Cursusaanbod

Introduction to Custom Operator Development

  • Why build custom operators? Use cases and constraints
  • CANN runtime structure and operator integration points
  • Overview of TBE, TIK, and TVM in the Huawei AI ecosystem

Using TIK for Low-Level Operator Programming

  • Understanding the TIK programming model and supported APIs
  • Memory management and tiling strategy in TIK
  • Creating, compiling, and registering a custom op with CANN

Testing and Validating Custom Ops

  • Unit testing and integration testing of ops in 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 ops
  • 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 in custom op deployment across devices

Summary and Next Steps

Vereisten

  • Strong knowledge 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 ops or targeting novel AI workloads
 14 Uren

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