Building Custom AI Operators with CANN TIK and TVM Training Course

Foundations of Custom Operator Development

• Rationale for custom operators: addressing specific use cases and system constraints
• Exploring the CANN runtime architecture and operator integration touchpoints
• Overview of TBE, TIK, and TVM within the broader Huawei AI ecosystem

Low-Level Operator Programming with TIK

• Deep dive into the TIK programming model and its supported APIs
• Strategies for memory management and tiling within the TIK framework
• End-to-end workflow: creation, compilation, and registration of custom operations in CANN

Validation and Testing of Custom Operations

• Conducting unit and integration tests for operations within the computation graph
• Diagnosing and resolving kernel-level performance bottlenecks
• Visualizing execution flows and buffer dynamics

Scheduling and Optimization via TVM

• Understanding TVM as a specialized compiler for tensor operations
• Designing and implementing custom schedules for operators in TVM
• Applying TVM tuning, benchmarking, and code generation techniques for Ascend targets

Framework and Model Integration

• Registering custom operations for compatibility with MindSpore and ONNX standards
• Ensuring model integrity and managing fallback mechanisms
• Handling multi-operator graphs that utilize mixed precision data types

Real-World Case Studies and Advanced Optimizations

• Case study: optimizing high-efficiency convolution for small input dimensions
• Case study: enhancing memory-aware attention operators
• Best practices for deploying custom operations across diverse device environments

Conclusions and Future Directions