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HNSW

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Fix algorithm
pull/1/head
unknown 3 years ago
parent 8b5bf38dd5
commit 1f967908b9
4 changed files with 302 additions and 198 deletions
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25
TestHNSW/HNSWDemo/Program.cs
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@ -84,6 +84,7 @@ namespace HNSWDemo
var sw = new Stopwatch();
var world = new SmallWorld<float[]>(NSWOptions<float[]>.Create(6, 4, 120, 120, CosineDistance.ForUnits));
for (int i = 0; i < (count / batchSize); i++)
{
var batch = samples.Skip(i * batchSize).Take(batchSize).ToArray();
@ -101,18 +102,18 @@ namespace HNSWDemo
//HNSWFilter filter = new HNSWFilter(ids => ids.Where(id => { var p = _database[id]; return p.Age > 45 && p.Gender == Gender.Feemale; }));
/*var fackupCount = 0;
foreach (var v in vectors)
{
var result = world.Search(v, 10, filter);
foreach (var r in result)
{
if (_database[r.Item1].Age <= 45 || _database[r.Item1].Gender != Gender.Feemale)
{
Interlocked.Increment(ref fackupCount);
}
}
}*/
/*var fackupCount = 0;
foreach (var v in vectors)
{
var result = world.Search(v, 10, filter);
foreach (var r in result)
{
if (_database[r.Item1].Age <= 45 || _database[r.Item1].Gender != Gender.Feemale)
{
Interlocked.Increment(ref fackupCount);
}
}
}*/
//Console.WriteLine($"Completed. Fackup count: {fackupCount}");
Console.ReadKey();

236
ZeroLevel.HNSW/Layer.cs
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@ -13,6 +13,11 @@ namespace ZeroLevel.HNSW
private readonly VectorSet<TItem> _vectors;
private CompactBiDirectionalLinksSet _links = new CompactBiDirectionalLinksSet();
/// <summary>
/// Count nodes at layer
/// </summary>
public int Count => (_links.Count >> 1);
public Layer(NSWOptions<TItem> options, VectorSet<TItem> vectors)
{
_options = options;
@ -50,148 +55,107 @@ namespace ZeroLevel.HNSW
}
}
public int GetEntryPointFor(int q)
{
var randomLinkId = DefaultRandomGenerator.Instance.Next(0, _links.Count);
var entryId = _links[randomLinkId].Item1;
var v = new VisitedBitSet(_vectors._set.Count, _options.M);
// v ← ep // set of visited elements
var (ep, ed) = DFS_SearchMinFrom(entryId, q, v);
return ep;
}
private (int, float) DFS_SearchMinFrom(int entryId, int id, VisitedBitSet visited)
{
visited.Add(entryId);
int candidate = entryId;
var candidateDistance = _options.Distance(_vectors[entryId], _vectors[id]);
int counter = 0;
do
{
var (mid, dist) = GetMinNearest(visited, entryId, candidate, candidateDistance);
if (dist > candidateDistance)
{
break;
}
candidate = mid;
candidateDistance = dist;
counter++;
} while (counter < _options.EFConstruction);
return (candidate, candidateDistance);
}
private (int, float) GetMinNearest(VisitedBitSet visited, int entryId, int id, float entryDistance)
{
var minId = entryId;
var minDist = entryDistance;
foreach (var candidate in _links.FindLinksForId(entryId).Select(l => l.Item2))
{
if (visited.Contains(candidate) == false)
{
var dist = _options.Distance(_vectors[candidate], _vectors[id]);
if (dist < minDist)
{
minDist = dist;
minId = candidate;
}
visited.Add(candidate);
}
}
return (minId, minDist);
}
#region Implementation of https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf
/// <summary>
/// Algorithm 2
/// </summary>
/// <param name="q">query element</param>
/// <param name="ep">enter points ep</param>
/// <returns>Output: ef closest neighbors to q</returns>
public IDictionary<int, float> SEARCH_LAYER(int q, int ep, int ef)
public void RunKnnAtLayer(int entryPointId, Func<int, float> targetCosts, IDictionary<int, float> W, int ef)
{
var v = new VisitedBitSet(_vectors._set.Count, _options.M);
/*
* v ← ep // set of visited elements
* C ← ep // set of candidates
* W ← ep // dynamic list of found nearest neighbors
* while │C│ > 0
* c ← extract nearest element from C to q
* f ← get furthest element from W to q
* if distance(c, q) > distance(f, q)
* break // all elements in W are evaluated
* for each e ∈ neighbourhood(c) at layer lc // update C and W
* if e ∉ v
* v ← v ⋃ e
* f ← get furthest element from W to q
* if distance(e, q) < distance(f, q) or │W│ < ef
* C ← C ⋃ e
* W ← W ⋃ e
* if │W│ > ef
* remove furthest element from W to q
* return W
*/
var v = new VisitedBitSet(_vectors.Count, _options.M);
// v ← ep // set of visited elements
v.Add(ep);
v.Add(entryPointId);
// C ← ep // set of candidates
var C = new Dictionary<int, float>();
C.Add(ep, _options.Distance(_vectors[ep], _vectors[q]));
C.Add(entryPointId, targetCosts(entryPointId));
// W ← ep // dynamic list of found nearest neighbors
var W = new Dictionary<int, float>();
W.Add(ep, C[ep]);
// while │C│ > 0
W.Add(entryPointId, C[entryPointId]);
// run bfs
while (C.Count > 0)
{
// c ← extract nearest element from C to q
var nearest = W.OrderBy(p => p.Value).First();
var c = nearest.Key;
var md = nearest.Value;
// var (c, md) = GetMinimalDistanceIndex(C, q);
C.Remove(c);
// f ← get furthest element from W to q
var f = W.OrderBy(p => p.Value).First().Key;
//var f = GetMaximalDistanceIndex(W, q);
// if distance(c, q) > distance(f, q)
if (_options.Distance(_vectors[c], _vectors[q]) > _options.Distance(_vectors[f], _vectors[q]))
// get next candidate to check and expand
var toExpand = popCandidate();
var farthestResult = fartherFromResult();
if (toExpand.Item2 > farthestResult.Item2)
{
// break // all elements in W are evaluated
// the closest candidate is farther than farthest result
break;
}
// for each e ∈ neighbourhood(c) at layer lc // update C and W
foreach (var l in _links.FindLinksForId(c))
// expand candidate
var neighboursIds = GetNeighbors(toExpand.Item1).ToArray();
for (int i = 0; i < neighboursIds.Length; ++i)
{
var e = l.Item2;
// if e ∉ v
if (v.Contains(e) == false)
int neighbourId = neighboursIds[i];
if (!v.Contains(neighbourId))
{
// v ← v ⋃ e
v.Add(e);
// f ← get furthest element from W to q
f = W.OrderByDescending(p => p.Value).First().Key;
//f = GetMaximalDistanceIndex(W, q);
// if distance(e, q) < distance(f, q) or │W│ < ef
var ed = _options.Distance(_vectors[e], _vectors[q]);
if (ed > _options.Distance(_vectors[f], _vectors[q])
|| W.Count < ef)
// enqueue perspective neighbours to expansion list
farthestResult = fartherFromResult();
var neighbourDistance = targetCosts(neighbourId);
if (W.Count < ef || neighbourDistance < farthestResult.Item2)
{
// C ← C ⋃ e
C.Add(e, ed);
// W ← W ⋃ e
W.Add(e, ed);
// if │W│ > ef
C.Add(neighbourId, neighbourDistance);
W.Add(neighbourId, neighbourDistance);
if (W.Count > ef)
{
// remove furthest element from W to q
f = W.OrderByDescending(p => p.Value).First().Key;
//f = GetMaximalDistanceIndex(W, q);
W.Remove(f);
fartherPopFromResult();
}
}
v.Add(neighbourId);
}
}
}
// return W
return W;
C.Clear();
v.Clear();
}
/// <summary>
/// Algorithm 3
/// </summary>
/// <param name="q">base element</param>
/// <param name="C">candidate elements</param>
/// <returns>Output: M nearest elements to q</returns>
public IDictionary<int, float> SELECT_NEIGHBORS_SIMPLE(int q, IDictionary<int, float> C)
public IDictionary<int, float> SELECT_NEIGHBORS_SIMPLE(Func<int, float> distance, IDictionary<int, float> candidates, int M)
{
if (C.Count <= _options.M)
var bestN = M;
var W = new Dictionary<int, float>(candidates);
if (W.Count > bestN)
{
return new Dictionary<int, float>(C);
var popFarther = new Action(() => { var pair = W.OrderByDescending(e => e.Value).First(); W.Remove(pair.Key); });
while (W.Count > bestN)
{
popFarther();
}
}
var output = new Dictionary<int, float>();
// return M nearest elements from C to q
return new Dictionary<int, float>(C.OrderBy(p => p.Value).Take(_options.M));
return W;
}
/// <summary>
/// Algorithm 4
/// </summary>
@ -200,41 +164,56 @@ namespace ZeroLevel.HNSW
/// <param name="extendCandidates">flag indicating whether or not to extend candidate list</param>
/// <param name="keepPrunedConnections">flag indicating whether or not to add discarded elements</param>
/// <returns>Output: M elements selected by the heuristic</returns>
public IDictionary<int, float> SELECT_NEIGHBORS_HEURISTIC(int q, IDictionary<int, float> C, bool extendCandidates, bool keepPrunedConnections)
public IDictionary<int, float> SELECT_NEIGHBORS_HEURISTIC(Func<int, float> distance, IDictionary<int, float> candidates, int M, bool extendCandidates, bool keepPrunedConnections)
{
// R ← ∅
var R = new Dictionary<int, float>();
// W ← C // working queue for the candidates
var W = new List<int>(C.Select(p => p.Key));
var W = new Dictionary<int, float>(candidates);
// if extendCandidates // extend candidates by their neighbors
if (extendCandidates)
{
var extendBuffer = new HashSet<int>();
// for each e ∈ C
foreach (var e in C)
foreach (var e in W)
{
var neighbors = GetNeighbors(e.Key);
// for each e_adj ∈ neighbourhood(e) at layer lc
foreach (var l in _links.FindLinksForId(e.Key))
foreach (var e_adj in neighbors)
{
var e_adj = l.Item2;
// if eadj ∉ W
if (W.Contains(e_adj) == false)
if (extendBuffer.Contains(e_adj) == false)
{
// W ← W ⋃ eadj
W.Add(e_adj);
extendBuffer.Add(e_adj);
}
}
}
// W ← W ⋃ eadj
foreach (var id in extendBuffer)
{
W.Add(id, distance(id));
}
}
// Wd ← ∅ // queue for the discarded candidates
var Wd = new Dictionary<int, float>();
var popCandidate = new Func<(int, float)>(() => { var pair = W.OrderBy(e => e.Value).First(); W.Remove(pair.Key); return (pair.Key, pair.Value); });
var fartherFromResult = new Func<(int, float)>(() => { if (R.Count == 0) return (-1, 0f); var pair = R.OrderByDescending(e => e.Value).First(); return (pair.Key, pair.Value); });
var popNearestDiscarded = new Func<(int, float)>(() => { var pair = Wd.OrderBy(e => e.Value).First(); W.Remove(pair.Key); return (pair.Key, pair.Value); });
// while │W│ > 0 and │R│< M
while (W.Count > 0 && R.Count < _options.M)
while (W.Count > 0 && R.Count < M)
{
// e ← extract nearest element from W to q
var (e, ed) = GetMinimalDistanceIndex(W, q);
W.Remove(e);
var (e, ed) = popCandidate();
var (fe, fd) = fartherFromResult();
// if e is closer to q compared to any element from R
if (ed < R.Min(pair => pair.Value))
if (R.Count == 0 ||
ed < fd)
{
// R ← R ⋃ e
R.Add(e, ed);
@ -248,37 +227,20 @@ namespace ZeroLevel.HNSW
if (keepPrunedConnections)
{
// while │Wd│> 0 and │R│< M
while (Wd.Count > 0 && R.Count < _options.M)
while (Wd.Count > 0 && R.Count < M)
{
// R ← R ⋃ extract nearest element from Wd to q
var nearest = Wd.Aggregate((l, r) => l.Value < r.Value ? l : r);
Wd.Remove(nearest.Key);
R.Add(nearest.Key, nearest.Value);
var nearest = popNearestDiscarded();
R.Add(nearest.Item1, nearest.Item2);
}
}
}
// return R
return R;
}
#endregion
private (int, float) GetMinimalDistanceIndex(IList<int> self, int q)
{
float min = _options.Distance(_vectors[self[0]], _vectors[q]);
int minIndex = 0;
for (int i = 1; i < self.Count; ++i)
{
var dist = _options.Distance(_vectors[self[i]], _vectors[q]);
if (dist < min)
{
min = self[i];
minIndex = i;
}
}
return (minIndex, min);
}
private IEnumerable<int> GetNeighbors(int id) => _links.FindLinksForId(id).Select(d => d.Item2);
}
}

34
ZeroLevel.HNSW/Services/VectorSet.cs
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@ -1,15 +1,16 @@
using System.Collections.Generic;
using System;
using System.Collections.Generic;
using System.Threading;
namespace ZeroLevel.HNSW
{
public class VectorSet<T>
{
public IList<T> _set = new List<T>();
private List<T> _set = new List<T>();
private SpinLock _lock = new SpinLock();
public T this[int index] => _set[index];
SpinLock _lock = new SpinLock();
public int Count => _set.Count;
public int Append(T vector)
{
@ -27,5 +28,30 @@ namespace ZeroLevel.HNSW
if (gotLock) _lock.Exit();
}
}
public int[] Append(IEnumerable<T> vectors)
{
bool gotLock = false;
int startIndex, endIndex;
gotLock = false;
try
{
_lock.Enter(ref gotLock);
startIndex = _set.Count;
_set.AddRange(vectors);
endIndex = _set.Count;
}
finally
{
// Only give up the lock if you actually acquired it
if (gotLock) _lock.Exit();
}
var ids = new int[endIndex - startIndex];
for (int i = startIndex, j = 0; i < endIndex; i++, j++)
{
ids[j] = i;
}
return ids;
}
}
}

201
ZeroLevel.HNSW/SmallWorld.cs
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@ -1,9 +1,38 @@
using System;
using System.Collections.Generic;
using System.Linq;
using System.Threading;
namespace ZeroLevel.HNSW
{
public class ProbabilityLayerNumberGenerator
{
private const float DIVIDER = 4.362f;
private readonly float[] _probabilities;
public ProbabilityLayerNumberGenerator(int maxLayers, int M)
{
_probabilities = new float[maxLayers];
var probability = 1.0f / DIVIDER;
for (int i = 0; i < maxLayers; i++)
{
_probabilities[i] = probability;
probability /= DIVIDER;
}
}
public int GetRandomLayer()
{
var probability = DefaultRandomGenerator.Instance.NextFloat();
for (int i = 0; i < _probabilities.Length; i++)
{
if (probability > _probabilities[i])
return i;
}
return 0;
}
}
public class SmallWorld<TItem>
{
private readonly NSWOptions<TItem> _options;
@ -12,12 +41,17 @@ namespace ZeroLevel.HNSW
private Layer<TItem> EnterPointsLayer => _layers[_layers.Length - 1];
private Layer<TItem> LastLayer => _layers[0];
private int EntryPoint = -1;
private int MaxLayer = -1;
private readonly ProbabilityLayerNumberGenerator _layerLevelGenerator;
private ReaderWriterLockSlim _lockGraph = new ReaderWriterLockSlim();
public SmallWorld(NSWOptions<TItem> options)
{
_options = options;
_vectors = new VectorSet<TItem>();
_layers = new Layer<TItem>[_options.LayersCount];
_layerLevelGenerator = new ProbabilityLayerNumberGenerator(_options.LayersCount, _options.M);
for (int i = 0; i < _options.LayersCount; i++)
{
_layers[i] = new Layer<TItem>(_options, _vectors);
@ -31,93 +65,174 @@ namespace ZeroLevel.HNSW
public int[] AddItems(IEnumerable<TItem> vectors)
{
var insert = vectors.ToArray();
var ids = new int[insert.Length];
for (int i = 0; i < insert.Length; i++)
_lockGraph.EnterWriteLock();
try
{
var item = insert[i];
ids[i] = Insert(item);
var ids = _vectors.Append(vectors);
for (int i = 0; i < ids.Length; i++)
{
INSERT(ids[i]);
}
return ids;
}
finally
{
_lockGraph.ExitWriteLock();
}
return ids;
}
public int Insert(TItem item)
public void TestLevelGenerator()
{
var id = _vectors.Append(item);
INSERT(id);
return id;
var levels = new Dictionary<int, float>();
for (int i = 0; i < 10000; i++)
{
var level = _layerLevelGenerator.GetRandomLayer();
if (levels.ContainsKey(level) == false)
{
levels.Add(level, 1);
}
else
{
levels[level] += 1.0f;
}
}
foreach (var pair in levels.OrderBy(l => l.Key))
{
Console.WriteLine($"[{pair.Key}]: {pair.Value / 100.0f}% ({pair.Value})");
}
}
#region https://arxiv.org/ftp/arxiv/papers/1603/1603.09320.pdf
/// <summary>
/// Algorithm 1
/// </summary>
/// <param name="q">new element</param>
public void INSERT(int q)
{
var distance = new Func<int, float>(candidate => _options.Distance(_vectors[q], _vectors[candidate]));
// W ← ∅ // list for the currently found nearest elements
IDictionary<int, float> W;
IDictionary<int, float> W = new Dictionary<int, float>();
// ep ← get enter point for hnsw
var ep = EnterPointsLayer.GetEntryPointFor(q);
var ep = EntryPoint == -1 ? 0 : EntryPoint;
var epDist = 0.0f;
// L ← level of ep // top layer for hnsw
var L = _layers.Length - 1;
var L = MaxLayer;
// l ← ⌊-ln(unif(0..1))∙mL⌋ // new element’s level
int l = DefaultRandomGenerator.Instance.Next(0, _options.LayersCount - 1);
int l = _layerLevelGenerator.GetRandomLayer();
if (L == -1)
{
L = l;
MaxLayer = l;
}
// for lc ← L … l+1
for (int lc = L; lc > l; lc--)
// Проход с верхнего уровня до уровня где появляется элемент, для нахождения точки входа
for (int lc = L; lc > l; --lc)
{
// W ← SEARCH-LAYER(q, ep, ef = 1, lc)
W = _layers[lc].SEARCH_LAYER(q, ep, 1);
_layers[lc].RunKnnAtLayer(ep, distance, W, 1);
// ep ← get the nearest element from W to q
ep = W.OrderBy(p => p.Value).First().Key;
var nearest = W.OrderBy(p => p.Value).First();
ep = nearest.Key;
epDist = nearest.Value;
W.Clear();
}
//for lc ← min(L, l) … 0
for (int lc = Math.Min(L, l); lc >= 0; lc--)
// connecting new node to the small world
for (int lc = Math.Min(L, l); lc >= 0; --lc)
{
// W ← SEARCH - LAYER(q, ep, efConstruction, lc)
W = _layers[lc].SEARCH_LAYER(q, ep, _options.EFConstruction);
_layers[lc].RunKnnAtLayer(ep, distance, W, _options.EFConstruction);
// neighbors ← SELECT-NEIGHBORS(q, W, M, lc) // alg. 3 or alg. 4
var neighbors = _layers[lc].SELECT_NEIGHBORS_SIMPLE(q, W);
var neighbors = SelectBestForConnecting(lc, distance, W);;
// add bidirectionall connectionts from neighbors to q at layer lc
// for each e ∈ neighbors // shrink connections if needed
foreach (var e in neighbors)
{
// eConn ← neighbourhood(e) at layer lc
_layers[lc].AddBidirectionallConnectionts(q, e.Key, e.Value);
// if distance from newNode to newNeighbour is better than to bestPeer => update bestPeer
if (e.Value < epDist)
{
ep = e.Key;
epDist = e.Value;
}
}
// ep ← W
ep = W.OrderBy(p => p.Value).First().Key;
W.Clear();
}
// if l > L
if (l > L)
{
// set enter point for hnsw to q
L = l;
MaxLayer = l;
EntryPoint = ep;
}
// if l > L
// set enter point for hnsw to q
}
/// <summary>
/// Get maximum allowed connections for the given level.
/// </summary>
/// <remarks>
/// Article: Section 4.1:
/// "Selection of the Mmax0 (the maximum number of connections that an element can have in the zero layer) also
/// has a strong influence on the search performance, especially in case of high quality(high recall) search.
/// Simulations show that setting Mmax0 to M(this corresponds to kNN graphs on each layer if the neighbors
/// selection heuristic is not used) leads to a very strong performance penalty at high recall.
/// Simulations also suggest that 2∙M is a good choice for Mmax0;
/// setting the parameter higher leads to performance degradation and excessive memory usage."
/// </remarks>
/// <param name="layer">The level of the layer.</param>
/// <returns>The maximum number of connections.</returns>
internal int GetM(int layer)
{
return layer == 0 ? 2 * _options.M : _options.M;
}
private IDictionary<int, float> SelectBestForConnecting(int layer, Func<int, float> distance, IDictionary<int, float> candidates)
{
return _layers[layer].SELECT_NEIGHBORS_SIMPLE(distance, candidates, GetM(layer));
}
/// <summary>
/// Algorithm 5
/// </summary>
/// <param name="q">query element</param>
/// <param name="K">number of nearest neighbors to return</param>
/// <returns>: K nearest elements to q</returns>
public IList<int> K_NN_SEARCH(int q, int K)
internal IEnumerable<(int, float)> KNearest(TItem q, int k)
{
// W ← ∅ // set for the current nearest elements
IDictionary<int, float> W;
// ep ← get enter point for hnsw
var ep = EnterPointsLayer.GetEntryPointFor(q);
// L ← level of ep // top layer for hnsw
var L = _options.LayersCount - 1;
// for lc ← L … 1
for (var lc = L; lc > 0; lc--)
_lockGraph.EnterReadLock();
try
{
// W ← SEARCH-LAYER(q, ep, ef = 1, lc)
W = _layers[lc].SEARCH_LAYER(q, ep, 1);
// ep ← get nearest element from W to q
ep = W.OrderBy(p => p.Value).First().Key;
if (_vectors.Count == 0)
{
return Enumerable.Empty<(int, float)>();
}
var distance = new Func<int, float>(candidate => _options.Distance(q, _vectors[candidate]));
// W ← ∅ // set for the current nearest elements
var W = new Dictionary<int, float>(k + 1);
// ep ← get enter point for hnsw
var ep = EntryPoint;
// L ← level of ep // top layer for hnsw
var L = MaxLayer;
// for lc ← L … 1
for (int layer = L; layer > 0; --layer)
{
// W ← SEARCH-LAYER(q, ep, ef = 1, lc)
_layers[layer].RunKnnAtLayer(ep, distance, W, 1);
// ep ← get nearest element from W to q
ep = W.OrderBy(p => p.Value).First().Key;
W.Clear();
}
// W ← SEARCH-LAYER(q, ep, ef, lc =0)
_layers[0].RunKnnAtLayer(ep, distance, W, k);
// return K nearest elements from W to q
return W.Select(p => (p.Key, p.Value));
}
finally
{
_lockGraph.ExitReadLock();
}
// W ← SEARCH-LAYER(q, ep, ef, lc =0)
W = LastLayer.SEARCH_LAYER(q, ep, _options.EF);
// return K nearest elements from W to q
return W.OrderBy(p => p.Value).Take(K).Select(p => p.Key).ToList();
}
#endregion
}

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