The specifications of ONNX are defined under onnx/defs directory in ONNX projects.
There is a python script onnx/defs/gen_doc.py that automatically generate documents about operations in ONNX (docs/Operations.md).
ONNF modified this script to import ONNX specifications into ONNF. There are two files generated for ONNF with the modified gen_doc.py:
1. src/dialect/onnx/onnxop.inc: Operation defintion for MLIR tablegen. Will be included in src/dialect/onnx/onnx.td
2. src/builder/op_build_table.inc: c code for ONNF frontend to import operation nodes from ONNX model. Will be included in src/builder/frontend_dialect_transformer.cpp
## How to use the script
1. Get ONNX. You can use ONNF/third_party/onnx
2. In your ONNX directory, copy the script docs/gen_doc.py in your ONNF to onnx/defs in ONNX
3. Run the script: python onnx/defs/gen_doc.py
4. Two files, onnxop.inc and op_buid_table.inc should be generated in current directory
5. copy the two file into your ONNF: cp onnxop.inc your_ONNF/src/dialect/onnx/onnxop.inc; cp op_build_table.inc your_ONNF/src/builder
The Operators.md generated by gen_doc.py is copied into doc. Please refer to this specification, not the one in onnx github, to make sure operators are consistent in version with onnxop.inc.
In addition to following the ONNF specification, the modified gen_doc.py provides some mechanism for you to customize the output.
Several tables are defined at the beginning of the script:
1. special_attr_defaults: gives attribute special default value.
2. special_op_handler: creates special import function in frontend_dialect_transformer.cpp. Currently special handler is used for operations with oprational arguments
3. ShapeInferenceList: list of operations which has shape inference defined
4. CanonicalList : list of operations which has canonical form
5. manual_code_in_op_def: provides a way to specify any code for an operation in its tablegen
