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Course Outline
Introduction to Custom Operator Development
- Rationale for custom operators: Use cases and constraints.
- CANN runtime architecture and points of operator integration.
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.
Low-Level Operator Programming with TIK
- Comprehension of the TIK programming model and its supported APIs.
- Memory management strategies and tiling approaches in TIK.
- Steps to create, compile, and register a custom operator with CANN.
Testing and Validation of Custom Operators
- Conducting unit and integration tests for operators within the graph.
- Troubleshooting kernel-level performance bottlenecks.
- Visualizing operator execution and buffer behaviors.
TVM-Driven Scheduling and Optimization
- Overview of TVM as a compiler for tensor operations.
- Crafting schedules for custom operators in TVM.
- Performing TVM tuning, benchmarking, and code generation for Ascend.
Integration with Frameworks and Models
- Registering custom operators for MindSpore and ONNX compatibility.
- Ensuring model integrity and analyzing fallback mechanisms.
- Enabling support for multi-operator graphs utilizing mixed precision.
Case Studies and Specialized Optimizations
- Case study: Achieving high-efficiency convolution 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.
- Familiarity with 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 targeting emerging AI workloads.
14 Hours
