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Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: Use cases and constraints.
- CANN runtime structure and points for operator integration.
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.
Low-Level Operator Programming with TIK
- Comprehending 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 Operations
- Unit testing and integration testing of ops within the graph.
- Debugging kernel-level performance issues.
- 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 behavior.
- 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 for custom op deployment across devices.
Summary and Next Steps
Requirements
- Profound understanding of AI model internals and operator-level computation.
- Hands-on experience with Python and Linux development environments.
- Familiarity with neural network compilers or graph-level optimizers.
Target Audience
- Compiler engineers involved in AI toolchain development.
- Systems developers specializing in low-level AI optimization.
- Developers constructing custom operations or targeting novel AI workloads.
14 Hours
