Changelog¶

Notable API and core changes only. This project follows semantic versioning.

[0.2.0] - 23. September 2026¶

These can require edits to code written for 0.1.

align_inputs returns a 1-D int64 column index, not a tensor. Index your data with it: X[:, col].
Hop stores packed source_index / target_index instead of a mask, so PackedLinear now works on hops. Call Hop.to_mask() for the dense mask.
Skip.source_index / target_index are renamed to source_in_layer / target_in_layer.
MaskedLinear.weight is a plain parameter (state_dict key weight, was parametrizations.weight.original); read effective_weight() for the masked map. 0.1 checkpoints still load.
MaskedLinear, PackedLinear, and PackedMultiheadAttention ignore torch.autocast and compute in their parameter dtype.
PackedLinear.forward raises Kpnn2Error when the input's last dimension is not in_features.

PackedMultiheadAttention: attention restricted to the prior's edges, with optional chunk_size to bound memory.
aggregate_node_attributions and list_aggregation_methods: fold named node scores with a registered method. The default method, rauter_mangano_2026, is not yet implemented.
PackedLinear.transpose() and scatter_hop_outputs, for tied decoders.
widths= on both parsers, so a node can own several units, and ranks= on parse_layered, to set depths yourself.
constraint= on MaskedLinear and PackedLinear (for example positive or frozen edges), with effective_weight() and init_bound().
identity= on all three layers: a spec fingerprint stored in the checkpoint and checked on load.
generator= on all three layers, for isolated parameter init.
edge_location() and node_units() on both specs, and hop_units() on LayeredSpec: find a named edge's weight slots or a node's units.
hop_input=, hop_output=, and axis="inputs" on map_node_attributions: say which side of a hop, or which input units, a tensor holds.

map_node_attributions no longer shares memory with the input tensor, and it accepts bfloat16 scores (stored as float32).

First release of kpnn2.