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Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: Use cases and constraints
- CANN runtime structure and points for operator integration
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem
Utilizing TIK for Low-Level Operator Programming
- Understanding the TIK programming model and supported APIs
- Memory management and tiling strategies in TIK
- Creating, compiling, and registering a custom op with CANN
Testing and Validating Custom Ops
- Unit testing and integration testing of ops within the graph
- Debugging kernel-level performance issues
- Visualizing op execution and buffer behavior
TVM-Based Scheduling and Optimization
- Overview of TVM as a compiler for tensor operations
- Writing a schedule for a custom op in TVM
- TVM tuning, benchmarking, and code generation for Ascend
Integration with Frameworks and Models
- Registering custom ops for MindSpore and ONNX
- Verifying model integrity and fallback behavior
- Supporting multi-operator graphs with mixed precision
Case Studies and Specialized Optimizations
- Case study: High-efficiency convolution for small input shapes
- Case study: Memory-aware attention operator optimization
- Best practices for deploying custom ops across devices
Summary and Next Steps
Requirements
- Solid understanding of AI model internals and operator-level computation
- Proficiency in Python and Linux development environments
- Familiarity with neural network compilers or graph-level optimization tools
Target Audience
- Compiler engineers working on AI toolchains
- Systems developers focused on low-level AI optimization
- Developers creating custom operations or targeting novel AI workloads
14 Hours
