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arXiv Models Data

Self-Proving Models

Self-Proving Models prove the correctness of their outputs to a verifier using an Interactive Proof System. This repository includes tools to train these models, specifically for the Greatest Common Divisor (GCD) problem, based on the theory described in our paper.

This repository provides:

  • A straightforward framework for training a Self-Proving GPT.
  • Data generation scripts for the GCD problem.
  • Scripts for reproducing experiments from the paper.

Setup

  1. Create a new conda environment (recommended):
conda create -n spm python=3.12
conda activate spm
  1. Clone the repository:
git clone https://github.com/orrp/self-proving-models.git
  1. Install the package:
cd self-proving-models
pip install -e .

Data

You can download pre-generated Data, and extract it to the data/ directory. To generate this data yourself, run

python spm/data/generate_data.py

This populates the data/ directory with Transcripts and Annotated Transcripts of interactions between an honest prover and the verifier.

Transcript datasets are named according to the following convention:

TL_{UPPER_BOUND}_m{NUM_TRAIN_SAMPLES}_b{BASE_OF_REPRESENTATION}

For Annotated Transcripts, TL is replaced with ATL{ANNOTATION_LENGTH}.

Training

Once data/ is populated, you can train a Self-Proving GPT via Transcript Learning:

python spm/train.py --data DATASET_NAME

where DATASET_NAME is the name of the dataset you want to use.

Example

To train on about 10 million samples with an upper bound of 10,000 encoded in base 210:

python spm/train.py --data TL_1e4_m1e7_b210

Useful arguments

  • --help: Show all arguments.
  • --device DEVICE: Specify the device to train on (cpu or cuda).
  • --epochs EPOCHS: Number of epochs to train. Each epoch looks at a number of samples equal to the dataset size.
  • --data DATA, -d DATA: Name of the dataset to use.
  • --seed SEED: Set random seed
  • --save_iters SAVE_ITERS [SAVE_ITERS ...]: Save model at these iterations. -1 For the last iteration. None to disable. Checkpoints are saved to models/ as: {N_LAYERS}x{N_HEAD}x{DIM_EMBED}x{N_ITERATIONS}_{DATASET_NAME}_iter{ITERATION}.pt
  • --load_ckpt LOAD_CKPT: Load model from checkpoint (name). When you load a model, specify the checkpont name as described above (not the full path).
  • --wandb: Enable tracking with Weights & Biases. Use --wandb_proj WANDB_PROJ to specify the project name.

Reproducing results from the paper

Once you obtain data, you can train models on the datasets to reproduce the experimental section of the paper.

Annotation length

To reproduce the ablation on the annotation length, run

./runs/annot_len.sh

Logs of these runs will be saved at logs/. The Figure 2 can be generated with

./figs/annotation.py        # Fig 2

Base of representation

The paper shows that the number of unique primes in the base of representation determines Verifiability of the model. This ablation requires generating many different datasets (one for each base). For convenience, there is a script that first samples a random base with a given number of unique primes in its factorization, then trains a model and deletes the dataset.

python spm/train_diff_bases.py --num_unique_primes NUM_UNIQUE_PRIMES --seed SEED

Run this script for twenty seeds. Tip: You can use a WandB sweep to easily schedule runs from different machines, and then aggregate the results onto your local machine for plotting. Use wandb sweep runs/diff_bases.yaml to create the sweep.

Each run will be logged to logs/diff_bases/. You can then generate the figure with

./figs/diff_bases.py  # Fig 3

Citation

If you use Self-Proving Models or components of this codebase in your research, please cite the following paper:

@article{AGPR2024,
  author       = {Noga Amit and
                  Shafi Goldwasser and
                  Orr Paradise and
                  Guy N. Rothblum},
  title        = {Models That Prove Their Own Correctness},
  journal      = {CoRR},
  volume       = {abs/2405.15722},
  year         = {2024},
  url          = {https://doi.org/10.48550/arXiv.2405.15722},
  doi          = {10.48550/ARXIV.2405.15722},
  eprinttype    = {arXiv},
  eprint       = {2405.15722},
  timestamp    = {Wed, 19 Jun 2024 08:52:57 +0200},
  biburl       = {https://dblp.org/rec/journals/corr/abs-2405-15722.bib},
  bibsource    = {dblp computer science bibliography, https://dblp.org}
}

Acknowledgements

This codebase adapts Andrej Karpathy's nanoGPT as its GPT implementation. The model can be found in self-proving-models/gpt/. Cassidy Laidlaw's boilerplate was used for repo structure and linting (self-proving-models/lint.sh).

Contribution

Contributions are welcome! Please open an issue or a pull request.

To install the package in development mode, run

pip install -e '.[dev]'
ln -s post-commit.sh .git/hooks/post-commit

The last command sets up a git hook to copy the git hash in spm/__init__.py. This hash is then logged by WandB for reproducibility. This is useful when experiments are run from a server to which code is deployed from a local machine (not via git).

Package structure

Root spm/

  • train.py: Main entry point for training models.
  • train_diff_bases.py: Alternative entry point for training models on datasets with different bases of representation. Generates and cleans up datasets automatically.
  • utils.py: Common utilities (e.g. implementation of the extended Euclidean algorithm).
  • __init__.py: Common paths and constants.
  • systematic_models.py: Systematic models for the GCD problem, useful for testing.

Data spm/data/

  • generate_data.py: Generates datasets for training.
  • samples.py: The Samples represents a dataset of input-output sequences to the GCD problem. Transcripts add a proof (Bézout coefficients) to the samples. AnnotatedTranscripts add a proof and its annotation.
  • samplers.py: Samplers for generating Samples, Transcripts, and AnnotatedTranscripts.
  • str_repr.py: A string representation of data samples (encoded in a given base).
  • tensor_repr.py: A tensor representation of data samples. Uses str_repr to encode samples as strings, and handles delimiters and padding. Contains utility methods for encoding, decoding, and saving.

Model spm/gpt/

  • model.py: Defines the GPT model architecture. An object-oriented adaptation of nanoGPT.
  • trainer.py: Trainer for GPT models. Handles training, evaluation, and logging.
  • config.py: Config for a trainer.