Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Developing Custom Operators

• The rationale for creating custom operators: use cases and constraints.
• Structure of the CANN runtime and key operator integration points.
• Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.

Utilizing TIK for Low-Level Operator Programming

• Exploring the TIK programming model and its supported APIs.
• Managing memory and implementing tiling strategies in TIK.
• Creating, compiling, and registering custom operators with CANN.

Testing and Validating Custom Operations

• Conducting unit and integration testing for operators within the graph.
• Troubleshooting kernel-level performance bottlenecks.
• Visualizing operator execution flows and buffer behaviors.

Scheduling and Optimization via TVM

• Overview of TVM as a compiler for tensor operations.
• Crafting schedules for custom operators using TVM.
• Executing TVM tuning, benchmarking, and code generation for Ascend.

Integration with Frameworks and Models

• Registering custom operators for MindSpore and ONNX frameworks.
• Ensuring model integrity and managing fallback behaviors.
• Supporting multi-operator graphs with mixed-precision capabilities.

Case Studies and Advanced Optimizations

• Case study: Optimizing high-efficiency convolution for small input shapes.
• Case study: Memory-conscious optimization of attention operators.
• Best practices for deploying custom operators across various devices.

Summary and Future Directions