# Copyright Contributors to the Pyro project.
# SPDX-License-Identifier: Apache-2.0
from contextlib import ExitStack
from torch.distributions import biject_to
from torch.distributions.transforms import ComposeTransform
import pyro
import pyro.distributions as dist
from pyro.poutine.plate_messenger import block_plate
from .reparam import Reparam
[docs]class UnitJacobianReparam(Reparam):
"""
Reparameterizer for :class:`~torch.distributions.transforms.Transform`
objects whose Jacobian determinant is one.
:param transform: A transform whose Jacobian has determinant 1.
:type transform: ~torch.distributions.transforms.Transform
:param str suffix: A suffix to append to the transformed site.
:param bool experimental_allow_batch: EXPERIMENTAL allow coupling across a
batch dimension. The targeted batch dimension and all batch dimensions
to the right will be converted to event dimensions. Defaults to False.
"""
def __init__(self, transform, suffix="transformed", *,
experimental_allow_batch=False):
self.transform = transform.with_cache()
self.suffix = suffix
self.experimental_allow_batch = experimental_allow_batch
[docs] def __call__(self, name, fn, obs):
assert obs is None, "TransformReparam does not support observe statements"
event_dim = fn.event_dim
transform = self.transform
with ExitStack() as stack:
shift = max(0, transform.event_dim - event_dim)
if shift:
if not self.experimental_allow_batch:
raise ValueError("Cannot transform along batch dimension; try either"
"converting a batch dimension to an event dimension, or "
"setting experimental_allow_batch=True.")
# Reshape and mute plates using block_plate.
from pyro.contrib.forecast.util import (
reshape_batch,
reshape_transform_batch,
)
old_shape = fn.batch_shape
new_shape = old_shape[:-shift] + (1,) * shift + old_shape[-shift:]
fn = reshape_batch(fn, new_shape).to_event(shift)
transform = reshape_transform_batch(transform,
old_shape + fn.event_shape,
new_shape + fn.event_shape)
for dim in range(-shift, 0):
stack.enter_context(block_plate(dim=dim, strict=False))
# Draw noise from the base distribution.
transform = ComposeTransform([biject_to(fn.support).inv.with_cache(),
self.transform])
x_trans = pyro.sample("{}_{}".format(name, self.suffix),
dist.TransformedDistribution(fn, transform))
# Differentiably transform.
x = transform.inv(x_trans) # should be free due to transform cache
if shift:
x = x.reshape(x.shape[:-2 * shift - event_dim] + x.shape[-shift - event_dim:])
# Simulate a pyro.deterministic() site.
new_fn = dist.Delta(x, event_dim=event_dim)
return new_fn, x