Source code for pyro.infer.reparam.unit_jacobian

# 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(, 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