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Execute tensor L2 norm with RowFn - #9768

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Execute tensor L2 norm with RowFn#9768
connortsui20 wants to merge 1 commit into
ct/row-fn-tensor-rowsfrom
ct/row-fn-tensor-l2-v2

Execute tensor L2 norm with RowFn

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CodSpeed HQ / CodSpeed Performance Analysis failed Sep 8, 2026 in 0s

Performance Regression: -14.26%

⚠️ Unknown Walltime execution environment detected

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For the most accurate results, we recommend using CodSpeed Macro Runners: bare-metal machines fine-tuned for performance measurement consistency.

⚠️ Different runtime environments detected

Some benchmarks with significant performance changes were compared across different runtime environments,
which may affect the accuracy of the results.

Open the report in CodSpeed to investigate

⚡ 4 improved benchmarks
❌ 33 regressed benchmarks
✅ 2150 untouched benchmarks
⏩ 218 skipped benchmarks1

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

Mode Benchmark BASE HEAD Efficiency
❌ Simulation compress_alp[f64, (1000, 0.0, 1.0)] 104.8 µs 159 µs -34.08%
❌ Simulation compress_alp[f64, (1000, 0.0, 0.25)] 114.3 µs 168.2 µs -32.05%
❌ Simulation compress_alp[f64, (1000, 0.0, 0.95)] 114.1 µs 167.2 µs -31.74%
❌ Simulation compress_alp[f64, (1000, 0.1, 1.0)] 150.6 µs 211.9 µs -28.91%
❌ Simulation compress_alp[f64, (1000, 0.01, 1.0)] 144.5 µs 201.1 µs -28.14%
❌ Simulation alp_compress_f64 171.8 µs 237.8 µs -27.74%
❌ Simulation compress_alp[f64, (1000, 0.01, 0.25)] 155.2 µs 211.9 µs -26.73%
❌ Simulation column_x_extension_constant[32] 170.8 µs 232.6 µs -26.58%
❌ Simulation compress_alp[f64, (1000, 0.1, 0.25)] 168.9 µs 228.9 µs -26.22%
❌ Simulation column_x_extension_constant[256] 427.2 µs 573.8 µs -25.55%
❌ Simulation compress_alp[f64, (1000, 0.1, 0.95)] 177.7 µs 237.3 µs -25.12%
❌ Simulation compress_alp[f64, (1000, 0.01, 0.95)] 159.8 µs 213.2 µs -25.03%
❌ Simulation column_x_column[256] 88.9 µs 112.1 µs -20.74%
❌ Simulation column_x_column[32] 94.6 µs 116.9 µs -19.1%
❌ Simulation compress_alp[f32, (1000, 0.0, 1.0)] 51.6 µs 63.4 µs -18.51%
❌ Simulation compress_alp[f32, (1000, 0.0, 0.25)] 61 µs 73.3 µs -16.84%
❌ Simulation compress_alp[f32, (1000, 0.0, 0.95)] 61 µs 73.1 µs -16.63%
❌ Simulation column_x_constant[256] 622.7 µs 742.4 µs -16.13%
❌ Simulation compress_alp[f64, (10000, 0.0, 1.0)] 308 µs 361 µs -14.68%
❌ Simulation compress_alp[f64, (10000, 0.0, 0.95)] 318.3 µs 370.9 µs -14.18%
... ... ... ... ... ...

ℹ️ Only the first 20 benchmarks are displayed. Go to the app to view all benchmarks.

Tip

Investigate this regression by commenting @codspeedbot fix this regression on this PR, or directly use the CodSpeed MCP with your agent.

Comparing ct/row-fn-tensor-l2-v2 (929ae6d) with ct/row-fn-tensor-rows (5eba5b2)2

Open in CodSpeed

Footnotes

  1. 218 benchmarks were skipped, so the baseline results were used instead. If they were deleted from the codebase, click here and archive them to remove them from the performance reports. ↩

  2. No successful run was found on ct/row-fn-tensor-rows (6ca28c3) during the generation of this report, so 63d5d77 was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩

View more details on CodSpeed HQ