Add RFC Process and RFC 0001 — Multi-Vector Distance Functions

rfcs/0000-template.md

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+# RFC Title
+
+<!-- Replace "RFC Title" with a short, descriptive title for your proposal. -->
+
+<!--
+  This template is a recommended starting point. Feel free to add, remove, or
+  reorganize sections to best convey your proposal.
+-->
+
+| | |
+|---|---|
+| **Status** | Draft <!-- Draft, InReview, Accepted, Rejected --> |
+| **Authors** | Your Name |
+| **Contributors** | <!-- Others who contributed to this RFC --> |
+| **Created** | YYYY-MM-DD |
+| **Updated** | YYYY-MM-DD |
+
+## Summary
+
+<!-- One paragraph describing the proposal at a high level. What are you building and why? -->
+
+## Motivation
+
+### Background
+
+<!-- Context a reader needs to understand the problem. What exists today? -->
+
+### Problem Statement
+
+<!-- What specific problem does this RFC solve? Include quantitative data if available. -->
+
+### Goals
+
+<!-- Numbered list of concrete, measurable goals. -->
+
+1. Goal one
+2. Goal two
+
+## Proposal
+
+<!-- The core technical proposal. Include:
+     - Type definitions (structs, traits, type aliases)
+     - API signatures
+     - Module structure
+     - Algorithms / pseudocode where helpful
+
+     Use Rust code blocks for type/API definitions. -->
+
+## Trade-offs
+
+<!-- Describe the key design trade-offs and alternative approaches considered.
+     For each alternative, explain what it is, its pros/cons, and why it was
+     not chosen (or under what conditions it might be preferred). -->
+
+## Benchmark Results
+
+<!-- If applicable, include performance measurements.
+     State the configuration (hardware, dataset, parameters) and present results in tables. -->
+
+## Future Work
+
+<!-- Items explicitly deferred from this RFC. Use a checkbox list. -->
+
+- [ ] Future item one
+- [ ] Future item two
+
+## References
+
+<!-- Links to papers, prior art, related issues, or PRs. -->
+
+1. [Reference title](URL)

rfcs/0001-multi-vector-distance-functions.md

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+# Multi-Vector Distance Functions
+
+| | |
+|---|---|
+| **Status** | InReview |
+| **Authors** | Suryansh Gupta |
+| **Contributors** | Suryansh Gupta, Mark Hildebrand |
+| **Created** | 2026-01-06 |
+| **Updated** | 2026-02-06 |
+
+## Summary
+
+This RFC proposes a high-performance Chamfer distance implementation for multi-vector (ColBERT-style late interaction) representations in DiskANN. The design uses a **query-transposed tiling** approach that transposes queries into a block layout while keeping documents in row-major format, achieving up to **2.67x speedup** over SIMD baseline. The implementation builds on existing types from `diskann-quantization` (`Mat`, `MaxSim`, `Chamfer`) and implements `DistanceFunctionMut` from `diskann-vector` for ecosystem compatibility.
+
+## Motivation
+
+### Background
+
+Traditional vector search represents each document as a single embedding. Multi-vector representations (used in models like ColBERT) encode each document/query as a **bag of embeddings** — typically one per token. This enables:
+
+- **Fine-grained matching**: Token-level similarity captures nuanced semantic relationships
+- **Late interaction**: Document embeddings are pre-computed; only lightweight aggregation at query time
+- **Better recall**: If any query token matches any document token well, the document scores high
+
+### Problem Statement
+
+Chamfer distance for multi-vector search requires O(Q × D × Dim) operations per query-document pair, where:
+
+- Q = number of query tokens
+- D = number of document tokens
+- Dim = embedding dimensionality
+
+For typical configurations (Q=32, D=128, Dim=384), this is ~1.5M floating-point operations per pair. Naive implementations become a bottleneck for large-scale search.
+
+### Goals
+
+1. Implement high-performance Chamfer distance starting with `f32` embeddings, with future support for `f16` and `u8` types
+2. Achieve 2x+ speedup over baseline SIMD through memory layout optimization
+3. Maintain compatibility with DiskANN's `DistanceFunctionMut` trait
+4. Provide a clean API that enables standalone distance function usage without full index integration
+5. Achieve performance within 10–20% of `faer` SGEMM-based Chamfer computation, when both our implementation and `faer` are restricted to AVX2 (no AVX-512 on either side)
+
+## Proposal
+
+### Approach: Query-Transposed Tiling
+
+We propose the **query-transposed tiling** approach as the primary Chamfer distance implementation for DiskANN integration. This approach transposes the query into a block-transposed layout, keeps documents in row-major format, and processes pairs of document vectors together to amortize query memory loads. A pre-allocated scratch buffer tracks per-query max similarities and is reused across distance calls.
+
+This is the recommended default because it preserves the existing document storage format (no index migration), while still achieving significant speedups through SIMD tiling.
+
+### Chamfer Distance Definition
+
+For query multi-vector Q and document multi-vector D:
+
+```
+Chamfer(Q, D) = Σᵢ minⱼ -IP(qᵢ, dⱼ)
+```
+
+Since `InnerProduct::evaluate` in `diskann-vector` returns the negated inner product (`-IP`), the kernel finds the minimum negated IP per query vector (equivalent to finding the maximum similarity), then sums across all query vectors. The result is a distance compatible with DiskANN's min-heap.
+
+### Types
+
+The design builds on the multi-vector matrix types already defined in `diskann_quantization::multi_vector`:
+
+#### Query and Document (from `diskann-quantization`)
+
+```rust
+use diskann_quantization::multi_vector::{Mat, MatRef, Standard, QueryMatRef};
+
+/// Owning row-major matrix: rows = tokens, cols = dimensions
+type MultiVector = Mat<Standard<f32>>;
+
+/// Borrowed view into a multi-vector
+type MultiVectorRef<'a> = MatRef<'a, Standard<f32>>;
+```
+
+`Standard<f32>` provides contiguous row-major storage with `as_slice()` for BLAS compatibility and zero-copy views via `MatRef`. `QueryMatRef` (a newtype over `MatRef`) distinguishes query from document matrices for asymmetric distance functions.
+
+#### TransposedMultiVector (new type)
+
+A block-transposed representation for SIMD-optimized inner product computation. The full transposed type will be added in separate PRs; the below is a simplified depiction.
+
+This will implement a `Repr` trait similar to `Standard<T>` defined in `diskann_quantization::multi_vector`, backed by a block-transposed layout analogous to `BlockTranspose<16>`:
+
+```rust
+/// Block-transposed layout for SIMD-parallel inner products.
+/// Groups 16 vectors and stores dimensions contiguously:
+///
+///   Block 0: [v0_d0..v15_d0], [v0_d1..v15_d1], ...
+///   Block 1: [v16_d0..v31_d0], ...
+///
+/// This enables loading 8 values (f32x8) from 8 different vectors
+/// in a single SIMD load.
+pub struct TransposedMultiVector { ... }
+
+impl TransposedMultiVector {
+    pub fn from_view(view: MatRef<'_, Standard<f32>>) -> Self;
+    pub fn num_vectors(&self) -> usize;
+    pub fn vector_dim(&self) -> usize;
+}
+
+impl From<&MultiVector> for TransposedMultiVector { ... }
+```
+
+#### MaxSim (from `diskann-quantization`)
+
+The existing `MaxSim` type in `diskann_quantization::multi_vector::distance` provides a mutable buffer for per-query-vector maximum similarities. The query-transposed tiling approach uses an analogous scratch pattern internally:
+
+```rust
+/// Per-query-vector maximum similarity scores.
+pub struct MaxSim<'a> {
+    scores: &'a mut [f32],
+}
+```
+
+#### Chamfer (from `diskann-quantization`)
+
+The existing unit type `Chamfer` in `diskann_quantization::multi_vector::distance` identifies the Chamfer distance function:
+
+```rust
+/// Unit type identifying the Chamfer (MaxSim) distance function.
+pub struct Chamfer;
+
+impl PureDistanceFunction for Chamfer { ... }
+```
+
+`Chamfer` implements `PureDistanceFunction` from `diskann-vector`, enabling it to be used as a distance identifier throughout the DiskANN ecosystem without carrying computation state.
+
+### Algorithm
+
+The query-transposed tiling approach:
+
+1. Transpose the query into block-transposed layout (once, before distance calls)
+2. For each pair of document vectors `(d1, d2)`:
+   - Load each query block once, compute inner products against both documents simultaneously
+   - Update per-query min negated IPs in the score array in `MaxSim` struct
+3. Handle remainder if document count is odd
+4. Sum scores to produce the final Chamfer distance
+
+```
+// `scores` corresponds to the scores array inside MaxSim
+// InnerProduct::evaluate returns -IP(q, d), so we track min (= best match)
+scores = [f32::MAX; num_queries]  // reused across calls
+
+for i in (0..num_docs step 2):
+    d1, d2 = doc[i], doc[i+1]
+    for each query block:
+        // Load query block ONCE, use for BOTH documents
+        for each query vector q:
+            scores[q] = min(scores[q], -IP(q, d1))
+            scores[q] = min(scores[q], -IP(q, d2))
+
+if num_docs is odd:
+    for each query vector q:
+        scores[q] = min(scores[q], -IP(q, last_doc))
+
+score = sum(scores[i] for i in 0..num_queries)
+```
+
+**Key optimizations**:
+
+- Query blocks loaded once, reused for 2 document vectors — reduces memory bandwidth by ~50%
+- Scratch buffer stored in `MaxSim` struct and reused across distance calls — zero allocation on hot path
+- 4 SIMD accumulators per document vector to hide FMA latency
+- Uses `SIMDMinMax` from `diskann-wide` for hardware-accelerated SIMD reductions
+
+### Trait Implementation
+
+`MaxSim` implements `DistanceFunctionMut` to populate its per-query-vector max similarities. The caller then sums the scores to produce the Chamfer distance. We propose adding a `chamfer_score()` convenience method on `MaxSim` for this:
+
+```rust
+// DistanceFunctionMut populates the MaxSim scores buffer with per-query max similarities
+impl DistanceFunctionMut<&TransposedMultiVector, MatRef<'_, Standard<f32>>, Result<(), MaxSimError>> for MaxSim<'_> {
+    fn evaluate(&mut self, query: &TransposedMultiVector, doc: MatRef<'_, Standard<f32>>) -> Result<(), MaxSimError>;
+}
+
+impl MaxSim<'_> {
+    /// Sums the per-query min negated IPs to produce the Chamfer distance.
+    /// Call after `evaluate()` has populated the scores buffer.
+    ///
+    /// Returns: Σᵢ scores[i]  (each score is already minⱼ -IP(qᵢ, dⱼ))
+    pub fn chamfer_score(&self) -> f32 {
+        self.scores.iter().sum()
+    }
+
+    /// Resets all scores to `f32::MAX` for the next document comparison.
+    pub fn reset(&mut self) {
+        self.scores.fill(f32::MAX);
+    }
+}
+```
+
+**Thread safety**: `MaxSim` holds `&mut [f32]`, so a `DistanceFunctionMut` implementation borrowing it is `!Sync` by construction. For concurrent search, each thread should own its own `MaxSim` buffer.
+
+### API Usage
+
+The end-to-end flow converts a row-major query into block-transposed layout, then uses `MaxSim` for repeated distance computations against document vectors:
+
+```rust
+use diskann_quantization::multi_vector::{Mat, MatRef, Standard};
+use diskann_quantization::multi_vector::distance::MaxSim;
+use diskann_vector::DistanceFunctionMut;
+
+// 1. Query arrives as row-major multi-vector (e.g., 32 tokens × 384 dims)
+let query: Mat<Standard<f32>> = /* from encoder/upstream layers */;
+
+// 2. Transpose query into SIMD-friendly block layout (once per query)
+let transposed_query = TransposedMultiVector::from_view(query.as_ref());
+
+// 3. Create MaxSim buffer (reused across all document comparisons for this query)
+let mut buffer = vec![f32::MAX; query.num_vectors()];
+let mut max_sim = MaxSim::new(&mut buffer).unwrap();
+
+// 4. For each candidate document:
+for doc in candidate_documents {
+    // Populate per-query-vector max similarities
+    max_sim.evaluate(&transposed_query, doc.as_ref());
+
+    // Sum to get final Chamfer distance
+    let score = max_sim.chamfer_score();
+
+    // Reset for next document
+    max_sim.reset();
+}
+```
+
+The `TransposedMultiVector::from_view` conversion is O(Q × Dim) — a single pass that rearranges the query into block-transposed layout. This cost is amortized over all document comparisons for that query, making it negligible in practice (typically thousands of comparisons per query).
+
+## Trade-offs
+
+### Alternative Approaches
+
+The experimental crate explored six approaches total. The query-transposed tiling approach was selected as the proposal, but the alternatives remain available and may be better for specific workloads.
+
+#### Document-Transposed Approaches
+
+Instead of transposing the query, documents can be block-transposed at index time. This is implemented as `TransposedApproach` and `TransposedWithTilingApproach`.
+
+| Aspect | Query-Transposed (Proposed) | Document-Transposed |
+|--------|---------------------------|---------------------|
+| **Document Layout** | Row-major (no change) | Block-transposed |
+| **Query Layout** | Block-transposed (once per query) | Row-major |
+| **Index Migration** | None | Required |
+| **Hot Path Allocation** | None (scratch reused) | None |
+| **Best For** | Many query tokens (≥16) | Few query tokens (≤8) |
+
+**Backwards Compatibility**: Locking documents into a transposed layout creates backward compatibility obligations — if we later discover a better layout (e.g., for AVX-512), we're stuck supporting the legacy format. Query transpositions are ephemeral and not persisted, so the query-side layout can evolve freely.
+
+#### SGEMM Approach
+
+Uses BLAS matrix multiplication to compute the full Q×D similarity matrix, then SIMD row-max reduction. Dominates at large scale (≥32×128) with up to 2.16x speedup, but materializes the full similarity matrix (Q×D×4 bytes). Custom tiling approaches fuse max-reduction with dot product, avoiding this materialization, which makes them faster for small/medium Q×D.
+
+#### Baseline Approaches
+
+- **NaiveApproach**: Scalar O(n²) implementation for correctness verification
+- **SimdApproach**: SIMD-accelerated inner product via `diskann_vector::InnerProduct`, iterating documents sequentially (1.0x baseline)
+
+## Benchmark Results
+
+**Machine:** Intel Core i7-1365U, AVX2 supported, AVX-512 not supported, 32 GB RAM
+
+Median over 50 measurements. Each measurement computes 10 consecutive distance evaluations across 100 points.
+
+### Speedup vs SIMD Baseline (Median, Lower Latency = Better)
+
+| Configuration | SIMD (µs) | transposed_simd | transposed_tiling | query_transposed_tiling | sgemm |
+|--------------|-----------|-----------------|-------------------|------------------------|-------|
+| dim=128, doc=32, query=8 | 2,237 | 1.34x | **1.75x** | 1.05x | 1.13x |
+| dim=128, doc=64, query=16 | 9,224 | 1.42x | 2.07x | **2.35x** | 1.48x |
+| dim=128, doc=128, query=32 | 47,882 | 1.32x | 1.86x | **2.64x** | 1.88x |
+| dim=256, doc=32, query=8 | 4,654 | 1.26x | **1.69x** | 1.13x | 0.96x |
+| dim=256, doc=64, query=16 | 25,809 | 1.56x | 1.94x | **2.40x** | 1.87x |
+| dim=256, doc=128, query=32 | 101,093 | 1.41x | 1.71x | **2.67x** | 1.96x |
+| dim=256, doc=16, query=32 | 8,239 | 1.22x | 1.77x | **2.02x** | 1.57x |
+| dim=384, doc=32, query=8 | 8,412 | 1.41x | **1.65x** | 1.30x | 1.24x |
+| dim=384, doc=64, query=16 | 38,162 | 1.30x | 1.47x | **1.70x** | 1.66x |
+| dim=384, doc=128, query=32 | 171,431 | 1.53x | 1.94x | 2.04x | **2.16x** |
+| **Average** | — | **1.38x** | **1.79x** | **1.93x** | **1.59x** |
+
+### Analysis
+
+- **query_transposed_tiling** is the best overall approach (avg **1.93x**), winning 7 of 10 configurations
+- **transposed_tiling** wins for small query counts (≤8 tokens) with up to **1.75x** speedup
+- **sgemm** catches up at large scale (dim=384, 32×128) where BLAS cache-blocking dominates (**2.16x**)
+- All tiling approaches exceed the **2x speedup** goal at ≥16×64 token configurations
+
+## Future Work
+
+- [ ] **FFI types**: View types for `MultiVector` / `TransposedMultiVector` for C/C++ callers
+- [ ] **Integration into `diskann` crate**: Graduate from `experimental-` prefix into the main library with a clean API and appropriate module structure
+- [ ] **Quantized types**: `f16`, `u8` support for memory efficiency
+- [ ] **AVX-512 support**: Larger registers could enable tile size 4 (processing 4 queries simultaneously)
+- [ ] **SIMD-accelerated horizontal MinMax**: Hardware-accelerated horizontal min/max reductions across SIMD lanes for faster per-query score aggregation
+
+## References
+
+1. [ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction](https://arxiv.org/abs/2004.12832)
+2. [Chamfer Distance](https://en.wikipedia.org/wiki/Chamfer_distance)
+3. Experimental implementation: [experimental-multi-vector-bench crate (PR #730)](https://github.com/microsoft/DiskANN/pull/730)