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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 operator integration points.
- 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 techniques and tiling strategies in TIK.
- Steps to create, compile, and register a custom operator with CANN.
Testing and Validating Custom Operators
- Conducting unit and integration testing of operators within the graph.
- Debugging kernel-level performance bottlenecks.
- Visualizing operator execution flows and buffer behaviors.
Scheduling and Optimization via TVM
- Understanding TVM as a compiler designed for tensor operators.
- Writing custom schedules for operators in TVM.
- Performing TVM tuning, benchmarking, and code generation specifically for Ascend.
Integration with Frameworks and Models
- Registering custom operators for compatibility with MindSpore and ONNX.
- Verifying model integrity and analyzing fallback behaviors.
- Supporting multi-operator graphs with mixed precision capabilities.
Case Studies and Specialized Optimizations
- Case study: Implementing high-efficiency convolution for small input shapes.
- Case study: Optimizing attention operators with a focus on memory awareness.
- Best practices for deploying custom operators across various devices.
Summary and Next Steps
Requirements
- Profound 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 involved in AI toolchain development.
- Systems developers specializing in low-level AI optimization.
- Developers creating custom operators or targeting emerging AI workloads.
14 Hours
