codebook-features 0.1.1

Sparse and discrete interpretability tool for neural networks

Codebook Features

Alex Tamkin, Mohammad Taufeeque and Noah D. Goodman: "Codebook Features: Sparse and Discrete Interpretability for Neural Networks", 2023. [arXiv]

Codebook Features is a method for training neural networks with a set of learned sparse and discrete hidden states, enabling interpretability and control of the resulting model.

Codebook features work by inserting vector quantization bottlenecks called codebooks into each layer of a neural network. The library provides a range of features to train and interpret codebook models, including by analyzing the activations of codes, searching for codes that activate on a pattern, and performing code interventions to verify the causal effect of a code on the output of a model. Many of these features are also available through an easy-to-use webapp that helps in analyzing and experimenting with the codebook models.

Installation

Create a virtual environment and then:

git clone https://github.com/taufeeque9/codebook-features
cd codebook-features
pip install -e .

Usage

Training a codebook model

We adapt the run_clm.py script from HuggingFace to train/finetune conventional models or the codebook models. We also use the hydra library for configuration management of the training scripts. The default config for training codebooks is available in codebook_features/config/main.yaml. The hydra syntax can be used to override any of the default config values, which includes arguments for the codebook model and arguments inherited from Huggingface's TrainingArguments. For example, to train a codebook model using gpt2-small on the wikitext dataset, run:

python -m codebook_features.train_codebook model_args.model_name_or_path=roneneldan/TinyStories-1M 'data_args.dataset_name=roneneldan/TinyStories'

Interpretability WebApp for Codebook Models

Once a codebook model has been trained and saved on disk, we can use the interpretability webapp to visualize the codebook. First, we need to generate the relevant cache files for the codebook model that is required for the webapp. This can be done by running the script codebook_features/code_search_cache.py:

python -m codebook_features.code_search_cache --model_name <path to codebook model> --pretrained_path --dataset_name <dataset name> --dataset_config_name <dataset config name> --output_base_dir <path to output directory>

Once the cache files have been generated, we can run the webapp using the following command:

python -m streamlit run codebook_features/webapp/Code_Browser.py -- --cache_dir <path to cache directory>

Code Intervention

To control a network, one can intervene on codes by causing them to always be activated during the forward pass. This can be useful to influence the sampled generations, e.g., to cause the network to discuss certain topics. For a general tutorial on using codebook models and seeing how you can perform code intervention, please see the Code Intervention Tutorial.

Guide to the codebase [click to expand]

Codebook Model

codebook_features/models is the main module used to define codebooks. It has the following classes:

• CodebookLayer: defines a torch.nn.Module that implements the codebook layer. It takes in arguments like num_codes, dim, snap_fn kcodes that define the codebook. It provides various functionalities including logging methods, hook function that can disable specific codes during inference, etc.
  • GroupCodebookLayer: defines a torch.nn.Module that implements a group of codebook layer each of which are applied to a different part of the input vector. This is useful for applying a group of codebooks on the attention head outputs of a transformer model.
• CodebookWrapper: is an abstract class to wrap a codebook around any torch.nn.Module. It takes in the module_layer, codebook_cls, and arguments for the codebook class to instantiate the codebook layer. The wrapper provides a snap boolean field that can be used to enable/disable the codebook layer.
  • TransformerLayerWrapper: subclasses CodebookWrapper to wrap a codebook around a transformer layer, i.e. a codebook is applied on the output of the a whole transformer block.
  • MLPWrapper: subclasses CodebookWrapper to wrap a codebook around an MLP layer, i.e. a codebook is applied on the output of the MLP block.
• CodebookModelConfig: defines the config to be used by a codebook model. It contains important parameters like codebook_type, num_codes, num_codebooks, layers_to_snap, similarity_metric, codebook_at, etc.
• CodebookModel: defines the abstract base class for a codebook model. It takes in a neural network model through the model argument and the config through the config argument and return a codebook model.
  • GPT2CodebookModel: subclasses CodebookModel to define a codebook model specifically for GPT2.
  • GPTNeoCodebookModel: subclasses CodebookModel to define a codebook model specifically for GPTNeo.
  • GPTNeoXCodebookModel: subclasses CodebookModel to define a codebook model specifically for GPTNeoX.
  • HookedTransformerCodebookModel: subclasses CodebookModel to define a codebook model for any transformer model defined using the HookedTransformer class of transformer_lens. This is mostly while interpreting the codebooks while the other classes are used for training the codebook models. The convert_to_hooked_model() function can be used to convert a trained codebook model to a HookedTransformerCodebookModel.

Codebook Training

The codebook_features/train_codebook.py script is used to train a codebook model based on a causal language model. We use the run_clm.py script provided by the transformers library for training. It can take in a dataset name available in the datasets library or a custom dataset. The default arguments for the training script is available in codebook_features/config/main.yaml. The hydra syntax can be used to override any of the default config values.

TokFSM Experiment

The codebook_features/train_fsm_model.py script provides an algorithmic sequence modeling task to analyse the codebook models. The task is to predict the next element in a sequence of numbers generated using a Finite State Machine (FSM). The train_fsm_model/FSM class defines the FSM by taking in the number of states through N, the number of outbound edges from each state through edges, and the base in which to represent the state using representation_base. The train_fsm_model/TokFSMDataset class defines an iterable torch dataset using the FSM that generates the dataset on the fly. The train_fsm_model/TokFSMModelTrainer provides additional logging feature specific to the fsm models like logging the transition accuracy of a model.

The codebook_features/train_fsm_model.py script can be used to train a codebook model on the TokFSM dataset. The syntax for the arguments and training procedure is similar to the train_codebook.py script. The default arguments for the training script is available in codebook_features/config/fsm_main.yaml.

For tutorials on how to use the library, please see the Codebook Features Tutorials.

Citation

@misc{tamkin2023codebook,
      title={Codebook Features: Sparse and Discrete Interpretability for Neural Networks},
      author={Alex Tamkin and Mohammad Taufeeque and Noah D. Goodman},
      year={2023},
      eprint={2310.17230},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}