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LEANN/packages/leann-backend-diskann/third_party/DiskANN/python/apps/insert-in-clustered-order.py
yichuan520030910320 46f6cc100b Initial commit
2025-06-30 09:05:05 +00:00

103 lines
3.3 KiB
Python

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT license.
import argparse
import diskannpy
import numpy as np
import utils
def insert_and_search(
dtype_str,
indexdata_file,
querydata_file,
Lb,
graph_degree,
num_clusters,
num_insert_threads,
K,
Ls,
num_search_threads,
gt_file,
):
npts, ndims = utils.get_bin_metadata(indexdata_file)
if dtype_str == "float":
dtype = np.float32
elif dtype_str == "int8":
dtype = np.int8
elif dtype_str == "uint8":
dtype = np.uint8
else:
raise ValueError("data_type must be float, int8 or uint8")
index = diskannpy.DynamicMemoryIndex(
distance_metric="l2",
vector_dtype=dtype,
dimensions=ndims,
max_vectors=npts,
complexity=Lb,
graph_degree=graph_degree
)
queries = diskannpy.vectors_from_file(querydata_file, dtype)
data = diskannpy.vectors_from_file(indexdata_file, dtype)
offsets, permutation = utils.cluster_and_permute(
dtype_str, npts, ndims, data, num_clusters
)
i = 0
timer = utils.Timer()
for c in range(num_clusters):
cluster_index_range = range(offsets[c], offsets[c + 1])
cluster_indices = np.array(permutation[cluster_index_range], dtype=np.uint32)
cluster_data = data[cluster_indices, :]
index.batch_insert(cluster_data, cluster_indices + 1, num_insert_threads)
print('Inserted cluster', c, 'in', timer.elapsed(), 's')
tags, dists = index.batch_search(queries, K, Ls, num_search_threads)
print('Batch searched', queries.shape[0], 'queries in', timer.elapsed(), 's')
res_ids = tags - 1
if gt_file != "":
recall = utils.calculate_recall_from_gt_file(K, res_ids, gt_file)
print(f"recall@{K} is {recall}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(
prog="in-mem-dynamic",
description="Inserts points dynamically in a clustered order and search from vectors in a file.",
)
parser.add_argument("-d", "--data_type", required=True)
parser.add_argument("-i", "--indexdata_file", required=True)
parser.add_argument("-q", "--querydata_file", required=True)
parser.add_argument("-Lb", "--Lbuild", default=50, type=int)
parser.add_argument("-Ls", "--Lsearch", default=50, type=int)
parser.add_argument("-R", "--graph_degree", default=32, type=int)
parser.add_argument("-TI", "--num_insert_threads", default=8, type=int)
parser.add_argument("-TS", "--num_search_threads", default=8, type=int)
parser.add_argument("-C", "--num_clusters", default=32, type=int)
parser.add_argument("-K", default=10, type=int)
parser.add_argument("--gt_file", default="")
args = parser.parse_args()
insert_and_search(
args.data_type,
args.indexdata_file,
args.querydata_file,
args.Lbuild,
args.graph_degree, # Build args
args.num_clusters,
args.num_insert_threads,
args.K,
args.Lsearch,
args.num_search_threads, # search args
args.gt_file,
)
# An ingest optimized example with SIFT1M
# python3 ~/DiskANN/python/apps/insert-in-clustered-order.py -d float \
# -i sift_base.fbin -q sift_query.fbin --gt_file gt100_base \
# -Lb 10 -R 30 -Ls 200 -C 32