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Simple but powerful

Design, train, and sample from complex probability distributions using only a few lines of code. Advanced features such as conditionality, caching, and structured representations are planned for future released.

Community focused

We help you be a successful user or contributor through detailed user, developer, and API guides. Educational tutorials and research benchmarks are planned for the future. We welcome your feedback!

Modular and extendable

Combine multiple bijections to form complex normalizing flows, and mix-and-match conditioning networks with bijections. FlowTorch has a well-defined interface so you easily create your own components!

Production ready

Proven code migrated from Pyro, with improved unit testing and continuous integration. And it is easy to add standard unit tests to components you write yourself!

Examples
import torch
import flowtorch.bijectors as B
import flowtorch.distributions as D
import flowtorch.parameters as P
# Lazily instantiated flow plus base and target distributions
params_fn = P.DenseAutoregressive(hidden_dims=(32,))
bijectors = B.AffineAutoregressive(params_fn=params_fn)
base_dist = torch.distributions.Independent(
torch.distributions.Normal(torch.zeros(2), torch.ones(2)),
1
)
target_dist = torch.distributions.Independent(
torch.distributions.Normal(torch.zeros(2)+5, torch.ones(2)*0.5),
1
)
# Instantiate transformed distribution and parameters
flow = D.Flow(base_dist, bijectors)
# Training loop
opt = torch.optim.Adam(flow.parameters(), lr=5e-3)
frame = 0
for idx in range(3001):
opt.zero_grad()
# Minimize KL(p || q)
y = target_dist.sample((1000,))
loss = -flow.log_prob(y).mean()
if idx % 500 == 0:
print('epoch', idx, 'loss', loss)
loss.backward()
opt.step()