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Fast Per-Example Gradient Clipping

This repository contains the source code for the paper "Scaling Up Differentially Private Deep Learning with Fast Per-Example Gradient Clipping" (to appear in POPETS 2021).

The provided fastgc package provides a fast and scalable PyTorch implementation of gradient clipping method used for satisfying differential privacy. It provides a set of wrapper classes for neural network layers. To compute per-example gradients for differential privacy, you can simply import the fastgc package and replace the original PyTorch layers in your network with the provided wrapper classes. The support types of layers include:

• Linear layer
• Convolutional layer
• Reccurent layer
• LayerNorm layer
• Transformer encoding layer

Installing dependecies

• The code was written and tested using Python 3.7 and Pytorch 1.5.0.
• To install the required packages, run

pip install -r requirements.txt

• The code expects that fastgc package is available in your system. This means that fastgc directory is in your python's search path. An easy way to meet this requirement is to place fastgc directory as a subdirectory of one of directories in your PYTHONPATH environment variable.

Running the code

The main function is implemented in run_algo.py. To train a neural network using the proposed ReweightGP algorithm, you can execute

python run_algo.py --train_alg reweight --data_dir <path to the directory containing the dataset> --download True

• You will need to make sure the --data_dir is correctly set to the location containing the datasets to use. If the dataset to use is not available on the machine, please make sure to set ---download True (by default it is set to True) so that the program can download it from the internet.
• If one or multiple GPUs are available on the machine, the program tries to choose a cuda device using nvidia-smi command. On Windows OS, it expects the executable is located in C:\Program Files\NVIDIA Corporation\NVSMI directory. On a Linux machine, it assumes the command is available in the current directory. Either add the location of nvidia-smi binary to the system's PATH environment variable or manually set the ID of cuda device using --gpu_id flag.

Example usage

• Running non-private algorithm to train a CNN model on MNIST dataset

python run_algo.py --train_alg batch --model_name CNN --data_dir <path to data> --dname mnist --download True

• Running the proposed ReweightGP algorithm to train an RNN on mnist dataset, using mini-batches of size 128

python run_algo.py --train_alg reweight --model_name RNN --data_dir <path to data> --dname mnist --batch_size 128 --download True

Important Input Arguments

The script takes several input arguments. To see available command line input arguments and allowed values, simply run

python run_algo.py --help

Here we introduce each optional argument:

• --data_dir: string, path to the root directory in which input datasets are stored.
• --dname: the name of input dataset, Allowed values are {mnist, cifar10, fmnist, lsum}.
• --train_alg: the name of algorith to use for training, It can be batch, reweight, or naive.
• --model_name: the name of neural network architecture to use, Available values include:
  • MLP: a multi-layer perceptron
  • CNN: a convolutional neural network
  • RNN: a recurrent neural network
  • LSTM: an LSTM network
  • Transformer: an encoder block of Transformer network
  • ResNet: resnet18, resnet34, resnet50, resnet101
  • VGG: vgg11, vgg13, vgg16, vgg19
• --hidden_size: an integer specifying the number of units in RNN
• --hidden_sizes: a list of integers, specifying the number of units in each layer of MLP model. For example, [128, 256] will create an MLP with two hidden layers with 128 and 256 hidden units, respectively.
• --kernel_sizes: a list of integers. This parameter sets the sizes of kernel in each layer of CNN model.
• --fc_sizes: a list of integers. This parameter is used to specify the number of hidden units in linear layers that are added after convolutional layers in the CNN model.
• --batch_size: integer, the number of observations in a mini-batch
• --clip_thresh: float, clipping threshold
• --test_batch_size: integer, how many observations to load in a mini-batch for testing
• --act_func: string, the name of activation function. Available values include: {sigmoid, tanh, relu, lrelu}
• --optimizer: string, name of optimization algorithm to use. There are 3 optimizers available: Adam, SGD, and RMSprop.
• --lr: float, learning rate (or step size)
• --sigma: float, scale parameter of noise distribution
• --delta: float, privacy parameter in $(\epsilon, \delta)$-differential privacy
• --rep: integer, how many times to repeat the experiment
• --deterministic: If this flag is set, the program fixes the randomness of numpy and pytorch (i.e., sets the seed to a fixed value)
• --epochs: integer, the number of epochs to run
• --vervose: If this option is provided, the program prints out detailed messages.
• --img_size: integer, this flag is used to set the image size of LSUN dataset. For example, if img_size is set to 64, the program resizes the images to 64 x 64.
• --embedding_size: integer, the length of embedding vectors for IMDB dataset (only used for Transformer model)
• --max_vocab_size: integer, the maximum number of words in the vocabulary (only used for Transformer model)
• --num_layers: integer, number of layers in a transformer block (only used for Transformer model)
• --num_heads: integer, number of attention head to put in a transformer block
• --max_seq_len: integer, maximum length of sequence for IMDB dataset
• --niter: integer, number of iterations to run the algorithm. If this parameter is given, the program ignores epochs parameter.
• --download: boolean (True or False) If true, the program will try to download the dataset from the internet if it is not found in the location specified by data_dir
• --gpu_id: integer, the id of cuda device to use

Result Files

After the program runs for the specified number of iterations, it stores the results (avg. loss, avg. per-epoch execution time, etc) into a .csv file. The resulting csv file has the following naming convetion:

[training algorithm name]_[model name]_[dataset_name]_B[batch_size]E[epochs]C[clipping_threshold]SIG[sigma].csv

Datasets

The paper uses 5 different datasets for the evaluation of proposed algorithm:

• Pytorch built-in datasets: MNIST, FMNIST, CIFAR10, IMDB API document
• LSUN dataset can be downloaded from here.