Building Custom AI Operators with CANN TIK and TVM Training Course

Foundamentals of Custom Operator Development

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

Leveraging TIK for Low-Level Operator Programming

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

Testing and Verification of Custom Operations

• Conducting unit and integration tests for operators within the execution graph.
• Techniques for identifying and resolving kernel-level performance bottlenecks.
• Methods for visualizing operator execution flows and buffer dynamics.

Scheduling and Optimization via TVM

• Overview of TVM’s role as a compiler for tensor operations.
• Authoring efficient schedules for custom operators using TVM.
• Executing TVM-based tuning, benchmarking, and code generation targeted at Ascend.

Framework and Model Integration

• Registering custom operators for compatibility with MindSpore and ONNX.
• Ensuring model integrity and managing fallback behaviors effectively.
• Supporting complex, multi-operator graphs that utilize mixed precision.

Practical Case Studies and Specialized Optimizations

• Case study: implementing high-efficiency convolution for small input shapes.
• Case study: optimizing attention operators with memory constraints in mind.
• Best practices for deploying custom operators across diverse hardware devices.

Conclusion and Forward-Looking Strategies