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ONNX Optimizer

WARNING: ONNX Optimizer has been moved to https://github.com/onnx/optimizer.

All further enhancements and fixes to optimizers will be done in this new repo.

The optimizer code in onnx/onnx repo will be removed in 1.9 release.

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).

Invoking The Optimizer

The optimizer may be invoked either via C++ or Python. The Python API is described, with example, here.

The C++ API consists of three functions

const std::vector<std::string> GetAvailablePasses();

ModelProto Optimize(
    const ModelProto& mp_in,
    const std::vector<std::string>& names);

ModelProto OptimizeFixed(
    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.

OptimizeFixed() accepts an ModelProto and a list of optimization passes to apply. It then applies fixed point optimization of the passes respecting the order of the optimization passes. The strategy used by OptimizeFixed() is for every pass to be run as many times as needed until the graph is unchanged by the pass. We then rerun this ordered set of passes until the group of passes do not alter the graph.

Implementing Optimization Passes

The majority of optimization passes can be implemented using the PredicateBasedPass, which run transforms given a pattern matching constraint. Furthermore every pass written within ONNX must formally give an annotation of what the pass does.

explicit PredicateBasedPass(
      PassType pass_type,
      PassEfficiency pass_efficiency,
      PassOptimizationType pass_optimization_type)

PassType is a classification of the type of pass (e.g. Nop, Fuse). PassEfficiency describes the fixed point behavior of the pass, if a pass is annotated as PassEfficiency::Complete we assume that a two repetitive applications of this pass yield the same graph as one. If the pass cannot guarantee this, mark it as PassEfficiency::Partial. Our last annotation PassOptimizationType describes what you're attempting to optimize for (e.g. Memory, Compute, Stability).

Once you've annotated your pass the two functions needed to be implemented are:

bool patternMatchPredicate(Node* node);
bool runTransform(Node* node, Graph& graph, NodeDestroyType& destroy_current);

runTransform will be run for every node in the graph where patternMatchPredicate is true.

To register your pass use GlobalPassRegistry::registerPass. Optimization passes operate 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.