Building Custom AI Operators with CANN TIK and TVM Training Course

Foundations of Custom Operator Development

• The rationale for custom operators: use cases and technical constraints.
• Exploring the CANN runtime architecture and operator integration points.
• A comprehensive look at TBE, TIK, and TVM within the Huawei AI ecosystem.

Low-Level Operator Programming with TIK

• Navigating the TIK programming model and its supported APIs.
• Strategies for memory management and tiling in TIK.
• The process of creating, compiling, and registering custom ops with CANN.

Validation and Testing of Custom Operators

• Implementing unit and integration tests for operators within the graph.
• Techniques for debugging kernel-level performance bottlenecks.
• Visualizing operator execution flows and buffer dynamics.

Scheduling and Optimization via TVM

• Understanding TVM as a compiler for tensor operations.
• Crafting schedules for custom operators within TVM.
• Utilizing TVM for tuning, benchmarking, and code generation tailored for Ascend.

Framework Integration and Model Compatibility

• Registering custom operators for compatibility with MindSpore and ONNX.
• Ensuring model integrity and managing fallback mechanisms.
• Supporting multi-operator graphs that utilize mixed precision.

Real-World Case Studies and Advanced Optimizations

• Case study: Optimizing high-efficiency convolution for small input dimensions.
• Case study: Refining attention operators with memory-aware strategies.
• Best practices for deploying custom operators across various devices.

Conclusion and Future Pathways