ONNXRuntime Extensions

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Project description

ONNXRuntime-Extensions

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 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.

Quickstart

Python installation

pip install onnxruntime-extensions

Nightly Build

on Windows

pip install --index-url https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/ORT-Nightly/pypi/simple/ onnxruntime-extensions

Please ensure that you have met the prerequisites of onnxruntime-extensions (e.g., onnx and onnxruntime) in your Python environment.

on Linux/macOS

Please make sure the compiler toolkit like gcc(later than g++ 8.0) or clang are installed before the following command

python -m pip install git+https://github.com/microsoft/onnxruntime-extensions.git

Usage

1. Generation of Pre-/Post-Processing ONNX Model

The onnxruntime-extensions Python package provides a convenient way to generate the ONNX processing graph. This can be achieved by converting the Huggingface transformer data processing classes into the desired format. For more detailed information, please refer to the API below:

help(onnxruntime_extensions.gen_processing_models)

NOTE:

The generation of model processing requires the ONNX package to be installed. The data processing models generated in this manner can be merged with other models using the onnx.compose if needed.

2. Using Extensions for ONNX Runtime inference

Python

There are individual packages for the following languages, please install it for the build.

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++

  // 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

var env = OrtEnvironment.getEnvironment();
var sess_opt = new OrtSession.SessionOptions();

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

C#

SessionOptions options = new SessionOptions()
options.RegisterOrtExtensions()
session = new InferenceSession(model, options)

Project details

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Project links

Homepage

Development Status
- 4 - Beta
Environment
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Intended Audience
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License
- OSI Approved :: MIT License
Operating System
Programming Language

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0.13.0

Oct 31, 2024

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Aug 16, 2024

This version

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May 28, 2024

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May 26, 2023

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Oct 7, 2021

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Sep 28, 2021

0.3.1

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onnxruntime_extensions 0.11.0

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ONNXRuntime-Extensions

What's ONNXRuntime-Extensions

Quickstart

Python installation

Nightly Build

on Windows

on Linux/macOS

Usage

1. Generation of Pre-/Post-Processing ONNX Model

NOTE:

2. Using Extensions for ONNX Runtime inference

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Java

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