General API for Deep Bayesian Variational Inference by Backpropagation.
The repository has been designed to work with Transformers like architectures.
Compatible with the HuggingFace Transformers models.

Setup

Installation of the required python libraries is done through pip.

$ cd BayeFormers
$ (sudo) pip3 install -r requirements.txt

Usage

from bayeformers import to_bayesian

import bayeformers.nn as bnn
import torch
import torch.nn as nn
import torch.nn.functional as F

# Frequentist Model Definition
class Model(nn.Module):
    pass

# Train Frequentist Model
model = Model()

predictions = model(inputs)
loss = F.nll(inputs, labels, reduction="sum")

# Turn Frequentist Model to Bayesian Model (MOPED Initializatipn)
bayesian_model = to_bayesian(model, delta=0.05, freeze=True)

# Train Bayesian Model
predictions = torch.zeros(samples, batch_size, *output_dim)
log_prior = torch.zeros(samples, batch_size)
log_variational_posterior = torch.zeros(samples, batch_size)

for s in samples:
    predictions[s] = bayesian_model(inputs)
    log_prior[s] = bayesian_model.log_prior()
    log_variational_posterior[s] = bayesian_model.log_variational_posterior()

predictions = predictions.mean(0)
log_prior = log_prior.mean(0)
log_variational_posterior = log_variational_posterior.mean(0)

nll = F.nll(predictions, labels, reduction="sum")
loss = (log_variational_posterior - log_prior) / n_batches + nll

Examples

$ python3 -m examples.mlp_mnist
$ python3 -m examples.bert_glue --help
$ python3 -m examples.bert_squad --help

References

Libraries

• PyTorch
• HuggingFace Transformers

Papers

• "Weight Uncertainty in Neural Networks", Blundell et al., ICML 2015, Arxiv
• "Specifying Weight Priors in Bayesian Deep Neural Networks with Empirical Bayes", Krishnan et al., AAAI 2020, Arxiv

Articles

• "Bayesian inference: How we are able to chase the Posterior", Ritchie Vink, Blog
• "Weight Uncertainty in Neural Networks", Nitarshan Rajkumar, Blog