Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

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

Low-Level Operator Programming with TIK

• Understanding the TIK programming model and supported APIs.
• Memory management and tiling strategies in TIK.
• Creating, compiling, and registering custom ops with CANN.

Testing and Validating Custom Operators

• Unit and integration testing of operators within the graph.
• Debugging kernel-level performance issues.
• Visualizing operator execution and buffer behavior.

TVM-Based Scheduling and Optimization

• TVM as a compiler for tensor operations: an overview.
• Developing schedules for custom operators in TVM.
• TVM tuning, benchmarking, and code generation for Ascend.

Framework and Model Integration

• Registering custom operators for MindSpore and ONNX.
• Verifying model integrity and fallback behavior.
• Supporting multi-operator graphs with mixed precision.

Case Studies and Specialized Optimizations

• Case study: High-efficiency convolution for small input shapes.
• Case study: Memory-aware attention operator optimization.
• Best practices for deploying custom ops across various devices.

Summary and Next Steps