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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 operator integration points.
- 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 strategies and tiling approaches in TIK.
- Creating, compiling, and registering custom operators with CANN.
Testing and Validating Custom Operators
- Conducting unit and integration tests for operators within the graph.
- Debugging kernel-level performance bottlenecks.
- Visualizing operator execution and buffer behaviour.
TVM-Based Scheduling and Optimization
- Understanding TVM as a compiler for tensor operations.
- Writing schedules for custom operators in TVM.
- TVM tuning, benchmarking, and code generation for Ascend devices.
Integration with Frameworks and Models
- Registering custom operators for MindSpore and ONNX.
- Verifying model integrity and fallback behaviour.
- Supporting mixed-precision execution across multi-operator graphs.
Case Studies and Specialized Optimizations
- Case study: High-efficiency convolution for small input shapes.
- Case study: Memory-aware optimization for attention operators.
- Best practices for deploying custom operators across various devices.
Summary and Next Steps
Requirements
- Solid 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 techniques.
Target Audience
- Compiler engineers working within AI toolchains.
- Systems developers specializing in low-level AI optimization.
- Developers creating custom operators or targeting innovative AI workloads.
14 Hours
