# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import torch
import pyro
import pyro.distributions as dist
from .reparam import Reparam
[docs]class GumbelSoftmaxReparam(Reparam):
"""
Reparametrizer for :class:`~pyro.distributions.RelaxedOneHotCategorical`
latent variables.
This is useful for transforming multimodal posteriors to unimodal
posteriors. Note this increases the latent dimension by 1 per event.
This reparameterization works only for latent variables, not likelihoods.
"""
[docs] def __call__(self, name, fn, obs):
fn, event_dim = self._unwrap(fn)
assert isinstance(fn, dist.RelaxedOneHotCategorical)
assert obs is None, "SoftmaxReparam does not support observe statements"
# Draw parameter-free noise.
proto = fn.logits
new_fn = dist.Uniform(torch.zeros_like(proto), torch.ones_like(proto))
u = pyro.sample("{}_uniform".format(name), self._wrap(new_fn, event_dim))
# Differentiably transform.
logits = fn.logits - u.log().neg().log()
value = (logits / fn.temperature).softmax(dim=-1)
# Simulate a pyro.deterministic() site.
new_fn = dist.Delta(value, event_dim=event_dim).mask(False)
return new_fn, value