PiPNN 3/6: add core graph construction by SeliMeli · Pull Request #1290 · microsoft/DiskANN

SeliMeli · 2026-07-29T11:52:44Z

Adds the provider-independent PiPNN graph-construction pipeline.

Code map

lib.rs defines configuration, validates graph-policy compatibility, and orchestrates partition → leaf candidates → finalization inside the caller-owned Rayon pool.
partitioning.rs runs deterministic replicas. Each oversized work item samples leaders, gathers point/leader rows, uses GEMM plus partition_kernel for assignments, and recurses only on oversized clusters.
Large assignment scatters use at most one partial per pool worker, then merge each leader in parallel. Assignment row tiles are rounded down to power-of-two sizes under the 512 KiB target to avoid repeated GEMM tail shapes.
global_merge_small combines sub-c_min leaves without exceeding c_max; final validation rejects empty/oversized leaves.
leaf_build.rs gives each Rayon job reusable buffers. Active prefixes are passed explicitly because numeric buffers retain their high-water length. Each leaf gathers rows, computes lower A · Aᵀ, runs the dual-endpoint top-k kernel, and merges symmetric candidates.
finalization.rs leaves degree-bounded rows unchanged and sends only overfull rows through the shared RobustPrune kernel.

Review path

Verify seed derivation and output ordering across replicas, recursion levels, worker-count scatter, and small-leaf merge.
In leaf_build, check the sorted-ID duplicate fast path and its HashSet fallback, plus every active-prefix slice after grow-only buffer reuse.
Confirm scratch ownership: partition chunks lease buffers from a stage-owned pool and return them after computation; the mutex is held only for pop/push, never across GEMM. Leaf jobs still own their MapInit state, and the outer leaf value is consumed by the stage. There is no TLS or cleanup broadcast.
The crate boundary intentionally contains no provider, start-point, PQ, serialization, or search lifecycle.

Stack 3/6: #1288 → #1291

Copilot

Pull request overview

This PR introduces the “core” PiPNN build pipeline in the diskann-pipnn crate, wiring together deterministic partitioning, leaf-local candidate construction, and final pruning into a public build_graph API with a validated build context.

Changes:

Adds PiPNNConfig validation and a PiPNNBuildContext that binds PiPNN policy to DiskANN graph pruning policy and a caller-owned Rayon thread pool.
Implements the three main stages: partitioning (partitioning.rs), leaf candidate construction (leaf_build.rs), and final pruning via shared Vamana robust prune (finalization.rs).
Adds comprehensive unit/integration tests and a Criterion benchmark for core scenarios; updates dependencies, lockfile, and mutation-test exclusions.

Reviewed changes

Copilot reviewed 13 out of 14 changed files in this pull request and generated 1 comment.

Show a summary per file

File	Description
diskann-pipnn/src/lib.rs	Adds public PiPNN API (`PiPNNConfig`, `PiPNNBuildContext`, `build_graph`) and stage orchestration.
diskann-pipnn/src/partitioning.rs	Implements deterministic overlapping partition construction and leader assignment/scatter.
diskann-pipnn/src/partitioning/tests.rs	Adds unit tests covering partition determinism, invariants, error cases, and helpers.
diskann-pipnn/src/leaf_build.rs	Builds leaf-local symmetric k-NN candidates and accumulates global candidates safely in parallel.
diskann-pipnn/src/leaf_build/tests.rs	Adds unit tests for candidate correctness, invariants, type support, and error handling.
diskann-pipnn/src/finalization.rs	Orders/prunes candidate rows using shared `robust_prune` and validates candidate IDs/shape.
diskann-pipnn/src/finalization/tests.rs	Adds unit tests for pruning behavior and candidate validation failures.
diskann-pipnn/src/tests.rs	Tests `effective_metric` behavior for integer cosine-normalized handling.
diskann-pipnn/tests/config.rs	Integration tests for config validation and graph-policy compatibility checks.
diskann-pipnn/tests/build_graph.rs	Integration tests for end-to-end graph building, invariants, determinism, and type/metric support.
diskann-pipnn/benches/core.rs	Adds a Criterion benchmark for stage-focused core build scenarios.
diskann-pipnn/Cargo.toml	Updates crate dependencies/dev-dependencies and registers the new `core` benchmark target.
Cargo.lock	Records dependency graph changes for the updated `diskann-pipnn` crate dependencies.
.cargo/mutants.toml	Adds mutation-test exclusions for key PiPNN public boundary checks and partitioning invariants.

Comments suppressed due to low confidence (1)

diskann-pipnn/src/partitioning.rs:604

size_of::<f32>() is used without being in scope (no use std::mem::size_of; and not qualified), so this function won’t compile as written.

fn assignment_stripe_rows(leaders: usize) -> usize {
    (ASSIGNMENT_CACHE_TARGET_BYTES / (leaders.max(1) * size_of::<f32>()))
        .clamp(MIN_ASSIGNMENT_STRIPE_ROWS, MAX_ASSIGNMENT_STRIPE_ROWS)
}

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+        *scale = FastL2NormSquared.evaluate(row);
+        if metric == Metric::Cosine {
+            *scale = scale.sqrt();
+        }
+    }


Copilot

Pull request overview

Copilot reviewed 13 out of 14 changed files in this pull request and generated 1 comment.

+fn assignment_stripe_rows(leaders: usize) -> usize {
+    (ASSIGNMENT_CACHE_TARGET_BYTES / (leaders.max(1) * size_of::<f32>()))
+        .clamp(MIN_ASSIGNMENT_STRIPE_ROWS, MAX_ASSIGNMENT_STRIPE_ROWS)
+}


Copilot

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Copilot reviewed 14 out of 15 changed files in this pull request and generated no new comments.

Comments suppressed due to low confidence (2)

diskann-pipnn/src/partitioning.rs:637

size_of is used without being in scope (std::mem::size_of), which will not compile. Qualify the call or import it.

fn assignment_stripe_rows(leaders: usize) -> usize {
    (ASSIGNMENT_CACHE_TARGET_BYTES / (leaders.max(1) * size_of::<f32>()))
        .clamp(MIN_ASSIGNMENT_STRIPE_ROWS, MAX_ASSIGNMENT_STRIPE_ROWS)
}

diskann-pipnn/src/partitioning.rs:19

Norm is imported but never used in this module, which will trip unused_imports warnings (and can become CI failures under -D warnings). Remove it from the import list.

use diskann::{utils::VectorRepr, ANNError, ANNResult};
use diskann_linalg::Transpose;
use diskann_utils::views::MatrixView;
use diskann_vector::{distance::Metric, norm::FastL2NormSquared, Norm};
use rand::{prelude::IndexedRandom, SeedableRng};
use rayon::prelude::*;

Copilot

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Copilot reviewed 14 out of 15 changed files in this pull request and generated no new comments.

Comments suppressed due to low confidence (1)

diskann-pipnn/src/partitioning.rs:390

gather_rows uses TypeId::of::<T>(), which implicitly requires T: 'static. Making that bound explicit here avoids surprising/indirect trait-bound errors later and matches the public build_graph boundary (which already requires 'static).

fn gather_rows<T>(data: MatrixView<'_, T>, indices: &[u32], output: &mut [f32]) -> ANNResult<()>
where
    T: VectorRepr,

Copilot

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Copilot reviewed 14 out of 15 changed files in this pull request and generated 1 comment.

Comments suppressed due to low confidence (1)

diskann-pipnn/src/partitioning.rs:641

size_of::<f32>() is used without being imported or qualified, which will fail to compile. Qualify it with std::mem::size_of (or add an explicit import).

    let rows = ASSIGNMENT_CACHE_TARGET_BYTES / (leaders.max(1) * size_of::<f32>());

+use diskann::{utils::VectorRepr, ANNError, ANNResult};
+use diskann_linalg::Transpose;
+use diskann_utils::views::MatrixView;
+use diskann_vector::{distance::Metric, norm::FastL2NormSquared, Norm};


Copilot

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Copilot reviewed 14 out of 15 changed files in this pull request and generated 1 comment.

Comments suppressed due to low confidence (4)

diskann-pipnn/src/leaf_build.rs:222

build_leaf is executed from a Rayon parallel context (via build_leaf_candidates), so it should also explicitly require T: Send + Sync to reflect the actual thread-safety requirement.

where
    T: VectorRepr + 'static,
{

diskann-pipnn/src/leaf_build/tests.rs:154

assert_source_type forwards T into the parallel leaf build path, so it should also include Send + Sync bounds to match the production requirements.

fn assert_source_type<T>(data: &[T])
where
    T: diskann::utils::VectorRepr + 'static,
{

diskann-pipnn/src/leaf_build.rs:193

build_leaf_candidates uses Rayon parallel iteration over data, so T must be Send + Sync. Making this explicit in the signature avoids confusing trait-bound errors at call sites and documents the thread-safety requirement.

This issue also appears on line 220 of the same file.

where
    T: VectorRepr + 'static,
{

diskann-pipnn/src/leaf_build/tests.rs:35

This test helper calls build_leaf_candidates, which (via Rayon) requires T: Send + Sync. Add the bounds here so the test continues to compile once the production signature is tightened.

This issue also appears on line 151 of the same file.

where
    T: diskann::utils::VectorRepr + 'static,
{

+    fanout: usize,
+    leaders: usize,
+) -> ANNResult<Vec<Vec<u32>>> {
+    let mut sizes = filled_vec(leaders, 0usize)?;


Copilot

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Copilot reviewed 14 out of 15 changed files in this pull request and generated 1 comment.

Comments suppressed due to low confidence (1)

diskann-pipnn/src/finalization.rs:76

prune::robust_prune requires the candidate pool to be sorted by increasing distance, but pool is currently populated in candidate-ID order (from the AdjacencyList) and never sorted by the computed distances. This can change pruning behavior substantially and break determinism/quality.

            let pool = workspace.prune.candidates_mut();
            pool.clear();
            pool.try_reserve(row.len()).map_err(ANNError::opaque)?;
            pool.extend(row.iter().copied().map(|candidate| {
                Neighbor::new(
                    candidate,
                    distance.evaluate_similarity(source_vector, data.row(candidate as usize)),
                )
            }));
            let candidate_count = pool.len();
            let mut context = workspace.prune.as_context(candidate_count);
            prune::robust_prune(

+fn assignment_stripe_rows(leaders: usize) -> usize {
+    let rows = ASSIGNMENT_CACHE_TARGET_BYTES / (leaders.max(1) * size_of::<f32>());
+    let rows = if rows.is_power_of_two() {
+        rows
+    } else {
+        rows.next_power_of_two() / 2
+    };
+    rows.clamp(MIN_ASSIGNMENT_STRIPE_ROWS, MAX_ASSIGNMENT_STRIPE_ROWS)
+}


magdalendobson · 2026-07-30T16:44:26Z

+    /// Number of nearest leaders retained at each overlapping partition level.
+    pub fanout: Vec<usize>,
+    /// Number of nearest neighbors selected within each leaf.
+    pub k: usize,


Request to use something like partition_k to not overload k

magdalendobson · 2026-07-30T16:45:53Z

+    /// Fraction of a cluster sampled as partition leaders.
+    pub p_samp: f64,
+    /// Number of nearest leaders retained at each overlapping partition level.
+    pub fanout: Vec<usize>,


Could you explain why this is a vector? What would it mean if I passed in an arbitrary vector here?

magdalendobson · 2026-07-30T16:47:16Z

+                self.c_min, self.c_max
+            )));
+        }
+        if !self.p_samp.is_finite() || !(0.0..=1.0).contains(&self.p_samp) || self.p_samp == 0.0 {


Isn't the middle statement sufficient? Why do we need to explicitly check finite and nonzero?

magdalendobson · 2026-07-30T16:49:24Z

+        pool: &'a ThreadPool,
+    ) -> ANNResult<Self> {
+        config.validate()?;
+        if !graph.alpha().is_finite() || graph.alpha() < 1.0 {


0.0 < Alpha < 1.0 should not be disallowed. It is meaningful. Also, the Config should check for these issues already. If it doesn't, the errors should be moved into the validation of the config itself.

magdalendobson · 2026-07-30T16:53:16Z

+/// Policy owned by the partition stage. Leaf-neighbor and merge settings do
+/// not cross this boundary.
+#[derive(Clone, Debug)]
+pub(crate) struct PartitionConfig {
+    c_max: usize,
+    c_min: usize,
+    p_samp: f64,
+    fanout: Vec<usize>,
+    replicas: usize,
+}
+
+impl From<&PiPNNConfig> for PartitionConfig {
+    fn from(config: &PiPNNConfig) -> Self {
+        Self {
+            c_max: config.c_max,
+            c_min: config.c_min,
+            p_samp: config.p_samp,
+            fanout: config.fanout.clone(),
+            replicas: config.replicas,
+        }
+    }


Why can't we just use PiPNNConfig? What would go wrong if we did?

microsoft-github-policy-service · 2026-07-31T04:24:33Z

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Copilot reviewed 13 out of 14 changed files in this pull request and generated no new comments.

Suppressed comments (1)

diskann-pipnn/src/partitioning.rs:172

The doc comment claims coverage is validated ("every input point must remain covered once per replica"), but partition() currently only calls validate_leaves(&leaves, config.c_max) which checks empties/oversized leaves. Either add an explicit per-replica coverage check, or adjust the docs so they don’t promise validation that isn’t performed.

/// Levels beyond `fanout.len()` retain one leader assignment. Completed small
/// leaves are merged without exceeding `c_max`; every input point must remain
/// covered once per replica. The caller installs the operation in its pool.

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Comment thread diskann-pipnn/src/partitioning.rs

Comment on lines +285 to +289

*scale = FastL2NormSquared.evaluate(row);

if metric == Metric::Cosine {

*scale = scale.sqrt();

}

}

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Comment thread diskann-pipnn/src/partitioning.rs

Comment on lines +606 to +609

fn assignment_stripe_rows(leaders: usize) -> usize {

(ASSIGNMENT_CACHE_TARGET_BYTES / (leaders.max(1) * size_of::<f32>()))

.clamp(MIN_ASSIGNMENT_STRIPE_ROWS, MAX_ASSIGNMENT_STRIPE_ROWS)

}

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SeliMeli changed the title ~~Pipnn stack/03 core~~ PiPNN 3/6: add core graph construction Jul 29, 2026

This was referenced Jul 29, 2026

PiPNN 2/6: extract shared RobustPrune #1288

Open

PiPNN 4/6: integrate disk build pipeline #1291

Open

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Comment thread diskann-pipnn/src/partitioning.rs

use diskann::{utils::VectorRepr, ANNError, ANNResult};

use diskann_linalg::Transpose;

use diskann_utils::views::MatrixView;

use diskann_vector::{distance::Metric, norm::FastL2NormSquared, Norm};

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Comment thread diskann-pipnn/src/partitioning.rs

fanout: usize,

leaders: usize,

) -> ANNResult<Vec<Vec<u32>>> {

let mut sizes = filled_vec(leaders, 0usize)?;

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SeliMeli added 17 commits July 31, 2026 04:16

pipnn: assemble direct-candidate graph builder

e5254bf

pipnn: benchmark core graph stages

1a62429

pipnn: cover partition validation boundaries

b38a12a

pipnn: harden core graph construction

9a49d93

bench(pipnn): keep core workloads scoped

2df098c

docs(pipnn): describe integer cosine policy

5af01b3

pipnn: expose core config validation

b687ab0

refactor(pipnn): consume leaves during leaf build

850c3aa

fix(pipnn): preserve established replica seeds

e56c7c6

docs(pipnn): define graph-construction boundary

03dd2e7

refactor(pipnn): own partition-stage configuration

6ac967a

perf(pipnn): release owned leaves after leaf stage

3f1450f

docs(pipnn): explain partition stage contract

2b492b4

perf(pipnn): reduce partition and leaf overhead

f6a2255

perf(pipnn): reuse partition scratch across work items

32aed80

docs(pipnn): document core stage invariants

118b428

docs(pipnn): diagram core stage ownership

a7ce31c

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SeliMeli commented Jul 29, 2026 •

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