Restructure in preparation for parallelism
This commit is contained in:
Dirkjan Ochtman
parent
98a673fea2
commit
f8d1941d7c
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@ -23,13 +23,77 @@ version = "0.1.10"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "4785bdd1c96b2a846b2bd7cc02e86b6b3dbf14e7e53446c4f54c92a361040822"
|
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|
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[[package]]
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name = "cfg-if"
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version = "1.0.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
|
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checksum = "baf1de4339761588bc0619e3cbc0120ee582ebb74b53b4efbf79117bd2da40fd"
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|
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[[package]]
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name = "const_fn"
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version = "0.4.4"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "cd51eab21ab4fd6a3bf889e2d0958c0a6e3a61ad04260325e919e652a2a62826"
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|
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[[package]]
|
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name = "crossbeam-channel"
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version = "0.5.0"
|
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "dca26ee1f8d361640700bde38b2c37d8c22b3ce2d360e1fc1c74ea4b0aa7d775"
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dependencies = [
|
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"cfg-if 1.0.0",
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"crossbeam-utils",
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]
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[[package]]
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name = "crossbeam-deque"
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version = "0.8.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "94af6efb46fef72616855b036a624cf27ba656ffc9be1b9a3c931cfc7749a9a9"
|
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dependencies = [
|
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"cfg-if 1.0.0",
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"crossbeam-epoch",
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"crossbeam-utils",
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]
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[[package]]
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name = "crossbeam-epoch"
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version = "0.9.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "a1aaa739f95311c2c7887a76863f500026092fb1dce0161dab577e559ef3569d"
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dependencies = [
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"cfg-if 1.0.0",
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"const_fn",
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"crossbeam-utils",
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"lazy_static",
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"memoffset",
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"scopeguard",
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]
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[[package]]
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name = "crossbeam-utils"
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version = "0.8.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "02d96d1e189ef58269ebe5b97953da3274d83a93af647c2ddd6f9dab28cedb8d"
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dependencies = [
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"autocfg",
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"cfg-if 1.0.0",
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"lazy_static",
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]
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[[package]]
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name = "either"
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version = "1.6.1"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "e78d4f1cc4ae33bbfc157ed5d5a5ef3bc29227303d595861deb238fcec4e9457"
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[[package]]
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name = "getrandom"
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version = "0.1.15"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "fc587bc0ec293155d5bfa6b9891ec18a1e330c234f896ea47fbada4cadbe47e6"
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dependencies = [
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"cfg-if",
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"cfg-if 0.1.10",
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"libc",
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"wasi",
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]
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@ -40,11 +104,20 @@ version = "0.2.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "ee8025cf36f917e6a52cce185b7c7177689b838b7ec138364e50cc2277a56cf4"
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dependencies = [
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"cfg-if",
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"cfg-if 0.1.10",
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"libc",
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"wasi",
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]
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[[package]]
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name = "hermit-abi"
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version = "0.1.17"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "5aca5565f760fb5b220e499d72710ed156fdb74e631659e99377d9ebfbd13ae8"
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dependencies = [
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"libc",
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]
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[[package]]
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name = "hinasmawo"
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version = "0.1.0"
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@ -52,6 +125,7 @@ dependencies = [
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"ahash",
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"ordered-float",
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"rand",
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"rayon",
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]
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[[package]]
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@ -66,6 +140,15 @@ version = "0.2.80"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "4d58d1b70b004888f764dfbf6a26a3b0342a1632d33968e4a179d8011c760614"
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[[package]]
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name = "memoffset"
|
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version = "0.6.1"
|
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "157b4208e3059a8f9e78d559edc658e13df41410cb3ae03979c83130067fdd87"
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dependencies = [
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"autocfg",
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]
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[[package]]
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name = "num-traits"
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version = "0.2.14"
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@ -75,6 +158,16 @@ dependencies = [
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"autocfg",
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]
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[[package]]
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name = "num_cpus"
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version = "1.13.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "05499f3756671c15885fee9034446956fff3f243d6077b91e5767df161f766b3"
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dependencies = [
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"hermit-abi",
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"libc",
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]
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[[package]]
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name = "ordered-float"
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version = "2.0.0"
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@ -141,6 +234,37 @@ dependencies = [
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"rand_core",
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]
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[[package]]
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name = "rayon"
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version = "1.5.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "8b0d8e0819fadc20c74ea8373106ead0600e3a67ef1fe8da56e39b9ae7275674"
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dependencies = [
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"autocfg",
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"crossbeam-deque",
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"either",
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"rayon-core",
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]
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[[package]]
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name = "rayon-core"
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version = "1.9.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "9ab346ac5921dc62ffa9f89b7a773907511cdfa5490c572ae9be1be33e8afa4a"
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dependencies = [
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"crossbeam-channel",
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"crossbeam-deque",
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"crossbeam-utils",
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"lazy_static",
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"num_cpus",
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]
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[[package]]
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name = "scopeguard"
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version = "1.1.0"
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source = "registry+https://github.com/rust-lang/crates.io-index"
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checksum = "d29ab0c6d3fc0ee92fe66e2d99f700eab17a8d57d1c1d3b748380fb20baa78cd"
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[[package]]
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name = "version_check"
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version = "0.9.2"
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@ -7,4 +7,5 @@ edition = "2018"
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[dependencies]
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ahash = "0.6.1"
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rand = { version = "0.7.3", features = ["small_rng"] }
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rayon = "1.5"
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ordered-float = "2.0"
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508
src/lib.rs
508
src/lib.rs
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@ -1,5 +1,6 @@
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use std::cmp::{max, Ordering};
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use std::ops::{Index, IndexMut};
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use std::cmp::{max, min, Ordering};
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use std::hash::Hash;
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use std::ops::Index;
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use ahash::AHashSet as HashSet;
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use ordered_float::OrderedFloat;
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@ -7,153 +8,159 @@ use rand::rngs::SmallRng;
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use rand::{RngCore, SeedableRng};
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pub struct Hnsw<P> {
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ef_construction: usize,
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ef_search: usize,
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points: Vec<P>,
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zero: Vec<ZeroNode>,
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layers: Vec<Vec<UpperNode>>,
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rng: SmallRng,
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}
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impl<P> Hnsw<P>
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where
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P: Point,
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P: Point + std::fmt::Debug,
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{
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pub fn new(ef_construction: usize) -> Self {
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Self {
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ef_construction,
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points: Vec::new(),
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zero: Vec::new(),
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layers: Vec::new(),
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rng: SmallRng::from_entropy(),
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}
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}
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/// Insert a point into the `Hnsw`, returning a `PointId`
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///
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/// `PointId` implements `Hash`, `Eq` and friends, so it can be linked to some value.
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pub fn insert(&mut self, point: P, search: &mut Search) -> PointId {
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let layer = self.rng.next_u32() as f32 / u32::MAX as f32;
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let layer = LayerId((-(layer.ln() * (M as f32).ln())).floor() as usize);
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self.insert_at(point, layer, search)
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}
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/// Deterministic implementation of insertion that takes the `layer` as an argument
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///
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/// Implements the paper's algorithm 1, although there is a slight difference in that
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/// new elements are always inserted from their selected layer, rather than delaying the
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/// addition of new layers until after the selection of a particular layer.
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fn insert_at(&mut self, point: P, layer: LayerId, search: &mut Search) -> PointId {
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let empty = self.points.is_empty();
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let pid = PointId(self.points.len());
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self.points.push(point);
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let top = LayerId(self.layers.len());
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if layer > top {
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self.layers.resize_with(layer.0, Default::default);
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pub fn new(points: &[P], ef_construction: usize, ef_search: usize) -> (Self, Vec<PointId>) {
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if points.is_empty() {
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return (
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Self {
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ef_search,
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zero: Vec::new(),
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points: Vec::new(),
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layers: Vec::new(),
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},
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Vec::new(),
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);
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}
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search.reset(1, top);
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for cur in max(top, layer).descend() {
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search.num = if cur <= layer {
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self.ef_construction
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} else {
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1
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};
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// Give all points a random layer and sort the list of nodes by descending order for
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// construction. This allows us to copy higher layers to lower layers as construction
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// progresses, while preserving randomness in each point's layer and insertion order.
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// If this layer already existed, search it for the 1 nearest neighbor
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// (this roughly corresponds to the first loop in the paper's algorithm 1).
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if cur <= top {
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debug_assert_eq!(search.layer, cur);
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let mut rng = SmallRng::from_entropy();
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let mut nodes = (0..points.len())
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.map(|i| (LayerId::random(&mut rng), i))
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.collect::<Vec<_>>();
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nodes.sort_unstable_by(|l, r| r.cmp(&l));
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// At the first layer that already existed, insert the first element as an initial
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// candidate. Because the zero-th layer always exists, also check if it was empty.
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if cur == top && !empty {
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search.push(NodeId(0), &self[pid], self);
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// Sort the original `points` in layer order.
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// TODO: maybe optimize this? https://crates.io/crates/permutation
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let mut new_points = Vec::with_capacity(points.len());
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let mut new_nodes = Vec::with_capacity(points.len());
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let mut out = vec![PointId::invalid(); points.len()];
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for (i, &(layer, idx)) in nodes.iter().enumerate() {
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let pid = PointId(i);
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new_points.push(points[idx].clone());
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new_nodes.push((layer, pid));
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out[idx] = pid;
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}
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let (points, nodes) = (new_points, new_nodes);
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// The layer from the first node is our top layer, or the zero layer if we have no nodes.
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let top = match nodes.first() {
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Some((top, _)) => *top,
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None => LayerId(0),
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};
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// Figure out how many nodes will go on each layer. This helps us allocate memory capacity
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// for each layer in advance, and also helps enable batch insertion of points.
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let mut sizes = vec![0; top.0 + 1];
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for (layer, _) in nodes.iter().copied() {
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sizes[layer.0] += 1;
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}
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let mut start = 0;
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let mut ranges = Vec::with_capacity(top.0);
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for (i, size) in sizes.into_iter().enumerate().rev() {
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// Skip the first point, since we insert the enter point separately
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ranges.push((LayerId(i), max(start, 1)..start + size));
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start += size;
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}
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// Insert the first point so that we have an enter point to start searches with.
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let mut layers = vec![vec![]; top.0];
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let mut zero = Vec::with_capacity(points.len());
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zero.push(ZeroNode::default());
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let mut search = Search::default();
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for (layer, range) in ranges {
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let num = if layer.0 > 0 { M } else { M * 2 };
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for &(_, pid) in &nodes[range] {
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search.reset();
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let point = &points[pid];
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search.push(PointId(0), &points[pid], &points);
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for cur in top.descend() {
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search.num = if cur <= layer { ef_construction } else { 1 };
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zero.search(point, &mut search, &points, num);
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match cur > layer {
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true => search.cull(),
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false => break,
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}
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}
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self.search_layer(cur, pid, search);
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// If we're still above the layer to insert at, we're going to skip the
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// insertion code below and continue to the next iteration. Before we do so,
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// we update the `Search` so it's ready for the next layer coming up.
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if cur > layer {
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search.lower(self);
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}
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zero.insert_node(pid, &search.nearest, &points);
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}
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// If we're above the layer to start inserting links at, skip the rest of this loop.
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if cur > layer {
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continue;
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}
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if cur.is_zero() {
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let nid = NodeId(self.zero.len());
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let mut node = ZeroNode {
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nearest: Default::default(),
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};
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self.link(cur, (nid, &mut node.nearest), &search.nearest);
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self.zero.push(node);
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} else {
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let nid = NodeId(self.layers[cur.0 - 1].len());
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let lower = match cur.0 == 1 {
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false => NodeId(self.layers[cur.0 - 2].len()),
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true => NodeId(self.zero.len()),
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};
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let mut node = UpperNode {
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pid,
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lower,
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nearest: Default::default(),
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};
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self.link(cur, (nid, &mut node.nearest), &search.nearest);
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self.layers[cur.0 - 1].push(node);
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}
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if search.layer == cur && !cur.is_zero() {
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search.lower(self);
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// For layers above the zero layer, make a copy of the current state of the zero layer
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// with `nearest` truncated to `M` elements.
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if layer.0 > 0 {
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let mut upper = Vec::with_capacity(zero.len());
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upper.extend(zero.iter().map(|zero| {
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let mut upper = UpperNode::default();
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upper.nearest.copy_from_slice(&zero.nearest[..M]);
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upper
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}));
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layers[layer.0 - 1] = upper;
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}
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}
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pid
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(
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Self {
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ef_search,
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zero,
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points,
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layers,
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},
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out,
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)
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}
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/// Bidirectionally insert links between newly detected neighbors
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/// Search the index for the points nearest to the reference point `point`
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///
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/// `layer` is the layer we're at; `new` contains the `NodeId` for the new `Node` (which has
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/// not yet been added to the layer) and its still-empty list of nearest neighbors; `found` is
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/// a slice containing the `Candidate`s found during searching (ordered from near to far).
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/// The results are returned in the `out` parameter; the number of neighbors to search for
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/// is limited by the size of the `out` parameter, and the number of results found is returned
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/// in the return value.
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///
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/// This just defers to the `Layer`'s `link()` implementation, which specializes on layer type.
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fn link(&mut self, layer: LayerId, new: (NodeId, &mut [Option<NodeId>]), found: &[Candidate]) {
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match layer.0 {
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0 => self.zero.link(new, found, &self.points),
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l => self.layers[l - 1].link(new, found, &self.points),
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/// `PointId` values can be initialized with `PointId::invalid()`.
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pub fn search(&self, point: &P, out: &mut [PointId], search: &mut Search) -> usize {
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if self.points.is_empty() {
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return 0;
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}
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}
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/// Search the given `layer` for neighbors closed to the point identified by `pid`
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///
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/// This implements the outer loop of algorithm 2 from the paper, deferring the state mutation
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/// in the inner loop to the `Search::push()` implementation.
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fn search_layer(&self, layer: LayerId, pid: PointId, search: &mut Search) {
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debug_assert_eq!(search.layer, layer);
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let point = &self[pid];
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while let Some(candidate) = search.candidates.pop() {
|
||||
if let Some(found) = search.nearest.last() {
|
||||
if candidate.distance > found.distance {
|
||||
break;
|
||||
}
|
||||
search.reset();
|
||||
search.push(PointId(0), point, &self.points);
|
||||
for cur in LayerId(self.layers.len()).descend() {
|
||||
search.num = if cur.is_zero() { self.ef_search } else { 1 };
|
||||
|
||||
let num = if cur.0 > 0 { M } else { M * 2 };
|
||||
match cur.0 {
|
||||
0 => self.zero.search(point, search, &self.points, num),
|
||||
l => self.layers[l - 1].search(point, search, &self.points, num),
|
||||
}
|
||||
|
||||
let iter = match layer.0 {
|
||||
0 => self.zero[candidate.nid].nearest_iter(),
|
||||
l => self.layers[l - 1][candidate.nid].nearest_iter(),
|
||||
};
|
||||
|
||||
for nid in iter {
|
||||
search.push(nid, point, self);
|
||||
if !cur.is_zero() {
|
||||
search.cull();
|
||||
}
|
||||
}
|
||||
|
||||
let found = min(search.nearest.len(), out.len());
|
||||
for (i, candidate) in search.nearest.iter().take(found).enumerate() {
|
||||
out[i] = candidate.pid;
|
||||
}
|
||||
found
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -163,42 +170,33 @@ where
|
|||
/// initialized by using `push()` to add the initial enter points.
|
||||
pub struct Search {
|
||||
/// Nodes visited so far (`v` in the paper)
|
||||
visited: HashSet<NodeId>,
|
||||
visited: HashSet<PointId>,
|
||||
/// Candidates for further inspection (`C` in the paper)
|
||||
candidates: Vec<Candidate>,
|
||||
/// Nearest neighbors found so far (`W` in the paper)
|
||||
nearest: Vec<Candidate>,
|
||||
/// Maximum number of nearest neighbors to retain (`ef` in the paper)
|
||||
num: usize,
|
||||
/// Current layer
|
||||
layer: LayerId,
|
||||
}
|
||||
|
||||
impl Search {
|
||||
/// Resets the state to be ready for a new search
|
||||
fn reset(&mut self, num: usize, layer: LayerId) {
|
||||
fn reset(&mut self) {
|
||||
self.visited.clear();
|
||||
self.candidates.clear();
|
||||
self.nearest.clear();
|
||||
self.num = num;
|
||||
self.layer = layer;
|
||||
}
|
||||
|
||||
/// Track node `nid` as a potential new neighbor for the given `point`
|
||||
/// Track node `pid` as a potential new neighbor for the given `point`
|
||||
///
|
||||
/// Will immediately return if the node has been considered before. This implements
|
||||
/// the inner loop from the paper's algorithm 2.
|
||||
fn push<P: Point>(&mut self, nid: NodeId, point: &P, hnsw: &Hnsw<P>) {
|
||||
if !self.visited.insert(nid) {
|
||||
fn push<P: Point>(&mut self, pid: PointId, point: &P, points: &[P]) {
|
||||
if !self.visited.insert(pid) {
|
||||
return;
|
||||
}
|
||||
|
||||
let pid = match self.layer.0 {
|
||||
0 => hnsw.zero.pid(nid),
|
||||
l => hnsw.layers[l - 1].pid(nid),
|
||||
};
|
||||
|
||||
let other = &hnsw[pid];
|
||||
let other = &points[pid];
|
||||
let distance = OrderedFloat::from(point.distance(other));
|
||||
if self.nearest.len() >= self.num {
|
||||
if let Some(found) = self.nearest.last() {
|
||||
|
|
@ -212,7 +210,7 @@ impl Search {
|
|||
self.nearest.pop();
|
||||
}
|
||||
|
||||
let new = Candidate { distance, nid };
|
||||
let new = Candidate { distance, pid };
|
||||
let idx = self.candidates.binary_search(&new).unwrap_or_else(|e| e);
|
||||
self.candidates.insert(idx, new);
|
||||
|
||||
|
|
@ -222,25 +220,14 @@ impl Search {
|
|||
|
||||
/// Lower the search to the next lower level
|
||||
///
|
||||
/// Resets `visited`, `candidates` to match `nearest`.
|
||||
///
|
||||
/// Panics if called while the `Search` is at level 0.
|
||||
fn lower<P: Point>(&mut self, hnsw: &Hnsw<P>) {
|
||||
debug_assert!(!self.layer.is_zero());
|
||||
|
||||
/// Re-initialize the `Search`: `nearest`, the output `W` from the last round, now becomes
|
||||
/// the set of enter points, which we use to initialize both `candidates` and `visited`.
|
||||
fn cull(&mut self) {
|
||||
self.nearest.truncate(self.num); // Limit size of the set of nearest neighbors
|
||||
let old = hnsw.layers[self.layer.0 - 1].nodes();
|
||||
for cur in self.nearest.iter_mut() {
|
||||
cur.nid = old[cur.nid].lower;
|
||||
}
|
||||
|
||||
// Re-initialize the `Search`: `nearest`, the output `W` from the last round, now becomes
|
||||
// the set of enter points, which we use to initialize both `candidates` and `visited`.
|
||||
self.layer = self.layer.lower();
|
||||
self.candidates.clear();
|
||||
self.candidates.extend(&self.nearest);
|
||||
self.visited.clear();
|
||||
self.visited.extend(self.nearest.iter().map(|c| c.nid));
|
||||
self.visited.extend(self.nearest.iter().map(|c| c.pid));
|
||||
}
|
||||
}
|
||||
|
||||
|
|
@ -250,7 +237,6 @@ impl Default for Search {
|
|||
visited: HashSet::new(),
|
||||
candidates: Vec::new(),
|
||||
nearest: Vec::new(),
|
||||
layer: LayerId(0),
|
||||
num: 1,
|
||||
}
|
||||
}
|
||||
|
|
@ -264,7 +250,7 @@ impl<P> Index<PointId> for Hnsw<P> {
|
|||
}
|
||||
}
|
||||
|
||||
impl<P: Point> Index<PointId> for [P] {
|
||||
impl<P: Point> Index<PointId> for Vec<P> {
|
||||
type Output = P;
|
||||
|
||||
fn index(&self, index: PointId) -> &Self::Output {
|
||||
|
|
@ -272,46 +258,10 @@ impl<P: Point> Index<PointId> for [P] {
|
|||
}
|
||||
}
|
||||
|
||||
impl Index<NodeId> for Vec<UpperNode> {
|
||||
type Output = UpperNode;
|
||||
impl<P: Point> Index<PointId> for [P] {
|
||||
type Output = P;
|
||||
|
||||
fn index(&self, index: NodeId) -> &Self::Output {
|
||||
&self[index.0]
|
||||
}
|
||||
}
|
||||
|
||||
impl IndexMut<NodeId> for Vec<UpperNode> {
|
||||
fn index_mut(&mut self, index: NodeId) -> &mut Self::Output {
|
||||
&mut self[index.0]
|
||||
}
|
||||
}
|
||||
|
||||
impl Index<NodeId> for [UpperNode] {
|
||||
type Output = UpperNode;
|
||||
|
||||
fn index(&self, index: NodeId) -> &Self::Output {
|
||||
&self[index.0]
|
||||
}
|
||||
}
|
||||
|
||||
impl Index<NodeId> for Vec<ZeroNode> {
|
||||
type Output = ZeroNode;
|
||||
|
||||
fn index(&self, index: NodeId) -> &Self::Output {
|
||||
&self[index.0]
|
||||
}
|
||||
}
|
||||
|
||||
impl IndexMut<NodeId> for Vec<ZeroNode> {
|
||||
fn index_mut(&mut self, index: NodeId) -> &mut Self::Output {
|
||||
&mut self[index.0]
|
||||
}
|
||||
}
|
||||
|
||||
impl Index<NodeId> for [ZeroNode] {
|
||||
type Output = ZeroNode;
|
||||
|
||||
fn index(&self, index: NodeId) -> &Self::Output {
|
||||
fn index(&self, index: PointId) -> &Self::Output {
|
||||
&self[index.0]
|
||||
}
|
||||
}
|
||||
|
|
@ -321,17 +271,17 @@ impl Layer for Vec<ZeroNode> {
|
|||
|
||||
type Node = ZeroNode;
|
||||
|
||||
fn pid(&self, nid: NodeId) -> PointId {
|
||||
PointId(nid.0)
|
||||
}
|
||||
|
||||
fn nodes(&self) -> &[Self::Node] {
|
||||
self
|
||||
fn push(&mut self, new: ZeroNode) {
|
||||
self.push(new);
|
||||
}
|
||||
|
||||
fn nodes_mut(&mut self) -> &mut [Self::Node] {
|
||||
self
|
||||
}
|
||||
|
||||
fn nodes(&self) -> &[Self::Node] {
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
impl Layer for Vec<UpperNode> {
|
||||
|
|
@ -339,17 +289,17 @@ impl Layer for Vec<UpperNode> {
|
|||
|
||||
type Node = UpperNode;
|
||||
|
||||
fn pid(&self, nid: NodeId) -> PointId {
|
||||
self.nodes()[nid].pid
|
||||
}
|
||||
|
||||
fn nodes(&self) -> &[Self::Node] {
|
||||
self
|
||||
fn push(&mut self, new: UpperNode) {
|
||||
self.push(new);
|
||||
}
|
||||
|
||||
fn nodes_mut(&mut self) -> &mut [Self::Node] {
|
||||
self
|
||||
}
|
||||
|
||||
fn nodes(&self) -> &[Self::Node] {
|
||||
self
|
||||
}
|
||||
}
|
||||
|
||||
trait Layer {
|
||||
|
|
@ -357,41 +307,56 @@ trait Layer {
|
|||
|
||||
type Node: Node;
|
||||
|
||||
fn pid(&self, nid: NodeId) -> PointId;
|
||||
|
||||
fn nodes(&self) -> &[Self::Node];
|
||||
|
||||
fn nodes_mut(&mut self) -> &mut [Self::Node];
|
||||
|
||||
/// Bidirectionally insert links between newly detected neighbors
|
||||
/// Search this layer for nodes near the given `point`
|
||||
///
|
||||
/// `new` contains the `NodeId` for the new `Node` (which has not yet been added to the layer)
|
||||
/// and its still-empty list of nearest neighbors; `found` is a slice containing all
|
||||
/// This contains the loops from the paper's algorithm 2. `point` represents `q`, the query
|
||||
/// element; `search.candidates` contains the enter points `ep`. `points` contains all the
|
||||
/// points, which is required to calculate distances between two points.
|
||||
///
|
||||
/// The `num` argument represents the number of links from each candidate to consider. This
|
||||
/// function may be called for a higher layer (with M links per node) or the zero layer (with
|
||||
/// M * 2 links per node), but for performance reasons we often call this function on the data
|
||||
/// representation matching the zero layer even when we're referring to a higher layer. In that
|
||||
/// case, we use `num` to constrain the number of per-candidate links we consider for search.
|
||||
fn search<P: Point>(&self, point: &P, search: &mut Search, points: &[P], num: usize) {
|
||||
while let Some(candidate) = search.candidates.pop() {
|
||||
if let Some(found) = search.nearest.last() {
|
||||
if candidate.distance > found.distance {
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
for pid in self.nodes()[candidate.pid.0].nearest_iter().take(num) {
|
||||
search.push(pid, point, points);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
/// Insert new node in this layer
|
||||
///
|
||||
/// `new` contains the `PointId` for the new node; `found` is a slice containing all
|
||||
/// `Candidate`s found during searching (ordered from near to far).
|
||||
///
|
||||
/// Initializes both the new node's neighbors (in `new.1`) and updates the nearest neighbors
|
||||
/// Creates the new node, initializing its `nearest` array and updates the nearest neighbors
|
||||
/// for the new node's neighbors if necessary.
|
||||
fn link<P: Point>(
|
||||
&mut self,
|
||||
new: (NodeId, &mut [Option<NodeId>]),
|
||||
found: &[Candidate],
|
||||
points: &[P],
|
||||
) {
|
||||
fn insert_node<P: Point>(&mut self, new: PointId, found: &[Candidate], points: &[P]) {
|
||||
let mut node = Self::Node::default();
|
||||
let new_nearest = node.nearest_mut();
|
||||
|
||||
// Just make sure the candidates are all unique
|
||||
debug_assert_eq!(
|
||||
found.len(),
|
||||
found.iter().map(|c| c.nid).collect::<HashSet<_>>().len()
|
||||
found.iter().map(|c| c.pid).collect::<HashSet<_>>().len()
|
||||
);
|
||||
|
||||
// Only use the `Self::LINKS` nearest candidates found
|
||||
for (i, candidate) in found.iter().take(Self::LINKS).enumerate() {
|
||||
// `candidate` here is the new node's neighbor
|
||||
let &Candidate { distance, nid } = candidate;
|
||||
new.1[i] = Some(nid); // Update the new node's `nearest`
|
||||
let &Candidate { distance, pid } = candidate;
|
||||
new_nearest[i] = Some(pid); // Update the new node's `nearest`
|
||||
|
||||
let pid = self.pid(nid);
|
||||
let old = &points[pid.0];
|
||||
let nearest = self.nodes()[nid.0].nearest();
|
||||
let old = &points[pid];
|
||||
let nearest = self.nodes()[pid.0].nearest();
|
||||
|
||||
// Find the correct index to insert at to keep the neighbor's neighbors sorted
|
||||
let idx = nearest
|
||||
|
|
@ -403,8 +368,7 @@ trait Layer {
|
|||
None => return Ordering::Greater,
|
||||
};
|
||||
|
||||
let pid = self.pid(third);
|
||||
let third_distance = OrderedFloat::from(old.distance(&points[pid.0]));
|
||||
let third_distance = OrderedFloat::from(old.distance(&points[third.0]));
|
||||
distance.cmp(&third_distance)
|
||||
})
|
||||
.unwrap_or_else(|e| e);
|
||||
|
|
@ -415,39 +379,41 @@ trait Layer {
|
|||
continue;
|
||||
}
|
||||
|
||||
let nearest = self.nodes_mut()[nid.0].nearest_mut();
|
||||
let nearest = self.nodes_mut()[pid.0].nearest_mut();
|
||||
if nearest[idx].is_none() {
|
||||
nearest[idx] = Some(new.0);
|
||||
nearest[idx] = Some(new);
|
||||
continue;
|
||||
}
|
||||
|
||||
let end = Self::LINKS - 1;
|
||||
nearest.copy_within(idx..end, idx + 1);
|
||||
nearest[idx] = Some(new.0);
|
||||
nearest[idx] = Some(new);
|
||||
}
|
||||
|
||||
self.push(node);
|
||||
}
|
||||
|
||||
fn push(&mut self, new: Self::Node);
|
||||
|
||||
fn nodes_mut(&mut self) -> &mut [Self::Node];
|
||||
|
||||
fn nodes(&self) -> &[Self::Node];
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
#[derive(Clone, Copy, Debug, Default)]
|
||||
struct UpperNode {
|
||||
/// This node's point
|
||||
pid: PointId,
|
||||
/// The point's node on the next level down
|
||||
///
|
||||
/// This is only used when lowering the search.
|
||||
lower: NodeId,
|
||||
/// The nearest neighbors on this layer
|
||||
///
|
||||
/// This is always kept in sorted order (near to far).
|
||||
nearest: [Option<NodeId>; M],
|
||||
nearest: [Option<PointId>; M],
|
||||
}
|
||||
|
||||
impl Node for UpperNode {
|
||||
fn nearest(&self) -> &[Option<NodeId>] {
|
||||
fn nearest(&self) -> &[Option<PointId>] {
|
||||
&self.nearest
|
||||
}
|
||||
|
||||
fn nearest_mut(&mut self) -> &mut [Option<NodeId>] {
|
||||
fn nearest_mut(&mut self) -> &mut [Option<PointId>] {
|
||||
&mut self.nearest
|
||||
}
|
||||
|
||||
|
|
@ -458,20 +424,20 @@ impl Node for UpperNode {
|
|||
}
|
||||
}
|
||||
|
||||
#[derive(Debug)]
|
||||
#[derive(Clone, Copy, Debug, Default)]
|
||||
struct ZeroNode {
|
||||
/// The nearest neighbors on this layer
|
||||
///
|
||||
/// This is always kept in sorted order (near to far).
|
||||
nearest: [Option<NodeId>; M * 2],
|
||||
nearest: [Option<PointId>; M * 2],
|
||||
}
|
||||
|
||||
impl Node for ZeroNode {
|
||||
fn nearest(&self) -> &[Option<NodeId>] {
|
||||
fn nearest(&self) -> &[Option<PointId>] {
|
||||
&self.nearest
|
||||
}
|
||||
|
||||
fn nearest_mut(&mut self) -> &mut [Option<NodeId>] {
|
||||
fn nearest_mut(&mut self) -> &mut [Option<PointId>] {
|
||||
&mut self.nearest
|
||||
}
|
||||
|
||||
|
|
@ -482,18 +448,18 @@ impl Node for ZeroNode {
|
|||
}
|
||||
}
|
||||
|
||||
trait Node {
|
||||
fn nearest(&self) -> &[Option<NodeId>];
|
||||
fn nearest_mut(&mut self) -> &mut [Option<NodeId>];
|
||||
trait Node: Default {
|
||||
fn nearest(&self) -> &[Option<PointId>];
|
||||
fn nearest_mut(&mut self) -> &mut [Option<PointId>];
|
||||
fn nearest_iter(&self) -> NearestIter<'_>;
|
||||
}
|
||||
|
||||
struct NearestIter<'a> {
|
||||
nearest: &'a [Option<NodeId>],
|
||||
nearest: &'a [Option<PointId>],
|
||||
}
|
||||
|
||||
impl<'a> Iterator for NearestIter<'a> {
|
||||
type Item = NodeId;
|
||||
type Item = PointId;
|
||||
|
||||
fn next(&mut self) -> Option<Self::Item> {
|
||||
let (&first, rest) = self.nearest.split_first()?;
|
||||
|
|
@ -509,11 +475,9 @@ impl<'a> Iterator for NearestIter<'a> {
|
|||
struct LayerId(usize);
|
||||
|
||||
impl LayerId {
|
||||
/// Return a `LayerId` for the layer one lower
|
||||
///
|
||||
/// Panics when called for `LayerId(0)`.
|
||||
fn lower(&self) -> LayerId {
|
||||
LayerId(self.0 - 1)
|
||||
fn random(rng: &mut SmallRng) -> Self {
|
||||
let layer = rng.next_u32() as f32 / u32::MAX as f32;
|
||||
LayerId((-(layer.ln() * (M as f32).ln())).floor() as usize)
|
||||
}
|
||||
|
||||
fn descend(&self) -> DescendingLayerIter {
|
||||
|
|
@ -546,14 +510,14 @@ impl Iterator for DescendingLayerIter {
|
|||
}
|
||||
}
|
||||
|
||||
pub trait Point {
|
||||
pub trait Point: Clone {
|
||||
fn distance(&self, other: &Self) -> f32;
|
||||
}
|
||||
|
||||
#[derive(Clone, Copy, Debug, Eq, Ord, PartialEq, PartialOrd)]
|
||||
struct Candidate {
|
||||
distance: OrderedFloat<f32>,
|
||||
nid: NodeId,
|
||||
pid: PointId,
|
||||
}
|
||||
|
||||
/// References a node in a particular layer (usually the same layer)
|
||||
|
|
@ -566,6 +530,12 @@ struct NodeId(usize);
|
|||
#[derive(Clone, Copy, Debug, Eq, Hash, Ord, PartialEq, PartialOrd)]
|
||||
pub struct PointId(usize);
|
||||
|
||||
impl PointId {
|
||||
pub fn invalid() -> Self {
|
||||
PointId(usize::MAX)
|
||||
}
|
||||
}
|
||||
|
||||
/// The parameter `M` from the paper
|
||||
///
|
||||
/// This should become a generic argument to `Hnsw` when possible.
|
||||
|
|
@ -576,15 +546,27 @@ mod tests {
|
|||
use super::*;
|
||||
|
||||
#[test]
|
||||
fn test_insertion() {
|
||||
fn basic() {
|
||||
let (hnsw, pids) = Hnsw::new(
|
||||
&[
|
||||
Point(0.1, 0.4),
|
||||
Point(-0.324, 0.543),
|
||||
Point(0.87, -0.33),
|
||||
Point(0.452, 0.932),
|
||||
],
|
||||
100,
|
||||
100,
|
||||
);
|
||||
|
||||
let mut search = Search::default();
|
||||
let mut hnsw = Hnsw::new(100);
|
||||
hnsw.insert(Point(0.1, 0.4), &mut search);
|
||||
hnsw.insert(Point(-0.324, 0.543), &mut search);
|
||||
hnsw.insert(Point(0.87, -0.33), &mut search);
|
||||
hnsw.insert(Point(0.452, 0.932), &mut search);
|
||||
let mut results = vec![PointId::invalid()];
|
||||
let p = Point(0.1, 0.35);
|
||||
let found = hnsw.search(&p, &mut results, &mut search);
|
||||
assert_eq!(found, 1);
|
||||
assert_eq!(&results, &[pids[0]]);
|
||||
}
|
||||
|
||||
#[derive(Clone, Copy, Debug)]
|
||||
struct Point(f32, f32);
|
||||
|
||||
impl super::Point for Point {
|
||||
|
|
|
|||
Loading…
Reference in New Issue