UPSTREAM PR #17064: convert: add dequant function for compressed_tensor (kimi-k2-thinking)

[DajanaV]

Mirrored from ggml-org/llama.cpp#17064

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Model: https://huggingface.co/moonshotai/Kimi-K2-Thinking

[loci-agentic-ai]

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Performance Analysis Summary

Overview

Analysis of project 2621b8c0-b5ce-11f0-b333-453f42058aa1 comparing versions e896b8cd-fcd4-4014-a127-0aaff6f38112 against 52cd5469-e814-4a51-818a-57d1618fc442 reveals minimal performance variations with no meaningful impact on core inference functionality.

Key Findings

Performance Metrics:

• Highest Response Time change: llama_supports_rpc in build.bin.libllama.so (+0.08%, +0.024 ns absolute)
• Highest Throughput change: _Optional_base constructor in build.bin.llama-tts (-0.17%, -0.040 ns absolute improvement)
• Changes are within measurement precision limits and represent microarchitectural noise rather than functional modifications

Core Function Impact:

• No changes detected in critical inference functions (llama_decode, llama_encode, llama_tokenize)
• The affected functions (llama_supports_rpc, _Optional_base) are utility/capability detection functions, not core inference paths
• Tokens per second impact: None - No performance-critical functions show meaningful changes

Power Consumption Analysis:

• System-wide power consumption remains effectively unchanged across all binaries
• Largest change: build.bin.libllama.so (-0.0002%, -0.66 nJ absolute improvement)
• All other binaries show zero measurable power consumption change
• Changes fall within measurement precision limits

Flame Graph and CFG Analysis:

• llama_supports_rpc shows identical control flow structure between versions
• Assembly code remains identical across all basic blocks
• The 0.02 ns timing difference represents compiler backend variations or instruction cache effects rather than code changes
• Linear execution path with no branching complexity changes

GitHub Code Review:

• PR UPSTREAM PR #17064: convert: add dequant function for compressed_tensor (kimi-k2-thinking) #108 modifies only Python conversion script (convert_hf_to_gguf.py) for compressed tensor dequantization
• No C++ runtime code modifications that would affect the measured performance functions
• Changes are isolated to model conversion pipeline, not inference runtime

Conclusion:
The analysis reveals no functional code changes affecting performance. Measured variations represent normal microarchitectural noise within expected precision limits for identical code execution.

[loci-agentic-ai]

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Performance Analysis Summary

Overview

Analysis of llama.cpp version 519a9af2 compared to baseline 52cd5469 reveals minimal performance impact. The highest Response Time change was a 0.10% improvement in std::vector<llm_bigram_spm>::pop_back() (67 ns vs 67 ns baseline), while the highest Throughput change was a 0.12% degradation in std::make_unique<llm_graph_input_pos_bucket>() (104 ns vs 104 ns baseline).

Key Findings

Performance Metrics:

• Response Time Leader: std::vector<llm_bigram_spm>::pop_back() improved by 0.067 ns (-0.10%)
• Throughput Leader: std::make_unique<llm_graph_input_pos_bucket>() degraded by 0.122 ns (+0.12%)
• Core Function Impact: No changes detected in critical inference functions (llama_decode, llama_encode, llama_tokenize)

Inference Performance Impact:
No impact on tokens per second expected. The affected functions are peripheral STL operations in tokenization subsystems, not core inference paths. Based on the reference model performance (7% tokens/sec reduction per 2ms llama_decode slowdown), the nanosecond-level changes observed would have negligible effect on overall throughput.

Power Consumption Analysis:
Minimal power consumption changes across all binaries:

• build.bin.libllama.so: -0.0% change (280,780 nJ vs 280,781 nJ baseline)
• build.bin.llama-cvector-generator: -100% (binary removed/disabled, eliminating 314,116 nJ)
• Other binaries show zero or negligible changes

Technical Analysis:

• Flame Graph: pop_back() shows simple, flat execution profile with 66 ns concentrated in single operation
• CFG Comparison: Identical control flow graphs between versions, confirming no algorithmic changes
• Code Review: PR UPSTREAM PR #17064: convert: add dequant function for compressed_tensor (kimi-k2-thinking) #108 only modifies Python conversion script (convert_hf_to_gguf.py) for compressed tensor support, unrelated to observed C++ performance metrics

Conclusion:
The performance variations represent compiler optimization differences or measurement noise rather than functional changes. No actionable performance improvements required as changes are within normal statistical variation and don't affect core inference capabilities.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary

Overview

Analysis of llama.cpp version 519a9af2 compared to baseline 52cd5469 reveals minimal performance impact. The highest Response Time change was a 0.10% improvement in std::vector<llm_bigram_spm>::pop_back() (67 ns vs 67 ns baseline), while the highest Throughput change was a 0.12% degradation in std::make_unique<llm_graph_input_pos_bucket>() (104 ns vs 104 ns baseline).

Key Findings

Performance Metrics:

• Response Time Leader: std::vector<llm_bigram_spm>::pop_back() improved by 0.067 ns (-0.10%)
• Throughput Leader: std::make_unique<llm_graph_input_pos_bucket>() degraded by 0.122 ns (+0.12%)
• Core Function Impact: No changes detected in critical inference functions (llama_decode, llama_encode, llama_tokenize)

Inference Performance Impact:
No impact on tokens per second expected. The affected functions are peripheral STL operations in tokenization subsystems, not core inference paths. Based on the reference model performance (7% tokens/sec reduction per 2ms llama_decode slowdown), the nanosecond-level changes observed would have negligible effect on overall throughput.

Power Consumption Analysis:
Minimal power consumption changes across all binaries:

• build.bin.libllama.so: -0.0% change (280,780 nJ vs 280,781 nJ baseline)
• build.bin.llama-cvector-generator: -100% (binary removed/disabled, eliminating 314,116 nJ)
• Other binaries show zero or negligible changes

Technical Analysis:

• Flame Graph: pop_back() shows simple, flat execution profile with 66 ns concentrated in single operation
• CFG Comparison: Identical control flow graphs between versions, confirming no algorithmic changes
• Code Review: PR UPSTREAM PR #17064: convert: add dequant function for compressed_tensor (kimi-k2-thinking) #108 only modifies Python conversion script (convert_hf_to_gguf.py) for compressed tensor support, unrelated to observed C++ performance metrics

Conclusion:
The performance variations represent compiler optimization differences or measurement noise rather than functional changes. No actionable performance improvements required as changes are within normal statistical variation and don't affect core inference capabilities.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary

Overview

Analysis of llama.cpp version 519a9af2 compared to baseline 52cd5469 reveals minimal performance impact. The highest Response Time change was a 0.10% improvement in std::vector<llm_bigram_spm>::pop_back() (67 ns vs 67 ns baseline), while the highest Throughput change was a 0.12% degradation in std::make_unique<llm_graph_input_pos_bucket>() (104 ns vs 104 ns baseline).

Key Findings

Performance Metrics:

• Response Time Leader: std::vector<llm_bigram_spm>::pop_back() improved by 0.067 ns (-0.10%)
• Throughput Leader: std::make_unique<llm_graph_input_pos_bucket>() degraded by 0.122 ns (+0.12%)
• Core Function Impact: No changes detected in critical inference functions (llama_decode, llama_encode, llama_tokenize)

Inference Performance Impact:
No impact on tokens per second expected. The affected functions are peripheral STL operations in tokenization subsystems, not core inference paths. Based on the reference model performance (7% tokens/sec reduction per 2ms llama_decode slowdown), the nanosecond-level changes observed would have negligible effect on overall throughput.

Power Consumption Analysis:
Minimal power consumption changes across all binaries:

• build.bin.libllama.so: -0.0% change (280,780 nJ vs 280,781 nJ baseline)
• build.bin.llama-cvector-generator: -100% (binary removed/disabled, eliminating 314,116 nJ)
• Other binaries show zero or negligible changes

Technical Analysis:

• Flame Graph: pop_back() shows simple, flat execution profile with 66 ns concentrated in single operation
• CFG Comparison: Identical control flow graphs between versions, confirming no algorithmic changes
• Code Review: PR UPSTREAM PR #17064: convert: add dequant function for compressed_tensor (kimi-k2-thinking) #108 only modifies Python conversion script (convert_hf_to_gguf.py) for compressed tensor support, unrelated to observed C++ performance metrics

Conclusion:
The performance variations represent compiler optimization differences or measurement noise rather than functional changes. No actionable performance improvements required as changes are within normal statistical variation and don't affect core inference capabilities.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary: llama.cpp PR #108

Overview

Analysis of PR #108 reveals minimal performance impact on the llama.cpp codebase. The pull request adds compressed tensor dequantization support for Kimi-K2-Thinking model conversion, modifying only the Python conversion script convert_hf_to_gguf.py. No C++ runtime code was changed.

Performance Metrics Analysis

Highest Performance Changes:

• Response Time: llama_supports_rpc function showed +0.082% increase (+0.024 ns)
• Throughput: _ZNK14llama_kv_cells11seq_pos_minEi (seq_pos_min) showed +0.113% increase (+0.34 ns)

Core Function Impact: None of the critical inference functions (llama_decode, llama_encode, llama_tokenize) were modified or showed meaningful performance changes. The observed micro-variations are within measurement noise levels and do not affect tokens-per-second performance.

Power Consumption: All binaries showed negligible power consumption changes (≤0.001%), with total estimated consumption remaining at ~1.73 millijoules across all binaries.

Technical Analysis

Flame Graph Insights: The llama_supports_rpc function maintains a simple two-level execution structure with 76% self-time and 24% PLT resolution overhead. No structural changes were observed.

CFG Comparison: Control flow graphs for llama_supports_rpc showed identical assembly code and structure between versions, confirming that performance variations stem from external factors (build environment, linking) rather than code modifications.

Code Review Findings: The PR introduces robust compressed tensor handling with proper dequantization logic for 4-bit quantized weights. Implementation is limited to specific quantization parameters (32-group, 4-bit) with hardcoded assertions that may require broader validation for production use.

Key Findings

The analysis confirms this PR has no impact on inference performance. Changes are isolated to model conversion functionality, with all performance variations falling within normal measurement variance. The implementation successfully adds support for compressed tensor formats while maintaining the existing runtime performance characteristics of the llama.cpp inference engine.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary

Overview

Analysis of llama.cpp project comparing version 3a2a4a9c-b3d7-48cf-bcb4-915b18859db3 against base version 52cd5469-e814-4a51-818a-57d1618fc442 reveals minimal performance variations with no meaningful impact on core inference functionality.

Key Findings

Performance Metrics:

• Highest Response Time Change: _ZNSt7__cxx1112regex_traitsIcE10_RegexMaskC1Eth in build.bin.llama-run (+0.082%, +0.018 ns)
• Highest Throughput Change: llama_supports_rpc in build.bin.libllama.so (+0.110%, +0.024 ns)

Core Function Impact Assessment:
The measured changes affect non-critical utility functions rather than core inference components. Neither llama_decode, llama_encode, nor llama_tokenize show performance variations, indicating zero impact on tokens per second throughput.

Power Consumption Analysis:
Total estimated power consumption change across all binaries is <0.001%. Affected binaries show negligible variations:

• build.bin.libllama.so: -0.0% (280,780 nJ → 280,780 nJ)
• build.bin.llama-run: -0.0% (266,868 nJ → 266,867 nJ)

Flame Graph and CFG Analysis:
The _ZNSt7__cxx1112regex_traitsIcE10_RegexMaskC1Eth function shows identical assembly code between versions with single-block execution (22 ns runtime). The 0.01 ns timing difference stems from micro-architectural factors rather than code changes, as confirmed by byte-for-byte identical instruction sequences.

GitHub Code Review:
The concurrent PR #108 adds compressed tensor dequantization support for Kimi-K2-Thinking models in the Python conversion script. This change is isolated to the conversion pipeline and does not affect runtime inference performance.

Conclusion:
The performance variations represent measurement noise rather than functional changes. All core inference functions maintain identical performance characteristics, ensuring consistent tokens per second throughput. The system demonstrates stable performance with no actionable optimization requirements.