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  • Quick tour
  • Getting Started
  • Design
  • API
  • Operators
    • How-To
    • Miscellaneous
  • GitHub
  • Quick tour
  • Getting Started
  • Design
  • API
  • Operators
  • How-To
  • Miscellaneous
  • GitHub

Section Navigation

ONNX, Concepts, link to protobuf

  • Introduction to ONNX
    • ONNX Concepts
    • ONNX with Python
  • Uncompromising Objective
  • Detailed Differences between onnx and onnx_light
  • Protobuf format applied to ONNX
  • ORT flatbuffer format: parallelization and alignment
  • ModelProto creation and no-copy ownership
  • Complex loading and saving scenarios

Library Split

  • How the C++ libraries are split
  • Linking onnx-light in C++

Kernels and Backend Tests

  • C++ Kernels
  • Using backend tests to evaluate a runtime
  • Backend test-case coverage
  • Runtime test coverage (onnxruntime and shape_inference)
  • Custom kernels for ReferenceEvaluator

Shape Inference

  • Shape inference
    • Symbolic expression library (onnx_light.onnx_optim.expressions)
    • Value-as-shape propagation
    • Symbolic-dimension constraint mechanism
    • Sequences, maps and subgraphs
    • Shape-inference events (ShapeEvent)
    • Shape-inference coverage (onnx_optim)

Fuzzing

  • Fuzzing
  • Design

Design#

It replicates the same Python API and the same C++ API to enable a smooth replacement.

ONNX, Concepts, link to protobuf

  • Introduction to ONNX
  • Uncompromising Objective
  • Detailed Differences between onnx and onnx_light
  • Protobuf format applied to ONNX
  • ORT flatbuffer format: parallelization and alignment
  • ModelProto creation and no-copy ownership
  • Complex loading and saving scenarios

Library Split

  • How the C++ libraries are split
  • Linking onnx-light in C++

Kernels and Backend Tests

  • C++ Kernels
  • Using backend tests to evaluate a runtime
  • Backend test-case coverage
  • Runtime test coverage (onnxruntime and shape_inference)
  • Custom kernels for ReferenceEvaluator

Shape Inference

  • Shape inference

Fuzzing

  • Fuzzing

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Getting Started

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Introduction to ONNX

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