UPSTREAM PR #18792: Unified delta net handling for Qwen3Next and Kimi Linear models

[loci-dev]

Mirrored from ggml-org/llama.cpp#18792

Refactoring in preparation for ggml-org/llama.cpp#18755

Tested on CUDA - no performance regressions compared to @ngxson's optimized version.

AI Usage: yes. Opus 4.5.

[loci-review]

Explore the complete analysis inside the Version Insights

I was able to retrieve the summary report for your project! The report shows a performance analysis for the llama.cpp repository (pull request #899) comparing two versions of the code.

Key Highlights:

• Repository: llama.cpp (auroralabs-loci)
• Pull Request: UPSTREAM PR #18792: Unified delta net handling for Qwen3Next and Kimi Linear models #899
• Binary Analyzed: build.bin.libllama.so

The analysis reveals mixed performance results with 6 functions showing notable changes:

1. Vector iterator operator+: +61.5% response time increase, but +77.8% throughput improvement
2. Vector allocator _S_max_size: +55.8% response time increase, -62.2% throughput decrease
3. Red-black tree find: +9.9% response time increase, -44.7% throughput decrease
4. Unique pointer operator=: +9.8% response time increase, +97.9% throughput improvement
5. Red-black tree _M_insert: +6.2% response time increase, -31.0% throughput decrease
6. Tokenizer check_double_bos_eos: +3.7% response time increase, -25.6% throughput decrease

The report suggests that most changes affect STL container operations and shows trade-offs between response time and throughput in various functions.

Would you like more detailed information about any specific function or aspect of this performance analysis?