NVIDIA/cuEquivariance v0.9.0

v0.9.0

Repository: NVIDIA/cuEquivariance

Tag: v0.9.0

Published: 2026-02-17T22:56:08Z

Prerelease: no

Release notes:

0.9.0 (2026-02-17)

Added

• GB10 (DGX Spark) support
• Support for Python 3.13 and 3.14
• [JAX] Support for Triton 3.6.0
• [JAX] flax.nnx MACE example
• [Torch/JAX] Deterministic indexing mode for uniform 1d kernels
• [Torch/JAX] Parallel JIT compilation for uniform_1d kernels with per-kernel caching, significantly reducing compilation time. New optional environment variable CUEQUIVARIANCE_OPS_NVRTC_CACHE_DIR allows setting a directory for caching compiled kernels.
• Documentation: new tutorials for JAX and PyTorch segmented polynomials

Bug fix

• [JAX] Fixed Triton tuning issue for triangular multiplicative update
• [JAX] Compatibility with JAX 0.8.2: fixed FFI interface and dtype casting issues when x64 mode is not enabled
• [JAX] Improved triangle attention error messages
• [Torch/JAX] Fixed yx_rotation descriptor
• [Torch] TensorRT QDP plugin workaround

Breaking Changes

• [Torch/JAX] The environment variable CUEQUIVARIANCE_OPS_USE_JIT no longer exists. JIT compilation is now the default behavior for uniform_1d kernels (already since few releases).
• [Torch/JAX] Renamed filter_drop_unsued_operands to filter_drop_unused_operands (typo fix)
• [Torch/JAX] Removed nvfatbin optional dependency
• [Torch] Removed deprecated primitive classes: TensorProduct, EquivariantTensorProduct, SymmetricTensorProduct, and IWeightedSymmetricTensorProduct. Use cuet.SegmentedPolynomial with method='uniform_1d' instead, or the high-level APIs (cuet.ChannelWiseTensorProduct, cuet.FullyConnectedTensorProduct, cuet.SymmetricContraction). Attempting to import these classes will raise an ImportError with migration instructions.
• [Torch] Removed deprecated low-level wrapper classes: TensorProductUniform1d, TensorProductUniform4x1d, TensorProductUniform3x1dIndexed, TensorProductUniform4x1dIndexed, and SymmetricTensorContraction from cuequivariance_ops_torch. Use torch.ops.cuequivariance.uniform_1d or cuet.SegmentedPolynomial instead.

Notes

• [JAX] DGX Spark/GB10 (sm_121) with CUDA 12.9: This release uses PTX 87, which works correctly for most architectures but is not compatible with DGX Spark/GB10 on CUDA 12.9. To enable DGX Spark/GB10 support with CUDA 12.9, refer to #250 for a simple frontend integration tweak that restricts PTX 88 to sm_121 only. This fix will be merged after the 0.9.0 release.