Building Custom AI Operators with CANN TIK and TVM Training Course

Fundamentals of Custom Operator Development

• Rationale for custom operator development: use cases and technical constraints.
• Architecture of the CANN runtime and key operator integration points.
• Positioning of TBE, TIK, and TVM within the Huawei AI ecosystem.

Low-Level Operator Programming with TIK

• Detailed exploration of the TIK programming model and available APIs.
• Strategies for memory management and tiling within the TIK framework.
• Workflow for creating, compiling, and registering custom ops in CANN.

Verification and Validation of Custom Ops

• Conducting unit and integration tests for ops within the execution graph.
• Troubleshooting kernel-level performance bottlenecks.
• Visualizing execution flows and buffer behaviors for custom operations.

Scheduling and Optimization Using TVM

• Overview of TVM as a compiler infrastructure for tensor operations.
• Developing efficient schedules for custom ops using TVM.
• Applying TVM for tuning, benchmarking, and code generation targeted at Ascend.

Framework and Model Integration

• Registering custom operations for compatibility with MindSpore and ONNX.
• Ensuring model integrity and managing fallback mechanisms.
• Implementing multi-operator graphs with mixed precision support.

Case Studies and Advanced Optimizations

• Case study: Optimizing convolution operations for small input shapes.
• Case study: Memory-efficient optimization of attention operators.
• Best practices for deploying custom ops across diverse hardware devices.

Conclusion and Future Directions