UPSTREAM PR #19108: ggml-cpu: arm64: Q4_K repack (i8mm) scale unroll and vectorization

[loci-dev]

Mirrored from ggml-org/llama.cpp#19108

While working on ggml-org/llama.cpp#18860 I found out a small perf optimization when loading the subblock scales.
Behavior unchanged, it's a manual unroll + vectorization.

Llama-bench:

model	test	old t/s	new t/s	speedup
lfm2 1.2B Q4_K	pp512	658.53	682.69	1.04
lfm2 350M Q4_K	pp512	2052.76	2159.47	1.05
Qwen 8B Q4_K - Medium	pp512	94.21	99.51	1.06

No changes observed in the perplexities for Qwen3 8B 128K Q4_K_M and lfm2 1.2B Q4_K_M

cc: @tdakhran

[loci-review]

Performance Review Report

Summary

No functions were identified for performance analysis between the base and target versions. This indicates that no meaningful performance changes occurred in this code revision.

Analysis

The absence of functions with significant response time or throughput time changes suggests that:

1. No Performance-Critical Modifications: The changes between versions did not impact the performance-sensitive areas of llama.cpp, such as:

   • Matrix multiplication kernels (GEMM operations)
   • Attention computation paths
   • KV cache management
   • Quantization/dequantization routines
   • Token processing loops

2. Stable Performance Profile: The inference pipeline and core computational paths maintained their performance characteristics across versions.

3. Non-Performance Changes: Any modifications made were likely focused on:

   • Bug fixes without performance impact
   • Documentation updates
   • Code refactoring that preserved performance
   • Feature additions in non-critical paths

Conclusion

The target version exhibits no measurable performance regression or improvement compared to the base version. The core inference engine and computational kernels remain performance-neutral across this revision.

See the complete breakdown in Version Insights
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