest similarity wrong_passages = [ passage_id for passage_id in top_k_passages if passage_id not in gold_passages ][: self.max_negatives] hard_negatives_list[prediction["sample_idx"]] = wrong_passages trainer.datamodule.train_dataset.hn_manager = HardNegativesManager( tokenizer=trainer.datamodule.train_dataset.tokenizer, max_length=trainer.datamodule.train_dataset.max_passage_length, data=hard_negatives_list, ) # normalize predictions as in the original GoldenRetrieverPredictionCallback predictions = {0: predictions} return predictions