def test_selects_batch_members_from_list_of_arrays(self):
# Shape of each array: [2, 3] = [batch_size, event_size]
# This test verifies that is_accepted selects batch members, despite the
# "usual" broadcasting being applied on the right first (event first).
zeros_states = [np.zeros((2, 3))]
ones_states = [np.ones((2, 3))]
chosen = util.choose(
tf.constant([True, False]),
zeros_states,
ones_states)
chosen_ = self.evaluate(chosen)
# Make sure outer list wasn't interpreted as a dimenion of an array.
self.assertIsInstance(chosen_, list)
expected_array = np.array([
[0., 0., 0.], # zeros_states selected for first batch
[1., 1., 1.], # ones_states selected for second
])
expected = [expected_array]
self.assertAllEqual(expected, chosen_)
def _swap_tensor(x):
return mcmc_util.choose(
is_swap_accepted_mask,
mcmc_util.index_remapping_gather(x, swaps), x)
def one_step(self, current_state, previous_kernel_results):
with tf.name_scope(
mcmc_util.make_name(self.name, 'simple_step_size_adaptation',
'one_step')):
# Set the step_size.
inner_results = self.step_size_setter_fn(
previous_kernel_results.inner_results,
previous_kernel_results.new_step_size)
# Step the inner kernel.
new_state, new_inner_results = self.inner_kernel.one_step(
current_state, inner_results)
# Get the new step size.
log_accept_prob = self.log_accept_prob_getter_fn(new_inner_results)
log_target_accept_prob = tf.math.log(
tf.cast(previous_kernel_results.target_accept_prob,
dtype=log_accept_prob.dtype))
state_parts = tf.nest.flatten(current_state)
step_size = self.step_size_getter_fn(new_inner_results)
step_size_parts = tf.nest.flatten(step_size)
log_accept_prob_rank = prefer_static.rank(log_accept_prob)
new_step_size_parts = []
for step_size_part, state_part in zip(step_size_parts,
state_parts):
# Compute new step sizes for each step size part. If step size part has
# smaller rank than the corresponding state part, then the difference is
# averaged away in the log accept prob.
#
# Example:
#
# state_part has shape [2, 3, 4, 5]
# step_size_part has shape [1, 4, 1]
# log_accept_prob has shape [2, 3, 4]
#
# Since step size has 1 rank fewer than the state, we reduce away the
# leading dimension of log_accept_prob to get a Tensor with shape [3,
# 4]. Next, since log_accept_prob must broadcast into step_size_part on
# the left, we reduce the dimensions where their shapes differ, to get a
# Tensor with shape [1, 4], which now is compatible with the leading
# dimensions of step_size_part.
#
# There is a subtlety here in that step_size_parts might be a length-1
# list, which means that we'll be "structure-broadcasting" it for all
# the state parts (see logic in, e.g., hmc.py). In this case we must
# assume that that the lone step size provided broadcasts with the event
# dims of each state part. This means that either step size has no
# dimensions corresponding to chain dimensions, or all states are of the
# same shape. For the former, we want to reduce over all chain
# dimensions. For the later, we want to use the same logic as in the
# non-structure-broadcasted case.
#
# It turns out we can compute the reduction dimensions for both cases
# uniformly by taking the rank of any state part. This obviously works
# in the second case (where all state ranks are the same). In the first
# case, all state parts have the rank L + D_i + B, where L is the rank
# of log_accept_prob, D_i is the non-shared dimensions amongst all
# states, and B are the shared dimensions of all the states, which are
# equal to the step size. When we subtract B, we will always get a
# number >= L, which means we'll get the full reduction we want.
num_reduce_dims = prefer_static.minimum(
log_accept_prob_rank,
prefer_static.rank(state_part) -
prefer_static.rank(step_size_part))
reduced_log_accept_prob = reduce_logmeanexp(
log_accept_prob, axis=prefer_static.range(num_reduce_dims))
# reduced_log_accept_prob must broadcast into step_size_part on the
# left, so we do an additional reduction over dimensions where their
# shapes differ.
reduce_indices = get_differing_dims(reduced_log_accept_prob,
step_size_part)
reduced_log_accept_prob = reduce_logmeanexp(
reduced_log_accept_prob,
axis=reduce_indices,
keepdims=True)
one_plus_adaptation_rate = 1. + tf.cast(
previous_kernel_results.adaptation_rate,
dtype=step_size_part.dtype)
new_step_size_part = mcmc_util.choose(
reduced_log_accept_prob > log_target_accept_prob,
step_size_part * one_plus_adaptation_rate,
step_size_part / one_plus_adaptation_rate)
new_step_size_parts.append(
tf.where(
previous_kernel_results.step <
self.num_adaptation_steps, new_step_size_part,
step_size_part))
new_step_size = tf.nest.pack_sequence_as(step_size,
new_step_size_parts)
return new_state, previous_kernel_results._replace(
inner_results=new_inner_results,
step=1 + previous_kernel_results.step,
new_step_size=new_step_size)
def _swap(is_exchange_accepted, x, y):
"""Swap batches of x, y where accepted."""
with tf.compat.v1.name_scope('swap_where_exchange_accepted'):
new_x = mcmc_util.choose(is_exchange_accepted, y, x)
new_y = mcmc_util.choose(is_exchange_accepted, x, y)
return new_x, new_y
def one_step(self, current_state, previous_kernel_results, seed=None):
"""Takes one step of the TransitionKernel.
Args:
current_state: `Tensor` or Python `list` of `Tensor`s representing the
current state(s) of the Markov chain(s).
previous_kernel_results: A (possibly nested) `tuple`, `namedtuple` or
`list` of `Tensor`s representing internal calculations made within the
previous call to this function (or as returned by `bootstrap_results`).
seed: PRNG seed; see `tfp.random.sanitize_seed` for details.
Returns:
next_state: `Tensor` or Python `list` of `Tensor`s representing the
next state(s) of the Markov chain(s).
kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
`Tensor`s representing internal calculations made within this function.
Raises:
ValueError: if `inner_kernel` results doesn't contain the member
"target_log_prob".
"""
is_seeded = seed is not None
seed = samplers.sanitize_seed(seed) # Retain for diagnostics.
proposal_seed, acceptance_seed = samplers.split_seed(seed)
with tf.name_scope(mcmc_util.make_name(self.name, 'mh', 'one_step')):
# Take one inner step.
inner_kwargs = dict(seed=proposal_seed) if is_seeded else {}
[
proposed_state,
proposed_results,
] = self.inner_kernel.one_step(
current_state, previous_kernel_results.accepted_results,
**inner_kwargs)
if mcmc_util.is_list_like(current_state):
proposed_state = tf.nest.pack_sequence_as(
current_state, proposed_state)
if (not has_target_log_prob(proposed_results)
or not has_target_log_prob(
previous_kernel_results.accepted_results)):
raise ValueError('"target_log_prob" must be a member of '
'`inner_kernel` results.')
# Compute log(acceptance_ratio).
to_sum = [
proposed_results.target_log_prob,
-previous_kernel_results.accepted_results.target_log_prob
]
try:
if (not mcmc_util.is_list_like(
proposed_results.log_acceptance_correction)
or proposed_results.log_acceptance_correction):
to_sum.append(proposed_results.log_acceptance_correction)
except AttributeError:
warnings.warn(
'Supplied inner `TransitionKernel` does not have a '
'`log_acceptance_correction`. Assuming its value is `0.`')
log_accept_ratio = mcmc_util.safe_sum(
to_sum, name='compute_log_accept_ratio')
# If proposed state reduces likelihood: randomly accept.
# If proposed state increases likelihood: always accept.
# I.e., u < min(1, accept_ratio), where u ~ Uniform[0,1)
# ==> log(u) < log_accept_ratio
log_uniform = tf.math.log(
samplers.uniform(shape=prefer_static.shape(
proposed_results.target_log_prob),
dtype=dtype_util.base_dtype(
proposed_results.target_log_prob.dtype),
seed=acceptance_seed))
is_accepted = log_uniform < log_accept_ratio
next_state = mcmc_util.choose(is_accepted,
proposed_state,
current_state,
name='choose_next_state')
kernel_results = MetropolisHastingsKernelResults(
accepted_results=mcmc_util.choose(
is_accepted,
# We strip seeds when populating `accepted_results` because unlike
# other kernel result fields, seeds are not a per-chain value.
# Thus it is impossible to choose between a previously accepted
# seed value and a proposed seed, since said choice would need to
# be made on a per-chain basis.
mcmc_util.strip_seeds(proposed_results),
previous_kernel_results.accepted_results,
name='choose_inner_results'),
is_accepted=is_accepted,
log_accept_ratio=log_accept_ratio,
proposed_state=proposed_state,
proposed_results=proposed_results,
extra=[],
seed=seed,
)
return next_state, kernel_results
def one_step(self, current_state, previous_kernel_results):
"""Takes one step of the TransitionKernel.
Args:
current_state: `Tensor` or Python `list` of `Tensor`s representing the
current state(s) of the Markov chain(s).
previous_kernel_results: A (possibly nested) `tuple`, `namedtuple` or
`list` of `Tensor`s representing internal calculations made within the
previous call to this function (or as returned by `bootstrap_results`).
Returns:
next_state: `Tensor` or Python `list` of `Tensor`s representing the
next state(s) of the Markov chain(s).
kernel_results: A (possibly nested) `tuple`, `namedtuple` or `list` of
`Tensor`s representing internal calculations made within this function.
Raises:
ValueError: if `inner_kernel` results doesn't contain the member
"target_log_prob".
"""
with tf1.name_scope(name=mcmc_util.make_name(self.name, 'mh',
'one_step'),
values=[current_state, previous_kernel_results]):
# Take one inner step.
[
proposed_state,
proposed_results,
] = self.inner_kernel.one_step(
current_state, previous_kernel_results.accepted_results)
if (not has_target_log_prob(proposed_results)
or not has_target_log_prob(
previous_kernel_results.accepted_results)):
raise ValueError('"target_log_prob" must be a member of '
'`inner_kernel` results.')
# Compute log(acceptance_ratio).
to_sum = [
proposed_results.target_log_prob,
-previous_kernel_results.accepted_results.target_log_prob
]
try:
if (not mcmc_util.is_list_like(
proposed_results.log_acceptance_correction)
or proposed_results.log_acceptance_correction):
to_sum.append(proposed_results.log_acceptance_correction)
except AttributeError:
warnings.warn(
'Supplied inner `TransitionKernel` does not have a '
'`log_acceptance_correction`. Assuming its value is `0.`')
log_accept_ratio = mcmc_util.safe_sum(
to_sum, name='compute_log_accept_ratio')
# If proposed state reduces likelihood: randomly accept.
# If proposed state increases likelihood: always accept.
# I.e., u < min(1, accept_ratio), where u ~ Uniform[0,1)
# ==> log(u) < log_accept_ratio
log_uniform = tf.math.log(
tf.random.uniform(
shape=tf.shape(input=proposed_results.target_log_prob),
dtype=proposed_results.target_log_prob.dtype.base_dtype,
seed=self._seed_stream()))
is_accepted = log_uniform < log_accept_ratio
next_state = mcmc_util.choose(is_accepted,
proposed_state,
current_state,
name='choose_next_state')
kernel_results = MetropolisHastingsKernelResults(
accepted_results=mcmc_util.choose(
is_accepted,
proposed_results,
previous_kernel_results.accepted_results,
name='choose_inner_results'),
is_accepted=is_accepted,
log_accept_ratio=log_accept_ratio,
proposed_state=proposed_state,
proposed_results=proposed_results,
extra=[],
)
return next_state, kernel_results
def _do_flip(state, i):
new_state = sampler._flip_feature(state,
tf.gather(flip_idxs, i))
return mcmc_util.choose(tf.gather(should_flip, i), new_state,
state)
def one_step(self, current_state, previous_kernel_results, seed=None):
with tf.name_scope(
mcmc_util.make_name(self.name,
'gradient_based_trajectory_length_adaptation',
'one_step')):
jitter_seed, inner_seed = samplers.split_seed(seed)
dtype = previous_kernel_results.adaptation_rate.dtype
current_state = tf.nest.map_structure(
lambda x: tf.convert_to_tensor(x, dtype=dtype), current_state)
step_f = tf.cast(previous_kernel_results.step, dtype)
if self.use_halton_sequence_jitter:
trajectory_jitter = _halton_sequence(step_f)
else:
trajectory_jitter = samplers.uniform((), seed=jitter_seed, dtype=dtype)
jitter_amount = previous_kernel_results.jitter_amount
trajectory_jitter = (
trajectory_jitter * jitter_amount + (1. - jitter_amount))
adapting = previous_kernel_results.step < self.num_adaptation_steps
max_trajectory_length = tf.where(
adapting, previous_kernel_results.max_trajectory_length,
previous_kernel_results.averaged_max_trajectory_length)
jittered_trajectory_length = (max_trajectory_length * trajectory_jitter)
step_size = _ensure_step_size_is_scalar(
self.step_size_getter_fn(previous_kernel_results), self.validate_args)
num_leapfrog_steps = tf.cast(
tf.maximum(
tf.ones([], dtype),
tf.math.ceil(jittered_trajectory_length / step_size)), tf.int32)
previous_kernel_results_with_jitter = self.num_leapfrog_steps_setter_fn(
previous_kernel_results, num_leapfrog_steps)
new_state, new_inner_results = self.inner_kernel.one_step(
current_state, previous_kernel_results_with_jitter.inner_results,
inner_seed)
proposed_state = self.proposed_state_getter_fn(new_inner_results)
proposed_velocity = self.proposed_velocity_getter_fn(new_inner_results)
accept_prob = tf.exp(self.log_accept_prob_getter_fn(new_inner_results))
new_kernel_results = _update_trajectory_grad(
previous_kernel_results_with_jitter,
previous_state=current_state,
proposed_state=proposed_state,
proposed_velocity=proposed_velocity,
trajectory_jitter=trajectory_jitter,
accept_prob=accept_prob,
step_size=step_size,
criterion_fn=self.criterion_fn,
max_leapfrog_steps=self.max_leapfrog_steps)
# Undo the effect of adaptation if we're not in the burnin phase. We keep
# the criterion, however, as that's a diagnostic. We also keep the
# leapfrog steps setting, as that's an effect of jitter (and also doubles
# as a diagnostic).
criterion = new_kernel_results.criterion
new_kernel_results = mcmc_util.choose(
adapting, new_kernel_results, previous_kernel_results_with_jitter)
new_kernel_results = new_kernel_results._replace(
inner_results=new_inner_results,
step=previous_kernel_results.step + 1,
criterion=criterion)
return new_state, new_kernel_results
def one_step(self, current_state, previous_kernel_results, seed=None):
with tf.name_scope(
mcmc_util.make_name(self.name,
'diagonal_mass_matrix_adaptation',
'one_step')):
variance_parts = previous_kernel_results.running_variance
inner_results = previous_kernel_results.inner_results
# Step the inner kernel.
inner_kwargs = {} if seed is None else dict(seed=seed)
new_state, new_inner_results = self.inner_kernel.one_step(
current_state, inner_results, **inner_kwargs)
def update_running_variance():
diags = [
variance_part.variance()
for variance_part in variance_parts
]
new_state_parts = tf.nest.flatten(new_state)
new_variance_parts = []
for variance_part, diag, state_part in zip(
variance_parts, diags, new_state_parts):
# Compute new variance for each variance part, accounting for partial
# batching of the variance calculation across chains (ie, some, all,
# or none of the chains may share the estimated mass matrix).
#
# For example, say
#
# state_part has shape [2, 3, 4] + [5, 6] (batch + event)
# variance_part has shape [4] + [5, 6]
# log_prob has shape [2, 3, 4]
#
# i.e., we have a batch of chains of shape [2, 3, 4], and 4 mass
# matrices, each being shared across a [2, 3]-batch of chains. Note
# this division is inferred from the shapes of the state part, the
# log_prob, and the user-provided initial running variances.
#
# Until RunningVariance supports rank > 1 chunking, we need to flatten
# the states that go into updating the variance estimates. In the
# above example, `state_part` will be reshaped to `[6, 4, 5, 6]`, and
# fed to `RunningVariance.update(state_part, axis=0)`, recording
# 6 new observations in the running variance calculation.
# `RunningVariance.variance()` will then be of shape `[4, 5, 6]`, and
# the resulting momentum distribution will have batch shape of
# `[2, 3, 4]` and event_shape of `[5, 6]`, matching the state_part.
state_rank = ps.rank(state_part)
variance_rank = ps.rank(diag)
num_reduce_dims = state_rank - variance_rank
state_part_shape = ps.shape(state_part)
# This reshape adds a 1 when reduce_dims==0, and collapses all the
# lead dimensions to a single one otherwise.
reshaped_state = ps.reshape(
state_part,
ps.concat([[
ps.reduce_prod(state_part_shape[:num_reduce_dims])
], state_part_shape[num_reduce_dims:]],
axis=0))
# The `axis=0` here removes the leading dimension we got from the
# reshape above, so the new_variance_parts have the correct shape
# again.
new_variance_parts.append(
variance_part.update(reshaped_state, axis=0))
return new_variance_parts
def update_momentum():
diags = [
variance_part.variance()
for variance_part in new_variance_parts
]
# Update the momentum.
prev_momentum_distribution = self.momentum_distribution_getter_fn(
new_inner_results)
new_momentum_distribution = (
preconditioning_utils.update_momentum_distribution(
prev_momentum_distribution, diags))
updated_new_inner_results = self.momentum_distribution_setter_fn(
new_inner_results, new_momentum_distribution)
return updated_new_inner_results
step = previous_kernel_results.step + 1
if self.num_estimation_steps is None:
new_variance_parts = update_running_variance()
new_inner_results = update_momentum()
else:
new_variance_parts = mcmc_util.choose(
step <= previous_kernel_results.num_estimation_steps,
update_running_variance(), variance_parts)
new_inner_results = mcmc_util.choose(
tf.equal(step,
previous_kernel_results.num_estimation_steps),
update_momentum(), new_inner_results)
new_kernel_results = previous_kernel_results._replace(
inner_results=new_inner_results,
running_variance=new_variance_parts,
step=step)
return new_state, new_kernel_results
