AI Edge Torch is a python library that supports converting PyTorch models into a .tflite format, which can then be run with TensorFlow Lite and MediaPipe. This enables applications for Android, iOS and IOT that can run models completely on-device. AI Edge Torch offers broad CPU coverage, with initial GPU and NPU support. AI Edge Torch seeks to closely integrate with PyTorch, building on top of torch.export() and providing good coverage of Core ATen operators.
To get started converting PyTorch models to TF Lite, see additional details in the PyTorch converter section. For the particular case of Large Language Models (LLMs) and transformer-based models, the Generative API supports model authoring and quantization to enable improved on device performance.
Although part of the same PyPi package, the PyTorch converter is a Beta release, while the Generative API is an Alpha release. Please see the release notes for additional information.
Here are the steps needed to convert a PyTorch model to a TFLite flatbuffer:
import torch
import torchvision
import ai_edge_torch
# Use resnet18 with pre-trained weights.
resnet18 = torchvision.models.resnet18(torchvision.models.ResNet18_Weights.IMAGENET1K_V1)
sample_inputs = (torch.randn(1, 3, 224, 224),)
# Convert and serialize PyTorch model to a tflite flatbuffer. Note that we
# are setting the model to evaluation mode prior to conversion.
edge_model = ai_edge_torch.convert(resnet18.eval(), sample_inputs)
edge_model.export("resnet18.tflite")The getting started Jupyter notebook gives an initial walkthrough of the conversion process and can be tried out with Google Colab.
Additional technical details of the PyTorch Converter are here.
The AI Edge Torch Generative API is a Torch native library for authoring mobile-optimized PyTorch Transformer models, which can be converted to TFLite, allowing users to easily deploy Large Language Models (LLMs) on mobile devices. Users can convert the models using the AI Edge Torch PyTorch Converter, and run them via the TensorFlow Lite runtime. See here.
Mobile app developers can also use the Edge Generative API to integrate PyTorch LLMs directly with the MediaPipe LLM Inference API for easy integration within their application code. See here.
More detailed documentation can be found here.
The Generative API is currently CPU-only, with planned support for GPU and NPU. A further future direction is to collaborate with the PyTorch community to ensure that frequently used transformer abstractions can be directly supported without reauthoring.
| Build Type | Status |
|---|---|
| Generative API (Linux) |  |
| Model Coverage (Linux) |  |
| Unit tests (Linux) |  |
| Nightly Release |  |
- Python versions: 3.9, 3.10, 3.11
- Operating system: Linux
- PyTorch:
- TensorFlow:
Set up a Python virtualenv:
python -m venv --prompt ai-edge-torch venv
source venv/bin/activateThe latest stable release can be installed with:
pip install ai-edge-torchAlternately, the nightly version can be installed with:
pip install ai-edge-torch-nightlyTorch XLA builds a shared library, _XLAC.so that needs to link to the version of Python
it was built with (currently 3.10 or 3.11). In order to ensure that import _XLAC can succeed,
update the LD_LIBRARY_PATH to the lib directory of your Python environment:
export LD_LIBRARY_PATH=<path to Python installation>/lib:$LD_LIBRARY_PATH- The list of versioned releases can be seen here.
- The full list of PyPi releases (including nightly builds) can be seen here.
See our contribution documentation.
Please create a GitHub issue with any questions.