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Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: Use cases and constraints.
- Structure of the CANN runtime and key integration points for operators.
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.
Leveraging TIK for Low-Level Operator Programming
- Understanding the TIK programming model and its supported APIs.
- Memory management techniques 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 bottlenecks.
- Visualizing operator execution flow and buffer behavior.
TVM-Based Scheduling and Optimization
- Overview of TVM as a compiler for tensor operations.
- Writing schedules for custom operators in TVM.
- TVM tuning, benchmarking, and code generation specifically for Ascend.
Integration with Frameworks and Models
- Registering custom operators for MindSpore and ONNX.
- Verifying model integrity and managing fallback behaviors.
- Supporting multi-operator graphs with mixed-precision processing.
Case Studies and Specialized Optimizations
- Case study: High-efficiency convolution strategies for small input shapes.
- Case study: Memory-aware optimization for attention operators.
- Best practices for custom operator deployment across diverse devices.
Summary and Next Steps
Requirements
- Proficient 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 constructing custom operators or targeting emerging AI workloads.
14 Hours
