Tight PAC-Bayes Compression Bounds

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This repository hosts the code for [PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization]() by Sanae Lotfi*, Marc Finzi*, Sanyam Kapoor*, Andres Potapczynski*, Micah Goldblum, and Andrew Gordon Wilson.

Setup

conda env create -f environment.yml -n pactl

Setup the pactl package.

pip install -e .

Usage

We use Fire for CLI parsing.

Training Intrinsic Dimensionality Models

python experiments/train.py --dataset=cifar10 \
                            --model-name=resnet18k \
                            --base-width=64 \
                            --optimizer=adam \
                            --epochs=500 \
                            --lr=1e-3 \
                            --intrinsic_dim=1000 \
                            --intrinsic_mode=rdkronqr \
                            --seed=137

All arguments in the main method of experiments/train.py are valid CLI arguments. The most imporant ones are noted here:

--seed: Setting the seed is important so that any subsequent runs using the checkpoint can reconstruct the same random parameter projection matrices used during training.
--data_dir: Parent path to directory containing root directory of the dataset.
--dataset: Dataset name. See data.py for list of dataset strings.
--intrinsic_dim: Dimension of the training subspace of parameters.
--intrinsic_mode: Method used to generate (sparse) random projection matrices. See create_intrinsic_model method in projectors.py for a list of valid modes.

Distributed Training

Distributed training is helpful for large datasets like Imagenet to spread computation over multiple GPUs. We rely on torchrun.

To use multiple GPUs on a single node, we need:

GPU visibility flags appropriately via CUDA_VISIBLE_DEVICES.
Specify the number x of GPUs made visible via --nproc_per_node=<x>
Specify a random port yyyy on the host for inter-process communication via --rdzv_endpoint=localhost:yyyy.

For the same run as above, we simply replace python with torchrun as:

CUDA_VISIBLE_DEVICES=0,1 \
torchrun --nproc_per_node=2 --rdzv_endpoint=localhost:9999 experiments/train.py ...

All remaining CLI arguments remain unchanged.

Transfer Learning using Existing Checkpoints

The key argument needed for transfer is the path to the configuration file named net.cfg.yml of the pretrained network.

python experiments/train.py --dataset=fmnist \
                            --optimizer=adam \
                            --epochs=500 \
                            --lr=1e-3 \
                            --intrinsic_dim=1000 \
                            --intrinsic_mode=rdkronqr \
                            --prenet_cfg_path=<path/to/net.cfg.yml> \
                            --seed=137 \
                            --transfer

In addition to earlier arguments, there is only one new key argument:

--prenet_cfg_path: Path to net.cfg.yml configuration file of the pretrained network. This path is logged during the train command specified previously.

Training for Data-Dependent Bounds

Data-dependent bounds first require pre-training on a fixed subset of training data and then training an intrinsic dimensionality model on the remainder of the subset.

For such training, we use the following command:

python experiments/train_dd_priors.py --dataset=cifar10 \
                                      ...
                                      --indices_path=<path/to/index/list> \
                                      --train-subset=0.1 \
                                      --seed=137

The key new arguments here in addition to the ones seen previously are:

--indices-path: A fixed permutation of indices as a numpy list equal to the length of the dataset. If not specified, a random permutation is generated every time and the results may not be reproducible. See dataset_permutations.ipynb to see an example of how to generate such a file.
--train-subset: A fractional subset of the training data to use. If a negative fraction, then the complement is used.

Computing our Adaptive Compression Bounds

Once we have the checkpoints of intrinsic-dimensionality models, the bound can be computed using:

python experiments/compute_bound.py --dataset=mnist \
                                    --misc-extra-bits=7 \
                                    --quant-epochs=30 \
                                    --levels=50 \
                                    --lr=0.0001 \
                                    --prenet_cfg_path=<path/to/net.cfg.yml> \
                                    --use_kmeans=True

The key arguments here are:

--misc-extra-bit: Penalty for hyper-parameter optimization during bound computation, equals the bits required to encode all hyper-parameter configurations.
--levels: Number of quantization levels.
--quant-epochs: Number of epochs used for fine-tuning of quantization levels.
--lr: Learning rate used for fine-tuning of quantization levels.
--user_kmeans: When true, uses kMeans clustering for initialization of quantization levels. Otherwise, random initialization is used.

LICENSE

Apache 2.0

activatedgeek / tight-pac-bayes

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