Release 0.2 · aleximmer/Laplace

TL;DR

This release is all about laying down the groundwork for Laplace on foundation models. The main target for this version is LLMs. Support for other foundation models will added in versions >= 0.3.
Additionally, this release adds support for Laplace for sequential decision making e.g. Bayesian optimization.
A new backend, curvlinops, is added.
Added native serialization support.
Updated the code base to conform modern Python development standard (typehinting, enums, ruff, etc.).

Thanks to all contributors!

Bringing laplace-torch to foundation-model era by @wiseodd in #144
Add support for cross entropy loss inputs with multiple leading dimensions by @runame in #132
Add an option to reduce LLM features in LLLaplace by @wiseodd in #172
Update huggingface_example.md by @wiseodd in #204
Refactor reward-modeling likelihood by @runame in #200
Doc & example for reward modeling by @wiseodd in #167
Make the dict keys for models with dict-like inputs general by @wiseodd in #168
Feature caching mechanism in LLLA by @wiseodd in #170
Added more test coverage to the subset of params functionality by @wiseodd in #185

Add Curvlinops backend & add default functorch implementations of many curvature quantities by @wiseodd in #146
Point to curvlinops master branch in setup.cfg by @wiseodd in #151

Add native serialization support by @wiseodd in #148
Enable torch.save() + torch.load() for different map_locations by @elcorto in #159

Typehinting by @wiseodd in #180
Add "Contributing" to the README by @wiseodd in #198
Add ruff check, ruff format --check, and pytest Github actions by @wiseodd in #176
Remove depreciated loss_average argument of KFACLinearOperator and add makefile for ruff by @runame in #197

Fixes and features for SubnetLaplace by @edaxberger in #87
Improve predictive samples by @aleximmer in #95
Add experiments repo reference by @runame in #99
Use ASDL as the default for classification by @edaxberger in #96
Update Laplace bridge predictive by @runame in #101
Make last-layer Jacobians agnostic to NN output shape by @runame in #112
Fix device and dtype of block_diag used for Kron.to_matrix() by @runame in #117
Add KronDecomposed.diag() feature by @aleximmer in #121
Replacing torch.einsum() with opt_einsum by @Heatdh in #125
Computing the classification BMA, i.e. average of softmaxes, online. by @wiseodd in #133
Add asdl_fisher_kwargs argument by @runame in #134
Running metrics by @wiseodd in #135
Add support for diagonal Kronecker factors in Kron matrix class by @runame in #136
Add prior_structure argument to optimize_prior_precision by @runame in #123
Move back to ASDL's main repo as dependency by @aleximmer in #183
fix typo in Jacobian dimension by @ruili-pml in #190
Prevent computing posterior precision in KronLaplace when it's not fitted by @wiseodd in #173
Use backend-native Jacobians if available by @wiseodd in #187
Update docs by @wiseodd in #184
Remove try-except from gridsearch by @wiseodd in #199
Add fast computation of functional_variance for DiagLLLaplace and KronLLLaplace by @wiseodd in #145
Caveats by @wiseodd in #202
Add some checks in optimize_prior_precision by @wiseodd in #205
Add backend 'flowchart' by @wiseodd in #207

Full Changelog: 0.1a2...0.2