Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

• The rationale for building custom operators, including use cases and constraints
• The structure of the CANN runtime and key operator integration points
• An overview of TBE, TIK, and TVM within the Huawei AI ecosystem

Utilizing TIK for Low-Level Operator Programming

• Understanding the TIK programming model and its supported APIs
• Memory management strategies and tiling techniques in TIK
• The process of creating, compiling, and registering custom ops with CANN

Testing and Validating Custom Operations

• Conducting unit and integration tests for operations within the graph
• Diagnosing and resolving kernel-level performance issues
• Visualizing operation execution flow and buffer behavior

Scheduling and Optimization with TVM

• An overview of TVM as a compiler for tensor operations
• Defining schedules for custom operators within TVM
• Performing TVM tuning, benchmarking, and code generation for Ascend

Framework and Model Integration

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

Case Studies and Specialized Optimizations

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

Summary and Next Steps