Building Custom AI Operators with CANN TIK and TVM Training Course

Foundations of Custom Operator Development

• The rationale for developing custom operators: use cases and limitations
• Analysis of CANN runtime architecture and operator integration touchpoints
• 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
• Strategies for memory management and tiling in TIK
• The process of creating, compiling, and registering custom ops with CANN

Verification and Validation of Custom Ops

• Conducting unit and integration testing of ops within the computation graph
• Diagnosing kernel-level performance bottlenecks
• Visualizing operator execution flows and buffer behavior

Scheduling and Optimization via TVM

• Introduction to TVM as a compiler for tensor operations
• Formulating schedules for custom ops within TVM
• TVM-based tuning, benchmarking, and code generation targeting Ascend

Integration with Frameworks and Models

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

Applied Case Studies and Advanced Optimizations

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

Recap and Future Directions