Building Custom AI Operators with CANN TIK and TVM Training Course

Fundamentals of Custom Operator Development

• Rationale for custom operator creation, including specific use cases and architectural constraints.
• Analysis of CANN runtime structure and critical operator integration interfaces.
• Strategic overview of TBE, TIK, and TVM roles within the Huawei AI ecosystem.

Low-Level Operator Programming with TIK

• Exploring the TIK programming model and associated API capabilities.
• Advanced memory management and tiling strategies within the TIK framework.
• End-to-end process of creating, compiling, and registering custom ops with CANN.

Testing and Validation of Custom Operations

• Comprehensive unit and integration testing of operations within the execution graph.
• Methodologies for debugging kernel-level performance bottlenecks.
• Techniques for visualizing operator execution flow and buffer dynamics.

Scheduling and Optimization using TVM

• Introduction to TVM as a compilation engine for tensor operations.
• Crafting efficient schedules for custom operations using TVM.
• Leveraging TVM for tuning, benchmarking, and code generation tailored for Ascend.

Integration with Frameworks and Models

• Registering custom operations for compatibility with MindSpore and ONNX standards.
• Ensuring model integrity and verifying fallback mechanisms.
• Managing multi-operator graphs that utilize mixed precision strategies.

Case Studies and Specialized Optimization Techniques

• Case study: Optimizing high-efficiency convolution for compact input shapes.
• Case study: Enhancing memory-aware attention operator performance.
• Best practices for deploying custom operators across diverse device environments.

Conclusions and Recommended Next Steps