How the C++ libraries are split

onnx-light is intentionally split into several small static (or shared) C++ libraries (also referred to as assemblies) instead of a single monolithic archive. The goal is to let any C++ project link only what it actually needs: a program that just parses a ModelProto does not need operator schemas, a program that only registers schemas does not need shape inference, a program that only evaluates models does not need the full ONNX schema / checker stack, and so on.

Each library has a well defined responsibility and a minimal set of dependencies on the other libraries. The full dependency graph is shown below (arrows point from a library to the library it depends on). The SVG below is generated from library_split.dot with dot -Tsvg (Graphviz); regenerate it after editing the .dot source.

The same graph as an ASCII tree (rooted at lib_onnx_proto, the base dependency):

lib_onnx_proto
    ├── lib_onnx_op
    │       └── lib_onnx_optim
    └── lib_onnx_manipulations
            ├── lib_onnx_lib
            └── lib_onnx_kernels
                    └── lib_onnx_backend_test

lib_onnx_op and lib_onnx_manipulations are independent siblings: each links directly against lib_onnx_proto but neither depends on the other. lib_onnx_optim depends on lib_onnx_op only (it does not pull in lib_onnx_lib). lib_onnx_manipulations gathers the schema-independent ModelProto manipulation helpers (text parser / printer, attribute and tensor proto helpers, data-type name utilities and the graph-manipulation helpers); both lib_onnx_lib (full schemas, checker, shape inference, version converter) and lib_onnx_kernels depend publicly on it. lib_onnx_backend_test depends publicly on lib_onnx_kernels (and transitively on lib_onnx_manipulations / lib_onnx_proto) because every registered test case computes its expected outputs by invoking the bundled C++ reference kernels.

When installed (cmake --install) all libraries are exported under the onnx_light:: namespace and can be consumed individually through find_package(onnx_light).

Summary of each library

Library / CMake target and Sources	What it contains
onnx_light::lib_onnx_proto: onnx_light/onnx_proto/, onnx_light/onnx_helpers/onnx_light_helpers.cc	Protobuf-compatible message types (ModelProto, GraphProto, NodeProto, TensorProto, …), parser / serializer, external data and (optional) AES-256 encrypted save / load. Built as SHARED when ONNX_LIGHT_BUILD_PYTHON=ON so that every Python extension shares the same proto class registrations; built as STATIC for pure C++ consumers.
onnx_light::lib_onnx_op:onnx_light/onnx_op/	Lightweight LightOpSchema registrations for ONNX operator domains (math, logical, tensor, sequence, traditional ML, …). Does not depend on shape inference and does not pull in the full ONNX defs. Useful when only the operator catalogue is needed. Depends publicly on lib_onnx_proto.
onnx_light::onnx_manipulations (in-tree target lib_onnx_manipulations): onnx_light/onnx_manipulations/, onnx_light/onnx_lib/common/	Foundational common utilities (Status, assertions, IR / proto conversion, …) plus the schema-independent ModelProto / GraphProto manipulation helpers: the ONNX text-format parser (parser) and printer (printer), attribute and tensor proto construction helpers (attr_proto_util, tensor_proto_util, tensor_util), the data-type name utilities (data_type_utils) and the graph manipulation helpers (graph_manipulations, formerly onnx_proto/common_functions). Depends publicly on lib_onnx_proto. Both lib_onnx_lib and lib_onnx_kernels depend on it.
onnx_light::onnx_light (in-tree target lib_onnx_lib): onnx_light/onnx_lib/defs/, onnx_light/onnx_lib/checker.cc, onnx_light/onnx_lib/inliner/, onnx_light/onnx_lib/shape_inference/, onnx_light/onnx_lib/version_converter/	Full ONNX-compatible operator schemas (with history), checker, inliner, shape inference and version converter. This is the target to link for the complete ONNX-light experience. Depends publicly on lib_onnx_manipulations (and transitively on lib_onnx_proto).
lib_onnx_optim (exported as onnx_light::lib_onnx_optim): onnx_light/onnx_optim/	Shape-inference dispatch table, expression engine for small tensor / backward-propagation shape inference, and graph optimization helpers. Depends publicly on lib_onnx_op.
onnx_light::onnx_kernels (in-tree target lib_onnx_kernels): onnx_light/onnx_kernels/	C++ reference implementation of the ONNX operators used to evaluate models in-process. Contains the runtime data model (struct Tensor, struct Sequence, RuntimeContext, RunGraph / RunFunction / RunModel) and a kernel for each supported operator under onnx_kernels/kernels/<group>/ (math, logical, nn, tensor, sequence, controlflow, traditionalml, …). Together with the SplitMix64-based deterministic pseudo-random helpers it provides everything required to run a node or a full model without depending on any third-party runtime. Intentionally independent from lib_onnx_lib / lib_onnx_op; depends publicly on lib_onnx_manipulations (for the graph manipulation helpers) and transitively on lib_onnx_proto.
onnx_light::onnx_backend_test (in-tree target lib_onnx_backend_test): onnx_light/onnx_backend_test/	Backend test infrastructure (struct TestCase, the Expect helper and the CollectTestCases registry) plus the per-operator RegisterXxxCases functions that build single-node models and compute their expected outputs by invoking the reference kernels from lib_onnx_kernels. Depends publicly on lib_onnx_kernels (and transitively on lib_onnx_proto); intentionally independent from lib_onnx_lib / lib_onnx_op.

What to link for a given use case

The libraries are designed so that a downstream project links the smallest set that covers its needs. The most common scenarios are:

• Read / write ONNX models, including > 2 GB files, external data or encrypted models, without any operator notion — link onnx_light::lib_onnx_proto.

• Enumerate or look up ONNX operator schemas (for example to drive code generation or to validate node op types) without paying for shape inference — link onnx_light::lib_onnx_op.

• Parse / print ONNX text models and manipulate ``ModelProto`` / ``GraphProto`` (attribute and tensor proto helpers, data-type name utilities, graph-input collection) without pulling in the operator schemas — link onnx_light::onnx_manipulations.

• Full ONNX feature set (schemas with history, checker, inliner, shape inference, version conversion) — link onnx_light::onnx_light.

• Shape inference and graph optimization passes — link onnx_light::lib_onnx_optim (which transitively pulls lib_onnx_op and lib_onnx_proto).

• Evaluate ONNX nodes / graphs / models in C++ using the bundled reference kernels (runtime struct Tensor, RunGraph / RunFunction / RunModel, SplitMix64 RNG, …) without pulling in any backend-test fixtures — link onnx_light::onnx_kernels.

• Run backend tests / evaluate models in C++ using the built-in reference kernels — link onnx_light::onnx_backend_test (which transitively pulls lib_onnx_kernels and lib_onnx_proto). It can be combined with onnx_light::onnx_light when both schema validation and execution are needed.

The Python extensions follow the same split and each link to the minimal C++ library that provides their feature. See Linking onnx-light in C++ for the full discussion of the Python / C++ packaging and of the shared lib_onnx_proto runtime.