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