File size: 4,015 Bytes
828992f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
import os
import math
import faiss
import torch
import numpy as np
import threading
import queue
from colbert.utils.utils import print_message, grouper
from colbert.indexing.loaders import get_parts
from colbert.indexing.index_manager import load_index_part
from colbert.indexing.faiss_index import FaissIndex
def get_faiss_index_name(args, offset=None, endpos=None):
partitions_info = '' if args.partitions is None else f'.{args.partitions}'
range_info = '' if offset is None else f'.{offset}-{endpos}'
return f'ivfpq{partitions_info}{range_info}.faiss'
def load_sample(samples_paths, sample_fraction=None):
sample = []
for filename in samples_paths:
print_message(f"#> Loading {filename} ...")
part = load_index_part(filename)
if sample_fraction:
part = part[torch.randint(0, high=part.size(0), size=(int(part.size(0) * sample_fraction),))]
sample.append(part)
sample = torch.cat(sample).float().numpy()
print("#> Sample has shape", sample.shape)
return sample
def prepare_faiss_index(slice_samples_paths, partitions, sample_fraction=None):
training_sample = load_sample(slice_samples_paths, sample_fraction=sample_fraction)
dim = training_sample.shape[-1]
index = FaissIndex(dim, partitions)
print_message("#> Training with the vectors...")
index.train(training_sample)
print_message("Done training!\n")
return index
SPAN = 3
def index_faiss(args):
print_message("#> Starting..")
parts, parts_paths, samples_paths = get_parts(args.index_path)
if args.sample is not None:
assert args.sample, args.sample
print_message(f"#> Training with {round(args.sample * 100.0, 1)}% of *all* embeddings (provided --sample).")
samples_paths = parts_paths
num_parts_per_slice = math.ceil(len(parts) / args.slices)
for slice_idx, part_offset in enumerate(range(0, len(parts), num_parts_per_slice)):
part_endpos = min(part_offset + num_parts_per_slice, len(parts))
slice_parts_paths = parts_paths[part_offset:part_endpos]
slice_samples_paths = samples_paths[part_offset:part_endpos]
if args.slices == 1:
faiss_index_name = get_faiss_index_name(args)
else:
faiss_index_name = get_faiss_index_name(args, offset=part_offset, endpos=part_endpos)
output_path = os.path.join(args.index_path, faiss_index_name)
print_message(f"#> Processing slice #{slice_idx+1} of {args.slices} (range {part_offset}..{part_endpos}).")
print_message(f"#> Will write to {output_path}.")
assert not os.path.exists(output_path), output_path
index = prepare_faiss_index(slice_samples_paths, args.partitions, args.sample)
loaded_parts = queue.Queue(maxsize=1)
def _loader_thread(thread_parts_paths):
for filenames in grouper(thread_parts_paths, SPAN, fillvalue=None):
sub_collection = [load_index_part(filename) for filename in filenames if filename is not None]
sub_collection = torch.cat(sub_collection)
sub_collection = sub_collection.float().numpy()
loaded_parts.put(sub_collection)
thread = threading.Thread(target=_loader_thread, args=(slice_parts_paths,))
thread.start()
print_message("#> Indexing the vectors...")
for filenames in grouper(slice_parts_paths, SPAN, fillvalue=None):
print_message("#> Loading", filenames, "(from queue)...")
sub_collection = loaded_parts.get()
print_message("#> Processing a sub_collection with shape", sub_collection.shape)
index.add(sub_collection)
print_message("Done indexing!")
index.save(output_path)
print_message(f"\n\nDone! All complete (for slice #{slice_idx+1} of {args.slices})!")
thread.join()
|