ort-customops/README.md

# ONNXRuntime-Extensions

[![Build Status](https://aiinfra.visualstudio.com/Lotus/_apis/build/status/onnxruntime-extensions/extensions.wheel?branchName=main)](https://aiinfra.visualstudio.com/Lotus/_build/latest?definitionId=1085&branchName=main)

## What's ONNXRuntime-Extensions

Introduction: ONNXRuntime-Extensions is a library that extends the capability of the ONNX models and inference with ONNX Runtime, via ONNX Runtime Custom Operator ABIs. It includes a set of [ONNX Runtime Custom Operator](https://onnxruntime.ai/docs/reference/operators/add-custom-op.html) to support the common pre- and post-processing operators for vision, text, and nlp models. And it supports multiple languages and platforms, like Python on Windows/Linux/macOS, some mobile platforms like Android and iOS, and Web-Assembly etc. The basic workflow is to enhance a ONNX model firstly and then do the model inference with ONNX Runtime and ONNXRuntime-Extensions package.

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<td>⚠️</td>
<td>
<strong>NOTE:</strong> most ONNXRuntime-Extensions packages are in <strong><em>active development</em></strong> and most packages require building from source. The package information will be updated here if it is published.
</td>
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## Quickstart with the experimental Python package

#### <strong>on Windows</strong>
```bash
pip install --extra-index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime-extensions
```
#### <strong>on Linux/macOS</strong>
the packages are not ready yet, so it could be installed from source. Please make sure the compiler toolkit like gcc(later than g++ 8.0) or clang, and the tool <strong>cmake</strong> are installed before the following command
```bash
python -m pip install git+https://github.com/microsoft/onnxruntime-extensions.git
```


## Usage

## 1. Augment an ONNX model with a pre- and post-processing pipeline
check [tutorial](./tutorials) for a couple of examples on how to do it.
## 2. Using Extensions for ONNX Runtime inference

### Python

```python
import onnxruntime as _ort
from onnxruntime_extensions import get_library_path as _lib_path

so = _ort.SessionOptions()
so.register_custom_ops_library(_lib_path())

# Run the ONNXRuntime Session, as ONNXRuntime docs suggested.
# sess = _ort.InferenceSession(model, so)
# sess.run (...)
```
### C++

```c++
  // The line loads the customop library into ONNXRuntime engine to load the ONNX model with the custom op
  Ort::ThrowOnError(Ort::GetApi().RegisterCustomOpsLibrary((OrtSessionOptions*)session_options, custom_op_library_filename, &handle));

  // The regular ONNXRuntime invoking to run the model.
  Ort::Session session(env, model_uri, session_options);
  RunSession(session, inputs, outputs);
```
### Java
```java
var env = OrtEnvironment.getEnvironment();
var sess_opt = new OrtSession.SessionOptions();

/* Register the custom ops from onnxruntime-extensions */
sess_opt.registerCustomOpLibrary(OrtxPackage.getLibraryPath());
```

## Use exporters to generate graphs with custom operators

The PyTorch and TensorFlow converters support custom operator generation if the operation from the original framework cannot be interpreted as a standard ONNX operators. Check the following two examples on how to do this.

1. [CustomOp conversion by pytorch.onnx.exporter](https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/pytorch_custom_ops_tutorial.ipynb)
2. [CustomOp conversion by tf2onnx](https://github.com/microsoft/onnxruntime-extensions/blob/main/tutorials/tf2onnx_custom_ops_tutorial.ipynb)


## Add a new custom operator to onnxruntime-extensions

You can contribute customop C++ implementations directly in this repository if they have general applicability to other users. In addition, if you want to quickly verify the ONNX model with Python, you can wrap the custom operator with PyOp.

```python
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)
# ...
```
Check [development.md](./docs/development.md) for build and test

## Contributing

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.microsoft.com.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide
a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions
provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

## License

[MIT License](LICENSE)