UPSTREAM PR #16941: Model: add openPangu-Embedded

[DajanaV]

Mirrored from ggml-org/llama.cpp#16941

Add a new model openPangu-Embedded-1/7B-V1.1.
Yu can get the the model from model path.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Based on my analysis of the performance data and code changes, here's the comprehensive performance impact assessment:

Performance Analysis Summary

Critical Function Performance Changes

Primary Performance Degradation

• Function: std::vector<std::pair<...>>::begin() (regex_automaton.h:873-874)
• Response Time: 208% increase (85ns → 261ns)
• Throughput: 282% degradation (62ns → 239ns self-time)
• Root Cause: Assembly analysis reveals missing stack canary load instruction and inefficient block splitting

Secondary Performance Impact

• Function: std::vector<unsigned int>::back() (regex_automaton.h:1233-1237)
• Bottleneck: 538% increase (32ns → 204ns)
• Throughput: 266% degradation (70ns → 255ns)

KPI Impact Analysis

1. Tokens Per Second Impact

Status: No Direct Impact Expected

Analysis: The degraded functions are located in regex processing components, not in core inference functions:

• llama_decode() - No performance changes detected
• llama_encode() - No performance changes detected
• llama_tokenize() - No performance changes detected

Conclusion: Based on the reference that 2ms slower llama_decode() results in 7% tokens/second reduction, the current regex-related degradation should not impact inference throughput as these functions are not in the critical inference path.

2. Power Consumption Impact

Status: Minimal Impact

Affected Binary: build.bin.libllama.so

• Power Consumption Change: +0.169% (280,662nJ → 281,136nJ)
• Absolute Delta: +475nJ increase
• Other Binaries: No measurable changes across remaining 15 binaries

Root Cause: Increased CPU cycles from inefficient STL container operations in regex processing.

3. Quantization Efficiency

Status: No Impact

Analysis: No changes detected in quantization-related functions:

• llama_model_quantize() - No performance changes
• Quantization format handling - Unchanged
• GGUF loading mechanisms - Stable performance

4. Memory Usage

Status: Potential Indirect Impact

Affected Areas:

• Regex Container Operations: Degraded std::vector::begin() and back() operations suggest potential memory access inefficiencies
• Stack Management: Missing stack canary load instruction indicates memory protection overhead
• Container Growth: Performance degradation may correlate with increased container sizes in tokenization patterns

No Direct Impact on core memory management functions:

• llama_memory_clear() - No changes
• llama_memory_seq_rm() - No changes
• KV cache operations - Stable performance

5. Batch Processing

Status: No Impact

Analysis: Core batch processing functions show no performance degradation:

• llama_batch_init() - No changes
• llama_batch_get_one() - No changes
• llama_decode() with batches - No changes

Root Cause Analysis

Assembly-Level Issues

1. Missing Stack Canary Load: Critical security instruction absent in current version
2. Inefficient Block Splitting: Entry block unnecessarily divided, increasing execution overhead
3. Compiler Optimization Regression: Suboptimal code generation affecting STL container performance

Code Changes Context

The performance degradation appears unrelated to PR #69 (PanguEmbedded model addition), suggesting:

• Compiler optimization changes between builds
• Build system modifications affecting STL container code generation
• Separate changes not captured in the analyzed pull request

Action Items

Immediate Code-Level Actions

1. Investigate Compiler Settings: Review optimization flags and STL container code generation between versions
2. Assembly Code Analysis: Restore missing stack canary load instruction in std::vector::begin() implementation
3. Block Structure Optimization: Eliminate unnecessary block splitting in entry sequences

Build System Actions

1. Compiler Version Verification: Ensure consistent compiler versions and optimization settings
2. STL Implementation Check: Verify STL library versions and container implementation consistency
3. Debug Symbol Analysis: Compare debug information between versions to identify optimization differences

Performance Monitoring Focus

1. Regex Processing Paths: Monitor tokenization performance for patterns using regex automaton
2. Container Operation Profiling: Track STL container performance in text processing pipelines
3. Memory Access Patterns: Analyze cache efficiency in container operations

Conclusion

The identified performance regression is isolated to regex processing components and does not directly impact core inference performance metrics. The 0.169% power consumption increase in libllama.so represents minimal system impact. Primary concern is the missing security instruction which requires immediate attention for both performance and security reasons.