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Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: applications and limitations
- CANN runtime architecture and points for operator integration
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem
Low-Level Operator Programming with TIK
- Grasping the TIK programming model and its supported APIs
- Memory management and tiling strategies in TIK
- Constructing, compiling, and registering a custom operator with CANN
Testing and Validating Custom Operators
- Conducting unit and integration tests for operators within the graph
- Diagnosing kernel-level performance challenges
- Visualizing operator execution and buffer interactions
TVM-Driven Scheduling and Optimization
- Introduction to TVM as a compiler for tensor operators
- Writing schedules for custom operators in TVM
- TVM tuning, benchmarking, and code generation for Ascend platforms
Integration with Frameworks and Models
- Registering custom operators for MindSpore and ONNX compatibility
- Verifying model integrity and fallback mechanisms
- Supporting multi-operator graphs with mixed precision
Case Studies and Specialized Optimizations
- Case study: achieving high efficiency in convolutions for small input shapes
- Case study: memory-aware optimization of attention operators
- Best practices for deploying custom operators across various devices
Summary and Next Steps
Requirements
- Proficient 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 working on AI toolchains
- Systems developers specializing in low-level AI optimization
- Developers creating custom operators or targeting innovative AI workloads
14 Hours
