Probabilistic Torch is library for deep generative models that extends PyTorch. It is similar in spirit and design goals to Edward and Pyro, sharing many design characteristics with the latter.
The design of Probabilistic Torch is intended to be as PyTorchlike as possible. Probabilistic Torch models are written just like you would write any PyTorch model, but make use of three additional constructs:

A library of reparameterized distributions that implement methods for sampling and evaluation of the log probability mass and density functions (now available in PyTorch)

A Trace data structure, which is both used to instantiate and store random variables.

Objective functions that approximate the lower bound on the log marginal likelihood using Monte Carlo and Importanceweighted estimators.
This repository accompanies the NIPS 2017 paper:
@inproceedings{siddharth2017learning,
title = {Learning Disentangled Representations with SemiSupervised Deep Generative Models},
author = {Siddharth, N. and Paige, Brooks and van de Meent, JanWillem and Desmaison, Alban and Goodman, Noah D. and Kohli, Pushmeet and Wood,
Frank and Torr, Philip},
booktitle = {Advances in Neural Information Processing Systems 30},
editor = {I. Guyon and U. V. Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
pages = {59275937},
year = {2017},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/7174learningdisentangledrepresentationswithsemisuperviseddeepgenerativemodels.pdf}
}
Contributors
(in order of joining)
 JanWillem van de Meent
 Siddharth Narayanaswamy
 Brooks Paige
 Alban Desmaison
 Alican Bozkurt
 Amirsina Torfi
 Babak Esmaeli
 Eli Sennesh
Installation

Install PyTorch [instructions]

Install this repository from source
pip install git+git://github.com/probtorch/probtorch

Refer to the
examples/
subdirectory for Jupyter notebooks that illustrate usage. 
To build and read the API documentation, please do the following
git clone git://github.com/probtorch/probtorch
cd probtorch/docs
pip install r requirements.txt
make html
open build/html/index.html
MiniTutorial: Semisupervised MNIST
Models in Probabilistic Torch define variational autoencoders. Both the encoder and the decoder model can be implemented as standard PyTorch models that subclass nn.Module
.
In the __init__
method we initialize network layers, just as we would in a PyTorch model. In the forward
method, we additionally initialize a Trace
variable, which is a writeonce dictionarylike object. The Trace
data structure implements methods for instantiating named random variables, whose values and log probabilities are stored under the specifed key.
Here is an implementation for the encoder of a standard semisupervised VAE, as introduced by Kingma and colleagues [1]
import torch
import torch.nn as nn
import probtorch
class Encoder(nn.Module):
def __init__(self, num_pixels=784, num_hidden=50, num_digits=10, num_style=2):
super(self.__class__, self).__init__()
self.h = nn.Sequential(
nn.Linear(num_pixels, num_hidden),
nn.ReLU())
self.y_log_weights = nn.Linear(num_hidden, num_digits)
self.z_mean = nn.Linear(num_hidden + num_digits, num_style)
self.z_log_std = nn.Linear(num_hidden + num_digits, num_style)
def forward(self, x, y_values=None, num_samples=10):
q = probtorch.Trace()
x = x.expand(num_samples, *x.size())
if y_values is not None:
y_values = y_values.expand(num_samples, *y_values.size())
h = self.h(x)
y = q.concrete(logits=self.y_log_weights(h), temperature=0.66,
value=y_values, name='y')
h2 = torch.cat([y, h], 1)
z = q.normal(loc=self.z_mean(h2),
scale=torch.exp(self.z_log_std(h2)),
name='z')
return q
In the code above, the method q.concrete
samples or observes from a Concrete/GumbelSoftmax relaxation of the discrete distribution, depending on whether supervision values y_values
are provided. The method q.normal
samples from a univariate normal.
The resulting trace q
now contains two entries q['y']
and q['z']
, which are instances of a RandomVariable
class, which stores both the value and the log probability associated with the variable. The stored values are now used to condition execution of the decoder model:
def binary_cross_entropy(x_mean, x, EPS=1e9):
return  (torch.log(x_mean + EPS) * x +
torch.log(1  x_mean + EPS) * (1  x)).sum(1)
class Decoder(nn.Module):
def __init__(self, num_pixels=784, num_hidden=50, num_digits=10, num_style=2):
super(self.__class__, self).__init__()
self.num_digits = num_digits
self.h = nn.Sequential(
nn.Linear(num_style + num_digits, num_hidden),
nn.ReLU())
self.x_mean = nn.Sequential(
nn.Linear(num_hidden, num_pixels),
nn.Sigmoid())
def forward(self, x, q=None):
if q is None:
q = probtorch.Trace()
p = probtorch.Trace()
y = p.concrete(logits=torch.zeros(x.size(0), self.num_digits),
temperature=0.66,
value=q['y'], name='y')
z = p.normal(loc=0.0, scale=1.0, value=q['z'], name='z')
h = self.h(torch.cat([y, z], 1))
p.loss(binary_cross_entropy, self.x_mean(h), x, name='x')
return p
The model above can be used both for conditioned forward execution, but also for generation. The reason for this is that q[k]
returns None
for variable names k
that have not been instantiated.
To train the model components above, probabilistic Torch provides objectives that compute an estimate of a lower bound on the log marginal likelihood, which can now be maximized with standard PyTorch optimizers
from probtorch.objectives.montecarlo import elbo
from random import rand
# initialize model and optimizer
enc = Encoder()
dec = Decoder()
optimizer = torch.optim.Adam(list(enc.parameters())
+ list(dec.parameters()))
# define subset of batches that will be supervised
supervise = [rand() < 0.01 for _ in data]
# train model for 10 epochs
for epoch in range(10):
for b, (x, y) in data:
x = Variable(x)
if supervise[b]:
y = Variable(y)
q = enc(x, y)
else:
q = enc(x)
p = dec(x, q)
loss = elbo(q, p, sample_dim=0, batch_dim=1)
loss.backward()
optimizer.step()
For a more details, see the Jupyter notebooks in the examples/
subdirectory.
References
[1] Kingma, Diederik P, Danilo J Rezende, Shakir Mohamed, and Max Welling. 2014. “SemiSupervised Learning with Deep Generative Models.” http://arxiv.org/abs/1406.5298.