Building Custom AI Operators with CANN TIK and TVM Training Course

Foundations of Custom Operator Development

• Rationale for building custom operators: use cases and architectural constraints
• CANN runtime architecture and key operator integration points
• Contextual overview of TBE, TIK, and TVM within the Huawei AI ecosystem

Low-Level Operator Programming with TIK

• Deconstructing the TIK programming model and associated APIs
• Managing memory and implementing tiling strategies within TIK
• Authoring, compiling, and registering custom ops with the CANN runtime

Validation and Testing of Custom Operations

• Executing unit and integration tests for operators within the graph
• Diagnosing and resolving kernel-level performance bottlenecks
• Visualizing operator execution flows and buffer interactions

Scheduling and Optimization via TVM

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

Framework and Model Integration

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

Case Studies and Specialized Techniques

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

Conclusion and Future Pathways