ONNX Runtime GenAI

Note: between release candidate 0.7.0-rc2 and release 0.7.0 there is a breaking Python API change in tokenizer.encode(prompt). Previously this method returned a Python list and now returns a numpy array. When concatenating the tokens generated by two prompts to pass to append_tokens e.g a system prompt and a user prompt, you must use the following instead of system_prompt + input_tokens:

system_tokens = tokenizer.encode(system_prompt)
input_tokens = tokenizer.encode(prompt)
generator.append_tokens(np.concatenate([system_tokens, input_tokens]))

Run generative AI models with ONNX Runtime.

This API gives you an easy, flexible and performant way of running LLMs on device.

It implements the generative AI loop for ONNX models, including pre and post processing, inference with ONNX Runtime, logits processing, search and sampling, and KV cache management.

See documentation at https://onnxruntime.ai/docs/genai.

Support matrix	Supported now	Under development	On the roadmap
Model architectures	DeepSeek Gemma Llama * Mistral + Phi (language + vision) Qwen Nemotron Granite AMD OLMo	Whisper	Stable diffusion
API	Python C# C/C++ Java ^	Objective-C
Platform	Linux Windows Mac ^ Android ^		iOS
Architecture	x86 x64 Arm64 ~
Hardware Acceleration	CUDA DirectML	QNN OpenVINO ROCm
Features	MultiLoRA Continuous decoding (session continuation)^	Constrained decoding	Speculative decoding

* The Llama model architecture supports similar model families such as CodeLlama, Vicuna, Yi, and more.

+ The Mistral model architecture supports similar model families such as Zephyr.

^ Requires build from source

~ Windows builds available, requires build from source for other platforms

Installation

See installation instructions or build from source

Sample code for Phi-3 in Python

Download the model

huggingface-cli download microsoft/Phi-3-mini-4k-instruct-onnx --include cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4/* --local-dir .

Install the API

pip install numpy
pip install --pre onnxruntime-genai

Run the model

import onnxruntime_genai as og

model = og.Model('cpu_and_mobile/cpu-int4-rtn-block-32-acc-level-4')
tokenizer = og.Tokenizer(model)
tokenizer_stream = tokenizer.create_stream()
 
# Set the max length to something sensible by default,
# since otherwise it will be set to the entire context length
search_options = {}
search_options['max_length'] = 2048
search_options['batch_size'] = 1

chat_template = '<|user|>\n{input} <|end|>\n<|assistant|>'

text = input("Input: ")
if not text:
   print("Error, input cannot be empty")
   exit

prompt = f'{chat_template.format(input=text)}'

input_tokens = tokenizer.encode(prompt)

params = og.GeneratorParams(model)
params.set_search_options(**search_options)
generator = og.Generator(model, params)

print("Output: ", end='', flush=True)

try:
   generator.append_tokens(input_tokens)
   while not generator.is_done():
     generator.generate_next_token()

     new_token = generator.get_next_tokens()[0]
     print(tokenizer_stream.decode(new_token), end='', flush=True)
except KeyboardInterrupt:
    print("  --control+c pressed, aborting generation--")

print()
del generator

Choosing the Right Examples: Release vs. Main Branch

Due to evolving nature of this project and ongoing feature additions, examples in the main branch may not always align with the latest stable release. This section outlines how to ensure compatibility between the examples and the corresponding version. Majority of the steps would remain same, just the package installation and the model example file would change.

Stable version

Install the package according to the installation instructions. Let's say you installed 0.5.2 version of ONNX Runtime GenAI, so the instructions would look like this:

# Clone the repo
git clone https://github.com/microsoft/onnxruntime-genai.git && cd onnxruntime-genai
# Checkout the branch for the version you are using
git checkout v0.5.2
cd examples

Nightly version (Main Branch)

Build the package from source using these instructions. Now just go to the folder location where all the examples are present.

# Clone the repo
git clone https://github.com/microsoft/onnxruntime-genai.git && cd onnxruntime-genai
cd examples

Roadmap

See the Discussions to request new features and up-vote existing requests.

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.

License

xenova/onnxruntime-genai

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