def _log_abs_determinant(self):
log_det = tfp_math.reduce_kahan_sum(tf.math.log(tf.math.abs(
self._diag)),
axis=[-1]).total
if dtype_util.is_complex(self.dtype):
log_det = tf.cast(log_det, dtype=self.dtype)
return log_det
def inverse_log_det_jacobian_ratio(p,
x,
q,
y,
event_ndims,
use_kahan_sum=True):
"""Computes `p.ildj(x, ndims) - q.idlj(y, ndims)`, numerically stably.
Args:
p: A bijector instance.
x: A tensor from the image of `p.forward`.
q: A bijector instance of the same type as `p`, with matching shape.
y: A tensor from the image of `q.forward`.
event_ndims: The number of right-hand dimensions comprising the event shapes
of `x` and `y`.
use_kahan_sum: When `True`, the reduction of any remaining `event_ndims`
beyond the minimum is done using Kahan summation. This requires statically
known ranks.
Returns:
ildj_ratio: `log ((abs o det o jac p^-1)(x) / (abs o det o jac q^-1)(y))`,
i.e. in TFP code, `p.inverse_log_det_jacobian(x, event_ndims) -
q.inverse_log_det_jacobian(y, event_ndims)`. In some cases
this will be computed with better than naive numerical precision, e.g. by
moving differences inside of a sum reduction.
"""
assert type(p) == type(q) # pylint: disable=unidiomatic-typecheck
min_event_ndims = p.inverse_min_event_ndims
def default_ildj_ratio_fn(p, x, q, y):
return (p.inverse_log_det_jacobian(x, event_ndims=min_event_ndims) -
q.inverse_log_det_jacobian(y, event_ndims=min_event_ndims))
ildj_ratio_fn = None
fldj_ratio_fn = None
for cls in inspect.getmro(type(p)):
if cls in _ildj_ratio_registry:
ildj_ratio_fn = _ildj_ratio_registry[cls]
if cls in _fldj_ratio_registry:
fldj_ratio_fn = _fldj_ratio_registry[cls]
if ildj_ratio_fn is None:
if fldj_ratio_fn is None:
ildj_ratio_fn = default_ildj_ratio_fn
else:
# p.ildj(x) - q.ildj(y) = q.fldj(q^-1(y)) - p.fldj(p^-1(x))
ildj_ratio_fn = (lambda p, x, q, y: fldj_ratio_fn(
q, q.inverse(y), p, p.inverse(x)))
if use_kahan_sum:
sum_fn = lambda x, axis: tfp_math.reduce_kahan_sum(x, axis=axis).total
else:
sum_fn = tf.reduce_sum
return sum_fn(ildj_ratio_fn(p, x, q, y),
axis=-1 - ps.range(event_ndims - min_event_ndims))
def _independent_log_prob_ratio(p, x, q, y, name=None):
"""Sum-of-diffs log(p(x)/q(y)) for `Independent`s."""
with tf.name_scope(name or 'independent_log_prob_ratio'):
checks = []
if p.validate_args or q.validate_args:
checks.append(tf.debugging.assert_equal(
p.reinterpreted_batch_ndims, q.reinterpreted_batch_ndims))
if p._experimental_use_kahan_sum or q._experimental_use_kahan_sum: # pylint: disable=protected-access
sum_fn = lambda x, axis: tfp_math.reduce_kahan_sum(x, axis).total
else:
sum_fn = tf.reduce_sum
with tf.control_dependencies(checks):
return sum_fn(
log_prob_ratio.log_prob_ratio(p.distribution, x, q.distribution, y),
axis=-1 - ps.range(p.reinterpreted_batch_ndims))
def _markov_chain_log_prob_ratio(p, x, q, y, name=None):
"""Implements `log_prob_ratio` for tfd.MarkovChain."""
with tf.name_scope(name or 'markov_chain_log_prob_ratio'):
# TODO(davmre): In the case where `p` and `q` have components of the same
# families (in addition to just both being MarkovChains), we might prefer to
# recursively call `log_prob_ratio` instead of just subtracting log probs.
p_prior_lp, p_transition_lps = p._log_prob_parts(x)
q_prior_lp, q_transition_lps = q._log_prob_parts(y)
prior_lp_ratio = p_prior_lp - q_prior_lp
transition_lp_ratios = p_transition_lps - q_transition_lps
if (p._experimental_use_kahan_sum or q._experimental_use_kahan_sum):
transition_lp_ratio = tfp_math.reduce_kahan_sum(
transition_lp_ratios, axis=0).value
else:
transition_lp_ratio = tf.reduce_sum(transition_lp_ratios, axis=0)
return prior_lp_ratio + transition_lp_ratio
def unnormalized_log_prob_parts(self, value, name=None):
"""Unnormalized log probability density/mass function, part-wise.
Args:
value: `list` of `Tensor`s in `distribution_fn` order for which we compute
the `unnormalized_log_prob_parts` and to parameterize other
("downstream") distributions.
name: name prepended to ops created by this function.
Default value: `"unnormalized_log_prob_parts"`.
Returns:
log_prob_parts: a `tuple` of `Tensor`s representing the `log_prob` for
each `distribution_fn` evaluated at each corresponding `value`.
"""
with self._name_and_control_scope(name or 'unnormalized_log_prob_parts'):
sum_fn = tf.reduce_sum
if self._experimental_use_kahan_sum:
sum_fn = lambda x, axis: tfp_math.reduce_kahan_sum(x, axis=axis).total
return self._model_unflatten(
self._reduce_measure_over_dists(
self._map_measure_over_dists('unnormalized_log_prob', value),
sum_fn))
def _sum_fn(self):
if self._experimental_use_kahan_sum:
return lambda x, axis: tfp_math.reduce_kahan_sum(x, axis).total
return tf.math.reduce_sum
def _log_abs_determinant(self):
return tfp_math.reduce_kahan_sum(tf.math.log(
tf.math.abs(self._get_diag())),
axis=[-1]).total