Source code for pyro.poutine.broadcast_messenger

from __future__ import absolute_import, division, print_function

from pyro.util import ignore_jit_warnings
from .messenger import Messenger

[docs]class BroadcastMessenger(Messenger): """ `BroadcastMessenger` automatically broadcasts the batch shape of the stochastic function at a sample site when inside a single or nested plate context. The existing `batch_shape` must be broadcastable with the size of the :class:`~pyro.plate` contexts installed in the `cond_indep_stack`. """ @staticmethod @ignore_jit_warnings(["Converting a tensor to a Python boolean"]) def _pyro_sample(msg): """ :param msg: current message at a trace site. """ if msg["done"] or msg["type"] != "sample": return dist = msg["fn"] actual_batch_shape = getattr(dist, "batch_shape", None) if actual_batch_shape is not None: target_batch_shape = [None if size == 1 else size for size in actual_batch_shape] for f in msg["cond_indep_stack"]: if f.dim is None or f.size == -1: continue assert f.dim < 0 target_batch_shape = [None] * (-f.dim - len(target_batch_shape)) + target_batch_shape if target_batch_shape[f.dim] is not None and target_batch_shape[f.dim] != f.size: raise ValueError("Shape mismatch inside plate('{}') at site {} dim {}, {} vs {}".format(, msg['name'], f.dim, f.size, target_batch_shape[f.dim])) target_batch_shape[f.dim] = f.size # Starting from the right, if expected size is None at an index, # set it to the actual size if it exists, else 1. for i in range(-len(target_batch_shape) + 1, 1): if target_batch_shape[i] is None: target_batch_shape[i] = actual_batch_shape[i] if len(actual_batch_shape) >= -i else 1 msg["fn"] = msg["fn"].expand(target_batch_shape)