Changelog¶

All notable changes to metatensor-learn are documented here, following the keep a changelog format. This project follows Semantic Versioning.

Unreleased ¶

metatensor.learn.nn modules InvariantTanh, InvariantSiLU, InvariantReLU, InvariantLayerNorm, and EquivariantLinear have removed and replaced parameter. invariant_key_idxs is replaced by invariant_keys, a Labels object that selects for invariant blocks.
metatensor.learn.nn modules LayerNorm, InvariantLayerNorm, Linear, and EquivariantLinear have altered accepted types for certain parameters. Parameters eps, elementwise_affine, bias, and mean for the layer norm modules, and bias for the linear modules are affected. Previously these could be passed as list, but now can only be passed as a single value. For greater control over modules applied to individual blocks, users are encouraged to use the ModuleMap module from metatensor.learn.nn.

We now require Python >= 3.9
Dataset and DataLoader can now handle fields with a name which is not a valid Python identifier.

Added torch-style activation function module maps to metatensor.learn.nn: ReLU, InvariantReLU, SiLU, and InvariantSiLU (#597)
Added torch-style neural network module maps to metatensor.learn.nn: LayerNorm, InvariantLayerNorm, EquivariantLinear, Sequential, Tanh, and InvariantTanh (#513)

metatensor.learn.nn modules LayerNorm and InvariantLayerNorm now applies sample-independent transformations to input tensors.
Set correct device for output of when torch default device is different than input device (#595)

metatensor-learn is no longer re-exported from metatensor and metatensor.torch, all functions are still available inside metatensor.learn and metatensor.torch.learn.

Pluralization removed for special kwarg sample_ids of IndexedDataset -> sample_id, and provided collate functions group and group_and_join updated accordingly.

ModuleMap and Linear modules, following torch.nn.ModuleDict and torch.nn.Linear in PyTorch but adapted for TensorMap’s (#427)
Dataset and DataLoader facilities, following the corresponding classes in PyTorch (#428)