Building Custom AI Operators with CANN TIK and TVM Training Course

Fundamentals of Custom Operator Development

• Rationale for building custom operators: use cases and constraints
• CANN runtime architecture and operator integration touchpoints
• 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 and tiling strategies in TIK
• Creating, compiling, and registering custom operations in CANN

Testing and Validation of Custom Operations

• Unit and integration testing of operations within the graph
• Debugging kernel-level performance bottlenecks
• Visualizing operation execution and buffer dynamics

TVM-Driven Scheduling and Optimization

• Introduction to TVM as a compiler for tensor operations
• Crafting schedules for custom operations in TVM
• TVM tuning, benchmarking, and code generation for Ascend

Framework and Model Integration

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

Case Studies and Specialized Optimization Techniques

• Case study: efficient convolution for small input shapes
• Case study: memory-aware optimization of attention operators
• Best practices for deploying custom operations across various devices

Recap and Future Directions