Building Custom AI Operators with CANN TIK and TVM Training Course

Fundamentals of Custom Operator Creation

• Rationale for custom operator development, including use cases and limitations.
• Examination of the CANN runtime architecture and operator integration points.
• Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.

Implementing Low-Level Operator Programming with TIK

• Exploring the TIK programming model and its associated APIs.
• Strategies for memory management and tiling within TIK.
• Process of creating, compiling, and registering custom operations with CANN.

Verification and Validation of Custom Operations

• Conducting unit and integration tests for operators within the graph.
• Troubleshooting performance issues at the kernel level.
• Visualizing execution flows and buffer dynamics.

Optimization and Scheduling via TVM

• Introduction to TVM as a compiler framework for tensor operations.
• Crafting scheduling logic for custom operators in TVM.
• Applying TVM for tuning, benchmarking, and code generation tailored for Ascend.

Framework and Model Integration

• Registering custom operators for use with MindSpore and ONNX.
• Ensuring model consistency and validating fallback mechanisms.
• Managing multi-operator graphs involving mixed precision.

Applied Case Studies and Advanced Optimizations

• Case analysis: Achieving high-efficiency convolution for smaller input shapes.
• Case analysis: Optimizing attention operators with a focus on memory usage.
• Best practices for deploying custom operators across diverse devices.

Conclusions and Future Directions