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 technical constraints.
• An exploration of the CANN runtime structure and key operator integration points.
• A comprehensive overview of TBE, TIK, and TVM within the Huawei AI ecosystem.

Using TIK for Low-Level Operator Programming

• Comprehending the TIK programming model and its supported APIs.
• Managing memory effectively and applying tiling strategies in TIK.
• The process of creating, compiling, and registering a custom operator with CANN.

Testing and Validating Custom Operators

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

TVM-Based Scheduling and Optimization

• Understanding TVM as a specialized compiler for tensor operations.
• Writing efficient schedules for custom operators in TVM.
• Performing TVM tuning, benchmarking, and code generation optimized for Ascend.

Integration with Frameworks and Models

• Registering custom operators for use with MindSpore and ONNX frameworks.
• Verifying model integrity and managing fallback behaviors effectively.
• Supporting complex 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 operators across diverse devices.

Summary and Next Steps