ort-customops/docs/new_operator.md

Add a Custom Operator in ONNXRuntime-Extensions

Before implement a custom operator, you get the ONNX model with one or more ORT custom operators, created by ONNX converters, ONNX-Script, or ONNX model API and etc..

1. Quick verification with PythonOp (optional)

Before you actually develop a custom operator for the work, if you want to quickly verify the ONNX model with Python, you can wrap the custom operator with PyOp.

import numpy
from onnxruntime_extensions import PyOp, onnx_op

# Implement the CustomOp by decorating a function with onnx_op
@onnx_op(op_type="Inverse", inputs=[PyOp.dt_float])
def inverse(x):
    # the user custom op implementation here:
    return numpy.linalg.inv(x)

# Run the model with this custom op
# model_func = PyOrtFunction(model_path)
# outputs = model_func(inputs)
# ...

2. Generate the C++ template code of the Custom operator from the ONNX Model (optional)

python -m onnxruntime-extensions.cmd --cpp-gen <model_path> <repository_dir>`

If you are familiar with the ONNX model detail, you create the custom operator C++ classes directly.

3. Implement the CustomOp Kernel Compute method in the generated C++ files.

the custom operator kernel C++ code example can be found operators folder, like gaussian_blur. All C++ APIs that can be used in the kernel implementation are listed below

• ONNXRuntime Custom API docs
• the third libraries API docs integrated in ONNXRuntime Extensions the can be used in C++ code
  • OpenCV API docs https://docs.opencv.org/4.x/
  • Google SentencePiece Library docs https://github.com/google/sentencepiece/blob/master/doc/api.md
  • dlib(matrix and ML library) C++ API docs http://dlib.net/algorithms.html
  • BlingFire Library https://github.com/microsoft/BlingFire
  • Google RE2 Library https://github.com/google/re2/wiki/CplusplusAPI
  • JSON library https://json.nlohmann.me/api/basic_json/

3. Build and Test

• The unit tests can be implemented as Python or C++, check test folder for more examples
• Check build-package on how to build the different language package to be used for production.

Please check the contribution to see if it is possible to contribute the custom operator to onnxruntime-extensions.