ONNX Runtime Generative AI

Run generative AI models with ONNX Runtime.

This library provides the generative AI loop for ONNX models, including inference with ONNX Runtime, logits processing, search and sampling, and KV cache management.

Users can call a high level generate() method, or run each iteration of the model in a loop.

Support greedy/beam search and TopP, TopK sampling to generate token sequences
Built in logits processing like repetition penalties
Easy custom scoring

See full documentation at [https://onnxruntime.ai/docs/genai].

Features

Supported model architectures:
- Gemma
- LLaMA
- Mistral
- Phi-2
Supported targets:
- CPU
- GPU (CUDA)
Supported sampling features
- Beam search
- Greedy search
- Top P/Top K
APIs
- Python
- C#
- C/C++

Coming very soon

Support for DirectML
Support for the encoder decoder model architectures, such as whisper, T5 and BART.

Coming soon

Support for mobile devices (Android and iOS) with Java and Objective-C bindings

Roadmap

Stable diffusion pipeline
Automatic model download and cache
More model architectures

Sample code for phi-2 in Python

Install the onnxruntime-genai Python package.

import onnxruntime_genai as og

model = og.Model(f'models/microsoft/phi-2')

tokenizer = og.Tokenizer(model)

prompt = '''def print_prime(n):
    """
    Print all primes between 1 and n
    """'''

tokens = tokenizer.encode(prompt)

params = og.SearchParams(model)
params.set_search_options({"max_length":200})
params.input_ids = tokens

output_tokens = model.generate(params)

text = tokenizer.decode(output_tokens)

print("Output:")
print(text)

Model download and export

ONNX models are run from a local folder, via a string supplied to the Model() method.

You can bring your own ONNX model or use the model builder utility, included in this package.

Install model builder dependencies.

pip install numpy
pip install transformers
pip install torch
pip install onnx
pip install onnxruntime

Export int4 CPU version

huggingface-cli login --token <your HuggingFace token>
python -m onnxruntime_genai.models.builder -m microsoft/phi-2 -p int4 -e cpu -o <model folder>

Known issues

Mistral and Gemma support on CUDA only

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.opensource.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., status check, 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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Craigacp / onnxruntime-genai