Building Custom AI Operators with CANN TIK and TVM Training Course

Fundamentals of Custom Operator Development

• The rationale for building custom operators: use cases and constraints
• Architecture of the CANN runtime and operator integration points
• An overview of TBE, TIK, and TVM within the Huawei AI ecosystem

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and its supported APIs
• Memory management techniques and tiling strategies in TIK
• Creation, compilation, and registration of custom ops with CANN

Verification and Testing of Custom Ops

• Unit and integration testing of ops within the execution graph
• Troubleshooting kernel-level performance bottlenecks
• Visualizing op execution flows and buffer dynamics

Scheduling and Optimization via TVM

• Understanding TVM as a compiler for tensor operations
• Crafting schedules for custom ops within TVM
• Tuning, benchmarking, and code generation for Ascend using TVM

Framework and Model Integration

• Registering custom ops for MindSpore and ONNX compatibility
• Ensuring model integrity and managing fallback behaviors
• Handling multi-operator graphs with mixed precision support

Real-World Applications and Advanced Optimizations

• Case study: Achieving high-efficiency convolution for small input shapes
• Case study: Optimizing memory-aware attention operators
• Best practices for deploying custom ops across various devices

Conclusion and Future Directions