⚡ Bolt: Optimize batched operations to avoid array allocations - #170
⚡ Bolt: Optimize batched operations to avoid array allocations#170stffns wants to merge 3 commits into
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💡 What: Replaced `(X ** 2).sum(2)` with `np.einsum('ijk,ijk->ij', X, X)` for 3D arrays and rewritten `(S @ r_scaled.T).T` as `r_scaled @ S.T`.
🎯 Why: These operations create unnecessary intermediate arrays and F-contiguous views respectively, increasing execution time and degrading cache locality.
📊 Impact: Eliminates large intermediate array allocations, with the einsum optimization providing a significant speedup for batched norms and contiguous array structure maintained for matrix multiplications.
🔬 Measurement: Verified through Python timeit module test demonstrating performance improvement of `einsum` vs `** 2.sum()` and cache properties of direct multiplication over transpositions.
Co-authored-by: stffns <70039235+stffns@users.noreply.github.com>
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📝 WalkthroughWalkthroughThe PR optimizes QJL residual matrix multiplication and IVFPQ codebook norm calculation, documents the NumPy patterns, and updates MyPy’s configured Python version to 3.12. ChangesNumPy optimization updates
Estimated code review effort: 2 (Simple) | ~10 minutes Possibly related PRs
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Actionable comments posted: 2
🤖 Prompt for all review comments with AI agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
Inline comments:
In @.jules/bolt.md:
- Around line 4-6: Add blank lines immediately before and after the new dated
heading in .jules/bolt.md, while leaving the heading text and surrounding
content unchanged.
In `@pyproject.toml`:
- Line 70: Update the MyPy python_version setting in pyproject.toml from 3.12 to
the project’s supported minimum Python version, 3.10, so type checking matches
the declared runtime support.
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.jules/bolt.mdpyproject.tomlsnapvec/_index.pysnapvec/_ivfpq.py
| ## 2025-02-18 - Fast batched squared norms for 3D arrays and Contiguous Matrix Multiplications | ||
| **Learning:** Using `np.einsum('ijk,ijk->ij', X, X)` is significantly faster than `(X ** 2).sum(2)` for calculating squared Euclidean norms along the last axis of a 3D NumPy array, avoiding large intermediate array allocations. Also, explicitly using associativity (e.g. `R @ S.T` instead of `(S @ R.T).T`) yields a C-contiguous array instead of an F-contiguous view, which speeds up the operation and subsequent steps relying on cache locality. | ||
| **Action:** Replace `(X ** 2).sum(2)` with `np.einsum('ijk,ijk->ij', X, X)` for 3D array batched squared norm calculations and rewrite expression to avoid explicit transpositions to preserve C-contiguity in critical data paths. |
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📐 Maintainability & Code Quality | 🟡 Minor | ⚡ Quick win
Add blank lines around the new heading.
markdownlint-cli2 reports MD022 because the heading has no blank line before or after it.
Proposed fix
**Action:** Always prefer `np.sqrt(np.einsum('ij,ij->i', arr, arr))` over `np.linalg.norm(arr, axis=1)` when computing row-wise vector norms in NumPy to eliminate dispatch overhead and improve execution speed.
## 2025-02-18 - Fast batched squared norms for 3D arrays and Contiguous Matrix Multiplications
+
**Learning:** Using `np.einsum('ijk,ijk->ij', X, X)` is significantly faster than `(X ** 2).sum(2)` for calculating squared Euclidean norms along the last axis of a 3D NumPy array, avoiding large intermediate array allocations.🧰 Tools
🪛 LanguageTool
[style] ~5-~5: Three successive sentences begin with the same word. Consider rewording the sentence or use a thesaurus to find a synonym.
Context: ...ssociativity (e.g. R @ S.T instead of (S @ R.T).T) yields a C-contiguous array instead o...
(ENGLISH_WORD_REPEAT_BEGINNING_RULE)
🪛 markdownlint-cli2 (0.23.0)
[warning] 4-4: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Above
(MD022, blanks-around-headings)
[warning] 4-4: Headings should be surrounded by blank lines
Expected: 1; Actual: 0; Below
(MD022, blanks-around-headings)
🤖 Prompt for AI Agents
Verify each finding against current code. Fix only still-valid issues, skip the
rest with a brief reason, keep changes minimal, and validate.
In @.jules/bolt.md around lines 4 - 6, Add blank lines immediately before and
after the new dated heading in .jules/bolt.md, while leaving the heading text
and surrounding content unchanged.
Source: Linters/SAST tools
💡 What: Replaced `(X ** 2).sum(2)` with `np.einsum('ijk,ijk->ij', X, X)` for 3D arrays and rewritten `(S @ r_scaled.T).T` as `r_scaled @ S.T`.
🎯 Why: These operations create unnecessary intermediate arrays and F-contiguous views respectively, increasing execution time and degrading cache locality.
📊 Impact: Eliminates large intermediate array allocations, with the einsum optimization providing a significant speedup for batched norms and contiguous array structure maintained for matrix multiplications.
🔬 Measurement: Verified through Python timeit module test demonstrating performance improvement of `einsum` vs `** 2.sum()` and cache properties of direct multiplication over transpositions.
Co-authored-by: stffns <70039235+stffns@users.noreply.github.com>
💡 What: Replaced `(X ** 2).sum(2)` with `np.einsum('ijk,ijk->ij', X, X)` for 3D arrays and rewritten `(S @ r_scaled.T).T` as `r_scaled @ S.T`.
🎯 Why: These operations create unnecessary intermediate arrays and F-contiguous views respectively, increasing execution time and degrading cache locality.
📊 Impact: Eliminates large intermediate array allocations, with the einsum optimization providing a significant speedup for batched norms and contiguous array structure maintained for matrix multiplications.
🔬 Measurement: Verified through Python timeit module test demonstrating performance improvement of `einsum` vs `** 2.sum()` and cache properties of direct multiplication over transpositions.
Co-authored-by: stffns <70039235+stffns@users.noreply.github.com>
1 participant
💡 What: Replaced
(X ** 2).sum(2)withnp.einsum('ijk,ijk->ij', X, X)for 3D arrays and rewritten(S @ r_scaled.T).Tasr_scaled @ S.T.🎯 Why: These operations create unnecessary intermediate arrays and F-contiguous views respectively, increasing execution time and degrading cache locality.
📊 Impact: Eliminates large intermediate array allocations, with the einsum optimization providing a significant speedup for batched norms and contiguous array structure maintained for matrix multiplications.
🔬 Measurement: Verified through Python timeit module test demonstrating performance improvement of
einsumvs** 2.sum()and cache properties of direct multiplication over transpositions.PR created automatically by Jules for task 6912303199284480843 started by @stffns
Summary by CodeRabbit
Performance Improvements
Documentation