Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Introduction to Custom Operator Development
- Rationale for building custom operators: Use cases and constraints.
- CANN runtime structure and operator integration points.
- Overview of TBE, TIK, and TVM within the Huawei AI ecosystem.
Low-Level Operator Programming with TIK
- Understanding the TIK programming model and supported APIs.
- Memory management and tiling strategies in TIK.
- Creating, compiling, and registering a custom operation with CANN.
Testing and Validating Custom Operations
- Unit testing and integration testing of operations within the graph.
- Debugging kernel-level performance issues.
- Visualizing operation execution and buffer behavior.
TVM-Based Scheduling and Optimization
- Overview of TVM as a compiler for tensor operations.
- Writing a schedule for a custom operation in TVM.
- TVM tuning, benchmarking, and code generation for Ascend.
Integration with Frameworks and Models
- Registering custom operations for MindSpore and ONNX.
- Verifying model integrity and fallback behavior.
- Supporting multi-operation graphs with mixed precision.
Case Studies and Specialized Optimizations
- Case study: High-efficiency convolution for small input shapes.
- Case study: Memory-aware attention operation optimization.
- Best practices for custom operation deployment across devices.
Summary and Next Steps
Requirements
- Profound understanding of AI model internals and operator-level computation.
- Experience with Python and Linux development environments.
- Familiarity with neural network compilers or graph-level optimizers.
Audience
- Compiler engineers working on AI toolchains.
- Systems developers focused on low-level AI optimization.
- Developers building custom operations or targeting novel AI workloads.
14 Hours
