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Python extensions using tch to interact with PyTorch

This sample crate shows how to use tch to write a Python extension that manipulates PyTorch tensors via PyO3.

There is a single function exposed by the Python extension which adds one to the input tensor. The relevant code is as follows:

#[pyfunction]
fn add_one(tensor: PyTensor) -> PyResult<PyTensor> {
    let tensor = tensor.f_add_scalar(1.0).map_err(wrap_tch_err)?;
    Ok(PyTensor(tensor))
}

It is recommended to use the f_ methods so that potential errors in the tch crate do not result in a crash of the Python interpreter.

Compiling the Extension

In order to build the extension and test the plugin, run the following command from the root of the github repo. This requires a Python environment that has the appropriate torch version installed.

LIBTORCH_USE_PYTORCH=1 cargo build && cp -f target/debug/libtch_ext.so tch_ext.so
python test.py

Setting LIBTORCH_USE_PYTORCH results in using the libtorch C++ library from the Python install in tch and ensures that this is at the proper version (having tch using a different libtorch version from the one used by the Python runtime may result in segfaults).

Colab Notebook

tch based plugins can easily be used from colab (though it might be a bit slow to download all the crates and compile), see this example notebook.