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# Copyright (c) 2023, NVIDIA CORPORATION. All rights reserved.
import abc
import numpy as np
import os
import torch
from megatron import get_retro_args
from tools.retro.external_libs import faiss
from .utils import get_index_dir
class Index(abc.ABC):
'''Abstract base class for indexes.
*Note* : While currently only Faiss-based classes are implemented, in the
future, this class will be extended with other types of indexes that have
different performance-accuracy trade-offs.
The primary methods to override are:
- train() : Train index on the sampled training chunks.
- add() : Add all training chunks to index.
'''
@classmethod
def c_verbose(cls, index, v):
'''Make index object verbose.'''
assert isinstance(v, bool)
faiss.ParameterSpace().set_index_parameter(index, "verbose", v)
def get_empty_index_path(self):
args = get_retro_args()
return os.path.join(
get_index_dir(),
"empty_%.3f.faissindex" % args.retro_index_train_load_fraction,
)
def get_empty_index(self):
return faiss.read_index(self.get_empty_index_path())
def get_added_index_path(self):
args = get_retro_args()
return os.path.join(
get_index_dir(),
"added_%.3f_%.3f.faissindex" % (
args.retro_index_train_load_fraction,
args.retro_index_add_load_fraction,
),
)
def get_added_index(self):
return faiss.read_index(self.get_added_index_path())
@abc.abstractmethod
def train(self, *args):
pass
@abc.abstractmethod
def add(self, *args):
pass
def embed_text_dataset_block(self, embedder, text_dataset, _range):
'''Embed a range of a text dataset.'''
sub_dataset = torch.utils.data.Subset(text_dataset, range(*_range))
return embedder.embed_text_dataset(sub_dataset)
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