Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

• Rationale for custom operators: use cases and technical constraints.
• Analysis of CANN runtime architecture and operator integration points.
• Contextualizing TBE, TIK, and TVM within the broader Huawei AI ecosystem.

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and its supported API surface.
• Strategies for memory management and tiling within the TIK framework.
• Process of creating, compiling, and registering custom operations in CANN.

Testing and Validation of Custom Operations

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

Scheduling and Optimization via TVM

• Understanding TVM as a compiler infrastructure for tensor operations.
• Defining execution schedules for custom operators using TVM.
• Implementing TVM tuning, benchmarking, and code generation for Ascend targets.

Framework and Model Integration

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

Case Studies and Specialized Optimizations

• Case analysis: optimizing convolution efficiency for small input shapes.
• Case analysis: memory-aware optimization of attention operators.
• Best practices for deploying custom operators across diverse device environments.

Conclusion and Future Directions