ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes. The library also provides a convenient in-memory representation that is much more convenient to manipulate than the raw protobuf structs, and converters to and from the protobuf format.
The primary motivation is to share work between the many ONNX backend implementations. Not all possible optimizations can be directly implemented on ONNX graphs - some will need additional backend-specific information - but many can, and our aim is to provide all such passes along with ONNX so that they can be re-used with a single function call.
You may be interested in invoking the provided passes, or in implementing new ones (or both).
The optimizer may be invoked either via C++ or Python. The Python API is described, with example, here.
The C++ API consists of two functions
const std::vector<std::string> GetAvailablePasses();
ModelProto Optimize(
const ModelProto& mp_in,
const std::vector<std::string>& names);
GetAvailablePasses() returns a list of available passes.
Optimize() accepts an input ModelProto and a list of optimization passes to
apply, and returns a new ModelProto which is the result of
applying each of the named passes in sequence to the model.
You can implement a new optimization pass by subclassing
OptimizePass, and registering your new pass with
Optimizer::registerOptimizer(). Optimization passes operate (at your
option) either directly on the protobuf structs, or on an in-memory
graph representation defined in ir.h. There are a
number of examples in the passes directory.
If your pass is at all generally applicable, please consider adding it to the core ONNX repository.