UPSTREAM PR #16958: Fix LoongArch test-quantize-fns f16 and q4_0 failed when use LSX

[DajanaV]

Mirrored from ggml-org/llama.cpp#16958

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary

Critical Function Performance Status

Core Inference Functions

• llama_decode: No performance changes detected

  • Response Time: 48,971,004 ns (unchanged)
  • Throughput: 71 ns (unchanged)
  • Bottleneck: 54 ns (unchanged)

• llama_encode: No performance changes detected

  • Response Time: 12,321,125 ns (unchanged)
  • Throughput: 56 ns (unchanged)
  • Bottleneck: 39 ns (unchanged)

• llama_tokenize: No performance changes detected

  • Response Time: 832,857 ns (unchanged)
  • Throughput: 22 ns (unchanged)
  • Bottleneck: 17 ns (unchanged)

Supporting Functions

• llama_model_quantize: No performance changes detected
• llama_batch_init: No performance changes detected
• ggml_vec_dot_q4_0_q8_0: No performance changes detected (despite PR UPSTREAM PR #16958: Fix LoongArch test-quantize-fns f16 and q4_0 failed when use LSX #47 modifications)

Key Performance Indicator Impact Analysis

1. Tokens Per Second

Status: No Impact

• Critical inference functions (llama_decode, llama_encode, llama_tokenize) show no measurable performance changes
• Based on the reference that 2 ms slower llama_decode results in 7% fewer tokens per second, the current unchanged performance maintains baseline throughput
• No degradation in tokenization or inference pipeline performance

2. Power Consumption

Status: Minimal Impact

• build.bin.libllama.so: Negligible decrease (-0.0%) from 306,894.27 nJ to 306,894.24 nJ
• Other binaries: No measurable changes in power consumption
• The observed regex function degradations have minimal impact on overall power consumption due to their limited usage in core inference paths

3. Quantization Efficiency

Status: No Impact

• llama_model_quantize: No performance changes detected
• ggml_vec_dot_q4_0_q8_0: No performance impact despite LoongArch LSX optimizations in PR UPSTREAM PR #16958: Fix LoongArch test-quantize-fns f16 and q4_0 failed when use LSX #47
• Quantization operations maintain baseline efficiency

4. Memory Usage

Status: No Impact

• Memory management functions show no performance degradation
• KV cache operations and memory allocation patterns remain unchanged
• No impact on memory efficiency or allocation overhead

5. Batch Processing

Status: No Impact

• llama_batch_init: No performance changes
• llama_decode (batch processing): No performance changes
• Batch processing efficiency remains at baseline levels

Observed Performance Issues

Regex Processing Degradation

The only measurable performance degradations occur in C++ standard library regex functions:

• ~_Scanner: +0.058% Response Time degradation (25.895 ms vs 25.880 ms)
• _M_match_multiline: +0.110% Throughput degradation (39.487 ms vs 39.443 ms)

These functions are not part of the core inference pipeline and have minimal impact on overall system performance.

Action Items

Code and Build Optimizations

1. Verify LoongArch optimizations: Validate that PR UPSTREAM PR #16958: Fix LoongArch test-quantize-fns f16 and q4_0 failed when use LSX #47 LoongArch LSX improvements are functioning correctly despite no measurable performance change
2. Investigate regex usage: Review tokenization paths for unnecessary regex operations that could be optimized
3. Build consistency: Ensure compiler flags and optimization settings are consistent between versions

Performance Monitoring

1. Focus on core functions: Continue monitoring llama_decode, llama_encode, and llama_tokenize as primary performance indicators
2. Quantization validation: Verify quantization efficiency on target hardware platforms
3. Memory profiling: Monitor KV cache and memory allocation patterns during inference

Conclusion

The analysis reveals stable performance across all critical LLaMA.cpp functions. The observed degradations are isolated to standard library regex components that do not impact core inference performance. The LoongArch optimizations in PR #47 appear to maintain performance parity while improving code quality. No immediate performance concerns affect tokens per second, power consumption, quantization efficiency, memory usage, or batch processing capabilities.