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Course Outline
Introduction to Custom Operator Development
- Reasons 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.
Low-Level Operator Programming with TIK
- Understanding the TIK programming model and supported APIs.
- Memory management and tiling strategies in TIK.
- Creating, compiling, and registering a custom operator with CANN.
Testing and Validating Custom Operators
- Unit testing and integration testing of operators within the graph.
- Debugging performance issues at the kernel level.
- Visualizing operator execution and buffer behavior.
TVM-Based Scheduling and Optimization
- Overview of TVM as a compiler for tensor operations.
- Writing a schedule for a custom operator in TVM.
- TVM tuning, benchmarking, and code generation for Ascend.
Integration with Frameworks and Models
- Registering custom operators 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 operators across devices.
Summary and Next Steps
Requirements
- Proficient understanding of AI model internals and operator-level computation.
- Experience with Python and Linux development environments.
- Familiarity with neural network compilers or graph-level optimizers.
Audience
- Compiler engineers working on AI toolchains.
- Systems developers focused on low-level AI optimization.
- Developers creating custom operators or targeting new AI workloads.
14 Hours
