PiPNN 3/6: add core graph construction

diskann/Cargo.toml

@@ -14,6 +14,7 @@ targets = ["x86_64-unknown-linux-gnu", "aarch64-pc-windows-msvc", "x86_64-pc-win
 [dependencies]
 anyhow.workspace = true
 bytemuck = { workspace = true, features = ["must_cast"]}
+diskann-linalg = { workspace = true, optional = true }
 diskann-utils = { workspace = true, default-features = false }
 futures-util = { workspace = true, default-features = false }
 half = { workspace = true, features = ["bytemuck", "num-traits"] }
@@ -22,6 +23,7 @@ half = { workspace = true, features = ["bytemuck", "num-traits"] }
 hashbrown = { version = "0.16.0", default-features = false, features = ["default-hasher"] }
 num-traits.workspace = true
 rand.workspace = true
+rayon = { workspace = true, optional = true }
 thiserror.workspace = true
 tokio = { workspace = true, features = ["rt", "rt-multi-thread"] }
 tracing = { workspace = true, optional = true }
@@ -57,7 +59,7 @@ panic = "warn"
 default = ["tracing"]
 
 # Enable PiPNN batch graph construction.
-pipnn = []
+pipnn = ["dep:diskann-linalg", "dep:rayon", "tracing"]
 
 # Enable "tracing" diagnostics.
 tracing = ["dep:tracing"]

diskann/src/graph/pipnn/finalization.rs

@@ -0,0 +1,293 @@
+/*
+ * Copyright (c) Microsoft Corporation.
+ * Licensed under the MIT license.
+ */
+
+//! Graph-degree enforcement with the Vamana RobustPrune kernel.
+//!
+//! Candidate merging can produce more than `R` IDs for one point. This module
+//! checks every global ID before parallel work starts. A list at or below `R`
+//! returns without distance calculations.
+//!
+//! For a longer list, the module computes each source distance. It sorts the
+//! candidates and calls RobustPrune. The module then writes the selected IDs into
+//! the original list allocation.
+//!
+//! RobustPrune defines occlusion and alpha-round behavior. This module supplies
+//! source vectors and metric distances.
+
+use crate::{
+    ANNError, ANNResult,
+    graph::{
+        AdjacencyList, Config,
+        internal::{SortedNeighbors, prune},
+    },
+    neighbor::Neighbor,
+    utils::VectorRepr,
+};
+use diskann_utils::views::MatrixView;
+use diskann_vector::{DistanceFunction, distance::Metric};
+use rayon::prelude::*;
+
+#[derive(Debug, thiserror::Error)]
+pub(crate) enum FinalizationError {
+    #[error("candidate list count {lists} does not match the dataset point count {points}")]
+    CandidateListCountMismatch { lists: usize, points: usize },
+    #[error(
+        "candidate ID {candidate} for source {source_index} is outside a {points}-point dataset"
+    )]
+    InvalidCandidateId {
+        source_index: usize,
+        candidate: u32,
+        points: usize,
+    },
+    #[error("candidate count {actual} exceeds the u16 position limit {max}")]
+    TooManyCandidates { actual: usize, max: usize },
+}
+
+/// RobustPrune state for one Rayon job.
+///
+/// `candidate_slots` and `prune_states` stay positionally aligned with
+/// `sorted_candidates`.
+#[derive(Default)]
+struct PruneWorkspace {
+    sorted_candidates: Vec<Neighbor<u32>>,
+    candidate_slots: Vec<(f32, Option<u32>)>,
+    prune_states: Vec<prune::State>,
+}
+
+/// Check candidate IDs and prune each list that exceeds the graph degree.
+pub(crate) fn prune_overfull<T>(
+    data: MatrixView<'_, T>,
+    candidates: Vec<AdjacencyList<u32>>,
+    graph: &Config,
+    metric: Metric,
+) -> ANNResult<Vec<AdjacencyList<u32>>>
+where
+    T: VectorRepr + Send + Sync,
+{
+    validate_candidate_lists(&candidates, data.nrows()).map_err(ANNError::new)?;
+
+    let degree = graph.pruned_degree().get();
+    let distance = T::distance(metric, Some(data.ncols()));
+
+    // `build_graph` runs this Rayon operation in the pool from the build context.
+    #[allow(clippy::disallowed_methods)]
+    candidates
+        .into_par_iter()
+        .enumerate()
+        .map_init(
+            PruneWorkspace::default,
+            |workspace, (source, mut source_candidates)| {
+                // Candidate merging already removes duplicate IDs. A list within
+                // the degree limit needs no distance calculation.
+                if source_candidates.len() <= degree {
+                    return Ok(source_candidates);
+                }
+
+                let source_id = u32::try_from(source).map_err(ANNError::new)?;
+                let source_vector = data.row(source);
+                workspace.sorted_candidates.clear();
+                workspace
+                    .sorted_candidates
+                    .try_reserve(source_candidates.len())
+                    .map_err(ANNError::new)?;
+                workspace
+                    .sorted_candidates
+                    .extend(source_candidates.iter().copied().map(|candidate| {
+                        Neighbor::new(
+                            candidate,
+                            distance
+                                .evaluate_similarity(source_vector, data.row(candidate as usize)),
+                        )
+                    }));
+
+                let candidate_count = workspace.sorted_candidates.len();
+                if candidate_count > u16::MAX as usize {
+                    return Err(ANNError::new(FinalizationError::TooManyCandidates {
+                        actual: candidate_count,
+                        max: u16::MAX as usize,
+                    }));
+                }
+                workspace.candidate_slots.clear();
+                workspace
+                    .candidate_slots
+                    .try_reserve(candidate_count)
+                    .map_err(ANNError::new)?;
+
+                // Sort all candidates before the code marks a self-edge as absent.
+                // Thus, self-edge removal cannot add a farther candidate. The
+                // `SortedNeighbors` value carries this order into RobustPrune.
+                let sorted =
+                    SortedNeighbors::new(&mut workspace.sorted_candidates, candidate_count);
+                workspace
+                    .candidate_slots
+                    .extend(sorted.iter().map(|neighbor| {
+                        let id = *neighbor.id();
+                        (*neighbor.distance(), (id != source_id).then_some(id))
+                    }));
+                workspace
+                    .prune_states
+                    .try_reserve(
+                        workspace
+                            .candidate_slots
+                            .len()
+                            .saturating_sub(workspace.prune_states.len()),
+                    )
+                    .map_err(ANNError::new)?;
+                workspace
+                    .prune_states
+                    .resize(workspace.candidate_slots.len(), prune::State::default());
+
+                let selected = prune::robust_prune(
+                    &sorted,
+                    &workspace.candidate_slots,
+                    workspace.prune_states.as_mut_slice(),
+                    degree,
+                    graph.alpha(),
+                    graph.prune_kind(),
+                    |left, right| {
+                        distance.evaluate_similarity(
+                            data.row(*left as usize),
+                            data.row(*right as usize),
+                        )
+                    },
+                );
+
+                let mut guard = source_candidates.resize(selected);
+                for (destination, state) in guard.iter_mut().zip(workspace.prune_states.iter()) {
+                    *destination = *sorted[state.neighbor as usize].id();
+                }
+                guard.finish(selected);
+                Ok(source_candidates)
+            },
+        )
+        .collect()
+}
+
+fn validate_candidate_lists(
+    candidates: &[AdjacencyList<u32>],
+    points: usize,
+) -> Result<(), FinalizationError> {
+    if candidates.len() != points {
+        return Err(FinalizationError::CandidateListCountMismatch {
+            lists: candidates.len(),
+            points,
+        });
+    }
+    for (source, source_candidates) in candidates.iter().enumerate() {
+        if let Some(&candidate) = source_candidates.iter().find(|&&id| id as usize >= points) {
+            return Err(FinalizationError::InvalidCandidateId {
+                source_index: source,
+                candidate,
+                points,
+            });
+        }
+    }
+    Ok(())
+}
+
+#[cfg(test)]
+mod tests {
+    use crate::graph::{
+        AdjacencyList,
+        config::{self, MaxDegree},
+    };
+    use diskann_utils::views::MatrixView;
+
+    use super::*;
+
+    fn graph_config(degree: usize) -> Config {
+        config::Builder::new_with(
+            degree,
+            MaxDegree::same(),
+            degree,
+            Metric::L2.into(),
+            |builder| {
+                builder.alpha(1.2);
+            },
+        )
+        .build()
+        .unwrap()
+    }
+
+    fn candidate_list(ids: impl IntoIterator<Item = u32>) -> AdjacencyList<u32> {
+        AdjacencyList::from_iter_untrusted(ids)
+    }
+
+    #[test]
+    fn preserves_lists_within_the_degree_bound() {
+        let data = [0.0_f32, 1.0, 2.0, 3.0];
+        let data = MatrixView::try_from(&data[..], 4, 1).unwrap();
+        let candidates = vec![
+            candidate_list([3, 1]),
+            candidate_list([]),
+            candidate_list([]),
+            candidate_list([]),
+        ];
+
+        let actual = prune_overfull(data, candidates, &graph_config(2), Metric::L2).unwrap();
+
+        assert_eq!(&*actual[0], &[1, 3]);
+    }
+
+    #[test]
+    fn prunes_an_overfull_list_with_the_vamana_kernel() {
+        let data = [0.0_f32, 1.0, 2.0, -3.0];
+        let data = MatrixView::try_from(&data[..], 4, 1).unwrap();
+        let candidates = vec![
+            candidate_list([3, 2, 1]),
+            candidate_list([]),
+            candidate_list([]),
+            candidate_list([]),
+        ];
+
+        let actual = prune_overfull(data, candidates, &graph_config(2), Metric::L2).unwrap();
+
+        assert_eq!(&*actual[0], &[1, 3]);
+    }
+
+    #[test]
+    fn rejects_invalid_candidate_ids_without_panicking() {
+        let data = [0.0_f32, 1.0, 2.0];
+        let data = MatrixView::try_from(&data[..], 3, 1).unwrap();
+        let candidates = vec![
+            candidate_list([1, 3]),
+            candidate_list([]),
+            candidate_list([]),
+        ];
+
+        let error = prune_overfull(data, candidates, &graph_config(1), Metric::L2).unwrap_err();
+
+        assert!(matches!(
+            error.downcast_ref::<FinalizationError>(),
+            Some(FinalizationError::InvalidCandidateId {
+                source_index: 0,
+                candidate: 3,
+                points: 3,
+            })
+        ));
+    }
+
+    #[test]
+    fn rejects_candidate_list_count_mismatch_without_panicking() {
+        let data = [0.0_f32, 1.0, 2.0];
+        let data = MatrixView::try_from(&data[..], 3, 1).unwrap();
+        let candidates = vec![
+            candidate_list([]),
+            candidate_list([]),
+            candidate_list([]),
+            candidate_list([]),
+        ];
+
+        let error = prune_overfull(data, candidates, &graph_config(1), Metric::L2).unwrap_err();
+
+        assert!(matches!(
+            error.downcast_ref::<FinalizationError>(),
+            Some(FinalizationError::CandidateListCountMismatch {
+                lists: 4,
+                points: 3
+            })
+        ));
+    }
+}