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onnxruntime/onnxruntime/core/flatbuffers/schema/README.md

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ORT File Format

This directory contains the ORT file format schema and the generated C++ header file for the ORT file format.

The ORT file format schema uses the FlatBuffers serialization library.

Please do not directly modify the generated C++ header file or the generated Python binding files.

The flatbuffers compiler (flatc) is built as part of an ONNX Runtime build. It is located in the external/flatbuffers subdirectory of the build output directory.

e.g.

  • Windows Debug build
    • \build\Windows\Debug\external\flatbuffers\Debug\flatc.exe
  • Linux Debug build
    • /build/Linux/external/flatbuffers/Debug/flatc

It is possible to use another flatc as well, e.g., from a separate installation. Note that ONNX Runtime uses FlatBuffers 1.12.

To update the ORT file format schema and generated files:

  1. Modify the ORT file format schema.

  2. Run compile_schema.py to generate the C++ and Python bindings.

    python onnxruntime/core/flatbuffers/schema/compile_schema.py --flatc <path to flatc>

ORT FB format version history

In ort_format_version.h, see IsOrtModelVersionSupported() for the supported versions and kOrtModelVersion for the current version.

Version 1

History begins.

Initial support for FlatBuffers that includes Model support. Graph support including Attributes, Tensors, Tensor Sequences, Maps and Sequences. Constant initializers are also supported. Constant nodes are converted to constant initializers in the ORT format.

Version 2

Support for sparse initializers. Sparse intializers are stored within ORT FlatBuffers format, which includes sparse initializers converted from a Constant node attribute.

Version 3

Support for storing graph_doc_string field in Model (ORT FlatBuffers format).

Version 4

Update kernel def hashing to not depend on ordering of type constraint types (NOT BACKWARDS COMPATIBLE).

Version 5

Remove kernel def hashes and add KernelTypeStrResolver info to replace them (LIMITED BACKWARDS COMPATIBILITY). The change to the ORT format itself is not backwards compatibility-breaking, but ORT provides limited backwards compatibility for processing older models with missing KernelTypeStrResolver info.

The motivation for this update is to support additional execution providers with statically registered kernels. The original approach of using kernel def hashes was not so extensible as it required the execution provider providing hashes to be enabled at model conversion time.