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Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: use cases and constraints
- CANN runtime architecture and points for operator integration
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem
Employing TIK for Low-Level Operator Programming
- Grasping the TIK programming model and its 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 bottlenecks
- 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 behaviour
- 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 in custom op deployment across devices
Summary and Next Steps
Requirements
- Profound understanding of AI model internals and operator-level computations
- Practical experience with Python and Linux development environments
- Knowledge of neural network compilers or graph-level optimization techniques
Target Audience
- Compiler engineers engaged in AI toolchain development
- Systems developers specializing in low-level AI optimization
- Developers creating custom operators or addressing novel AI workloads
14 Hours
