rten

Supported models

Operators

RTen supports most standard ONNX operators. See this tracking issue for details. Please open an issue if you find that you cannot run a model because an operator is not supported.

RTen additionally supports some "contrib" ops. These are non-standard operators supported by ONNX Runtime. They are supported for compatibility with widely deployed models. Support for new contrib ops is added on an as-needed basis.

Data types

RTen supports models with single-precision float (f32) weights and quantized models with int8 weights. Models with half-precision (f16) weights are partially supported - weights are upconverted to f32 when the model is loaded.

Getting started

The best way to get started is to clone this repository and try running some of the examples locally. Many of the examples use Hugging Face's Optimum or other Python-based tools to export the ONNX model, so you will need a recent Python version installed.

The examples are located in the rten-examples/ directory. See the README for descriptions of all the examples and steps to run them. As a quick-start, here are the steps to run the image classification example:

git clone https://github.com/robertknight/rten.git
cd rten

# Install dependencies for Python scripts
pip install -r tools/requirements.txt

# Export an ONNX model. We're using resnet-50, a classic image classification model.
python -m tools.export-timm-model timm/resnet50.a1_in1k

# Run image classification example. Replace `image.png` with your own image.
cargo run -p rten-examples --release --bin imagenet resnet50.a1_in1k.onnx image.png

Usage in JavaScript

To use this library in a JavaScript application, there are two approaches:

1. Prepare model inputs in JavaScript and use the rten library's built-in WebAssembly API to run the model and return a tensor which will then need to be post-processed in JS. This approach may be easiest for tasks where the pre-processing is simple.

   The image classification example uses this approach.

2. Create a Rust library that uses rten and does pre-processing of inputs and post-processing of outputs on the Rust side, exposing a domain-specific WebAssembly API. This approach is more suitable if you have complex and/or computationally intensive pre/post-processing to do.

Before running the examples, you will need to follow the steps under "Building the WebAssembly library" below.

The general steps for using RTen's built-in WebAssembly API to run models in a JavaScript project are:

1. In your JavaScript code, fetch the WebAssembly binary and initialize RTen using the init function.
2. Fetch the prepared .onnx model and use it to an instantiate the Model class from this library.
3. Each time you want to run the model, prepare one or more Float32Arrays containing input data in the format expected by the model, and call Model.run. This will return a TensorList that provides access to the shapes and data of the outputs.

After building the library, API documentation for the Model and TensorList classes is available in dist/rten.d.ts.

Building the WebAssembly library

Prerequisites

To build RTen for WebAssembly you will need:

• A recent stable version of Rust
• just
• (Optional) The wasm-opt tool from Binaryen can be used to optimize .wasm binaries for improved performance
• (Optional) A recent version of Node for running demos

Building rten

git clone https://github.com/robertknight/rten.git
cd rten
just wasm

The build created by just wasm requires support for WebAssembly SIMD, available since Chrome 91, Firefox 89 and Safari 16.4. It is possible to build the library without WebAssembly SIMD support using just wasm-nosimd, or both using just wasm-all. The non-SIMD builds are significantly slower.

At runtime, you can find out which build is supported by calling the binaryName() function exported by this package.