Rank-One Model Editing (ROME)

This repository provides an implementation of Rank-One Model Editing (ROME) on auto-regressive transformers (GPU-only). We currently support OpenAI's GPT-2 XL (1.5B) and EleutherAI's GPT-J (6B). The release of a 20B GPT-like model from EleutherAI is expected soon; we hope to support it ASAP.

Feel free to open an issue if you find any problems; we are actively developing this repository and will monitor tickets closely.

Table of Contents

1. Installation
2. Causal Tracing
3. Rank-One Model Editing (ROME)
4. CounterFact
5. Evaluation
   • Running the Full Evaluation Suite
   • Integrating New Editing Methods
6. How to Cite

Installation

We recommend conda for managing Python, CUDA, and PyTorch-related dependencies, and pip for everything else. To get started, simply install conda and run:

./scripts/setup_conda.sh

Causal Tracing

notebooks/causal_trace.ipynb demonstrates Causal Tracing, which can be modified to apply tracing to the processing of any statement.

Rank-One Model Editing (ROME)

notebooks/rome.ipynb demonstrates ROME. The API is simple; one simply has to specify a requested rewrite of the following form:

request = {
    "prompt": "{} plays the sport of",
    "subject": "LeBron James",
    "target_new": {
        "str": "football"
    }
}

Several similar examples are included in the notebook.

CounterFact

Details coming soon!

Evaluation

See baselines/ for a description of the available baselines.

Running the Full Evaluation Suite

experiments/evaluate.py can be used to evaluate any method in baselines/. To get started (e.g. using ROME on GPT-2 XL), run:

python3 -m experiments.evaluate \
    --alg_name=ROME \
    --model_name=gpt2-xl \
    --hparams_fname=gpt2-xl.json

Results from each run are stored at results/<method_name>/run_<run_id> in a specific format:

results/
|__ ROME/
    |__ run_<run_id>/
        |__ params.json
        |__ case_0.json
        |__ case_1.json
        |__ ...
        |__ case_10000.json

To summarize the results, you can use experiments/summarize.py:

python3 -m experiments.summarize --dir_name=ROME --runs=run_<run_id>

Running python3 -m experiments.evaluate -h or python3 -m experiments.summarize -h provides details about command-line flags.

Integrating New Editing Methods

Say you have a new method X and want to benchmark it on CounterFact. To integrate X with our runner:

• Subclass HyperParams into XHyperParams and specify all hyperparameter fields. See ROMEHyperParameters for an example implementation.
• Create a hyperparameters file at hparams/X/gpt2-xl.json and specify some default values. See hparams/ROME/gpt2-xl.json for an example.
• Define a function apply_X_to_model which accepts several parameters and returns (i) the rewritten model and (ii) the original weight values for parameters that were edited (in the dictionary format {weight_name: original_weight_value}). See rome/rome_main.py for an example.
• Add X to ALG_DICT in experiments/evaluate.py by inserting the line "X": (XHyperParams, apply_X_to_model).

Finally, run the main scripts:

python3 -m experiments.evaluate \
    --alg_name=X \
    --model_name=gpt2-xl \
    --hparams_fname=gpt2-xl.json

python3 -m experiments.summarize --dir_name=X --runs=run_<run_id>

Note on Cross-Platform Compatibility

We currently only support methods that edit autoregressive HuggingFace models using the PyTorch backend. We are working on a set of general-purpose methods (usable on e.g. TensorFlow and without HuggingFace) that will be released soon.

How to Cite

@article{meng2022locating,
  title={Locating and Editing Factual Associations in {GPT}},
  author={Kevin Meng and David Bau and Alex Andonian and Yonatan Belinkov},
  journal={Advances in Neural Information Processing Systems},
  volume={35},
  year={2022}
}