Header-only C++ HNSW implementation with python bindings. Paper's code for the HNSW 200M SIFT experiment

NEWS:

• hnswlib is now 0.5.0. Added support for pickling indices, support for PEP-517 and PEP-518 building, small speedups, bug and documentation fixes. Many thanks to @dbespalov, @dyashuni, @groodt,@uestc-lfs, @vinnitu, @fabiencastan, @JinHai-CN, @js1010!

• Thanks to Apoorv Sharma @apoorv-sharma, hnswlib now supports true element updates (the interface remained the same, but when you the performance/memory should not degrade as you update the element embeddings).

• Thanks to Dmitry @2ooom, hnswlib got a boost in performance for vector dimensions that are not multiple of 4

• Thanks to Louis Abraham (@louisabraham) hnswlib can now be installed via pip!

Highlights:

1. Lightweight, header-only, no dependencies other than C++ 11.
2. Interfaces for C++, python and R (https://github.com/jlmelville/rcpphnsw).
3. Has full support for incremental index construction. Has support for element deletions (currently, without actual freeing of the memory).
4. Can work with custom user defined distances (C++).
5. Significantly less memory footprint and faster build time compared to current nmslib's implementation.

Description of the algorithm parameters can be found in ALGO_PARAMS.md.

Note that inner product is not an actual metric. An element can be closer to some other element than to itself. That allows some speedup if you remove all elements that are not the closest to themselves from the index.

For other spaces use the nmslib library https://github.com/nmslib/nmslib.

• hnswlib.Index(space, dim) creates a non-initialized index an HNSW in space space with integer dimension dim.

hnswlib.Index methods:

• init_index(max_elements, ef_construction = 200, M = 16, random_seed = 100) initializes the index from with no elements.

  • max_elements defines the maximum number of elements that can be stored in the structure(can be increased/shrunk).
  • ef_construction defines a construction time/accuracy trade-off (see ALGO_PARAMS.md).
  • M defines tha maximum number of outgoing connections in the graph (ALGO_PARAMS.md).

• add_items(data, data_labels, num_threads = -1) - inserts the data(numpy array of vectors, shape:N*dim) into the structure.

  • labels is an optional N-size numpy array of integer labels for all elements in data.
  • num_threads sets the number of cpu threads to use (-1 means use default).
  • data_labels specifies the labels for the data. If index already has the elements with the same labels, their features will be updated. Note that update procedure is slower than insertion of a new element, but more memory- and query-efficient.
  • Thread-safe with other add_items calls, but not with knn_query.

• mark_deleted(data_label) - marks the element as deleted, so it will be omitted from search results.

• resize_index(new_size) - changes the maximum capacity of the index. Not thread safe with add_items and knn_query.

• set_ef(ef) - sets the query time accuracy/speed trade-off, defined by the ef parameter ( ALGO_PARAMS.md). Note that the parameter is currently not saved along with the index, so you need to set it manually after loading.

• knn_query(data, k = 1, num_threads = -1) make a batch query for k closest elements for each element of the

  • data (shape:N*dim). Returns a numpy array of (shape:N*k).
  • num_threads sets the number of cpu threads to use (-1 means use default).
  • Thread-safe with other knn_query calls, but not with add_items.

• load_index(path_to_index, max_elements = 0) loads the index from persistence to the uninitialized index.

  • max_elements(optional) resets the maximum number of elements in the structure.

• save_index(path_to_index) saves the index from persistence.

• set_num_threads(num_threads) set the default number of cpu threads used during data insertion/querying.

• get_items(ids) - returns a numpy array (shape:N*dim) of vectors that have integer identifiers specified in ids numpy vector (shape:N). Note that for cosine similarity it currently returns normalized vectors.

• get_ids_list() - returns a list of all elements' ids.

• get_max_elements() - returns the current capacity of the index

• get_current_count() - returns the current number of element stored in the index

Read-only properties of hnswlib.Index class:

• space - name of the space (can be one of "l2", "ip", or "cosine").

• dim - dimensionality of the space.

• M - parameter that defines the maximum number of outgoing connections in the graph.

• ef_construction - parameter that controls speed/accuracy trade-off during the index construction.

• max_elements - current capacity of the index. Equivalent to p.get_max_elements().

• element_count - number of items in the index. Equivalent to p.get_current_count().

Properties of hnswlib.Index that support reading and writing:

• ef - parameter controlling query time/accuracy trade-off.

• num_threads - default number of threads to use in add_items or knn_query. Note that calling p.set_num_threads(3) is equivalent to p.num_threads=3.

import hnswlib
import numpy as np
import pickle

dim = 128
num_elements = 10000

# Generating sample data
data = np.float32(np.random.random((num_elements, dim)))
data_labels = np.arange(num_elements)

# Declaring index
p = hnswlib.Index(space = 'l2', dim = dim) # possible options are l2, cosine or ip

# Initializing index - the maximum number of elements should be known beforehand
p.init_index(max_elements = num_elements, ef_construction = 200, M = 16)

# Element insertion (can be called several times):
p.add_items(data, data_labels)

# Controlling the recall by setting ef:
p.set_ef(50) # ef should always be > k

# Query dataset, k - number of closest elements (returns 2 numpy arrays)
labels, distances = p.knn_query(data, k = 1)

# Index objects support pickling
# WARNING: serialization via pickle.dumps(p) or p.__getstate__() is NOT thread-safe with p.add_items method!
# Note: ef parameter is included in serialization; random number generator is initialized with random_seed on Index load
p_copy = pickle.loads(pickle.dumps(p)) # creates a copy of index p using pickle round-trip

### Index parameters are exposed as class properties:
print(f"Parameters passed to constructor:  space={p_copy.space}, dim={p_copy.dim}") 
print(f"Index construction: M={p_copy.M}, ef_construction={p_copy.ef_construction}")
print(f"Index size is {p_copy.element_count} and index capacity is {p_copy.max_elements}")
print(f"Search speed/quality trade-off parameter: ef={p_copy.ef}")

An example with updates after serialization/deserialization:

import hnswlib
import numpy as np

dim = 16
num_elements = 10000

# Generating sample data
data = np.float32(np.random.random((num_elements, dim)))

# We split the data in two batches:
data1 = data[:num_elements // 2]
data2 = data[num_elements // 2:]

# Declaring index
p = hnswlib.Index(space='l2', dim=dim)  # possible options are l2, cosine or ip

# Initializing index
# max_elements - the maximum number of elements (capacity). Will throw an exception if exceeded
# during insertion of an element.
# The capacity can be increased by saving/loading the index, see below.
#
# ef_construction - controls index search speed/build speed tradeoff
#
# M - is tightly connected with internal dimensionality of the data. Strongly affects memory consumption (~M)
# Higher M leads to higher accuracy/run_time at fixed ef/efConstruction

p.init_index(max_elements=num_elements//2, ef_construction=100, M=16)

# Controlling the recall by setting ef:
# higher ef leads to better accuracy, but slower search
p.set_ef(10)

# Set number of threads used during batch search/construction
# By default using all available cores
p.set_num_threads(4)


print("Adding first batch of %d elements" % (len(data1)))
p.add_items(data1)

# Query the elements for themselves and measure recall:
labels, distances = p.knn_query(data1, k=1)
print("Recall for the first batch:", np.mean(labels.reshape(-1) == np.arange(len(data1))), "\n")

# Serializing and deleting the index:
index_path='first_half.bin'
print("Saving index to '%s'" % index_path)
p.save_index("first_half.bin")
del p

# Re-initializing, loading the index
p = hnswlib.Index(space='l2', dim=dim)  # the space can be changed - keeps the data, alters the distance function.

print("\nLoading index from 'first_half.bin'\n")

# Increase the total capacity (max_elements), so that it will handle the new data
p.load_index("first_half.bin", max_elements = num_elements)

print("Adding the second batch of %d elements" % (len(data2)))
p.add_items(data2)

# Query the elements for themselves and measure recall:
labels, distances = p.knn_query(data, k=1)
print("Recall for two batches:", np.mean(labels.reshape(-1) == np.arange(len(data))), "\n")

You can install from sources:

apt-get install -y python-setuptools python-pip
git clone https://github.com/nmslib/hnswlib.git
cd hnswlib
pip install .

or you can install via pip: pip install hnswlib

• Non-metric space library (nmslib) - main library(python, C++), supports exotic distances: https://github.com/nmslib/nmslib
• Faiss library by facebook, uses own HNSW implementation for coarse quantization (python, C++): https://github.com/facebookresearch/faiss
• Code for the paper "Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors" (current state-of-the-art in compressed indexes, C++): https://github.com/dbaranchuk/ivf-hnsw
• TOROS N2 (python, C++): https://github.com/kakao/n2
• Online HNSW (C++): https://github.com/andrusha97/online-hnsw)
• Go implementation: https://github.com/Bithack/go-hnsw
• Python implementation (as a part of the clustering code by by Matteo Dell'Amico): https://github.com/matteodellamico/flexible-clustering
• Java implementation: https://github.com/jelmerk/hnswlib
• Java bindings using Java Native Access: https://github.com/stepstone-tech/hnswlib-jna
• .Net implementation: https://github.com/microsoft/HNSW.Net
• CUDA implementation: https://github.com/js1010/cuhnsw

Contributions are highly welcome!

Please make pull requests against the develop branch.

To download and extract the bigann dataset (from root directory):

python3 download_bigann.py

To compile:

mkdir build
cd build
cmake ..
make all

To run the test on 200M SIFT subset:

./main

The size of the BigANN subset (in millions) is controlled by the variable subset_size_millions hardcoded in sift_1b.cpp.

To generate testing data (from root directory):

cd examples
python update_gen_data.py

To compile (from root directory):

mkdir build
cd build
cmake ..
make 

To run test without updates (from build directory)

./test_updates

To run test with updates (from build directory)

./test_updates update

• Visual search engine for 1M amazon products (MXNet + HNSW): website, code, demo by @ThomasDelteil

Malkov, Yu A., and D. A. Yashunin. "Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs." TPAMI, preprint: https://arxiv.org/abs/1603.09320