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 architecture of the CANN runtime and key points for operator integration.
• 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 and tiling strategies within TIK.
• The process of creating, compiling, and registering a custom operation with CANN.

Testing and Validating Custom Operations

• Performing unit and integration testing of operations within the graph.
• Diagnosing kernel-level performance bottlenecks.
• Visualizing operator execution and buffer dynamics.

Scheduling and Optimization Using TVM

• An introduction to TVM as a compiler for tensor operations.
• Developing a schedule for a custom operation in TVM.
• Tuning, benchmarking, and code generation for Ascend using TVM.

Integration with Frameworks and Models

• Registering custom operations for MindSpore and ONNX.
• Ensuring model integrity and handling fallback behaviors.
• Supporting multi-operator graphs that utilize mixed precision.

Case Studies and Specialized Optimizations

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

Conclusion and Future Steps