ONNX provides a C++ library for performing arbitrary optimizations on ONNX models, as well as a growing list of prepackaged optimization passes.
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).
You can install onnxoptimizer from PyPI:
pip3 install onnxoptimizerNote that you may need to upgrade your pip first if you have trouble:
pip3 install -U pipIf you want to build from source:
git clone --recursive https://github.com/onnx/optimizer onnxoptimizer
cd onnxoptimizer
pip3 install -e .Note that you need to install protobuf before building from source.
Now you can use command-line api in terminal instead of python script.
python -m onnxoptimizer input_model.onnx output_model.onnx
Arguments list is following:
# python3 -m onnxoptimizer -h
usage: python -m onnxoptimizer input_model.onnx output_model.onnx
onnxoptimizer command-line api
optional arguments:
-h, --help show this help message and exit
--print_all_passes print all available passes
--print_fuse_elimination_passes
print all fuse and elimination passes
-p [PASSES ...], --passes [PASSES ...]
list of optimization passes name, if no set, fuse_and_elimination_passes will be used
--fixed_point fixed point
- More built-in pass
- Separate graph rewriting and constant folding (or a pure graph rewriting mode, see issue #9 for the details)
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onnx-simplifier: A handy and popular tool based on onnxoptimizer
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convertmodel.com: onnx optimizer compiled as WebAssembly so that it can be used out-of-the-box