Microsoft.ML.OnnxRuntimeGenAI.Managed 0.1.0-rc4

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

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++

Coming very soon

• Support for the Whisper model architectures
• C# API
• Support for DirectML

Roadmap

• Automatic model download and cache
• More model architectures

Sample code for phi-2 in Python

Install onnxruntime-genai.

(Temporary) Build and install from source according to the instructions below.

import onnxruntime_genai as og

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

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)

Build from source

This step requires cmake to be installed.

1. Clone this repo

   git clone https://github.com/microsoft/onnxruntime-genai
   cd onnxruntime-genai
   

2. Install ONNX Runtime

   By default, the onnxruntime-genai build expects to find the ONNX Runtime include and binaries in a folder called ort in the root directory of onnxruntime-genai. You can put the ONNX Runtime files in a different location and specify this location to the onnxruntime-genai build. These instructions use ORT_HOME as the location.

   • Install from release

     These instructions are for the Linux GPU build of ONNX Runtime. Replace the location with the operating system and target of choice.

     cd $ORT_HOME
     wget https://github.com/microsoft/onnxruntime/releases/download/v1.17.1/onnxruntime-linux-x64-gpu-1.17.1.tgz
     tar xvzf onnxruntime-linux-x64-gpu-1.17.1.tgz 
     mv onnxruntime-linux-x64-gpu-1.17.1/include .
     mv onnxruntime-linux-x64-gpu-1.17.1/lib .
     

   • Or build from source

     git clone https://github.com/microsoft/onnxruntime.git
     cd onnxruntime
     

     Create include and lib folders in the ORT_HOME directory

     mkdir $ORT_HOME/include
     mkdir $ORT_HOME/lib
     

     Build from source and copy the include and libraries into ORT_HOME

     On Windows

     build.bat --config RelWithDebInfo --build_shared_lib --skip_tests --parallel [--use_cuda]
     copy include\onnxruntime\core\session\onnxruntime_c_api.h $ORT_HOME\include
     copy build\Windows\RelWithDebInfo\RelWithDebInfo\*.dll $ORT_HOME\lib
     

     On Linux

     ./build.sh --build_shared_lib --skip_tests --parallel [--use_cuda]
     cp include/onnxruntime/core/session/onnxruntime_c_api.h $ORT_HOME/include
     cp build/Linux/RelWithDebInfo/libonnxruntime*.so* $ORT_HOME/lib
     

3. Build onnxruntime-genai

   If you are building for CUDA, add the cuda_home argument.

   cd ..
   python build.py [--cuda_home <path_to_cuda_home>]
   

4. Install Python wheel

   cd build/wheel
   pip install *.whl
   

Model download and export

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

To source microsoft/phi-2 optimized for your target, download and run the following script. You will need to be logged into Hugging Face via the CLI to run the script.

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>

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.