| 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() | |