Source code for numpyro.primitives

from collections import namedtuple
import functools

import jax
from jax import lax

import numpyro
from numpyro.distributions.discrete import PRNGIdentity


CondIndepStackFrame = namedtuple('CondIndepStackFrame', ['name', 'dim', 'size'])

def apply_stack(msg):
    pointer = 0
    for pointer, handler in enumerate(reversed(_PYRO_STACK)):
        # When a Messenger sets the "stop" field of a message,
        # it prevents any Messengers above it on the stack from being applied.
        if msg.get("stop"):
    if msg['value'] is None:
        if msg['type'] == 'sample':
            msg['value'], msg['intermediates'] = msg['fn'](*msg['args'],
            msg['value'] = msg['fn'](*msg['args'], **msg['kwargs'])

    # A Messenger that sets msg["stop"] == True also prevents application
    # of postprocess_message by Messengers above it on the stack
    # via the pointer variable from the process_message loop
    for handler in _PYRO_STACK[-pointer-1:]:
    return msg

class Messenger(object):
    def __init__(self, fn=None):
        self.fn = fn
        functools.update_wrapper(self, fn, updated=[])

    def __enter__(self):

    def __exit__(self, *args, **kwargs):
        assert _PYRO_STACK[-1] is self

    def process_message(self, msg):

    def postprocess_message(self, msg):

    def __call__(self, *args, **kwargs):
        with self:
            return self.fn(*args, **kwargs)

[docs]def sample(name, fn, obs=None, rng_key=None, sample_shape=()): """ Returns a random sample from the stochastic function `fn`. This can have additional side effects when wrapped inside effect handlers like :class:`~numpyro.handlers.substitute`. .. note:: By design, `sample` primitive is meant to be used inside a NumPyro model. Then :class:`~numpyro.handlers.seed` handler is used to inject a random state to `fn`. In those situations, `rng_key` keyword will take no effect. :param str name: name of the sample site :param fn: Python callable :param numpy.ndarray obs: observed value :param jax.random.PRNGKey rng_key: an optional random key for `fn`. :param sample_shape: Shape of samples to be drawn. :return: sample from the stochastic `fn`. """ # if there are no active Messengers, we just draw a sample and return it as expected: if not _PYRO_STACK: return fn(rng_key=rng_key, sample_shape=sample_shape) # Otherwise, we initialize a message... initial_msg = { 'type': 'sample', 'name': name, 'fn': fn, 'args': (), 'kwargs': {'rng_key': rng_key, 'sample_shape': sample_shape}, 'value': obs, 'scale': 1.0, 'is_observed': obs is not None, 'intermediates': [], 'cond_indep_stack': [], } # ...and use apply_stack to send it to the Messengers msg = apply_stack(initial_msg) return msg['value']
def identity(x, *args, **kwargs): return x
[docs]def param(name, init_value=None, **kwargs): """ Annotate the given site as an optimizable parameter for use with :mod:`jax.experimental.optimizers`. For an example of how `param` statements can be used in inference algorithms, refer to :func:`~numpyro.svi.svi`. :param str name: name of site. :param numpy.ndarray init_value: initial value specified by the user. Note that the onus of using this to initialize the optimizer is on the user / inference algorithm, since there is no global parameter store in NumPyro. :return: value for the parameter. Unless wrapped inside a handler like :class:`~numpyro.handlers.substitute`, this will simply return the initial value. """ # if there are no active Messengers, we just draw a sample and return it as expected: if not _PYRO_STACK: return init_value # Otherwise, we initialize a message... initial_msg = { 'type': 'param', 'name': name, 'fn': identity, 'args': (init_value,), 'kwargs': kwargs, 'value': None, 'scale': 1.0, 'cond_indep_stack': [], } # ...and use apply_stack to send it to the Messengers msg = apply_stack(initial_msg) return msg['value']
[docs]def module(name, nn, input_shape=None): """ Declare a :mod:`~jax.experimental.stax` style neural network inside a model so that its parameters are registered for optimization via :func:`~numpyro.primitives.param` statements. :param str name: name of the module to be registered. :param tuple nn: a tuple of `(init_fn, apply_fn)` obtained by a :mod:`~jax.experimental.stax` constructor function. :param tuple input_shape: shape of the input taken by the neural network. :return: a `apply_fn` with bound parameters that takes an array as an input and returns the neural network transformed output array. """ module_key = name + '$params' nn_init, nn_apply = nn nn_params = param(module_key) if nn_params is None: if input_shape is None: raise ValueError('Valid value for `input_size` needed to initialize.') rng_key = numpyro.sample(name + '$rng_key', PRNGIdentity()) _, nn_params = nn_init(rng_key, input_shape) param(module_key, nn_params) return jax.partial(nn_apply, nn_params)
[docs]class plate(Messenger): """ Construct for annotating conditionally independent variables. Within a `plate` context manager, `sample` sites will be automatically broadcasted to the size of the plate. Additionally, a scale factor might be applied by certain inference algorithms if `subsample_size` is specified. :param str name: Name of the plate. :param int size: Size of the plate. :param int subsample_size: Optional argument denoting the size of the mini-batch. This can be used to apply a scaling factor by inference algorithms. e.g. when computing ELBO using a mini-batch. :param int dim: Optional argument to specify which dimension in the tensor is used as the plate dim. If `None` (default), the leftmost available dim is allocated. """ def __init__(self, name, size, subsample_size=None, dim=None): = name self.size = size self.subsample_size = size if subsample_size is None else subsample_size if dim is not None and dim >= 0: raise ValueError('dim arg must be negative.') self.dim = dim self._validate_and_set_dim() super(plate, self).__init__() def _validate_and_set_dim(self): msg = { 'type': 'plate', 'fn': identity, 'name':, 'args': (None,), 'kwargs': {}, 'value': None, 'scale': 1.0, 'cond_indep_stack': [], } apply_stack(msg) cond_indep_stack = msg['cond_indep_stack'] occupied_dims = {f.dim for f in cond_indep_stack} dim = -1 while True: if dim not in occupied_dims: break dim -= 1 if self.dim is None: self.dim = dim else: assert self.dim not in occupied_dims @staticmethod def _get_batch_shape(cond_indep_stack): n_dims = max(-f.dim for f in cond_indep_stack) batch_shape = [1] * n_dims for f in cond_indep_stack: batch_shape[f.dim] = f.size return tuple(batch_shape) def process_message(self, msg): cond_indep_stack = msg['cond_indep_stack'] frame = CondIndepStackFrame(, self.dim, self.subsample_size) cond_indep_stack.append(frame) expected_shape = self._get_batch_shape(cond_indep_stack) dist_batch_shape = msg['fn'].batch_shape if msg['type'] == 'sample' else () overlap_idx = len(expected_shape) - len(dist_batch_shape) if overlap_idx < 0: raise ValueError('Expected dimensions within plate = {}, which is less than the ' 'distribution\'s batch shape = {}.'.format(len(expected_shape), len(dist_batch_shape))) trailing_shape = expected_shape[overlap_idx:] # e.g. distribution with batch shape (1, 5) cannot be broadcast to (5, 5) broadcast_shape = lax.broadcast_shapes(trailing_shape, dist_batch_shape) if broadcast_shape != dist_batch_shape: raise ValueError('Distribution batch shape = {} cannot be broadcast up to {}. ' 'Consider using unbatched distributions.' .format(dist_batch_shape, broadcast_shape)) batch_shape = expected_shape[:overlap_idx] if 'sample_shape' in msg['kwargs']: batch_shape = lax.broadcast_shapes(msg['kwargs']['sample_shape'], batch_shape) msg['kwargs']['sample_shape'] = batch_shape msg['scale'] = msg['scale'] * self.size / self.subsample_size