Execute tensor product functions with RowFn

[connortsui20]

Summary

• Tracking Issue: Epic: Row-oriented scalar functions #9128
• Depends on: Execute tensor L2 norm with RowFn #9768

Moves inner product and cosine similarity to RowFn without changing their arithmetic contracts.

Changes

Inner product keeps the existing left-to-right multiply-and-sum order. Cosine computes that dot product followed by both L2 norms and the existing zero-denominator guard, with no encoded reduction, reassociation, or constant-norm shortcut. Tests cover nulls, empty and zero-width rows, IEEE overflow and underflow, and bitwise agreement between constant encodings and materialized rows.

[codspeed-hq]

Merging this PR will improve performance by 77.54%

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⚠️ Different runtime environments detected

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⚡ 14 improved benchmarks
✅ 2173 untouched benchmarks
⏩ 218 skipped benchmarks¹

Performance Changes

	Mode	Benchmark	BASE	HEAD	Efficiency
⚡	Simulation	column_x_constant[2]	486.9 µs	127.7 µs	×3.8
⚡	Simulation	column_x_constant[32]	422.2 µs	128.5 µs	×3.3
⚡	Simulation	column_x_constant[256]	637.6 µs	309.6 µs	×2.1
⚡	Simulation	column_x_column[2]	205.3 µs	101 µs	×2
⚡	Simulation	column_x_extension_constant[256]	540.2 µs	277.6 µs	+94.56%
⚡	Simulation	nullable[2]	592.5 µs	323.2 µs	+83.32%
⚡	Simulation	non_nullable[2]	594 µs	327.4 µs	+81.41%
⚡	Simulation	column_x_extension_constant[2]	235.4 µs	144.3 µs	+63.09%
⚡	Simulation	nullable[2]	630.5 µs	417.1 µs	+51.15%
⚡	Simulation	non_nullable[2]	631 µs	419.3 µs	+50.5%
⚡	Simulation	column_x_extension_constant[32]	191.7 µs	132.6 µs	+44.58%
⚡	Simulation	column_x_column[32]	96.2 µs	76.8 µs	+25.25%
⚡	Simulation	column_x_column[256]	90.6 µs	74.6 µs	+21.47%
⚡	Simulation	allocate_drop_bytes[0]	520.2 ns	466 ns	+11.62%

Tip

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_{Comparing ct/row-fn-tensor-products-v2 (4f0fa1a) with develop (a997c3f)²}

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-l2-v2 (5974184) during the generation of this report, so develop (a997c3f) was used instead as the comparison base. There might be some changes unrelated to this pull request in this report. ↩