# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
import pyro.distributions as dist
from .reparam import Reparam
Reparameterizer to split a random variable along a dimension, similar to
This is useful for treating different parts of a tensor with different
reparameterizers or inference methods. For example when performing HMC
inference on a time series, you can first apply
:class:`~pyro.infer.reparam.haar.HaarReparam`, then apply
:class:`SplitReparam` to split into low-frequency and high-frequency
components, and finally add the low-frequency components to the
``full_mass`` matrix together with globals.
:param sections: Size of a single chunk or list of sizes for
:param int dim: Dimension along which to split. Defaults to -1.
def __init__(self, sections, dim):
assert isinstance(dim, int) and dim < 0
assert isinstance(sections, list)
assert all(isinstance(size, int) for size in sections)
self.event_dim = -dim
self.sections = sections
[docs] def __call__(self, name, fn, obs):
assert fn.event_dim >= self.event_dim
assert obs is None, "SplitReparam does not support observe statements"
# Draw independent parts.
dim = fn.event_dim - self.event_dim
left_shape = fn.event_shape[:dim]
right_shape = fn.event_shape[1 + dim:]
parts = 
for i, size in enumerate(self.sections):
event_shape = left_shape + (size,) + right_shape
dist.ImproperUniform(fn.support, fn.batch_shape, event_shape)))
value = torch.cat(parts, dim=-self.event_dim)
# Combine parts.
log_prob = fn.log_prob(value)
new_fn = dist.Delta(value, event_dim=fn.event_dim, log_density=log_prob)
return new_fn, value