Execute tensor L2 norm with RowFn

[connortsui20]

Summary

• Tracking Issue: Epic: Row-oriented scalar functions #9128
• Depends on: Add tensor row inputs for RowFn #9776

Moves L2Norm to the current RowFn API while preserving its direct arithmetic and null behavior. L2Normalize remains on its handwritten one-pass implementation and is not part of this PR.

Changes

Uses the shared tensor row input for ordinary columns and both constant representations. Tests cover constant encodings, empty and zero-width inputs, nulls, and bitwise agreement with materialized rows.

[codspeed]

Merging this PR will degrade performance by 5.14%

⚠️ Unknown Walltime execution environment detected

Using the Walltime instrument on standard Hosted Runners will lead to inconsistent data.

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

⚡ 5 improved benchmarks
❌ 9 regressed benchmarks
✅ 2184 untouched benchmarks
⏩ 218 skipped benchmarks¹

Warning

Please fix the performance issues or acknowledge them on CodSpeed.

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
❌	Simulation	column_x_extension_constant[32]	170.5 µs	234.5 µs	-27.3%
❌	Simulation	column_x_extension_constant[256]	414.6 µs	563.8 µs	-26.46%
❌	Simulation	random_i8[0.5]	71.5 µs	95.4 µs	-24.98%
❌	Simulation	column_x_column[256]	88.9 µs	112.5 µs	-20.97%
❌	Simulation	column_x_column[32]	94.4 µs	117.7 µs	-19.78%
❌	Simulation	decompress[u64, (4000, 1024)]	71.9 µs	87.3 µs	-17.58%
❌	Simulation	column_x_constant[256]	591.1 µs	711.5 µs	-16.92%
❌	Simulation	column_x_constant[32]	392.9 µs	448.2 µs	-12.34%
❌	Simulation	column_x_extension_constant[2]	247.8 µs	276.8 µs	-10.48%
⚡	Simulation	nullable[2]	632.3 µs	416.6 µs	+51.8%
⚡	Simulation	non_nullable[2]	631.2 µs	419.1 µs	+50.59%
⚡	Simulation	random_i16[0.95]	99 µs	81 µs	+22.2%
⚡	Simulation	allocate_drop_arrow[0]	456.9 ns	402.7 ns	+13.45%
⚡	Simulation	allocate_drop_bytes[0]	575.7 ns	521.6 ns	+10.39%

Tip

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

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_{Comparing ct/row-fn-tensor-l2-v2 (b84f3f7) with develop (7286721)}

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. ↩

[robert3005]

I made #9874 by looking at profiles here