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].
- Supported model architectures:
- Phi-3
- Phi-2
- Gemma
- LLaMA
- Mistral
- Supported targets:
- GPU (DirectML)
- GPU (CUDA)
- CPU
- Supported sampling features
- Beam search
- Greedy search
- Top P/Top K
- APIs
- Python
- C#
- C/C++
- Support for the encoder decoder model architectures, such as whisper, T5 and BART.
- Support for mobile devices (Android and iOS) with Java and Objective-C bindings
- Stable diffusion pipeline
- Automatic model download and cache
- More model architectures
If you don't know which hardware capabilities is available on your device.
-
Windows GPU (use DirectML): Verify if you have Windows GPU
-
CUDA GPU: Verify if you have CUDA GPU
-
CPU and Mobile: For Windows, Mac, Android and other devices use the CPU and Mobile option below
pip install [--pre] numpy onnxruntime-genai-directml
pip install numpy onnxruntime-genai-cuda --pre --index-url=https://aiinfra.pkgs.visualstudio.com/PublicPackages/_packaging/onnxruntime-genai/pypi/simple/
pip install [--pre] numpy onnxruntime-genai
Install the onnxruntime-genai Python package.
- Build the model
python -m onnxruntime_genai.models.builder -m microsoft/phi-2 -e cpu -p int4 -o ./models/phi2
# You can append --extra_options enable_cuda_graph=1 to build an onnx model that supports using cuda graph in ORT.
- Run inference
import os
import onnxruntime_genai as og
model_path = os.path.abspath("./models/phi2")
model = og.Model(model_path)
tokenizer = og.Tokenizer(model)
prompt = '''def print_prime(n):
"""
Print all primes between 1 and n
"""'''
tokens = tokenizer.encode(prompt)
params = og.GeneratorParams(model)
params.set_search_options(max_length=200)
# Add the following line to enable cuda graph by passing the maximum batch size.
# params.try_use_cuda_graph_with_max_batch_size(16)
params.input_ids = tokens
output_tokens = model.generate(params)
text = tokenizer.decode(output_tokens)
print("Output:")
print(text)
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 install transformers torch onnx 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>
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