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This PR adds JaccardExpanded support for binary vectors in CAGRA and NN-descent.
The implementation reuses dot-product computation and applies the Jaccard expansion:
intersection = dot(x, q)
union = sum(x) + sum(q) - intersection
distance = 1 - intersection / union
Two empty vectors have distance zero. Dataset cardinalities are precomputed, and query cardinalities are computed within the search kernel. The expansion is applied before candidate comparisons during graph construction and traversal.
CAGRA supports Jaccard with the NN-descent and iterative-search graph builders. ACE is also permitted when every partition uses NN-descent; Python callers must explicitly select build_algo="nn_descent" or build_algo="iterative_cagra_search".
Datasets need transformation to binary first. Using MNIST, Fashion-MNIST, and SIFT datasets here. MNIST and Fashion-MNIST use x > 127.5. SIFT uses per-dimension median thresholds fitted on the base dataset. Ground truth is recomputed using exhaustive binary Jaccard search.
Added Jaccard distance support for binary vectors in CAGRA search and NN-Descent, across float, half, int8, and uint8 data.
CAGRA Jaccard indexes support NN-Descent and iterative CAGRA search. ACE is supported only when all partitions use NN-Descent; IVF-PQ partitions are rejected.
Jaccard distance between two empty sets is zero.
Documentation
Updated C++ and Python API documentation with Jaccard metric support, build options, and distance behavior.
This change adds binary Jaccard distance support to CAGRA search and graph construction, and to NN-Descent. It adds cardinality handling, restricts unsupported builder combinations, validates Python parameters, and adds tests and documentation.
Changes: Jaccard distance support
Layer / File(s)
Summary
NN-Descent cardinalities and metric cpp/src/neighbors/detail/nn_descent.cuh, cpp/src/neighbors/detail/nn_descent_gnnd.hpp, cpp/include/cuvs/neighbors/nn_descent.hpp, cpp/src/neighbors/nn_descent.cuh, fern/pages/cpp_api/cpp-api-neighbors-nn-descent.md
NN-Descent computes row cardinalities and uses them with dot products to calculate Jaccard distance. The metric is added to the supported-metrics lists for the documented build overloads.
CAGRA computes scaled dataset cardinalities and query counts. Jaccard search descriptors receive dataset norms, and the standard normalization path applies Jaccard distance normalization.
Graph construction and sorting cpp/src/neighbors/detail/cagra/cagra_build.cuh, cpp/src/neighbors/detail/cagra/graph_core.cuh, cpp/src/neighbors/detail/cagra/graph_shared.cu, cpp/src/neighbors/ivf_common.cuh, cpp/src/neighbors/cagra.cuh
Graph sorting accepts Jaccard and calculates distance from intersection and union. CAGRA rejects IVF-PQ graph construction for Jaccard.
API constraints and end-to-end coverage python/cuvs/cuvs/neighbors/cagra/cagra.pyx, python/cuvs/cuvs/tests/test_cagra.py, cpp/tests/CMakeLists.txt, cpp/tests/neighbors/ann_cagra/test_jaccard.cu, fern/pages/python_api/python-api-neighbors-cagra.md, fern/pages/cpp_api/cpp-api-neighbors-cagra.md
The Python API rejects unsupported Jaccard builder choices. C++ and Python tests cover Jaccard search, supported builders, data types, and distance results. The documentation describes Jaccard and its builder constraints.
ACE users may encounter a rejected search unless they attach a device dataset; the test suite also incurs avoidable build cost. The PR remains low risk after these follow-ups.
Pre-merge checks: 4 · 1
Failed checks: 1 warning
Check name
Status
Explanation
Resolution
Docstring Coverage
Docstring coverage is 6.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentat…
Write docstrings for the functions missing them to satisfy the coverage threshold.
Passed checks: 4 passed
Check name
Status
Explanation
Title check
The title "Add Jaccard distance support in CAGRA" matches the main change. The changeset adds JaccardExpanded support for binary vectors in CAGRA, including search, NN-descent graph construction, vali…
Linked Issues check
Issue #1158 requests Jaccard distance support in CAGRA. The issue sets no further acceptance criteria.
The whole-PR summary (merge base 3dd62c1 to head 1ddbd56) shows this implementation: - Metric …
Out of Scope Changes check
All described changes connect to Jaccard support in CAGRA or NN-descent, or to its tests and documentation: - Jaccard kernels, normalization headers, the tag_norm_jaccard JIT tag, planner registrati…
Description check
The PR description states that it adds JaccardExpanded support for binary vectors in CAGRA and NN-descent. It gives the distance formula, the empty-vector convention, the supported builders, benchmark…
Full details: Title check
Explanation
The title "Add Jaccard distance support in CAGRA" matches the main change. The changeset adds JaccardExpanded support for binary vectors in CAGRA, including search, NN-descent graph construction, validation, and tests. The title is short, specific, and states the primary change from the developer's perspective.
Full details: Linked Issues check
Explanation
Issue #1158 requests Jaccard distance support in CAGRA. The issue sets no further acceptance criteria.
The whole-PR summary (merge base 3dd62c1 to head 1ddbd56) shows this implementation:
Metric registration: JaccardExpanded is added in cagra_planner_base.hpp, compute_distance_standard_matrix.json, and apply_normalization_standard_matrix.json. The jaccard metric is exposed in cagra.pyx.
Distance computation: apply_normalization_standard_jaccard_impl.cuh computes 1 + distance / set_union per team, with zero for a non-positive union. Query cardinality is set up through setup_normalization_standard in setup_workspace_impl.cuh. Dataset cardinalities are computed in cagra.cuh and passed through cagra_search.cuh.
Graph construction: NN-descent supports Jaccard in nn_descent.cuh, nn_descent_gnnd.hpp, and graph_shared.cu (kern_sort). The IVF-PQ builder rejects Jaccard in cagra_build.cuh. The issue does not state this limit.
Tests: test_jaccard.cu covers float, half, int8, and uint8 inputs, three search algorithms, NN-descent and iterative builders, dataset replacement, and the IVF-PQ rejection. test_cagra.py covers Python parameter validation and distance results.
The summary does not show any requirement from #1158 that is unmet. The IVF-PQ restriction is a design limit that the issue does not require to change.
The summary shows an ACE path for Jaccard in cagra.pyx and test_cagra.py. This path changed since the previous review. The incremental diff could not be read. The ACE behavior rests on the whole-PR summary and does not change this verdict, because ACE support is an addition to the requested capability and does not remove any requirement.
Full details: Out of Scope Changes check
Explanation
All described changes connect to Jaccard support in CAGRA or NN-descent, or to its tests and documentation:
Jaccard kernels, normalization headers, the tag_norm_jaccard JIT tag, planner registration, and workspace setup implement the requested metric.
The compute_dataset_norms_ refactor to a shared row-reduction helper supports Jaccard cardinalities. The summary states that the cosine norm formula is preserved.
The noop and cosine entries in apply_normalization_standard_matrix.json now set compute_query_norm to false. This limits query setup to the Jaccard entry. The summary shows no cosine behavior change.
ivf_common.cuh adds JaccardExpanded to the no-op postprocessing branch. This keeps Jaccard distances unchanged.
validate_cagra_knn_graph_build_constraints, sort_knn_graph, get_build_config, and cagra.pyx accept Jaccard for supported builders and reject it for IVF-PQ.
The ACE path for Jaccard in cagra.pyx, cagra_search.cuh, and test_cagra.py extends the same metric to a CAGRA build path. The PR description does not mention ACE, and it states that callers must select nn_descent or iterative_cagra_search. This is a description gap, not an unrelated change.
Documentation in the C++ headers, fern/pages/*, and cagra.pyx describes the new metric and its builder restrictions.
No change was found that is unrelated to Jaccard support. The incremental diff from the previous reviewed head could not be read. The conclusion rests on the whole-PR summary.
Full details: Description check
Explanation
The PR description states that it adds JaccardExpanded support for binary vectors in CAGRA and NN-descent. It gives the distance formula, the empty-vector convention, the supported builders, benchmark tables for MNIST, Fashion-MNIST, and SIFT, and a list of test coverage. The changeset matches these claims: it adds the Jaccard fragment, normalization, NN-descent support, Python validation, and the new Jaccard test files. The description is related to the changeset and is specific, so the check passes.
Full details: Docstring Coverage
Explanation
Docstring coverage is 6.67% which is insufficient. The required threshold is 80.00%. Docstring coverage is scoped to functions touched by this diff with cyclomatic complexity of at least 2. Documentation-only edits and deletions within surviving functions are included; unchanged diff context and fully deleted functions are excluded. Analyzed 15 functions across 8 files. (3 skipped: 3 unsupported.)
Resolution
Write docstrings for the functions missing them to satisfy the coverage threshold.
Finishing Touches
Generate unit tests (beta)
Create a new PR
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The parameter product rebuilds the same ACE configuration three times per dtype. SearchParams(algo=...) selects the search path at query time and does not affect ACE construction. Build one ACE index per dtype, call cagra.update_dataset(index, padded_dataset) once, and run all three search algorithms against that index.
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Review comment at @python/cuvs/cuvs/tests/test_cagra.py around lines 365 - 367:
Update the parameterized test around the build_algo parameter so ACE
construction and cagra.update_dataset(index, padded_dataset) run only once per
dtype. Reuse that index to run searches with all three SearchParams(algo=...)
values, preserving the existing coverage for each search algorithm.
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Summary
This PR adds
JaccardExpandedsupport for binary vectors in CAGRA and NN-descent.The implementation reuses dot-product computation and applies the Jaccard expansion:
Two empty vectors have distance zero. Dataset cardinalities are precomputed, and query cardinalities are computed within the search kernel. The expansion is applied before candidate comparisons during graph construction and traversal.
CAGRA supports Jaccard with the NN-descent and iterative-search graph builders. ACE is also permitted when every partition uses NN-descent; Python callers must explicitly select
build_algo="nn_descent"orbuild_algo="iterative_cagra_search".Closes #1158
Benchmarks
Datasets
Datasets need transformation to binary first. Using MNIST, Fashion-MNIST, and SIFT datasets here. MNIST and Fashion-MNIST use
x > 127.5. SIFT uses per-dimension median thresholds fitted on the base dataset. Ground truth is recomputed using exhaustive binary Jaccard search.A100-PCIE-40GB
Configs:
k=10, batch_size=10000, intermediate_degree=128, graph_degree=[32, 64], max_iteration=20, itopk=[32, 64, 128, 256, 512]Tests
Added C++ and Python tests covering:
SINGLE_CTA,MULTI_CTA, andMULTI_KERNELsearch.