relik/retriever/indexers/voyager.py

import contextlib
import logging
import os
from typing import Callable, List, Optional, Union

import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
from voyager import Index, Space

from relik.common.log import get_logger
from relik.retriever.common.model_inputs import ModelInputs
from relik.retriever.data.base.datasets import BaseDataset
from relik.retriever.data.labels import Labels
from relik.retriever.indexers.base import BaseDocumentIndex
from relik.retriever.pytorch_modules import PRECISION_MAP, RetrievedSample

logger = get_logger(__name__, level=logging.INFO)


class VoyagerDocumentIndex(BaseDocumentIndex):
    DOCUMENTS_FILE_NAME = "documents.json"
    EMBEDDINGS_FILE_NAME = "embeddings.pt"

    def __init__(
        self,
        documents: Union[str, List[str], Labels, os.PathLike, List[os.PathLike]] = None,
        embeddings: Optional[torch.Tensor] = None,
        device: str = "cpu",
        precision: Optional[str] = None,
        name_or_path: Optional[Union[str, os.PathLike]] = None,
        *args,
        **kwargs,
    ) -> None:
        """
        An in-memory indexer.

        Args:
            documents (:obj:`Union[List[str], PassageManager]`):
                The documents to be indexed.
            embeddings (:obj:`Optional[torch.Tensor]`, `optional`, defaults to :obj:`None`):
                The embeddings of the documents.
            device (:obj:`str`, `optional`, defaults to "cpu"):
                The device to be used for storing the embeddings.
        """

        super().__init__(documents, embeddings, name_or_path)

        if embeddings is not None and documents is not None:
            logger.info("Both documents and embeddings are provided.")
            if documents.get_label_size() != embeddings.shape[0]:
                raise ValueError(
                    "The number of documents and embeddings must be the same."
                )

        self.embeddings = Index(
            Space.InnerProduct,
            num_dimensions=embeddings.shape[1],
            ef_construction=2000,
            M=2048,
        )
        self.embeddings.add_items(embeddings.numpy())

        # embeddings of the documents
        # self.embeddings = embeddings
        # does this do anything?
        del embeddings
        # convert the embeddings to the desired precision
        # if precision is not None:
        #     if (
        #         self.embeddings is not None
        #         and self.embeddings.dtype != PRECISION_MAP[precision]
        #     ):
        #         logger.info(
        #             f"Index vectors are of type {self.embeddings.dtype}. "
        #             f"Converting to {PRECISION_MAP[precision]}."
        #         )
        #         self.embeddings = self.embeddings.to(PRECISION_MAP[precision])
        # else:
        #     if (
        #         device == "cpu"
        #         and self.embeddings is not None
        #         and self.embeddings.dtype != torch.float32
        #     ):
        #         logger.info(
        #             "Index vectors are of type {}. Converting to float32.".format(
        #                 self.embeddings.dtype
        #             )
        #         )
        #         self.embeddings = self.embeddings.to(PRECISION_MAP[32])
        # # move the embeddings to the desired device
        # if self.embeddings is not None and not self.embeddings.device == device:
        #     self.embeddings = self.embeddings.to(device)

        # device to store the embeddings
        self.device = device
        # precision to be used for the embeddings
        self.precision = precision

    @torch.no_grad()
    @torch.inference_mode()
    def index(
        self,
        retriever,
        documents: Optional[List[str]] = None,
        batch_size: int = 32,
        num_workers: int = 4,
        max_length: Optional[int] = None,
        collate_fn: Optional[Callable] = None,
        encoder_precision: Optional[Union[str, int]] = None,
        compute_on_cpu: bool = False,
        force_reindex: bool = False,
        add_to_existing_index: bool = False,
    ) -> "VoyagerDocumentIndex":
        """
        Index the documents using the encoder.

        Args:
            retriever (:obj:`torch.nn.Module`):
                The encoder to be used for indexing.
            documents (:obj:`List[str]`, `optional`, defaults to :obj:`None`):
                The documents to be indexed.
            batch_size (:obj:`int`, `optional`, defaults to 32):
                The batch size to be used for indexing.
            num_workers (:obj:`int`, `optional`, defaults to 4):
                The number of workers to be used for indexing.
            max_length (:obj:`int`, `optional`, defaults to None):
                The maximum length of the input to the encoder.
            collate_fn (:obj:`Callable`, `optional`, defaults to None):
                The collate function to be used for batching.
            encoder_precision (:obj:`Union[str, int]`, `optional`, defaults to None):
                The precision to be used for the encoder.
            compute_on_cpu (:obj:`bool`, `optional`, defaults to False):
                Whether to compute the embeddings on CPU.
            force_reindex (:obj:`bool`, `optional`, defaults to False):
                Whether to force reindexing.
            add_to_existing_index (:obj:`bool`, `optional`, defaults to False):
                Whether to add the new documents to the existing index.

        Returns:
            :obj:`InMemoryIndexer`: The indexer object.
        """

        if documents is None and self.documents is None:
            raise ValueError("Documents must be provided.")

        if self.embeddings is not None and not force_reindex:
            logger.info(
                "Embeddings are already present and `force_reindex` is `False`. Skipping indexing."
            )
            if documents is None:
                return self

        if collate_fn is None:
            tokenizer = retriever.passage_tokenizer

            def collate_fn(x):
                return ModelInputs(
                    tokenizer(
                        x,
                        padding=True,
                        return_tensors="pt",
                        truncation=True,
                        max_length=max_length or tokenizer.model_max_length,
                    )
                )

        if force_reindex:
            if documents is not None:
                self.documents.add_labels(documents)
            data = [k for k in self.documents.get_labels()]

        else:
            if documents is not None:
                data = [k for k in Labels(documents).get_labels()]
            else:
                return self

        # if force_reindex:
        #     data = [k for k in self.documents.get_labels()]

        dataloader = DataLoader(
            BaseDataset(name="passage", data=data),
            batch_size=batch_size,
            shuffle=False,
            num_workers=num_workers,
            pin_memory=False,
            collate_fn=collate_fn,
        )

        encoder = retriever.passage_encoder

        # Create empty lists to store the passage embeddings and passage index
        passage_embeddings: List[torch.Tensor] = []

        encoder_device = "cpu" if compute_on_cpu else self.device

        # fucking autocast only wants pure strings like 'cpu' or 'cuda'
        # we need to convert the model device to that
        device_type_for_autocast = str(encoder_device).split(":")[0]
        # autocast doesn't work with CPU and stuff different from bfloat16
        autocast_pssg_mngr = (
            contextlib.nullcontext()
            if device_type_for_autocast == "cpu"
            else (
                torch.autocast(
                    device_type=device_type_for_autocast,
                    dtype=PRECISION_MAP[encoder_precision],
                )
            )
        )
        with autocast_pssg_mngr:
            # Iterate through each batch in the dataloader
            for batch in tqdm(dataloader, desc="Indexing"):
                # Move the batch to the device
                batch: ModelInputs = batch.to(encoder_device)
                # Compute the passage embeddings
                passage_outs = encoder(**batch).pooler_output
                # Append the passage embeddings to the list
                if self.device == "cpu":
                    passage_embeddings.extend([c.detach().cpu() for c in passage_outs])
                else:
                    passage_embeddings.extend([c for c in passage_outs])

        # move the passage embeddings to the CPU if not already done
        # the move to cpu and then to gpu is needed to avoid OOM when using mixed precision
        if not self.device == "cpu":  # this if is to avoid unnecessary moves
            passage_embeddings = [c.detach().cpu() for c in passage_embeddings]
        # stack it
        passage_embeddings: torch.Tensor = torch.stack(passage_embeddings, dim=0)
        # move the passage embeddings to the gpu if needed
        if not self.device == "cpu":
            passage_embeddings = passage_embeddings.to(PRECISION_MAP[self.precision])
            passage_embeddings = passage_embeddings.to(self.device)
        self.embeddings = passage_embeddings

        # free up memory from the unused variable
        del passage_embeddings

        return self

    @torch.no_grad()
    @torch.inference_mode()
    def search(self, query: torch.Tensor, k: int = 1) -> list[list[RetrievedSample]]:
        # k = min(k, self.embeddings.ntotal)

        if isinstance(query, torch.Tensor) and self.device == "cpu":
            query = query.detach().cpu().numpy()
        # Retrieve the indices of the top k passage embeddings
        retriever_out = self.embeddings.query(query, k)

        # get int values (second element of the tuple)
        batch_top_k: List[List[int]] = retriever_out[0].tolist()
        # get float values (first element of the tuple)
        batch_scores: List[List[float]] = retriever_out[1].tolist()
        # Retrieve the passages corresponding to the indices
        batch_passages = [
            [self.documents.get_label_from_index(i) for i in indices if i != -1]
            for indices in batch_top_k
        ]
        # build the output object
        batch_retrieved_samples = [
            [
                RetrievedSample(label=passage, index=index, score=score)
                for passage, index, score in zip(passages, indices, scores)
            ]
            for passages, indices, scores in zip(
                batch_passages, batch_top_k, batch_scores
            )
        ]
        return batch_retrieved_samples