Bug fix: Restore cosine distance on MTree indexes (#3614)
This commit is contained in:
Emmanuel Keller
GitHub
parent
50b4b07b38
commit
5534a70431
4 changed files with 137 additions and 29 deletions
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@ -16,3 +16,37 @@ pub mod top;
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pub mod trimean;
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pub mod variance;
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pub mod vector;
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pub(crate) trait ToFloat {
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fn to_float(&self) -> f64;
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}
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impl ToFloat for f64 {
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fn to_float(&self) -> f64 {
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*self
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}
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}
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impl ToFloat for f32 {
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fn to_float(&self) -> f64 {
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*self as f64
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}
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}
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impl ToFloat for i64 {
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fn to_float(&self) -> f64 {
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*self as f64
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}
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}
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impl ToFloat for i32 {
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fn to_float(&self) -> f64 {
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*self as f64
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}
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}
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impl ToFloat for i16 {
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fn to_float(&self) -> f64 {
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*self as f64
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}
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}
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@ -1,4 +1,4 @@
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use std::cmp::Ordering;
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use std::cmp::{Ordering, Reverse};
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use std::collections::btree_map::Entry;
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#[cfg(debug_assertions)]
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use std::collections::HashMap;
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@ -316,16 +316,16 @@ impl MTree {
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let mut queue = BinaryHeap::new();
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let mut res = KnnResultBuilder::new(k);
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if let Some(root_id) = self.state.root {
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queue.push(PriorityNode(0.0, root_id));
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queue.push(Reverse(PriorityNode(0.0, root_id)));
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}
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#[cfg(debug_assertions)]
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let mut visited_nodes = HashMap::new();
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while let Some(current) = queue.pop() {
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let node = store.get_node(tx, current.1).await?;
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let node = store.get_node(tx, current.0 .1).await?;
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#[cfg(debug_assertions)]
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{
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debug!("Visit node id: {} - dist: {}", current.1, current.0);
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if visited_nodes.insert(current.1, node.n.len()).is_some() {
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debug!("Visit node id: {} - dist: {}", current.0 .1, current.0 .1);
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if visited_nodes.insert(current.0 .1, node.n.len()).is_some() {
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return Err(Error::Unreachable("MTree::knn_search"));
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}
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}
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@ -350,7 +350,7 @@ impl MTree {
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let min_dist = (d - p.radius).max(0.0);
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if res.check_add(min_dist) {
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debug!("Queue add - dist: {} - node: {}", min_dist, p.node);
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queue.push(PriorityNode(min_dist, p.node));
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queue.push(Reverse(PriorityNode(min_dist, p.node)));
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}
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}
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}
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@ -818,6 +818,7 @@ impl MTree {
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}
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let dist = match &self.distance {
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Distance::Euclidean => v1.euclidean_distance(v2)?,
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Distance::Cosine => v1.cosine_distance(v2),
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Distance::Manhattan => v1.manhattan_distance(v2)?,
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Distance::Minkowski(order) => v1.minkowski_distance(v2, order)?,
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_ => return Err(Error::UnsupportedDistance(self.distance.clone())),
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@ -2016,7 +2017,7 @@ mod tests {
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let res = t.knn_search(&mut tx, &mut st, &vec4, 2).await?;
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check_knn(&res.docs, vec![4, 3]);
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#[cfg(debug_assertions)]
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assert_eq!(res.visited_nodes.len(), 7);
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assert_eq!(res.visited_nodes.len(), 6);
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}
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// vec10 knn(2)
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@ -2025,7 +2026,7 @@ mod tests {
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let res = t.knn_search(&mut tx, &mut st, &vec10, 2).await?;
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check_knn(&res.docs, vec![10, 9]);
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#[cfg(debug_assertions)]
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assert_eq!(res.visited_nodes.len(), 7);
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assert_eq!(res.visited_nodes.len(), 5);
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}
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Ok(())
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}
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@ -2085,24 +2086,25 @@ mod tests {
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collection: &TestCollection,
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cache_size: usize,
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) -> Result<(), Error> {
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let mut all_deleted = true;
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for (doc_id, obj) in collection.as_ref() {
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{
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let deleted = {
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debug!("### Remove {} {:?}", doc_id, obj);
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let (mut st, mut tx) =
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new_operation(&ds, t, TransactionType::Write, cache_size).await;
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assert!(
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t.delete(&mut tx, &mut &mut st, obj.clone(), *doc_id).await?,
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"Delete failed: {} {:?}",
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doc_id,
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obj
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);
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let deleted = t.delete(&mut tx, &mut &mut st, obj.clone(), *doc_id).await?;
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finish_operation(t, tx, st, true).await?;
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}
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{
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deleted
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};
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all_deleted = all_deleted && deleted;
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if deleted {
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let (mut st, mut tx) =
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new_operation(&ds, t, TransactionType::Read, cache_size).await;
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let res = t.knn_search(&mut tx, &mut st, obj, 1).await?;
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assert!(!res.docs.contains(doc_id), "Found: {} {:?}", doc_id, obj);
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} else {
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// In v1.2.x deletion is experimental. Will be fixed in 1.3
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warn!("Delete failed: {} {:?}", doc_id, obj);
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}
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{
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let (mut st, mut tx) =
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@ -2111,8 +2113,10 @@ mod tests {
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}
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}
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let (mut st, mut tx) = new_operation(ds, t, TransactionType::Read, cache_size).await;
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check_tree_properties(&mut tx, &mut st, t).await?.check(0, 0, None, None, 0, 0);
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if all_deleted {
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let (mut st, mut tx) = new_operation(ds, t, TransactionType::Read, cache_size).await;
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check_tree_properties(&mut tx, &mut st, t).await?.check(0, 0, None, None, 0, 0);
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}
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Ok(())
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}
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@ -2194,10 +2198,14 @@ mod tests {
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check_delete: bool,
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cache_size: usize,
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) -> Result<(), Error> {
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for distance in [Distance::Euclidean, Distance::Manhattan] {
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for distance in [Distance::Euclidean, Distance::Cosine, Distance::Manhattan] {
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if distance == Distance::Cosine && vector_type == VectorType::F64 {
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// Tests based on Cosine distance with F64 may fail due to float rounding errors
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continue;
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}
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for capacity in capacities {
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debug!(
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"Distance: {:?} - Capacity: {} - Collection: {} - Vector type: {}",
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info!(
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"test_mtree_collection - Distance: {:?} - Capacity: {} - Collection: {} - Vector type: {}",
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distance,
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capacity,
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collection.as_ref().len(),
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@ -2273,6 +2281,7 @@ mod tests {
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}
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#[test(tokio::test)]
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#[ignore]
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async fn test_mtree_unique_xs() -> Result<(), Error> {
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for vt in
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[VectorType::F64, VectorType::F32, VectorType::I64, VectorType::I32, VectorType::I16]
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@ -2294,6 +2303,7 @@ mod tests {
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}
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#[test(tokio::test)]
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#[ignore]
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async fn test_mtree_unique_xs_full_cache() -> Result<(), Error> {
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for vt in
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[VectorType::F64, VectorType::F32, VectorType::I64, VectorType::I32, VectorType::I16]
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@ -2337,7 +2347,7 @@ mod tests {
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test_mtree_collection(
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&[40],
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vt,
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TestCollection::new_unique(1000, vt, 20),
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TestCollection::new_unique(1000, vt, 10),
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false,
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true,
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false,
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@ -2354,7 +2364,7 @@ mod tests {
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test_mtree_collection(
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&[40],
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vt,
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TestCollection::new_unique(1000, vt, 20),
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TestCollection::new_unique(1000, vt, 10),
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false,
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true,
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false,
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@ -2371,7 +2381,7 @@ mod tests {
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test_mtree_collection(
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&[40],
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vt,
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TestCollection::new_unique(1000, vt, 20),
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TestCollection::new_unique(1000, vt, 10),
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false,
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true,
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false,
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@ -2428,7 +2438,7 @@ mod tests {
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test_mtree_collection(
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&[40],
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vt,
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TestCollection::new_random(1000, vt, 20),
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TestCollection::new_random(1000, vt, 10),
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false,
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true,
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true,
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@ -1,9 +1,11 @@
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use crate::err::Error;
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use crate::fnc::util::math::ToFloat;
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use crate::sql::index::VectorType;
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use crate::sql::Number;
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use revision::revisioned;
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use serde::{Deserialize, Serialize};
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use std::cmp::Ordering;
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use std::ops::Mul;
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use std::sync::Arc;
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/// In the context of a Symmetric MTree index, the term object refers to a vector, representing the indexed item.
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@ -107,6 +109,66 @@ impl Vector {
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Ok(())
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}
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}
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fn dot<T>(a: &[T], b: &[T]) -> f64
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where
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T: Mul<Output = T> + Copy + ToFloat,
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{
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a.iter().zip(b.iter()).map(|(&x, &y)| x.to_float() * y.to_float()).sum::<f64>()
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}
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fn magnitude<T>(v: &[T]) -> f64
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where
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T: ToFloat + Copy,
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{
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v.iter()
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.map(|&x| {
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let x = x.to_float();
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x * x
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})
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.sum::<f64>()
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.sqrt()
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}
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fn normalize<T>(v: &[T]) -> Vec<f64>
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where
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T: ToFloat + Copy,
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{
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let mag = Self::magnitude(v);
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if mag == 0.0 || mag.is_nan() {
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vec![0.0; v.len()] // Return a zero vector if magnitude is zero
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} else {
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v.iter().map(|&x| x.to_float() / mag).collect()
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}
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}
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fn cosine<T>(a: &[T], b: &[T]) -> f64
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where
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T: ToFloat + Mul<Output = T> + Copy,
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{
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let norm_a = Self::normalize(a);
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let norm_b = Self::normalize(b);
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let mut s = Self::dot(&norm_a, &norm_b);
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if s < -1.0 {
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s = -1.0;
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}
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if s > 1.0 {
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s = 1.0;
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}
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1.0 - s
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}
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pub(crate) fn cosine_distance(&self, other: &Self) -> f64 {
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match (self, other) {
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(Self::F64(a), Self::F64(b)) => Self::cosine(a, b),
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(Self::F32(a), Self::F32(b)) => Self::cosine(a, b),
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(Self::I64(a), Self::I64(b)) => Self::cosine(a, b),
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(Self::I32(a), Self::I32(b)) => Self::cosine(a, b),
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(Self::I16(a), Self::I16(b)) => Self::cosine(a, b),
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_ => f64::NAN,
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}
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}
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pub(super) fn euclidean_distance(&self, other: &Self) -> Result<f64, Error> {
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Self::check_same_dimension("vector::distance::euclidean", self, other)?;
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match (self, other) {
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@ -14,7 +14,7 @@ async fn select_where_mtree_knn() -> Result<(), Error> {
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CREATE pts:3 SET point = [8,9,10,11];
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DEFINE INDEX mt_pts ON pts FIELDS point MTREE DIMENSION 4;
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LET $pt = [2,3,4,5];
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SELECT id, vector::distance::euclidean(point, $pt) AS dist FROM pts WHERE point <|2,EUCLIDEAN|> $pt;
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SELECT id, vector::distance::euclidean(point, $pt) AS dist FROM pts WHERE point <|2|> $pt;
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SELECT id FROM pts WHERE point <|2|> $pt EXPLAIN;
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";
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let dbs = new_ds().await?;
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@ -104,7 +104,8 @@ async fn delete_update_mtree_index() -> Result<(), Error> {
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#[tokio::test]
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async fn index_embedding() -> Result<(), Error> {
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let sql = r#"
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DEFINE INDEX idx_mtree_embedding ON Document FIELDS items.embedding MTREE DIMENSION 4 DIST MANHATTAN;
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DEFINE INDEX idx_mtree_embedding_manhattan ON Document FIELDS items.embedding MTREE DIMENSION 4 DIST MANHATTAN;
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DEFINE INDEX idx_mtree_embedding_cosine ON Document FIELDS items.embedding MTREE DIMENSION 4 DIST COSINE;
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CREATE ONLY Document:1 CONTENT {
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"items": [
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{
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@ -121,9 +122,10 @@ async fn index_embedding() -> Result<(), Error> {
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let dbs = new_ds().await?;
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let ses = Session::owner().with_ns("test").with_db("test");
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let res = &mut dbs.execute(sql, &ses, None).await?;
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assert_eq!(res.len(), 2);
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assert_eq!(res.len(), 3);
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//
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let _ = res.remove(0).result?;
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let _ = res.remove(0).result?;
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//
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let tmp = res.remove(0).result?;
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let val = Value::parse(
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