UPSTREAM PR #17333: CANN: Refactor evaluate_and_capture_cann_graph

[DajanaV]

Mirrored from ggml-org/llama.cpp#17333

Description of the problem

• matched_graph is obtained even if graph mode is disabled.
• End of graph capture and graph replay are unnecessarily placed in different if blocks.

Proposed solution

• Obtain matched_graph only if graph mode is enabled.
• Place end of graph capture and graph replay inside the same if block.
• Unify graph related comments style.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary: PR #245 CANN Graph Refactoring

Overview

PR #245 implements a targeted refactoring of the evaluate_and_capture_cann_graph function in the CANN backend module (ggml/src/ggml-cann/ggml-cann.cpp). The changes address inefficient resource access patterns by moving matched_graph declaration inside conditional blocks and consolidating graph capture/execution logic.

Code Changes Analysis

The modifications represent a performance optimization rather than functional changes:

• Resource Access Optimization: matched_graph is now retrieved only when use_cann_graph is enabled, eliminating unnecessary LRU cache access
• Logic Consolidation: Graph capture end and execution operations are unified within a single conditional block
• Code Organization: Improved maintainability through better separation of concerns

Performance Impact Assessment

Condition 1 Applied: The analysis reveals no meaningful performance regressions or critical issues. The changes are internal optimizations that maintain identical execution semantics while improving resource efficiency.

Core Function Impact: The modifications affect the CANN backend's graph execution path but do not impact primary inference functions (llama_decode, llama_encode, llama_tokenize) that directly influence tokens-per-second performance.

Resource Efficiency: The refactoring eliminates unnecessary cache operations when graph mode is disabled, reducing potential cache miss penalties without affecting the critical inference pipeline.

Technical Benefits

• Reduced Overhead: Eliminates redundant LRU cache access in non-graph execution scenarios
• Improved Code Clarity: Consolidates related operations for better maintainability
• Preserved Functionality: Maintains backward compatibility with existing CANN graph usage patterns

Conclusion

This refactoring represents a well-executed internal optimization that improves code organization and resource utilization without introducing functional changes or performance regressions. The modifications enhance the CANN backend's efficiency while preserving the original execution semantics, contributing to overall codebase quality without impacting inference performance metrics.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary: PR #245 CANN Graph Refactoring

Overview

PR #245 implements a targeted refactoring of the evaluate_and_capture_cann_graph function in the CANN backend module (ggml/src/ggml-cann/ggml-cann.cpp). The changes address inefficient resource access patterns by moving matched_graph declaration inside conditional blocks and consolidating graph capture/execution logic.

Code Changes Analysis

The modifications represent a performance optimization rather than functional changes:

• Resource Access Optimization: matched_graph is now retrieved only when use_cann_graph is enabled, eliminating unnecessary LRU cache access
• Logic Consolidation: Graph capture end and execution operations are unified within a single conditional block
• Code Organization: Improved maintainability through better separation of concerns

Performance Impact Assessment

Condition 1 Applied: The analysis reveals no meaningful performance regressions or critical issues. The changes are internal optimizations that maintain identical execution semantics while improving resource efficiency.

Core Function Impact: The modifications affect the CANN backend's graph execution path but do not impact primary inference functions (llama_decode, llama_encode, llama_tokenize) that directly influence tokens-per-second performance.

Resource Efficiency: The refactoring eliminates unnecessary cache operations when graph mode is disabled, reducing potential cache miss penalties without affecting the critical inference pipeline.

Technical Benefits

• Reduced Overhead: Eliminates redundant LRU cache access in non-graph execution scenarios
• Improved Code Clarity: Consolidates related operations for better maintainability
• Preserved Functionality: Maintains backward compatibility with existing CANN graph usage patterns

Conclusion

This refactoring represents a well-executed internal optimization that improves code organization and resource utilization without introducing functional changes or performance regressions. The modifications enhance the CANN backend's efficiency while preserving the original execution semantics, contributing to overall codebase quality without impacting inference performance metrics.

[loci-agentic-ai]

Access the complete analysis in the LOCI Dashboard

Performance Analysis Summary: PR #245 CANN Graph Refactoring

Overview

PR #245 implements a targeted refactoring of the evaluate_and_capture_cann_graph function in the CANN backend module (ggml/src/ggml-cann/ggml-cann.cpp). The changes address inefficient resource access patterns by moving matched_graph declaration inside conditional blocks and consolidating graph capture/execution logic.

Code Changes Analysis

The modifications represent a performance optimization rather than functional changes:

• Resource Access Optimization: matched_graph is now retrieved only when use_cann_graph is enabled, eliminating unnecessary LRU cache access
• Logic Consolidation: Graph capture end and execution operations are unified within a single conditional block
• Code Organization: Improved maintainability through better separation of concerns

Performance Impact Assessment

Condition 1 Applied: The analysis reveals no meaningful performance regressions or critical issues. The changes are internal optimizations that maintain identical execution semantics while improving resource efficiency.

Core Function Impact: The modifications affect the CANN backend's graph execution path but do not impact primary inference functions (llama_decode, llama_encode, llama_tokenize) that directly influence tokens-per-second performance.

Resource Efficiency: The refactoring eliminates unnecessary cache operations when graph mode is disabled, reducing potential cache miss penalties without affecting the critical inference pipeline.

Technical Benefits

• Reduced Overhead: Eliminates redundant LRU cache access in non-graph execution scenarios
• Improved Code Clarity: Consolidates related operations for better maintainability
• Preserved Functionality: Maintains backward compatibility with existing CANN graph usage patterns

Conclusion

This refactoring represents a well-executed internal optimization that improves code organization and resource utilization without introducing functional changes or performance regressions. The modifications enhance the CANN backend's efficiency while preserving the original execution semantics, contributing to overall codebase quality without impacting inference performance metrics.