Building Custom AI Operators with CANN TIK and TVM Training Course

Introduction to Custom Operator Development

• Rationale for custom operators: exploring use cases and technical constraints.
• Analyzing CANN runtime architecture and key operator integration points.
• Contextualizing TBE, TIK, and TVM within the Huawei AI ecosystem.

Leveraging TIK for Low-Level Operator Programming

• Deep dive into the TIK programming model and its supported APIs.
• Strategies for memory management and tiling within the TIK framework.
• End-to-end process: creating, compiling, and registering custom operators with CANN.

Testing and Validating Custom Operators

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

TVM-Based Scheduling and Optimization

• Understanding TVM as a specialized compiler for tensor operations.
• Crafting efficient schedules for custom operators using TVM.
• Applying TVM tuning, benchmarking, and code generation techniques for Ascend.

Framework Integration and Model Compatibility

• Registering custom operators for compatibility with MindSpore and ONNX.
• Ensuring model integrity and proper fallback mechanism behavior.
• Managing multi-operator graphs with mixed-precision support.

Case Studies and Specialized Optimization Techniques

• Case study: Optimizing high-efficiency convolutions for small input shapes.
• Case study: Enhancing memory-aware attention operator performance.
• Establishing best practices for deploying custom operators across diverse devices.

Summary and Future Directions