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Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: Use cases and constraints
- CANN runtime structure and operator integration points
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem
Using 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 operation with CANN
Testing and Validating Custom Ops
- Unit testing and integration testing of operations within the graph
- Debugging kernel-level performance issues
- Visualizing operation execution and buffer behavior
TVM-Based Scheduling and Optimization
- Overview of TVM as a compiler for tensor operations
- Writing a schedule for a custom operation in TVM
- TVM tuning, benchmarking, and code generation for Ascend
Integration with Frameworks and Models
- Registering custom operations for MindSpore and ONNX
- Verifying model integrity and fallback behavior
- Supporting multi-operation 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 custom operation deployment across devices
Summary and Next Steps
Requirements
- Solid understanding of AI model internals and operator-level computations
- Practical experience with 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 specializing in low-level AI optimization
- Developers creating custom operations or addressing novel AI workloads
14 Hours
