PiPNN 2/6: add numerical kernels

[weiyaoluo (SeliMeli)]

Purpose

This PR adds the numerical kernels that PiPNN uses for partition ranking and leaf neighbor selection.

It does not add graph construction, providers, or serialization.

Main changes

• sgemm_aat_lower computes the diagonal and lower triangle of A · Aᵀ.
• PartitionMetric defines partition norm preparation and ranking formulas.
• LeafMetric defines leaf norm preparation and distance formulas.
• Metric traits prepare norm buffers and own norm access and SIMD loads.
• Shared functions define cosine distance for both stages.
• partition_kernel supports runtime fanout with reusable ranked-leader scratch.
• leaf_kernel scans each unordered pair once.
• Fixed insertion handles k from 1 through 3. A runtime loop handles larger k.
• diskann-wide adds SIMD division for supported f32 vector types.
• MatrixView rejects an overflowing declared area.

ScaleKind does not exist. All norm names state their units.

Numerical behavior

• Strict comparisons preserve scan order for ties.
• Strict comparisons do not rank NaN partition scores.
• Leaf distance clamps use diskann-wide SIMD min/max behavior.
• L2 partition SIMD groups and single values use fused arithmetic.
• Cosine preserves the DiskANN zero-norm rules.

This PR contains three temporary allow(dead_code) attributes. #1290 adds production callers and removes all three attributes.

Review order

1. Review diskann/src/graph/pipnn/kernel_metric.rs.
2. Review kernel_metric/partition.rs.
3. Review partition_kernel.rs.
4. Review kernel_metric/leaf.rs.
5. Review leaf_kernel.rs.
6. Review sgemm_aat_lower and SIMD division.

Validation

• 27 kernel tests cover all metrics, insertion widths, and SIMD boundaries.
• Tests cover 15, 16, 17, 31, 32, and 33 leaders or points.
• A 17-leader L2 test verifies matching fused SIMD and single-value rankings.
• Linux, Windows GNU, and AArch64 Clippy pass with -Dwarnings.
• This PR adds no submitted PiPNN benchmark target.

Stack

Stack 2/6. Depends on #1315. #1290 adds the production callers.

[Copilot]

Pull request overview

This PR adds the first set of PiPNN “kernel” building blocks to the DiskANN Rust workspace: SIMD-accelerated top‑k selection for partition assignment and leaf neighbor selection, along with supporting SIMD division and a new lower-triangular A·Aᵀ helper in diskann-linalg.

Changes:

• Add a new diskann-pipnn crate with partition_kernel and leaf_kernel implementations plus extensive correctness tests and Criterion benchmarks.
• Extend diskann-wide to support Div on relevant f32 SIMD types (native, doubled, and scalar/emulated) and add a corresponding division test macro.
• Add diskann_linalg::sgemm_aat_lower (lower-triangle-only AAT) and wire new crate/tests/CI/mutants exclusions into the workspace.

Reviewed changes

Copilot reviewed 26 out of 27 changed files in this pull request and generated 2 comments.

Show a summary per file

File	Description
diskann-wide/src/test_utils/ops.rs	Adds test_div! macro to validate lane-wise SIMD division correctness.
diskann-wide/src/emulated.rs	Adds Div for scalar/emulated Emulated<f32, N, A> to support division in scalar dispatch.
diskann-wide/src/doubled.rs	Adds Div for Doubled<T> to support composite SIMD widths.
diskann-wide/src/arch/x86_64/v4/f32x8_.rs	Adds AVX Div op mapping + division tests.
diskann-wide/src/arch/x86_64/v4/f32x4_.rs	Adds SSE Div op mapping + division tests.
diskann-wide/src/arch/x86_64/v4/f32x16_.rs	Adds AVX-512 Div op mapping + division tests.
diskann-wide/src/arch/x86_64/v3/f32x8_.rs	Adds AVX Div op mapping + division tests for V3.
diskann-wide/src/arch/x86_64/v3/f32x4_.rs	Adds SSE Div op mapping + division tests for V3.
diskann-wide/src/arch/x86_64/v3/f32x16_.rs	Adds division tests for the f32x16 V3 path (likely via doubled composition).
diskann-wide/src/arch/aarch64/f32x4_.rs	Adds Neon Div op mapping + division tests.
diskann-wide/src/arch/aarch64/f32x2_.rs	Adds Neon Div op mapping + division tests.
diskann-pipnn/tests/partition_kernel.rs	New integration tests for partition top‑k dispatch correctness and edge cases.
diskann-pipnn/tests/leaf_kernel.rs	New integration tests for leaf neighbor top‑k dispatch correctness and edge cases.
diskann-pipnn/src/partition_kernel/tests.rs	New unit tests comparing scalar reference vs runtime dispatch and metric contracts.
diskann-pipnn/src/partition_kernel.rs	New partition-assignment distance + top‑k kernel with validation and SIMD dispatch.
diskann-pipnn/src/lib.rs	New crate root exporting PiPNN kernel modules.
diskann-pipnn/src/leaf_kernel/tests.rs	New unit tests for scalar reference parity and workspace behavior.
diskann-pipnn/src/leaf_kernel.rs	New fused lower-triangle leaf neighbor kernel with SIMD dispatch and workspace support.
diskann-pipnn/Cargo.toml	Defines new diskann-pipnn crate, dev-deps, and benches.
diskann-pipnn/benches/kernels.rs	Adds benchmarks for partition top‑k, lower AAT, leaf top‑k, and full leaf workflow.
diskann-linalg/tests/sgemm_aat_lower.rs	New tests for lower-triangle AAT behavior and validation errors.
diskann-linalg/src/lib.rs	Adds public sgemm_aat_lower API with dimension checks.
diskann-linalg/src/faer.rs	Implements sgemm_aat_lower_impl using Faer triangular matmul.
Cargo.toml	Adds diskann-pipnn to workspace members and workspace dependencies.
Cargo.lock	Records the new diskann-pipnn package entry.
.github/workflows/ci.yml	Adds diskann-pipnn to CI test package lists.
.cargo/mutants.toml	Adds mutation-test exclusions for kernel code paths and equivalent transformations.

Comments suppressed due to low confidence (2)

diskann-pipnn/src/leaf_kernel.rs:651

• Same issue as the L2 arm: using max_simd for lower clamping can erase NaNs on the Scalar/Emulated backend, making NaN distances rankable. Clamp with lt_simd + select to preserve NaNs consistently.

        Metric::CosineNormalized => {
            let distance = F::splat(arch, 1.0) - dot;
            zero.max_simd(distance)
        }

diskann-pipnn/src/leaf_kernel.rs:664

• The cosine path also uses zero.max_simd(distance) for clamping, which can collapse NaNs to zero on the Scalar/Emulated backend (via f32::max). That contradicts the comment about preserving non-rankable NaNs and can change output ordering. Prefer an lt_simd + select clamp here as well.

            let distance = one - cosine;
            // Comparisons with NaN are false, so this explicit lower clamp
            // preserves non-rankable NaNs while matching the existing PiPNN
            // distance formulas for finite values.
            zero.max_simd(distance)

---

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[Copilot]

Pull request overview

Copilot reviewed 26 out of 27 changed files in this pull request and generated no new comments.

[Codecov Comments Bot (codecov-commenter)]

Codecov Report

✅ All modified and coverable lines are covered by tests.
⚠️ Please upload report for BASE (pipnn-stack/02-final-prune@c3fd818). Learn more about missing BASE report.

Additional details and impacted files

@@                      Coverage Diff                      @@
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Flag	Coverage Δ
miri	91.57% <100.00%> (?)
unittests	91.25% <100.00%> (?)

Flags with carried forward coverage won't be shown. Click here to find out more.

Files with missing lines	Coverage Δ
diskann-linalg/src/faer.rs	100.00% <100.00%> (ø)
diskann-linalg/src/lib.rs	99.72% <100.00%> (ø)
diskann-utils/src/views.rs	100.00% <100.00%> (ø)
diskann-wide/src/arch/x86_64/v3/f32x16_.rs	100.00% <ø> (ø)
diskann-wide/src/arch/x86_64/v3/f32x4_.rs	100.00% <ø> (ø)
diskann-wide/src/arch/x86_64/v3/f32x8_.rs	100.00% <ø> (ø)
diskann-wide/src/arch/x86_64/v4/f32x16_.rs	14.11% <ø> (ø)
diskann-wide/src/arch/x86_64/v4/f32x4_.rs	16.90% <ø> (ø)
diskann-wide/src/arch/x86_64/v4/f32x8_.rs	16.90% <ø> (ø)
diskann-wide/src/doubled.rs	85.77% <100.00%> (ø)
... and 2 more

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[Copilot]

Pull request overview

Copilot reviewed 26 out of 27 changed files in this pull request and generated no new comments.

Comments suppressed due to low confidence (2)

diskann-pipnn/src/partition_kernel/tests.rs:20

• The PartitionTopK contract for Metric::L2 expects leader_scales to contain squared leader norms (see docs and distance(Metric::L2, ..) test). This helper currently populates unsquared norms, which makes the test data inconsistent with the public API contract and could hide contract-related bugs.

    let leader_scales = match metric {
        Metric::L2 => (0..leaders).map(|leader| (leader + 1) as f32).collect(),
        Metric::Cosine => (0..leaders)
            .map(|leader| {

diskann-pipnn/src/partition_kernel.rs:61

• InvalidFanout’s error message says the maximum is {maximum}, but validation also rejects fanout > leaders. When leaders < maximum this message is misleading (it implies the only limit is {maximum}). Consider spelling out both constraints in the message so callers immediately see why it failed.

    #[error("invalid fanout {fanout} for {leaders} leaders; maximum is {maximum}")]

[Copilot]

Pull request overview

Copilot reviewed 25 out of 26 changed files in this pull request and generated no new comments.

Suppressed comments (1)

diskann-pipnn/src/partition_kernel.rs:294

• For Metric::Cosine, NaN norms currently produce a finite distance (1.0) because denominator.gt_simd(0) is false for NaN, so the lane falls back to cosine = 0. That makes NaN-derived pairs/leaders “rankable”, which contradicts the module’s stated NaN-rejection behavior and differs from diskann-vector cosine semantics (NaN norms propagate to a NaN similarity/distance). Consider explicitly preserving NaN denominators so the resulting distance stays NaN and is ignored by insert_topk.

        let denominator = row_norm * leader_norm;
        let valid = denominator.gt_simd(zero);
        let safe_denominator = valid.select(denominator, one);
        let cosine = valid.select(dot / safe_denominator, zero);
        one - cosine

[juchen-ms (partychen)]

Nice work overall. I found one correctness issue in the cosine handling that should be resolved before merge. The remaining comments are mostly about reducing duplicated or unsafe code and tightening the API contracts.

[Aditya Krishnan (arkrishn94)]

Thanks Weiyao, this is progress from the previous mega-PR. I still have some big-picture comments (we covered most of these offline) -

• Documentation: As I mentioned, we need thorough documentation in the diskann-pipnn crate. The main modules, partition_kernel and leaf_kernel need documentation up top, highlighting the main structures and how they are used - e.g. process_rows_binary/unary and nearest_leaders. Similarly with process_pairs_simd_* and nearest_leaf_neighbors
• Testing: I am concerned about the lack of testing for partition_kernel.rs and leaf_kernel.rs.
  • I notice some e2e integration tests but these kernels should be thoroughly tested, sweeping different input parameters, architectures and edge cases. This is especially needed given the amount of unsafe code.
  • That brings me to miri - there should be miri tests too.
  • I'm curious why are the tests in a separate submodule to the main files (for partition_kernel.rs and leaf_kernel.rs)? Let's try to keep tests along with the code being tested.
• Criterion: Since criterion is not a standard part of our library for benchmarking, let us not introduce it for this crate.
• Kernel dispatch: I left comments about you're disptaching the kernels, please take a look.

[Copilot]

Pull request overview

Copilot reviewed 26 out of 27 changed files in this pull request and generated no new comments.

Suppressed comments (2)

diskann-pipnn/src/partition_kernel/tests.rs:19

• PartitionTopK::leader_scales is documented as "squared leader norms for L2" (and cosine uses unsquared norms), but this test helper feeds unsquared values for the L2 case. That makes the test data inconsistent with the public contract and can mask mistakes in distance computation. Consider squaring the L2 norms here so the tests exercise the intended inputs.

    let leader_scales = match metric {
        Metric::L2 => (0..leaders).map(|leader| (leader + 1) as f32).collect(),
        Metric::Cosine => (0..leaders)

diskann-pipnn/src/partition_kernel.rs:252

• For the L2 path, the SIMD chunk uses mul_add_simd (fused multiply-add) but the scalar tail uses norm - 2.0 * dot (non-fused). This can introduce small rounding differences between SIMD and tail elements, which can change ordering/tie behavior right at SIMD-width boundaries. Use f32::mul_add for the scalar tail so both paths compute the same value shape.

                |dot, norm| F::splat(arch, -2.0).mul_add_simd(dot, norm),
                |dot, norm| norm - 2.0 * dot,

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