Merge branch 'nmslib:master' into fix-prefetch

Author: kishorenc

.github/workflows/build.yml

@@ -20,7 +20,9 @@ jobs:
 
       - name: Test
         timeout-minutes: 15
-        run: python -m unittest discover -v --start-directory python_bindings/tests --pattern "*_test*.py"
+        run: |
+          python -m unittest discover -v --start-directory examples/python --pattern "example*.py"
+          python -m unittest discover -v --start-directory tests/python --pattern "bindings_test*.py"
   
   test_cpp:
     runs-on: ${{matrix.os}}
@@ -48,7 +50,7 @@ jobs:
       - name: Prepare test data
         run: |
           pip install numpy
-          cd examples
+          cd tests/cpp/
           python update_gen_data.py
         shell: bash
 
@@ -59,6 +61,12 @@ jobs:
           if [ "$RUNNER_OS" == "Windows" ]; then
             cp ./Release/* ./
           fi
+          ./example_search
+          ./example_filter
+          ./example_replace_deleted
+          ./example_mt_search
+          ./example_mt_filter
+          ./example_mt_replace_deleted
           ./searchKnnCloserFirst_test
           ./searchKnnWithFilter_test
           ./multiThreadLoad_test

ALGO_PARAMS.md

@@ -27,5 +27,5 @@ ef_construction leads to longer construction, but better index quality. At some
 not improve the quality of the index. One way to check if the selection of ef_construction was ok is to measure a recall 
 for M nearest neighbor search when ```ef``` =```ef_construction```: if the recall is lower than 0.9, than there is room 
 for improvement.
-* ```num_elements``` - defines the maximum number of elements in the index. The index can be extened by saving/loading(load_index
+* ```num_elements``` - defines the maximum number of elements in the index. The index can be extended by saving/loading (load_index
 function has a parameter which defines the new maximum number of elements).

CMakeLists.txt

@@ -16,21 +16,41 @@ if(CMAKE_PROJECT_NAME STREQUAL PROJECT_NAME)
       SET( CMAKE_CXX_FLAGS  "-Ofast -lrt -DNDEBUG -std=c++11 -DHAVE_CXX0X -openmp -march=native -fpic -w -fopenmp -ftree-vectorize" )
     endif()
 
-    add_executable(test_updates examples/updates_test.cpp)
+    # examples
+    add_executable(example_search examples/cpp/example_search.cpp)
+    target_link_libraries(example_search hnswlib)
+
+    add_executable(example_filter examples/cpp/example_filter.cpp)
+    target_link_libraries(example_filter hnswlib)
+
+    add_executable(example_replace_deleted examples/cpp/example_replace_deleted.cpp)
+    target_link_libraries(example_replace_deleted hnswlib)
+
+    add_executable(example_mt_search examples/cpp/example_mt_search.cpp)
+    target_link_libraries(example_mt_search hnswlib)
+
+    add_executable(example_mt_filter examples/cpp/example_mt_filter.cpp)
+    target_link_libraries(example_mt_filter hnswlib)
+
+    add_executable(example_mt_replace_deleted examples/cpp/example_mt_replace_deleted.cpp)
+    target_link_libraries(example_mt_replace_deleted hnswlib)
+
+    # tests
+    add_executable(test_updates tests/cpp/updates_test.cpp)
     target_link_libraries(test_updates hnswlib)
 
-    add_executable(searchKnnCloserFirst_test examples/searchKnnCloserFirst_test.cpp)
+    add_executable(searchKnnCloserFirst_test tests/cpp/searchKnnCloserFirst_test.cpp)
     target_link_libraries(searchKnnCloserFirst_test hnswlib)
 
-    add_executable(searchKnnWithFilter_test examples/searchKnnWithFilter_test.cpp)
+    add_executable(searchKnnWithFilter_test tests/cpp/searchKnnWithFilter_test.cpp)
     target_link_libraries(searchKnnWithFilter_test hnswlib)
 
-    add_executable(multiThreadLoad_test examples/multiThreadLoad_test.cpp)
+    add_executable(multiThreadLoad_test tests/cpp/multiThreadLoad_test.cpp)
     target_link_libraries(multiThreadLoad_test hnswlib)
 
-    add_executable(multiThread_replace_test examples/multiThread_replace_test.cpp)
+    add_executable(multiThread_replace_test tests/cpp/multiThread_replace_test.cpp)
     target_link_libraries(multiThread_replace_test hnswlib)
 
-    add_executable(main main.cpp sift_1b.cpp)
+    add_executable(main tests/cpp/main.cpp tests/cpp/sift_1b.cpp)
     target_link_libraries(main hnswlib)
 endif()

Makefile

@@ -7,7 +7,7 @@ dist:
 	python3 -m build --sdist
 
 test:
-	python3 -m unittest discover --start-directory python_bindings/tests --pattern "*_test*.py"
+	python3 -m unittest discover --start-directory tests/python --pattern "bindings_test*.py"
 
 clean:
 	rm -rf *.egg-info build dist tmp var tests/__pycache__ hnswlib.cpython*.so

README.md

@@ -1,34 +1,22 @@
 # Hnswlib - fast approximate nearest neighbor search
-Header-only C++ HNSW implementation with python bindings.
+Header-only C++ HNSW implementation with python bindings, insertions and updates.
 
 **NEWS:**
 
+**version 0.7.0** 
 
-**version 0.6.2** 
-
-* Fixed a bug in saving of large pickles. The pickles with > 4GB could have been corrupted. Thanks Kai Wohlfahrt for reporting.
-* Thanks to ([@GuyAv46](https://github.com/GuyAv46)) hnswlib inner product now is more consitent accross architectures (SSE, AVX, etc). 
-* 
-
-**version 0.6.1** 
-
-* Thanks to ([@tony-kuo](https://github.com/tony-kuo)) hnswlib AVX512 and AVX builds are not backwards-compatible with older SSE and non-AVX512 architectures. 
-* Thanks to ([@psobot](https://github.com/psobot)) there is now a sencible message instead of segfault when passing a scalar to get_items.
-* Thanks to ([@urigoren](https://github.com/urigoren)) hnswlib has a lazy index creation python wrapper.
-
-**version 0.6.0** 
-* Thanks to ([@dyashuni](https://github.com/dyashuni)) hnswlib now uses github actions for CI, there is a search speedup in some scenarios with deletions. `unmark_deleted(label)` is now also a part of the python interface (note now it throws an exception for double deletions). 
-* Thanks to ([@slice4e](https://github.com/slice4e)) we now support AVX512; thanks to ([@LTLA](https://github.com/LTLA)) the cmake interface for the lib is now updated. 
-* Thanks to ([@alonre24](https://github.com/alonre24)) we now have a python bindings for brute-force (and examples for recall tuning: [TESTING_RECALL.md](TESTING_RECALL.md). 
-* Thanks to ([@dorosy-yeong](https://github.com/dorosy-yeong)) there is a bug fixed in the handling large quantities of deleted elements and large K. 
-
-  
+* Added support to filtering (#402, #430) by [@kishorenc](https://github.com/kishorenc)
+* Added python interface for filtering (though note its performance is limited by GIL) (#417) by [@gtsoukas](https://github.com/gtsoukas)
+* Added support for replacing the elements that were marked as delete with newly inserted elements (to control the size of the index, #418) by [@dyashuni](https://github.com/dyashuni)
+* Fixed data races/deadlocks in updates/insertion, added stress test for multithreaded operation (#418) by [@dyashuni](https://github.com/dyashuni)
+* Documentation, tests, exception handling, refactoring (#375, #379, #380, #395, #396, #401, #406, #404, #409, #410, #416, #415, #431, #432, #433) by [@jlmelville](https://github.com/jlmelville), [@dyashuni](https://github.com/dyashuni), [@kishorenc](https://github.com/kishorenc), [@korzhenevski](https://github.com/korzhenevski), [@yoshoku](https://github.com/yoshoku), [@jianshu93](https://github.com/jianshu93), [@PLNech](https://github.com/PLNech)
+* global linkages (#383) by [@MasterAler](https://github.com/MasterAler), USE_SSE usage in MSVC (#408) by [@alxvth](https://github.com/alxvth)
 
 
 ### Highlights:
 1) Lightweight, header-only, no dependencies other than C++ 11
-2) Interfaces for C++, Java, Python and R (https://github.com/jlmelville/rcpphnsw).
-3) Has full support for incremental index construction. Has support for element deletions 
+2) Interfaces for C++, Python, external support for Java and R (https://github.com/jlmelville/rcpphnsw).
+3) Has full support for incremental index construction and updating the elements. Has support for element deletions 
 (by marking them in index). Index is picklable.
 4) Can work with custom user defined distances (C++).
 5) Significantly less memory footprint and faster build time compared to current nmslib's implementation.
@@ -50,7 +38,7 @@ Note that inner product is not an actual metric. An element can be closer to som
 
 For other spaces use the nmslib library https://github.com/nmslib/nmslib. 
 
-#### Short API description
+#### API description
 * `hnswlib.Index(space, dim)` creates a non-initialized index an HNSW in space `space` with integer dimension `dim`.
 
 `hnswlib.Index` methods:
@@ -80,7 +68,7 @@ For other spaces use the nmslib library https://github.com/nmslib/nmslib.
 * `knn_query(data, k = 1, num_threads = -1, filter = None)` 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).
-    * `filter` filters elements by its labels, returns elements with allowed ids
+    * `filter` filters elements by its labels, returns elements with allowed ids. Note that search with a filter works slow in python in multithreaded mode. It is recommended to set `num_threads=1`
     * Thread-safe with other `knn_query` calls, but not with `add_items`.
 
 * `load_index(path_to_index, max_elements = 0, allow_replace_deleted = False)` loads the index from persistence to the uninitialized index.
@@ -123,6 +111,12 @@ Properties of `hnswlib.Index` that support reading and writing:
         
 
 #### Python bindings examples
+[See more examples here](examples/python/EXAMPLES.md):
+* Creating index, inserting elements, searching, serialization/deserialization
+* Filtering during the search with a boolean function
+* Deleting the elements and reusing the memory of the deleted elements for newly added elements
+
+An example of creating index, inserting elements, searching and pickle serialization:
 ```python
 import hnswlib
 import numpy as np
@@ -229,103 +223,13 @@ labels, distances = p.knn_query(data, k=1)
 print("Recall for two batches:", np.mean(labels.reshape(-1) == np.arange(len(data))), "\n")
 ```
 
-An example with a filter:
-```python
-import hnswlib
-import numpy as np
-
-dim = 16
-num_elements = 10000
-
-# Generating sample data
-data = np.float32(np.random.random((num_elements, dim)))
-
-# Declaring index
-hnsw_index = hnswlib.Index(space='l2', dim=dim)  # possible options are l2, cosine or ip
-
-# Initiating index
-# max_elements - the maximum number of elements, should be known beforehand
-#     (probably will be made optional in the future)
-#
-# ef_construction - controls index search speed/build speed tradeoff
-# M - is tightly connected with internal dimensionality of the data
-#     strongly affects the memory consumption
-
-hnsw_index.init_index(max_elements=num_elements, ef_construction=100, M=16)
-
-# Controlling the recall by setting ef:
-# higher ef leads to better accuracy, but slower search
-hnsw_index.set_ef(10)
-
-# Set number of threads used during batch search/construction
-# By default using all available cores
-hnsw_index.set_num_threads(4)
-
-print("Adding %d elements" % (len(data)))
-# Added elements will have consecutive ids
-hnsw_index.add_items(data, ids=np.arange(num_elements))
-
-print("Querying only even elements")
-# Define filter function that allows only even ids
-filter_function = lambda idx: idx%2 == 0
-# Query the elements for themselves and search only for even elements:
-labels, distances = hnsw_index.knn_query(data, k=1, filter=filter_function)
-# labels contain only elements with even id
-```
-
-An example with replacing of deleted elements:
-```python
-import hnswlib
-import numpy as np
-
-dim = 16
-num_elements = 1_000
-max_num_elements = 2 * num_elements
-
-# Generating sample data
-labels1 = np.arange(0, num_elements)
-data1 = np.float32(np.random.random((num_elements, dim)))  # batch 1
-labels2 = np.arange(num_elements, 2 * num_elements)
-data2 = np.float32(np.random.random((num_elements, dim)))  # batch 2
-labels3 = np.arange(2 * num_elements, 3 * num_elements)
-data3 = np.float32(np.random.random((num_elements, dim)))  # batch 3
-
-# Declaring index
-hnsw_index = hnswlib.Index(space='l2', dim=dim)
-
-# Initiating index
-# max_elements - the maximum number of elements, should be known beforehand
-#     (probably will be made optional in the future)
-#
-# ef_construction - controls index search speed/build speed tradeoff
-# M - is tightly connected with internal dimensionality of the data
-#     strongly affects the memory consumption
-
-# Enable replacing of deleted elements
-hnsw_index.init_index(max_elements=max_num_elements, ef_construction=200, M=16, allow_replace_deleted=True)
-
-# Controlling the recall by setting ef:
-# higher ef leads to better accuracy, but slower search
-hnsw_index.set_ef(10)
+#### C++ examples
+[See examples here](examples/cpp/EXAMPLES.md):
+* creating index, inserting elements, searching, serialization/deserialization
+* filtering during the search with a boolean function
+* deleting the elements and reusing the memory of the deleted elements for newly added elements
+* multithreaded usage
 
-# Set number of threads used during batch search/construction
-# By default using all available cores
-hnsw_index.set_num_threads(4)
-
-# Add batch 1 and 2 data
-hnsw_index.add_items(data1, labels1)
-hnsw_index.add_items(data2, labels2)  # Note: maximum number of elements is reached
-
-# Delete data of batch 2
-for label in labels2:
-    hnsw_index.mark_deleted(label)
-
-# Replace deleted elements
-# Maximum number of elements is reached therefore we cannot add new items,
-# but we can replace the deleted ones by using replace_deleted=True
-hnsw_index.add_items(data3, labels3, replace_deleted=True)
-# hnsw_index contains the data of batch 1 and batch 3 only
-```
 
 ### Bindings installation
 
@@ -346,9 +250,9 @@ Contributions are highly welcome!
 
 Please make pull requests against the `develop` branch.
 
-When making changes please run tests (and please add a test to `python_bindings/tests` in case there is new functionality):
+When making changes please run tests (and please add a test to `tests/python` in case there is new functionality):
 ```bash
-python -m unittest discover --start-directory python_bindings/tests --pattern "*_test*.py"
+python -m unittest discover --start-directory tests/python --pattern "bindings_test*.py"
 ```
 
 
@@ -360,20 +264,23 @@ https://github.com/facebookresearch/faiss
 ["Revisiting the Inverted Indices for Billion-Scale Approximate Nearest Neighbors"](https://arxiv.org/abs/1802.02422) 
 (current state-of-the-art in compressed indexes, C++):
 https://github.com/dbaranchuk/ivf-hnsw
+* Amazon PECOS https://github.com/amzn/pecos 
 * 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
+* Julia implmentation https://github.com/JuliaNeighbors/HNSW.jl
 * 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
+* .Net implementation: https://github.com/curiosity-ai/hnsw-sharp
 * CUDA implementation: https://github.com/js1010/cuhnsw
+* Rust implementation https://github.com/rust-cv/hnsw
 * Rust implementation for memory and thread safety purposes and There is  A Trait to enable the user to implement its own distances. It takes as data slices of types T satisfying T:Serialize+Clone+Send+Sync.: https://github.com/jean-pierreBoth/hnswlib-rs
 
 ### 200M SIFT test reproduction 
 To download and extract the bigann dataset (from root directory):
 ```bash
-python3 download_bigann.py
+python tests/cpp/download_bigann.py
 ```
 To compile:
 ```bash
@@ -393,7 +300,7 @@ The size of the BigANN subset (in millions) is controlled by the variable **subs
 ### Updates test
 To generate testing data (from root directory):
 ```bash
-cd examples
+cd tests/cpp
 python update_gen_data.py
 ```
 To compile (from root directory):