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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 operator with CANN.
Testing and Validating Custom Operators
- Unit testing and integration testing of operators within the graph.
- Debugging kernel-level performance issues.
- Visualising operator execution and buffer behaviour.
TVM-Based Scheduling and Optimisation
- 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 behaviour.
- Supporting multi-operator graphs with mixed precision.
Case Studies and Specialised Optimisations
- Case study: high-efficiency convolution for small input shapes.
- Case study: memory-aware attention operator optimisation.
- Best practices in custom operator deployment across devices.
Summary and Next Steps
Requirements
- Comprehensive understanding of AI model internals and operator-level computation.
- Proficiency 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 optimisation.
- Developers building custom operators or targeting novel AI workloads.
14 Hours
