e2e-sae

e2e_sae

This library is used to train and evaluate Sparse Autoencoders (SAEs). It handles the following training types:

• e2e (end-to-end): Loss function includes sparsity and final model kl_divergence.
• e2e + downstream reconstruction: Loss function includes sparsity, final model kl_divergence, and MSE at downstream layers.
• local (i.e. vanilla SAEs): Loss function includes sparsity and MSE at the SAE layer
• Any combination of the above.

See our paper which argues for training SAEs e2e rather than locally. All SAEs presented in the paper can be found at https://wandb.ai/sparsify/gpt2 and can be loaded using this library.

Usage

Installation

pip install e2e_sae

Train SAEs on any TransformerLens model

If you would like to track your run with Weights and Biases, place your api key and entity name in a new file called .env. An example is provided in .env.example.

Create a config file (see gpt2 configs here for examples). Then run

python e2e_sae/scripts/train_tlens_saes/run_train_tlens_saes.py <path_to_config>

If using a Colab notebook, see this example.

Sample wandb sweep configs are provided in e2e_sae/scripts/train_tlens_saes/.

The library also contains scripts for training mlps and SAEs on mlps, as well as training custom transformerlens models and SAEs on these models (see here).

Load a Pre-trained SAE

You can load any pre-trained SAE (and accompanying TransformerLens model) trained using this library from Weights and Biases or locally by running

from e2e_sae import SAETransformer
model = SAETransformer.from_wandb("<entity/project/run_id>")
# or, if stored locally
model = SAETransformer.from_local_path("/path/to/checkpoint/dir") 

All runs in our paper can be loaded this way (e.g.sparsify/gpt2/tvj2owza).

This will instantiate a SAETransformer class, which contains a TransformerLens model with SAEs attached. To do a forward pass without SAEs, use the forward_raw method, to do a forward pass with SAEs, use the forward method (or simply call the SAETansformer instance).

The dictionary elements of an SAE can be accessed via SAE.dict_elements. This is will normalize the decoder elements to have norm 1.

Analysis

To reproduce all of the analysis in our paper use the scripts in e2e_sae/scripts/analysis/.

Contributing

Developer dependencies are installed with make install-dev, which will also install pre-commit hooks.

Suggested extensions and settings for VSCode are provided in .vscode/. To use the suggested settings, copy .vscode/settings-example.json to .vscode/settings.json.

There are various make commands that may be helpful

make check  # Run pre-commit checks on all files (i.e. pyright, ruff linter, and ruff formatter)
make type  # Run pyright on all files
make format  # Run ruff linter and formatter on all files
make test  # Run tests that aren't marked `slow`
make test-all  # Run all tests

This library is maintained by Dan Braun.

Join the Open Source Mechanistic Interpretability Slack to chat about this library and other projects in the space!