def _prepare_args(target_log_prob_fn,
state,
step_size,
momentum_distribution,
target_log_prob=None,
grads_target_log_prob=None,
maybe_expand=False,
state_gradients_are_stopped=False):
"""Helper which processes input args to meet list-like assumptions."""
state_parts, _ = mcmc_util.prepare_state_parts(state, name='current_state')
if state_gradients_are_stopped:
state_parts = [tf.stop_gradient(x) for x in state_parts]
target_log_prob, grads_target_log_prob = mcmc_util.maybe_call_fn_and_grads(
target_log_prob_fn, state_parts, target_log_prob,
grads_target_log_prob)
step_sizes, _ = mcmc_util.prepare_state_parts(step_size,
dtype=target_log_prob.dtype,
name='step_size')
# Default momentum distribution is None, but if `store_parameters_in_results`
# is true, then `momentum_distribution` defaults to an empty list
if momentum_distribution is None or isinstance(momentum_distribution,
list):
batch_rank = ps.rank(target_log_prob)
def _batched_isotropic_normal_like(state_part):
event_ndims = ps.rank(state_part) - batch_rank
return independent.Independent(
normal.Normal(ps.zeros_like(state_part, tf.float32), 1.),
reinterpreted_batch_ndims=event_ndims)
momentum_distribution = jds.JointDistributionSequential([
_batched_isotropic_normal_like(state_part)
for state_part in state_parts
])
# The momentum will get "maybe listified" to zip with the state parts,
# and this step makes sure that the momentum distribution will have the
# same "maybe listified" underlying shape.
if not mcmc_util.is_list_like(momentum_distribution.dtype):
momentum_distribution = jds.JointDistributionSequential(
[momentum_distribution])
if len(step_sizes) == 1:
step_sizes *= len(state_parts)
if len(state_parts) != len(step_sizes):
raise ValueError(
'There should be exactly one `step_size` or it should '
'have same length as `current_state`.')
def maybe_flatten(x):
return x if maybe_expand or mcmc_util.is_list_like(state) else x[0]
return [
maybe_flatten(state_parts),
maybe_flatten(step_sizes),
momentum_distribution,
target_log_prob,
grads_target_log_prob,
]
def _prepare_args(target_log_prob_fn,
state,
step_size,
target_log_prob=None,
grads_target_log_prob=None,
maybe_expand=False,
state_gradients_are_stopped=False):
"""Helper which processes input args to meet list-like assumptions."""
state_parts, _ = mcmc_util.prepare_state_parts(state, name='current_state')
if state_gradients_are_stopped:
state_parts = [tf.stop_gradient(x) for x in state_parts]
target_log_prob, grads_target_log_prob = mcmc_util.maybe_call_fn_and_grads(
target_log_prob_fn, state_parts, target_log_prob,
grads_target_log_prob)
step_sizes, _ = mcmc_util.prepare_state_parts(step_size,
dtype=target_log_prob.dtype,
name='step_size')
if len(step_sizes) == 1:
step_sizes *= len(state_parts)
if len(state_parts) != len(step_sizes):
raise ValueError(
'There should be exactly one `step_size` or it should '
'have same length as `current_state`.')
def maybe_flatten(x):
return x if maybe_expand or mcmc_util.is_list_like(state) else x[0]
return [
maybe_flatten(state_parts),
maybe_flatten(step_sizes),
target_log_prob,
grads_target_log_prob,
]
def _swap_then_retemper(x):
x, is_multipart = mcmc_util.prepare_state_parts(x)
it_ratio_ = mcmc_util.left_justified_expand_dims_like(it_ratio, x[0])
x = [swap_tensor_fn(x_part) * it_ratio_ for x_part in x]
if not is_multipart:
x = x[0]
return x
def _prepare_args(target_log_prob_fn,
state,
step_size,
momentum_distribution,
target_log_prob=None,
grads_target_log_prob=None,
maybe_expand=False,
state_gradients_are_stopped=False,
experimental_shard_axis_names=None):
"""Helper which processes input args to meet list-like assumptions."""
state_parts, _ = mcmc_util.prepare_state_parts(state, name='current_state')
if state_gradients_are_stopped:
state_parts = [tf.stop_gradient(x) for x in state_parts]
target_log_prob, grads_target_log_prob = mcmc_util.maybe_call_fn_and_grads(
target_log_prob_fn, state_parts, target_log_prob,
grads_target_log_prob)
step_sizes, _ = mcmc_util.prepare_state_parts(step_size,
dtype=target_log_prob.dtype,
name='step_size')
# Default momentum distribution is None
if momentum_distribution is None:
momentum_distribution = pu.make_momentum_distribution(
state_parts,
ps.shape(target_log_prob),
shard_axis_names=experimental_shard_axis_names)
momentum_distribution = pu.maybe_make_list_and_batch_broadcast(
momentum_distribution, ps.shape(target_log_prob))
if len(step_sizes) == 1:
step_sizes *= len(state_parts)
if len(state_parts) != len(step_sizes):
raise ValueError(
'There should be exactly one `step_size` or it should '
'have same length as `current_state`.')
def maybe_flatten(x):
return x if maybe_expand or mcmc_util.is_list_like(state) else x[0]
return [
maybe_flatten(state_parts),
maybe_flatten(step_sizes),
momentum_distribution,
target_log_prob,
grads_target_log_prob,
]
def _prepare_step_size(step_size, dtype, n_state_parts):
step_sizes, _ = mcmc_util.prepare_state_parts(
step_size, dtype=dtype, name='step_size')
if len(step_sizes) == 1:
step_sizes *= n_state_parts
if n_state_parts != len(step_sizes):
raise ValueError('There should be exactly one `step_size` or it should '
'have same length as `current_state`.')
return step_sizes
def bootstrap_results(self, init_state):
with tf.name_scope(
mcmc_util.make_name(self.name, 'hmc', 'bootstrap_results')):
init_state, _ = mcmc_util.prepare_state_parts(init_state)
if self.state_gradients_are_stopped:
init_state = [tf.stop_gradient(x) for x in init_state]
[
init_target_log_prob,
init_grads_target_log_prob,
] = mcmc_util.maybe_call_fn_and_grads(self.target_log_prob_fn,
init_state)
if self._store_parameters_in_results:
return UncalibratedHamiltonianMonteCarloKernelResults(
log_acceptance_correction=tf.zeros_like(
init_target_log_prob),
target_log_prob=init_target_log_prob,
grads_target_log_prob=init_grads_target_log_prob,
initial_momentum=tf.nest.map_structure(
tf.zeros_like, init_state),
final_momentum=tf.nest.map_structure(
tf.zeros_like, init_state),
# TODO(b/142590314): Try to use the following code once we commit to
# a tensorization policy.
# step_size=mcmc_util.prepare_state_parts(
# self.step_size,
# dtype=init_target_log_prob.dtype,
# name='step_size')[0],
step_size=tf.nest.map_structure(
lambda x: tf.convert_to_tensor( # pylint: disable=g-long-lambda
x,
dtype=init_target_log_prob.dtype,
name='step_size'),
self.step_size),
num_leapfrog_steps=tf.convert_to_tensor(
self.num_leapfrog_steps,
dtype=tf.int32,
name='num_leapfrog_steps'))
else:
return UncalibratedHamiltonianMonteCarloKernelResults(
log_acceptance_correction=tf.zeros_like(
init_target_log_prob),
target_log_prob=init_target_log_prob,
grads_target_log_prob=init_grads_target_log_prob,
initial_momentum=tf.nest.map_structure(
tf.zeros_like, init_state),
final_momentum=tf.nest.map_structure(
tf.zeros_like, init_state),
step_size=[],
num_leapfrog_steps=[])
def bootstrap_results(self, init_state):
"""Creates initial `previous_kernel_results` using a supplied `state`."""
with tf.name_scope(self.name + '.bootstrap_results'):
if not tf.nest.is_nested(init_state):
init_state = [init_state]
state_parts, _ = mcmc_util.prepare_state_parts(init_state,
name='current_state')
current_target_log_prob, current_grads_log_prob = mcmc_util.maybe_call_fn_and_grads(
self.target_log_prob_fn, state_parts)
# Confirm that the step size is compatible with the state parts.
_ = _prepare_step_size(
self.step_size, current_target_log_prob.dtype, len(init_state))
momentum_distribution = self.momentum_distribution
if momentum_distribution is None:
momentum_distribution = pu.make_momentum_distribution(
state_parts, ps.shape(current_target_log_prob),
shard_axis_names=self.experimental_shard_axis_names)
momentum_distribution = pu.maybe_make_list_and_batch_broadcast(
momentum_distribution, ps.shape(current_target_log_prob))
momentum_parts = momentum_distribution.sample(seed=samplers.zeros_seed())
return PreconditionedNUTSKernelResults(
target_log_prob=current_target_log_prob,
grads_target_log_prob=current_grads_log_prob,
step_size=tf.nest.map_structure(
lambda x: tf.convert_to_tensor( # pylint: disable=g-long-lambda
x,
dtype=current_target_log_prob.dtype,
name='step_size'),
self.step_size),
log_accept_ratio=tf.zeros_like(
current_target_log_prob, name='log_accept_ratio'),
leapfrogs_taken=tf.zeros_like(
current_target_log_prob,
dtype=TREE_COUNT_DTYPE,
name='leapfrogs_taken'),
is_accepted=tf.zeros_like(
current_target_log_prob, dtype=tf.bool, name='is_accepted'),
reach_max_depth=tf.zeros_like(
current_target_log_prob, dtype=tf.bool, name='reach_max_depth'),
has_divergence=tf.zeros_like(
current_target_log_prob, dtype=tf.bool, name='has_divergence'),
energy=compute_hamiltonian(current_target_log_prob, momentum_parts,
momentum_distribution),
momentum_distribution=momentum_distribution,
# Allow room for one_step's seed.
seed=samplers.zeros_seed(),
)
def bootstrap_results(self, init_state):
with tf.name_scope(
mcmc_util.make_name(self.name, 'phmc', 'bootstrap_results')):
result = super(UncalibratedPreconditionedHamiltonianMonteCarlo,
self).bootstrap_results(init_state)
state_parts, _ = mcmc_util.prepare_state_parts(
init_state, name='current_state')
target_log_prob = self.target_log_prob_fn(*state_parts)
if (not self._store_parameters_in_results
or self.momentum_distribution is None):
momentum_distribution = pu.make_momentum_distribution(
state_parts, ps.shape(target_log_prob))
else:
momentum_distribution = pu.maybe_make_list_and_batch_broadcast(
self.momentum_distribution, ps.shape(target_log_prob))
result = UncalibratedPreconditionedHamiltonianMonteCarloKernelResults(
**result._asdict(), # pylint: disable=protected-access
momentum_distribution=momentum_distribution)
return result
def bootstrap_results(self, init_state):
"""Returns an object with the same type as returned by `one_step`.
Args:
init_state: `Tensor` or Python `list` of `Tensor`s representing the
initial state(s) of the Markov chain(s).
Returns:
kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
`Tensor`s representing internal calculations made within this function.
This inculdes replica states.
"""
with tf.name_scope(
mcmc_util.make_name(self.name, 'remc', 'bootstrap_results')):
init_state, unused_is_multipart_state = mcmc_util.prepare_state_parts(
init_state)
inverse_temperatures = tf.convert_to_tensor(
self.inverse_temperatures, name='inverse_temperatures')
if self._state_includes_replicas:
it_n_replica = inverse_temperatures.shape[0]
state_n_replica = init_state[0].shape[0]
if ((it_n_replica is not None)
and (state_n_replica is not None)
and (it_n_replica != state_n_replica)):
raise ValueError(
'Number of replicas implied by initial state ({}) must equal '
'number of replicas implied by inverse_temperatures ({}), but '
'did not'.format(it_n_replica, state_n_replica))
# We will now replicate each of a possible batch of initial stats, one for
# each inverse_temperature. So if init_state=[x, y] of shapes [Sx, Sy]
# then the new shape is [(T, Sx), (T, Sy)] where (a, b) means
# concatenation and T=shape(inverse_temperature).
num_replica = ps.size0(inverse_temperatures)
replica_shape = ps.convert_to_shape_tensor([num_replica])
if self._state_includes_replicas:
replica_states = init_state
else:
replica_states = [
tf.broadcast_to( # pylint: disable=g-complex-comprehension
x,
ps.concat([replica_shape, ps.shape(x)], axis=0),
name='replica_states') for x in init_state
]
target_log_prob_for_inner_kernel = _make_replica_target_log_prob_fn(
target_log_prob_fn=self.target_log_prob_fn,
inverse_temperatures=inverse_temperatures,
untempered_log_prob_fn=self.untempered_log_prob_fn,
tempered_log_prob_fn=self.tempered_log_prob_fn,
)
# TODO(b/159636942): Clean up the helpful error msg after 2020-11-10.
try:
inner_kernel = self.make_kernel_fn( # pylint: disable=not-callable
target_log_prob_for_inner_kernel)
except TypeError as e:
if 'argument' not in str(e):
raise
raise TypeError(
'`ReplicaExchangeMC`s `make_kernel_fn` no longer receives a second '
'(`seed`) argument. `TransitionKernel` instances now receive seeds '
'via `one_step`.')
replica_results = inner_kernel.bootstrap_results(replica_states)
pre_swap_replica_target_log_prob = _get_field(
replica_results, 'target_log_prob')
replica_and_batch_shape = ps.shape(
pre_swap_replica_target_log_prob)
batch_shape = replica_and_batch_shape[1:]
inverse_temperatures = bu.left_justified_broadcast_to(
inverse_temperatures, replica_and_batch_shape)
# Pretend we did a "null swap", which will always be accepted.
swaps = bu.left_justified_broadcast_to(tf.range(num_replica),
replica_and_batch_shape)
# is_swap_accepted.shape = [n_replica, n_replica] + batch_shape.
is_swap_accepted = distribution_util.rotate_transpose(tf.eye(
num_replica, batch_shape=batch_shape, dtype=tf.bool),
shift=2)
return ReplicaExchangeMCKernelResults(
post_swap_replica_states=replica_states,
pre_swap_replica_results=replica_results,
post_swap_replica_results=_set_swapped_fields_to_nan(
replica_results),
is_swap_proposed=is_swap_accepted,
is_swap_accepted=is_swap_accepted,
is_swap_proposed_adjacent=_sub_diag(is_swap_accepted),
is_swap_accepted_adjacent=_sub_diag(is_swap_accepted),
inverse_temperatures=self.inverse_temperatures,
swaps=swaps,
step_count=tf.zeros(shape=(), dtype=tf.int32),
seed=samplers.zeros_seed(),
potential_energy=tf.zeros_like(
pre_swap_replica_target_log_prob),
)
def bootstrap_results(self, init_state):
"""Returns an object with the same type as returned by `one_step`.
Args:
init_state: `Tensor` or Python `list` of `Tensor`s representing the
initial state(s) of the Markov chain(s).
Returns:
kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
`Tensor`s representing internal calculations made within this function.
This inculdes replica states.
"""
with tf.name_scope(
mcmc_util.make_name(self.name, 'remc', 'bootstrap_results')):
init_state, unused_is_multipart_state = mcmc_util.prepare_state_parts(
init_state)
inverse_temperatures = tf.convert_to_tensor(
self.inverse_temperatures, name='inverse_temperatures')
if self._state_includes_replicas:
it_n_replica = inverse_temperatures.shape[0]
state_n_replica = init_state[0].shape[0]
if ((it_n_replica is not None)
and (state_n_replica is not None)
and (it_n_replica != state_n_replica)):
raise ValueError(
'Number of replicas implied by initial state ({}) must equal '
'number of replicas implied by inverse_temperatures ({}), but '
'did not'.format(it_n_replica, state_n_replica))
# We will now replicate each of a possible batch of initial stats, one for
# each inverse_temperature. So if init_state=[x, y] of shapes [Sx, Sy]
# then the new shape is [(T, Sx), (T, Sy)] where (a, b) means
# concatenation and T=shape(inverse_temperature).
num_replica = ps.size0(inverse_temperatures)
replica_shape = tf.convert_to_tensor([num_replica])
if self._state_includes_replicas:
replica_states = init_state
else:
replica_states = [
tf.broadcast_to( # pylint: disable=g-complex-comprehension
x,
ps.concat([replica_shape, ps.shape(x)], axis=0),
name='replica_states') for x in init_state
]
target_log_prob_for_inner_kernel = _make_replica_target_log_prob_fn(
self.target_log_prob_fn, inverse_temperatures)
# Seed handling complexity is due to users possibly expecting an old-style
# stateful seed to be passed to `self.make_kernel_fn`.
# In other words:
# - We try `make_kernel_fn` without a seed first; this is the future. The
# kernel will receive a seed later, as part of `one_step`.
# - If the user code doesn't like that (Python complains about a missing
# required argument), we fall back to the previous behavior and warn.
try:
inner_kernel = self.make_kernel_fn( # pylint: disable=not-callable
target_log_prob_for_inner_kernel)
except TypeError as e:
if 'argument' not in str(e):
raise
warnings.warn(
'The second (`seed`) argument to `ReplicaExchangeMC`s '
'`make_kernel_fn` is deprecated. `TransitionKernel` instances now '
'receive seeds via `bootstrap_results` and `one_step`. This '
'fallback may become an error 2020-09-20.')
inner_kernel = self.make_kernel_fn( # pylint: disable=not-callable
target_log_prob_for_inner_kernel, self._seed_stream())
replica_results = inner_kernel.bootstrap_results(replica_states)
pre_swap_replica_target_log_prob = _get_field(
replica_results, 'target_log_prob')
replica_and_batch_shape = ps.shape(
pre_swap_replica_target_log_prob)
batch_shape = replica_and_batch_shape[1:]
inverse_temperatures = mcmc_util.left_justified_broadcast_to(
inverse_temperatures, replica_and_batch_shape)
# Pretend we did a "null swap", which will always be accepted.
swaps = mcmc_util.left_justified_broadcast_to(
tf.range(num_replica), replica_and_batch_shape)
# is_swap_accepted.shape = [n_replica, n_replica] + batch_shape.
is_swap_accepted = distribution_util.rotate_transpose(tf.eye(
num_replica, batch_shape=batch_shape, dtype=tf.bool),
shift=2)
post_swap_replica_results = _make_post_swap_replica_results(
replica_results,
inverse_temperatures,
inverse_temperatures,
is_swap_accepted[0],
lambda x: x,
)
return ReplicaExchangeMCKernelResults(
post_swap_replica_states=replica_states,
pre_swap_replica_results=replica_results,
post_swap_replica_results=post_swap_replica_results,
is_swap_proposed=is_swap_accepted,
is_swap_accepted=is_swap_accepted,
is_swap_proposed_adjacent=_sub_diag(is_swap_accepted),
is_swap_accepted_adjacent=_sub_diag(is_swap_accepted),
inverse_temperatures=self.inverse_temperatures,
swaps=swaps,
step_count=tf.zeros(shape=(), dtype=tf.int32),
seed=samplers.zeros_seed(),
)
def bootstrap_results(self, init_state):
"""Returns an object with the same type as returned by `one_step`.
Args:
init_state: `Tensor` or Python `list` of `Tensor`s representing the
initial state(s) of the Markov chain(s).
Returns:
kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
`Tensor`s representing internal calculations made within this function.
This inculdes replica states.
"""
with tf.name_scope(
mcmc_util.make_name(self.name, 'remc', 'bootstrap_results')):
init_state, unused_is_multipart_state = mcmc_util.prepare_state_parts(
init_state)
inverse_temperatures = tf.convert_to_tensor(
self.inverse_temperatures, name='inverse_temperatures')
# We will now replicate each of a possible batch of initial stats, one for
# each inverse_temperature. So if init_state=[x, y] of shapes [Sx, Sy]
# then the new shape is [(T, Sx), (T, Sy)] where (a, b) means
# concatenation and T=shape(inverse_temperature).
num_replica = prefer_static.size0(inverse_temperatures)
replica_shape = tf.convert_to_tensor([num_replica])
replica_states = [
tf.broadcast_to( # pylint: disable=g-complex-comprehension
x,
prefer_static.concat(
[replica_shape, prefer_static.shape(x)], axis=0),
name='replica_states') for x in init_state
]
inner_kernel = self.make_kernel_fn( # pylint: disable=not-callable
_make_replica_target_log_prob_fn(self.target_log_prob_fn,
inverse_temperatures),
self._seed_stream())
replica_results = inner_kernel.bootstrap_results(replica_states)
pre_swap_replica_target_log_prob = _get_field(
replica_results, 'target_log_prob')
replica_and_batch_shape = prefer_static.shape(
pre_swap_replica_target_log_prob)
batch_shape = replica_and_batch_shape[1:]
inverse_temperatures = mcmc_util.left_justified_broadcast_to(
inverse_temperatures, replica_and_batch_shape)
# Pretend we did a "null swap", which will always be accepted.
swaps = mcmc_util.left_justified_broadcast_to(
tf.range(num_replica), replica_and_batch_shape)
# is_swap_accepted.shape = [n_replica, n_replica] + batch_shape.
is_swap_accepted = distribution_util.rotate_transpose(tf.eye(
num_replica, batch_shape=batch_shape, dtype=tf.bool),
shift=2)
post_swap_replica_results = _make_post_swap_replica_results(
replica_results,
inverse_temperatures,
inverse_temperatures,
is_swap_accepted[0],
lambda x: x,
)
return ReplicaExchangeMCKernelResults(
post_swap_replica_states=replica_states,
pre_swap_replica_results=replica_results,
post_swap_replica_results=post_swap_replica_results,
is_swap_proposed=is_swap_accepted,
is_swap_accepted=is_swap_accepted,
is_swap_proposed_adjacent=_sub_diag(is_swap_accepted),
is_swap_accepted_adjacent=_sub_diag(is_swap_accepted),
inverse_temperatures=self.inverse_temperatures,
swaps=swaps,
)
def bootstrap_results(self, init_state):
"""Creates initial `previous_kernel_results` using a supplied `state`."""
with tf.name_scope(self.name + '.bootstrap_results'):
if not tf.nest.is_nested(init_state):
init_state = [init_state]
# Padding the step_size so it is compatable with the states
step_size = self.step_size
if len(step_size) == 1:
step_size = step_size * len(init_state)
if len(step_size) != len(init_state):
raise ValueError(
'Expected either one step size or {} (size of '
'`init_state`), but found {}'.format(
len(init_state), len(step_size)))
state_parts, _ = mcmc_util.prepare_state_parts(
init_state, name='current_state')
current_target_log_prob, current_grads_log_prob = mcmc_util.maybe_call_fn_and_grads(
self.target_log_prob_fn, state_parts)
momentum_distribution = self.momentum_distribution
if momentum_distribution is None:
momentum_distribution = pu.make_momentum_distribution(
state_parts, ps.shape(current_target_log_prob))
momentum_distribution = pu.maybe_make_list_and_batch_broadcast(
momentum_distribution, ps.shape(current_target_log_prob))
momentum_parts = momentum_distribution.sample()
def _init(shape_and_dtype):
"""Allocate TensorArray for storing state and velocity."""
return [ # pylint: disable=g-complex-comprehension
ps.zeros(ps.concat([[max(self._write_instruction) + 1], s],
axis=0),
dtype=d) for (s, d) in shape_and_dtype
]
get_shapes_and_dtypes = lambda x: [
(ps.shape(x_), x_.dtype) # pylint: disable=g-long-lambda
for x_ in x
]
velocity_state_memory = VelocityStateSwap(
velocity_swap=_init(get_shapes_and_dtypes(momentum_parts)),
state_swap=_init(get_shapes_and_dtypes(init_state)))
return PreconditionedNUTSKernelResults(
target_log_prob=current_target_log_prob,
grads_target_log_prob=current_grads_log_prob,
velocity_state_memory=velocity_state_memory,
step_size=step_size,
log_accept_ratio=tf.zeros_like(current_target_log_prob,
name='log_accept_ratio'),
leapfrogs_taken=tf.zeros_like(current_target_log_prob,
dtype=TREE_COUNT_DTYPE,
name='leapfrogs_taken'),
is_accepted=tf.zeros_like(current_target_log_prob,
dtype=tf.bool,
name='is_accepted'),
reach_max_depth=tf.zeros_like(current_target_log_prob,
dtype=tf.bool,
name='reach_max_depth'),
has_divergence=tf.zeros_like(current_target_log_prob,
dtype=tf.bool,
name='has_divergence'),
energy=compute_hamiltonian(current_target_log_prob,
momentum_parts,
momentum_distribution),
momentum_distribution=momentum_distribution,
# Allow room for one_step's seed.
seed=samplers.zeros_seed(),
)
def _prepare_args(target_log_prob_fn,
state,
step_size,
momentum_distribution,
target_log_prob=None,
grads_target_log_prob=None,
maybe_expand=False,
state_gradients_are_stopped=False):
"""Helper which processes input args to meet list-like assumptions."""
state_parts, _ = mcmc_util.prepare_state_parts(state, name='current_state')
if state_gradients_are_stopped:
state_parts = [tf.stop_gradient(x) for x in state_parts]
target_log_prob, grads_target_log_prob = mcmc_util.maybe_call_fn_and_grads(
target_log_prob_fn, state_parts, target_log_prob, grads_target_log_prob)
step_sizes, _ = mcmc_util.prepare_state_parts(
step_size, dtype=target_log_prob.dtype, name='step_size')
# Default momentum distribution is None, but if `store_parameters_in_results`
# is true, then `momentum_distribution` defaults to DefaultStandardNormal().
if (momentum_distribution is None or
isinstance(momentum_distribution, DefaultStandardNormal)):
batch_rank = ps.rank(target_log_prob)
def _batched_isotropic_normal_like(state_part):
return sample.Sample(
normal.Normal(ps.zeros([], dtype=state_part.dtype), 1.),
ps.shape(state_part)[batch_rank:])
momentum_distribution = jds.JointDistributionSequential(
[_batched_isotropic_normal_like(state_part)
for state_part in state_parts])
# The momentum will get "maybe listified" to zip with the state parts,
# and this step makes sure that the momentum distribution will have the
# same "maybe listified" underlying shape.
if not mcmc_util.is_list_like(momentum_distribution.dtype):
momentum_distribution = jds.JointDistributionSequential(
[momentum_distribution])
# If all underlying distributions are independent, we can offer some help.
# This code will also trigger for the output of the two blocks above.
if (isinstance(momentum_distribution, jds.JointDistributionSequential) and
not any(callable(dist_fn) for dist_fn in momentum_distribution.model)):
batch_shape = ps.shape(target_log_prob)
momentum_distribution = momentum_distribution.copy(model=[
batch_broadcast.BatchBroadcast(md, to_shape=batch_shape)
for md in momentum_distribution.model
])
if len(step_sizes) == 1:
step_sizes *= len(state_parts)
if len(state_parts) != len(step_sizes):
raise ValueError('There should be exactly one `step_size` or it should '
'have same length as `current_state`.')
def maybe_flatten(x):
return x if maybe_expand or mcmc_util.is_list_like(state) else x[0]
return [
maybe_flatten(state_parts),
maybe_flatten(step_sizes),
momentum_distribution,
target_log_prob,
grads_target_log_prob,
]
