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import contextlib
import logging
import os
from typing import Callable, List, Optional, Tuple, Union
import torch
from torch.utils.data import DataLoader
from tqdm import tqdm
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 InMemoryDocumentIndex(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_dir: 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_dir)
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."
)
# 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])
# 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,
) -> "InMemoryDocumentIndex":
"""
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]]:
"""
Search the documents using the query.
Args:
query (:obj:`torch.Tensor`):
The query to be used for searching.
k (:obj:`int`, `optional`, defaults to 1):
The number of documents to be retrieved.
Returns:
:obj:`List[RetrievedSample]`: The retrieved documents.
"""
# fucking autocast only wants pure strings like 'cpu' or 'cuda'
# we need to convert the model device to that
device_type_for_autocast = str(self.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=self.embeddings.dtype,
)
)
)
with autocast_pssg_mngr:
similarity = torch.matmul(query, self.embeddings.T)
# Retrieve the indices of the top k passage embeddings
retriever_out: Tuple = torch.topk(
similarity, k=min(k, similarity.shape[-1]), dim=1
)
# get int values
batch_top_k: List[List[int]] = retriever_out.indices.detach().cpu().tolist()
# get float values
batch_scores: List[List[float]] = retriever_out.values.detach().cpu().tolist()
# Retrieve the passages corresponding to the indices
batch_passages = [
[self.documents.get_label_from_index(i) for i in indices]
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
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