Reduced operator config file

The reduced operator config file is an input to the ONNX Runtime build-from-source script. It specifies which operators are included in the runtime. A reduced set of operators in ONNX Runtime permits a smaller build binary size. A smaller runtime is used in constrained environments, such as mobile and web deployments.

This article shows you how to generate the reduced operator config file using the create_reduced_build_config.py script. You can also generate the reduced operator config file by converting ONNX models to ORT format.

Contents

• The create_reduced_build_config.py script
• Configuration file format
• Type reduction format

The create_reduced_build_config.py script

To create a reduced operator configuration file, run the script create_reduced_build_config.py on your model/s.

The kernel configuration file can be manually edited as needed. The configuration can be created from either ONNX or ORT format models.

create_reduced_build_config.py --help
usage: Script to create a reduced build config file from ONNX or ORT format model/s. [-h] [-f {ONNX,ORT}] [-t] model_path_or_dir config_path

positional arguments:
  model_path_or_dir     Path to a single model, or a directory that will be recursively searched for models to process.
  config_path           Path to write configuration file to.

optional arguments:
  -h, --help            show this help message and exit
  -f {ONNX,ORT}, --format {ONNX,ORT}
                        Format of model/s to process. (default: ONNX)
  -t, --enable_type_reduction
                        Enable tracking of the specific types that individual operators require. Operator implementations MAY support limiting the type support included
                        in the build to these types. Only possible with ORT format models. (default: False)

Configuration file format

The basic format of the operator reduction configuration file is <operator domain>;<opset for domain>;<op1>[,op2]...

e.g.

#domain;opset;op1,op2...
ai.onnx;12;Add,Cast,Concat,Squeeze

The opset can match either the opset import for each model, or the initial ONNX opset that the operator version was first available in. If manually editing the configuration file, using the opset import value from the model is simplest.

e.g. if a model imports opset 12 of ONNX, all ONNX operators in that model can be listed under opset 12 for the ‘ai.onnx’ domain.

Netron can be used to view an ONNX model properties to discover the opset imports. Additionally, the ONNX operator specs for DNN and traditional ML operators list the individual operator versions.

Type reduction format

If the types an operator implementation supports can be limited to a specific set of types, this is specified in a JSON string immediately after the operator name in the configuration file.

It is highly recommended that you first generate the configuration file using ORT format models with type reduction enabled in order to see which operators support type reduction, and how the entry is defined for the individual operators.

The required types are generally listed per input and/or output of the operator. The type information is in a map, with ‘inputs’ and ‘outputs’ keys. The value for ‘inputs’ or ‘outputs’ is a map between the index number of the input/output and the required list of types.

For example, both the input and output types are relevant to ai.onnx:Cast. Type information for input 0 and output 0 could look like this: {"inputs": {"0": ["float", "int32_t"]}, "outputs": {"0": ["float", "int64_t"]}}

which is added directly after the operator name in the configuration file. e.g. ai.onnx;12;Add,Cast{"inputs": {"0": ["float", "int32_t"]}, "outputs": {"0": ["float", "int64_t"]}},Concat,Squeeze

If, for example, the types of inputs 0 and 1 were important, the entry may look like this (e.g. ai.onnx:Gather): {"inputs": {"0": ["float", "int32_t"], "1": ["int32_t"]}}

Finally some operators do non-standard things and store their type information under a ‘custom’ key. ai.onnx.OneHot is an example of this, where the three input types are combined into a triple. {"custom": [["float", "int64_t", "int64_t"], ["int64_t", "std::string", "int64_t"]]}

For these reasons, it is best to generate the configuration file first, and manually edit any entries if needed.