LTLA / rcpphnsw

Rcpp bindings for the approximate nearest neighbors library HNSW

RcppHNSW

Rcpp bindings for HNSW.

Status

October 20 2018. By inserting some preprocessor symbols into HNSW, these bindings no longer require a non-portable compiler flag and hence will pass R CMD CHECK without any warnings: previously you would be warned about -march=native. The price paid is not using specialized functions for the distance calculations that are architecture-specific. I have not checked how bad the performance hit is. The old settings remain in src/Makevars and src/Makevars.win (commented out), if you want to build the project from source directly. Otherwise, Release 0.0.0.9000 is the last version with the old behavior, which can be installed with something like:

devtools::install_github("jlmelville/rcpphnsw@v0.0.0.9000")

HNSW

HNSW is a header-only C++ library for finding approximate nearest neighbors (ANN) via Hierarchical Navigable Small Worlds (Yashunin and Malkov, 2016). It is part of the nmslib project.

RcppHNSW

An R package that interfaces with HNSW, taking enormous amounts of inspiration from Dirk Eddelbuettel's RcppAnnoy package which did the same for the Annoy ANN C++ library.

One difference is that I use roxygen2 to generate the man pages. The NAMESPACE is still built manually, however (I don't believe you can export the classes currently).

There is multithreading support for index searching using RcppParallel.

Installing

devtools::install_github("jlmelville/RcppHNSW")

Function example

# function interface returns results for all rows in nr x k matrices
all_knn <- RcppHNSW::hnsw_knn(data, k = 4, distance = "l2")
# other distance options: "euclidean", "cosine" and "ip" (inner product distance)

# or it can be split into two steps, so you can build with one set of data
# and search with another
ann <- hnsw_build(irism[1:100, ])
iris_nn <- hnsw_search(irism[101:150, ], ann, k = 5)

Class Example

library(RcppHNSW)
data <- as.matrix(iris[, -5])

# Create a new index using the L2 (squared Euclidean) distance
# nr and nc are the number of rows and columns of the data to be added, respectively
# ef and M determines speed vs accuracy trade off
# You must specify the maximum number of items to add to the index when it
# is created. But you can increase this number: see the next example
M <- 16
ef <- 200
ann <- new(HnswL2, ncol(data), nrow(data), M, ef)

# Add items to index
for (i in 1:nrow(data)) {
  ann$addItem(data[i, ])
}

# Find 4 nearest neighbors of row 1
# indexes are in res$item, distances in res$distance
# set include_distances = TRUE to get distances as well as index
res <- ann$getNNsList(data[1, ], k = 4, include_distances = TRUE)

# other distance classes:
# Cosine: HnswCosine
# Inner Product: HnswIP

And here's a rough equivalent of the serialization/deserialization example from the hnsw README. Although the index must have its initial size specified when its created, you can increase its size by saving it to disk, then specifying a new larger size when you read it back, as the following demonstrates:

library("RcppHNSW")
set.seed(12345)

dim <- 16
num_elements <- 100000

# Generate sample data
data <- matrix(stats::runif(num_elements * dim), nrow = num_elements)

# Split data into two batches
data1 <- data[1:(num_elements / 2), ]
data2 <- data[(num_elements / 2 + 1):num_elements, ]

# Create index
M <- 16
ef <- 10
# Set the initial index size to the size of the first batch
p <- new(HnswL2, dim, num_elements / 2, M, ef)

message("Adding first batch of ", nrow(data1), " elements")
p$addItems(data1)

# Query the elements for themselves and measure recall:
idx <- p$getAllNNs(data1, k = 1)
message("Recall for the first batch: ", formatC(mean(idx == 1:nrow(data1))))

filename <- "first_half.bin"
# Serialize index
p$save(filename)

# Reinitialize and load the index
rm(p)
message("Loading index from ", filename)
# Increase the total capacity, so that it will handle the new data
p <- new(HnswL2, dim, filename, num_elements)

message("Adding the second batch of ", nrow(data2), " elements")
p$addItems(data2)

# Query the elements for themselves and measure recall:
idx <- p$getAllNNs(data, k = 1)
# You can get distances with:
# res <- p$getAllNNsList(data, k = 1, include_distances = TRUE)
# res$dist contains the distance matrix, res$item stores the indexes

message("Recall for two batches: ", formatC(mean(idx == 1:num_elements)))
unlink(filename)

API

DO NOT USE NAMED PARAMETERS

Because these are wrappers around C++ code, you cannot use named parameters in the calling R code. Arguments are parsed by position. This is most annoying in constructors, which take multiple integer arguments, e.g.

### DO THIS ###
num_elements <- 100
M <- 200
ef_construction <- 16
index <- new(HnswL2, dim, num_elements, M, ef)

### DON'T DO THIS ###
index <- new(HnswL2, dim, ef_construction = 16, M = 200, num_elements = 100)
# treated as if you wrote:
index <- new(HnswL2, dim, 16, 200, 100)

OK onto the API

• new(HnswL2, dim, max_elements, M = 16, ef_contruction = 200) creates a new index using the squared L2 distance (i.e. square of the Euclidean distance), with dim dimensions and a maximum size of max_elements items. ef and M determine the speed vs accuracy trade off. Other classes for different distances are: HnswCosine for the cosine distance and HnswIp for the "Inner Product" distance (like the cosine distance without normalizing).
• new(HnswL2, dim, filename) load a previously saved index (see save below) with dim dimensions from the specified filename.
• new(HnswL2, dim, filename, max_elements) load a previously saved index (see save below) with dim dimensions from the specified filename, and a new maximum capacity of max_elements. This is a way to increase the capacity of the index without a complete rebuild.
• addItem(v) add vector v to the index.
• addItems(m) add the row vectors of the matrix m to the index.
• save(filename) saves an index to the specified filename. To load an index, use the new(HnswL2, dim, filename) constructor (see above).
• getNNs(v, k) return a vector of the indices of the k-nearest neighbors of the vector v. Indices are numbered from one.
• getNNsByList(v, k, include_distances = FALSE) return a list containing a vector named item with the indices of the k-nearest neighbors of the vector v. Indices are numbered from one. If include_distances = TRUE then also return a vector distance containing the distances.
• getAllNNs(m, k) return a matrix of the indices of the k-nearest neighbors of each row vector in m. Indices are numbered from one. This can be configured to use nthreads threads by calling RcppParallel::setThreadOptions(numThreads = nthreads) prior to calling this method.
• getAllNNsByList(m, k, include_distances = FALSE) return a list containing a matrix named item with the indices of the k-nearest neighbors of each row vector in m. Indices are numbered from one. If include_distances = TRUE then also return a matrix distance containing the distances. This can be configured to use nthreads threads by calling RcppParallel::setThreadOptions(numThreads = nthreads) prior to calling this method.

Differences from Python Bindings

• Multi-threading support is available only when searching the index, i.e. through getAllNNs or getAllNNsList. It's not currently turned on for building the index (addItems) because this gave disastrously bad results during my testing.
• Arbitrary integer labelling is not supported. Items are labelled 0, 1, 2 ... N.
• The interface roughly follows the Python one but deviates with naming and also rolls the declaration and initialization of the index into one call. And as noted above, you must pass arguments by position, not keyword.
• I have made a change to the C++ hnswalg.h code to use the showUpdate macro from RcppAnnoy, rather than std::cerr directly.

License

GPL-3 or later.