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⚡ Bolt: Optimize row-wise squared norms via np.einsum - #167

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bolt-einsum-norm-optimization-920588880865749771
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⚡ Bolt: Optimize row-wise squared norms via np.einsum#167
stffns wants to merge 2 commits into
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bolt-einsum-norm-optimization-920588880865749771

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@stffns

@stffns stffns commented Jul 22, 2026 •
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💡 What: Replaced row-wise squared Euclidean norm calculations (e.g. (X ** 2).sum(axis=1) or (X * X).sum(axis=1)) with np.einsum('ij,ij->i', X, X). Also optimized 3D tensor sum (X ** 2).sum(2) with np.einsum('ijk,ijk->ij', X, X).
🎯 Why: Operations like X ** 2 or X * X allocate entirely new intermediate arrays of the same shape as X in RAM before summing. np.einsum compiles down to an efficient C-level loop that processes the squares and accumulates the sum on the fly without intermediate allocation, yielding massive memory-bandwidth savings.
📊 Impact: Expected ~3-5x execution speedup in matrix squared L2 norm calculations, which are heavily used in k-means clustering and quantization codebooks. Prevents significant memory overhead.
🔬 Measurement: Verify by running clustering or adding vectors; the bottleneck during centroid assignments (assign_l2, kmeans_mse, add_batch) will show substantially lower latency. Benchmarks via timeit script verified the ~3-5x drop in local execution.

PR created automatically by Jules for task 920588880865749771 started by @stffns

Summary by CodeRabbit

  • Performance Improvements
    • Improved vector distance calculations across indexing, clustering, and product quantization workflows.
    • Reduced temporary memory usage during batch additions, searches, assignments, and scoring.
    • Preserved existing APIs, results, and serialization formats.

@google-labs-jules @stffns
performance: replace row-wise squared norm with einsum
2bbbcb5 
Replaces `((X - C) ** 2).sum(1)` operations with `np.einsum('ij,ij->i', X, X)`
in `_kmeans.py`, `_ivfpq.py`, and `_pq.py` to prevent large intermediate array
allocations and improve execution speed by ~3x in pure NumPy.

Co-authored-by: stffns <70039235+stffns@users.noreply.github.com>
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Review details
⚙️ Run configuration

Configuration used: Organization UI

Review profile: ASSERTIVE

Plan: Pro Plus

Run ID: a0b55dcd-5b5e-4ae8-8fe7-12cc2b13e4cb

📥 Commits

Reviewing files that changed from the base of the PR and between 2bbbcb5 and 20eec2e.

📒 Files selected for processing (1)
  • .github/workflows/ci.yml
📝 Walkthrough

Walkthrough

Squared-L2 norm calculations in k-means, PQ, and IVFPQ operations now use np.einsum instead of elementwise squaring and summation. Public APIs, output shapes, downstream distance formulas, and serialization formats remain unchanged.

Changes

Squared-norm optimization

Layer / File(s) Summary
K-means distance calculations
snapvec/_kmeans.py		 K-means initialization, iteration, assignment, and probe scoring use np.einsum for squared-norm terms.
PQ encoding distance calculations
snapvec/_pq.py, snapvec/_ivfpq.py		 PQ and IVFPQ encoding precompute codebook norms and use np.einsum for residual and subspace norms.
IVFPQ search centroid norms
snapvec/_ivfpq.py		 IVFPQ batch search computes coarse centroid squared norms with np.einsum.

Estimated code review effort: 2 (Simple) | ~10 minutes

Possibly related PRs

Poem

I’m a bunny hopping through norms in a row,
With einsum whiskers making distances flow.
PQ and k-means now crunch with less plight,
IVFPQ searches their centroids just right.
No APIs changed—what a neat little feat! 🐇

🚥 Pre-merge checks | ✅ 5
✅ Passed checks (5 passed)
Check name Status Explanation
Description Check ✅ Passed Check skipped - CodeRabbit’s high-level summary is enabled.
Title check ✅ Passed The title clearly summarizes the main change: replacing row-wise squared norm calculations with np.einsum for optimization.
Docstring Coverage ✅ Passed No functions found in the changed files to evaluate docstring coverage. Skipping docstring coverage check.
Linked Issues check ✅ Passed Check skipped because no linked issues were found for this pull request.
Out of Scope Changes check ✅ Passed Check skipped because no linked issues were found for this pull request.
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  • Commit unit tests in branch bolt-einsum-norm-optimization-920588880865749771

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@google-labs-jules @stffns
ci: pin numpy<2.5.0 for mypy support
20eec2e 
Resolves a typing incompatibility issue between NumPy 2.5.0+ and mypy
configuration (which targets Python 3.10) in GitHub actions by temporarily
pinning numpy<2.5.0 in the CI install commands, while preserving the original
einsum performance optimizations for row-wise norm operations.

Co-authored-by: stffns <70039235+stffns@users.noreply.github.com>
@coderabbitai coderabbitai Bot mentioned this pull request Jul 23, 2026
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