Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

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

Low-Level Operator Programming with TIK

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

Testing and Validating Custom Operations

• Conducting unit and integration tests for ops within the graph
• Diagnosing and resolving kernel-level performance bottlenecks
• Visualizing operator execution flow and buffer dynamics

Scheduling and Optimization via TVM

• Understanding TVM as a compiler framework for tensor operations
• Designing schedules for custom operators within TVM
• Executing TVM tuning, benchmarking, and code generation for Ascend

Framework and Model Integration

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

Case Studies and Specialized Optimizations

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

Summary and Future Steps