v0.3.0 (2025-06-22)

This release includes bugfixes and new opaque operations that compose with torch.compile for PT2.4-2.7. These will be unnecessary for PT2.8+.

Added:

1. Opaque variants of major operations via PyTorch custom_op declarations. These functions cannot be traced through and fail for JITScript / AOTI. They are shims that enable composition with torch.compile pre-PT2.8.
2. torch.load/torch.save functionality that, without torch.compile, is portable across GPU architectures.
3. .to() support to move TensorProduct and TensorProductConv between devices or change datatypes.

Fixed:

1. Gracefully records an error if libpython.so is not linked against C++ extension.
2. Resolves Kahan summation / various other bugs for HIP at O3 compiler-optimization level.
3. Removes multiple contexts spawning for GPU 0 when multiple devices are used.
4. Zero-initialized gradient buffers to prevent backward pass garbage accumulation.

OpenEquivariance v0.2.0 Release Notes

Our first stable release, v0.2.0, introduces several new features. Highlights include:

1. Full HIP support for all kernels.
2. Support for torch.compile, JITScript and export, preliminary support for AOTI.
3. Faster double backward performance for training.
4. Ability to install versioned releases from PyPI.
5. Support for CUDA streams and multiple devices.
6. An extensive test suite and newly released documentation.

If you successfully run OpenEquivariance on a GPU model not listed here, let us know! We can add your name to the list.

Known issues:

• Kahan summation is broken on HIP – fix planned.
• FX + Export + Compile has trouble with PyTorch dynamo; fix planned.
• AOTI broken on PT <2.8; you need the nightly build due to incomplete support for TorchBind in prior versions.