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Update README #219

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Update README
wiseodd Aug 14, 2024
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Update CI to not install `wheel` and `packaging`
wiseodd Aug 14, 2024
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2 changes: 1 addition & 1 deletion .github/workflows/pytest.yml
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Expand Up @@ -19,7 +19,7 @@ jobs:
# You can test your matrix by printing the current Python version
- name: Install dependencies
run: |
python -m pip install --upgrade pip wheel packaging
python -m pip install --upgrade pip
pip install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -e .
- name: Test with pytest
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50 changes: 25 additions & 25 deletions README.md
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Expand Up @@ -4,8 +4,8 @@
![pytest](https://github.com/aleximmer/laplace/actions/workflows/pytest.yml/badge.svg)
![lint](https://github.com/aleximmer/laplace/actions/workflows/lint-ruff.yml/badge.svg)
![format](https://github.com/aleximmer/laplace/actions/workflows/format-ruff.yml/badge.svg)
</div>

</div>

The laplace package facilitates the application of Laplace approximations for entire neural networks, subnetworks of neural networks, or just their last layer.
The package enables posterior approximations, marginal-likelihood estimation, and various posterior predictive computations.
Expand Down Expand Up @@ -49,30 +49,31 @@ The [code](https://github.com/runame/laplace-redux) to reproduce the experiments

## Setup

For full compatibility, install this package in a fresh virtual env.
We assume Python >= 3.9 since lower versions are [(soon to be) deprecated](https://devguide.python.org/versions/).
PyTorch version 2.0 and up is also required for full compatibility.
> [!IMPORTANT]
> We assume Python >= 3.9 since lower versions are [(soon to be) deprecated](https://devguide.python.org/versions/).
> PyTorch version 2.0 and up is also required for full compatibility.

To install laplace with `pip`, run the following:

```bash
pip install --upgrade pip wheel packaging
pip install git+https://github.com/aleximmer/laplace.git@0.2
pip install laplace-torch
```

> [!CAUTION]
> Unfortunately, we lost our PyPI account and so running `pip install laplace-torch`
> only installs the previous version (0.1)!

For development purposes, clone the repository and then install:
For development purposes, e.g. if you would like to make contributions,
clone the repository and then install:

```bash
# first install the build system:
pip install --upgrade pip wheel packaging

# then install the develop
# then install the develop
pip install -e ".[all]"
```

> [!NOTE]
> See [contributing guideline](#contributing).
> We're looking forward to your contributions!

## Example usage

### Simple usage
Expand Down Expand Up @@ -112,9 +113,9 @@ la = Laplace(model, "classification",
hessian_structure="diag")
la.fit(train_loader)
la.optimize_prior_precision(
method="gridsearch",
pred_type="glm",
link_approx="probit",
method="gridsearch",
pred_type="glm",
link_approx="probit",
val_loader=val_loader
)

Expand Down Expand Up @@ -291,19 +292,18 @@ cases. Each method has pros and cons, please see [this
discussion](https://github.com/aleximmer/Laplace/issues/217#issuecomment-2278311460)
for details. In summary

* Disable-grad: General method to perform Laplace on specific types of
- Disable-grad: General method to perform Laplace on specific types of
layer/parameter, e.g. in an LLM with LoRA. Can be used to emulate `LLLaplace`
as well. Always use `subset_of_weights='all'` for this method.
* subnet selection by disabling grads is more efficient than
`SubnetLaplace` since it avoids calculating full Jacobians first
* disabling grads can only be performed on `Parameter` level and not for
individual weights, so this doesn't cover all cases that `SubnetLaplace`
offers such as `Largest*SubnetMask` or `RandomSubnetMask`
* `LLLaplace`: last-layer specific code with improved performance (#145)
* `SubnetLaplace`: more fine-grained partitioning such as
- subnet selection by disabling grads is more efficient than
`SubnetLaplace` since it avoids calculating full Jacobians first
- disabling grads can only be performed on `Parameter` level and not for
individual weights, so this doesn't cover all cases that `SubnetLaplace`
offers such as `Largest*SubnetMask` or `RandomSubnetMask`
- `LLLaplace`: last-layer specific code with improved performance (#145)
- `SubnetLaplace`: more fine-grained partitioning such as
`LargestMagnitudeSubnetMask`


### Serialization

As with plain `torch`, we support to ways to serialize data.
Expand Down Expand Up @@ -368,7 +368,7 @@ torch.load(..., map_location="cpu")

The laplace package consists of two main components:

1. The subclasses of [`laplace.BaseLaplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/baselaplace.py) that implement different sparsity structures: different subsets of weights (`'all'`, `'subnetwork'` and `'last_layer'`) and different structures of the Hessian approximation (`'full'`, `'kron'`, `'lowrank'`, `'diag'` and `'gp'`). This results in _ten_ currently available options: `laplace.FullLaplace`, `laplace.KronLaplace`, `laplace.DiagLaplace`, `laplace.FunctionalLaplace` the corresponding last-layer variations `laplace.FullLLLaplace`, `laplace.KronLLLaplace`, `laplace.DiagLLLaplace` and `laplace.FunctionalLLLaplace` (which are all subclasses of [`laplace.LLLaplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/lllaplace.py)), [`laplace.SubnetLaplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/subnetlaplace.py) (which only supports `'full'` and `'diag'` Hessian approximations) and `laplace.LowRankLaplace` (which only supports inference over `'all'` weights). All of these can be conveniently accessed via the [`laplace.Laplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/laplace.py) function.
1. The subclasses of [`laplace.BaseLaplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/baselaplace.py) that implement different sparsity structures: different subsets of weights (`'all'`, `'subnetwork'` and `'last_layer'`) and different structures of the Hessian approximation (`'full'`, `'kron'`, `'lowrank'`, `'diag'` and `'gp'`). This results in _ten_ currently available options: `laplace.FullLaplace`, `laplace.KronLaplace`, `laplace.DiagLaplace`, `laplace.FunctionalLaplace` the corresponding last-layer variations `laplace.FullLLLaplace`, `laplace.KronLLLaplace`, `laplace.DiagLLLaplace` and `laplace.FunctionalLLLaplace` (which are all subclasses of [`laplace.LLLaplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/lllaplace.py)), [`laplace.SubnetLaplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/subnetlaplace.py) (which only supports `'full'` and `'diag'` Hessian approximations) and `laplace.LowRankLaplace` (which only supports inference over `'all'` weights). All of these can be conveniently accessed via the [`laplace.Laplace`](https://github.com/AlexImmer/Laplace/blob/main/laplace/laplace.py) function.
2. The backends in [`laplace.curvature`](https://github.com/AlexImmer/Laplace/blob/main/laplace/curvature/) which provide access to Hessian approximations of
the corresponding sparsity structures, for example, the diagonal GGN.

Expand Down
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