def _inverse_log_det_jacobian(self, y, use_saved_statistics=False):
if not self.batchnorm.built:
# Create variables.
self.batchnorm.build(y.shape)
event_dims = self.batchnorm.axis
reduction_axes = [i for i in range(len(y.shape)) if i not in event_dims]
# At training-time, ildj is computed from the mean and log-variance across
# the current minibatch.
# We use multiplication instead of tf.where() to get easier broadcasting.
log_variance = tf.math.log(
tf.where(
tf.logical_or(use_saved_statistics, tf.logical_not(self._training)),
self.batchnorm.moving_variance,
tf.nn.moments(x=y, axes=reduction_axes, keepdims=True)[1]) +
self.batchnorm.epsilon)
# TODO(b/137216713): determine whether it's unsafe for the reduce_sums below
# to happen across all axes.
# `gamma` and `log Var(y)` reductions over event_dims.
# Log(total change in area from gamma term).
log_total_gamma = tf.reduce_sum(tf.math.log(self.batchnorm.gamma))
# Log(total change in area from log-variance term).
log_total_variance = tf.reduce_sum(log_variance)
# The ildj is scalar, as it does not depend on the values of x and are
# constant across minibatch elements.
return log_total_gamma - 0.5 * log_total_variance
def _entropy(self):
if self._logits is None:
# If we only have probs, there's not much we can do to ensure numerical
# precision.
probs = tf.convert_to_tensor(self._probs)
return -tf.reduce_sum(
tf.math.multiply_no_nan(tf.math.log(probs), probs), axis=-1)
# The following result can be derived as follows. Write log(p[i]) as:
# s[i]-m-lse(s[i]-m) where m=max(s), then you have:
# sum_i exp(s[i]-m-lse(s-m)) (s[i] - m - lse(s-m))
# = -m - lse(s-m) + sum_i s[i] exp(s[i]-m-lse(s-m))
# = -m - lse(s-m) + (1/exp(lse(s-m))) sum_i s[i] exp(s[i]-m)
# = -m - lse(s-m) + (1/sumexp(s-m)) sum_i s[i] exp(s[i]-m)
# Write x[i]=s[i]-m then you have:
# = -m - lse(x) + (1/sum_exp(x)) sum_i s[i] exp(x[i])
# Negating all of this result is the Shanon (discrete) entropy.
logits = tf.convert_to_tensor(self._logits)
m = tf.reduce_max(logits, axis=-1, keepdims=True)
x = logits - m
lse_logits = m[..., 0] + tf.reduce_logsumexp(x, axis=-1)
sum_exp_x = tf.reduce_sum(tf.math.exp(x), axis=-1)
return lse_logits - tf.reduce_sum(tf.math.multiply_no_nan(
logits, tf.math.exp(x)),
axis=-1) / sum_exp_x
def _entropy(self):
concentration = tf.convert_to_tensor(self.concentration)
k = tf.cast(tf.shape(concentration)[-1], self.dtype)
total_concentration = tf.reduce_sum(concentration, axis=-1)
return (tf.math.lbeta(concentration) +
((total_concentration - k) * tf.math.digamma(total_concentration)) -
tf.reduce_sum((concentration - 1.) * tf.math.digamma(concentration),
axis=-1))
def _variance(self):
with tf.control_dependencies(self._runtime_assertions):
# Law of total variance: Var(Y) = E[Var(Y|X)] + Var(E[Y|X])
probs = distribution_utils.pad_mixture_dimensions(
self.mixture_distribution.probs_parameter(), self,
self.mixture_distribution, self._event_ndims) # [B, k, [1]*e]
mean_cond_var = tf.reduce_sum(
probs * self.components_distribution.variance(),
axis=-1 - self._event_ndims) # [B, E]
var_cond_mean = tf.reduce_sum(probs * tf.math.squared_difference(
self.components_distribution.mean(),
self._pad_sample_dims(self._mean())),
axis=-1 -
self._event_ndims) # [B, E]
return mean_cond_var + var_cond_mean # [B, E]
def _mean(self):
with tf.control_dependencies(self._runtime_assertions):
probs = distribution_utils.pad_mixture_dimensions(
self.mixture_distribution.probs_parameter(), self,
self.mixture_distribution, self._event_ndims) # [B, k, [1]*e]
return tf.reduce_sum(probs * self.components_distribution.mean(),
axis=-1 - self._event_ndims) # [B, E]
def _log_prob(self, x, **kwargs):
batch_ndims = prefer_static.rank_from_shape(
self.distribution.batch_shape_tensor,
self.distribution.batch_shape)
extra_sample_ndims = prefer_static.rank_from_shape(self.sample_shape)
event_ndims = prefer_static.rank_from_shape(
self.distribution.event_shape_tensor,
self.distribution.event_shape)
ndims = prefer_static.rank(x)
# (1) Expand x's dims.
d = ndims - batch_ndims - extra_sample_ndims - event_ndims
x = tf.reshape(x,
shape=tf.pad(
tf.shape(x),
paddings=[[prefer_static.maximum(0, -d), 0]],
constant_values=1))
sample_ndims = prefer_static.maximum(0, d)
# (2) Transpose x's dims.
sample_dims = prefer_static.range(0, sample_ndims)
batch_dims = prefer_static.range(sample_ndims,
sample_ndims + batch_ndims)
extra_sample_dims = prefer_static.range(
sample_ndims + batch_ndims,
sample_ndims + batch_ndims + extra_sample_ndims)
event_dims = prefer_static.range(
sample_ndims + batch_ndims + extra_sample_ndims, ndims)
perm = prefer_static.concat(
[sample_dims, extra_sample_dims, batch_dims, event_dims], axis=0)
x = tf.transpose(a=x, perm=perm)
# (3) Compute x's log_prob.
lp = self.distribution.log_prob(x, **kwargs)
# (4) Make the final reduction in x.
axis = prefer_static.range(sample_ndims,
sample_ndims + extra_sample_ndims)
return tf.reduce_sum(lp, axis=axis)
def log_combinations(n, counts, name='log_combinations'):
"""Multinomial coefficient.
Given `n` and `counts`, where `counts` has last dimension `k`, we compute
the multinomial coefficient as:
```n! / sum_i n_i!```
where `i` runs over all `k` classes.
Args:
n: Floating-point `Tensor` broadcastable with `counts`. This represents `n`
outcomes.
counts: Floating-point `Tensor` broadcastable with `n`. This represents
counts in `k` classes, where `k` is the last dimension of the tensor.
name: A name for this operation (optional).
Returns:
log_combinations: `Tensor` representing the multinomial coefficient between
`n` and `counts`.
"""
# First a bit about the number of ways counts could have come in:
# E.g. if counts = [1, 2], then this is 3 choose 2.
# In general, this is (sum counts)! / sum(counts!)
# The sum should be along the last dimension of counts. This is the
# 'distribution' dimension. Here n a priori represents the sum of counts.
with tf.name_scope(name):
n = tf.convert_to_tensor(n, name='n')
counts = tf.convert_to_tensor(counts, name='counts')
total_permutations = tf.math.lgamma(n + 1)
counts_factorial = tf.math.lgamma(counts + 1)
redundant_permutations = tf.reduce_sum(counts_factorial, axis=-1)
return total_permutations - redundant_permutations
def _variance(self): concentration = tf.convert_to_tensor(self.concentration) total_concentration = tf.reduce_sum(concentration, axis=-1, keepdims=True) mean = concentration / total_concentration scale = tf.math.rsqrt(1. + total_concentration) x = scale * mean return x * (scale - x)
def squared_frobenius_norm(x):
"""Helper to make KL calculation slightly more readable."""
# http://mathworld.wolfram.com/FrobeniusNorm.html
# The gradient of KL[p,q] is not defined when p==q. The culprit is
# tf.norm, i.e., we cannot use the commented out code.
# return tf.square(tf.norm(x, ord="fro", axis=[-2, -1]))
return tf.reduce_sum(tf.square(x), axis=[-2, -1])
def _log_prob(self, counts):
with tf.control_dependencies(self._maybe_assert_valid_sample(counts)):
log_p = (tf.math.log(self._probs) if self._logits is None else
tf.math.log_softmax(self._logits))
k = tf.convert_to_tensor(self.total_count)
return (tf.reduce_sum(counts * log_p, axis=-1) + # log_unnorm_prob
tfp_math.log_combinations(k, counts)) # -log_normalization
def matrix_rank(a, tol=None, validate_args=False, name=None):
"""Compute the matrix rank; the number of non-zero SVD singular values.
Arguments:
a: (Batch of) `float`-like matrix-shaped `Tensor`(s) which are to be
pseudo-inverted.
tol: Threshold below which the singular value is counted as 'zero'.
Default value: `None` (i.e., `eps * max(rows, cols) * max(singular_val)`).
validate_args: When `True`, additional assertions might be embedded in the
graph.
Default value: `False` (i.e., no graph assertions are added).
name: Python `str` prefixed to ops created by this function.
Default value: 'matrix_rank'.
Returns:
matrix_rank: (Batch of) `int32` scalars representing the number of non-zero
singular values.
"""
with tf.name_scope(name or 'matrix_rank'):
a = tf.convert_to_tensor(a, dtype_hint=tf.float32, name='a')
assertions = _maybe_validate_matrix(a, validate_args)
if assertions:
with tf.control_dependencies(assertions):
a = tf.identity(a)
s = tf.linalg.svd(a, compute_uv=False)
if tol is None:
if tensorshape_util.is_fully_defined(a.shape[-2:]):
m = np.max(a.shape[-2:].as_list())
else:
m = tf.reduce_max(tf.shape(a)[-2:])
eps = np.finfo(dtype_util.as_numpy_dtype(a.dtype)).eps
tol = (eps * tf.cast(m, a.dtype) *
tf.reduce_max(s, axis=-1, keepdims=True))
return tf.reduce_sum(tf.cast(s > tol, tf.int32), axis=-1)
def _log_prob(self, x):
logits = self._logits_parameter_no_checks()
event_size = self._event_size(logits)
x = tf.cast(x, logits.dtype)
x = self._maybe_assert_valid_sample(x, dtype=logits.dtype)
# broadcast logits or x if need be.
if (not tensorshape_util.is_fully_defined(x.shape)
or not tensorshape_util.is_fully_defined(logits.shape)
or x.shape != logits.shape):
broadcast_shape = tf.broadcast_dynamic_shape(
tf.shape(logits), tf.shape(x))
logits = tf.broadcast_to(logits, broadcast_shape)
x = tf.broadcast_to(x, broadcast_shape)
logits_shape = tf.shape(tf.reduce_sum(logits, axis=-1))
logits_2d = tf.reshape(logits, [-1, event_size])
x_2d = tf.reshape(x, [-1, event_size])
ret = -tf.nn.softmax_cross_entropy_with_logits(
labels=tf.stop_gradient(x_2d), logits=logits_2d)
# Reshape back to user-supplied batch and sample dims prior to 2D reshape.
ret = tf.reshape(ret, logits_shape)
return ret
def backward_step(most_likely_successor,
most_likely_given_successor):
return tf.reduce_sum(
input_tensor=(most_likely_given_successor *
tf.one_hot(most_likely_successor,
self._num_states,
dtype=tf.int64)),
axis=-1)
def _maybe_assert_valid_sample(self, x, dtype):
if not self.validate_args:
return x
one = tf.ones([], dtype=dtype)
return distribution_util.with_dependencies([
assert_util.assert_non_negative(x),
assert_util.assert_less_equal(x, one),
assert_util.assert_near(one, tf.reduce_sum(x, axis=[-1])),
], x)
def body(m, pchol, perm, matrix_diag):
"""Body of a single `tf.while_loop` iteration."""
# Here is roughly a numpy, non-batched version of what's going to happen.
# (See also Algorithm 1 of Harbrecht et al.)
# 1: maxi = np.argmax(matrix_diag[perm[m:]]) + m
# 2: maxval = matrix_diag[perm][maxi]
# 3: perm[m], perm[maxi] = perm[maxi], perm[m]
# 4: row = matrix[perm[m]][perm[m + 1:]]
# 5: row -= np.sum(pchol[:m][perm[m + 1:]] * pchol[:m][perm[m]]], axis=-2)
# 6: pivot = np.sqrt(maxval); row /= pivot
# 7: row = np.concatenate([[[pivot]], row], -1)
# 8: matrix_diag[perm[m:]] -= row**2
# 9: pchol[m, perm[m:]] = row
# Find the maximal position of the (remaining) permuted diagonal.
# Steps 1, 2 above.
permuted_diag = batch_gather(matrix_diag, perm[..., m:])
maxi = tf.argmax(permuted_diag, axis=-1,
output_type=tf.int64)[..., tf.newaxis]
maxval = batch_gather(permuted_diag, maxi)
maxi = maxi + m
maxval = maxval[..., 0]
# Update perm: Swap perm[...,m] with perm[...,maxi]. Step 3 above.
perm = _swap_m_with_i(perm, m, maxi)
# Step 4.
row = batch_gather(matrix, perm[..., m:m + 1], axis=-2)
row = batch_gather(row, perm[..., m + 1:])
# Step 5.
prev_rows = pchol[..., :m, :]
prev_rows_perm_m_onward = batch_gather(prev_rows, perm[...,
m + 1:])
prev_rows_pivot_col = batch_gather(prev_rows, perm[..., m:m + 1])
row -= tf.reduce_sum(prev_rows_perm_m_onward * prev_rows_pivot_col,
axis=-2)[..., tf.newaxis, :]
# Step 6.
pivot = tf.sqrt(maxval)[..., tf.newaxis, tf.newaxis]
# Step 7.
row = tf.concat([pivot, row / pivot], axis=-1)
# TODO(b/130899118): Pad grad fails with int64 paddings.
# Step 8.
paddings = tf.concat([
tf.zeros([prefer_static.rank(pchol) - 1, 2], dtype=tf.int32),
[[tf.cast(m, tf.int32), 0]]
],
axis=0)
diag_update = tf.pad(row**2, paddings=paddings)[..., 0, :]
reverse_perm = _invert_permutation(perm)
matrix_diag -= batch_gather(diag_update, reverse_perm)
# Step 9.
row = tf.pad(row, paddings=paddings)
# TODO(bjp): Defer the reverse permutation all-at-once at the end?
row = batch_gather(row, reverse_perm)
pchol_shape = pchol.shape
pchol = tf.concat([pchol[..., :m, :], row, pchol[..., m + 1:, :]],
axis=-2)
tensorshape_util.set_shape(pchol, pchol_shape)
return m + 1, pchol, perm, matrix_diag
def _rotate(self, samples):
"""Applies a Householder rotation to `samples`."""
event_dim = (tf.compat.dimension_value(self.event_shape[0])
or self._event_shape_tensor()[0])
basis = tf.concat(
[[1.], tf.zeros([event_dim - 1], dtype=self.dtype)], axis=0),
u = tf.math.l2_normalize(basis - self.mean_direction, axis=-1)
return samples - 2 * tf.reduce_sum(samples * u, axis=-1,
keepdims=True) * u
def _forward_log_det_jacobian(self, x):
# For a discussion of this (non-obvious) result, see Note 7.2.2 (and the
# sections leading up to it, for context) in
# http://neutrino.aquaphoenix.com/ReactionDiffusion/SERC5chap7.pdf
with tf.control_dependencies(self._assertions(x)):
matrix_dim = tf.cast(tf.shape(x)[-1],
dtype_util.base_dtype(x.dtype))
return -(matrix_dim + 1) * tf.reduce_sum(
tf.math.log(tf.abs(tf.linalg.diag_part(x))), axis=-1)
def _inverse_log_det_jacobian(self, y):
# The Jacobian of the inverse mapping is lower
# triangular, with the diagonal elements being:
# J[i,i] = 1 if i=1, and
# exp(y_i) if 1<i<=K
# which gives the absolute Jacobian determinant:
# |det(Jac)| = prod_{i=1}^{K} exp(y[i]).
# (1) - Stan Modeling Language User's Guide and Reference Manual
# Version 2.17.0 session 35.2
return tf.reduce_sum(y[..., 1:], axis=-1)
def _covariance(self):
concentration = tf.convert_to_tensor(self.concentration)
total_concentration = tf.reduce_sum(concentration, axis=-1, keepdims=True)
mean = concentration / total_concentration
scale = tf.math.rsqrt(1. + total_concentration)
x = scale * mean
variance = x * (scale - x)
return tf.linalg.set_diag(
tf.matmul(-x[..., tf.newaxis], x[..., tf.newaxis, :]),
variance)
def grad(dy):
"""Computes a derivative for the min and max parameters.
This function implements the derivative wrt the truncation bounds, which
get blocked by the sampler. We use a custom expression for numerical
stability instead of automatic differentiation on CDF for implicit
gradients.
Args:
dy: output gradients
Returns:
The standard normal samples and the gradients wrt the upper
bound and lower bound.
"""
# std_samples has an extra dimension (the sample dimension), expand
# lower and upper so they broadcast along this dimension.
# See note above regarding parameterized_truncated_normal, the sample
# dimension is the final dimension.
lower_broadcast = lower[..., tf.newaxis]
upper_broadcast = upper[..., tf.newaxis]
cdf_samples = ((special_math.ndtr(std_samples) -
special_math.ndtr(lower_broadcast)) /
(special_math.ndtr(upper_broadcast) -
special_math.ndtr(lower_broadcast)))
# tiny, eps are tolerance parameters to ensure we stay away from giving
# a zero arg to the log CDF expression.
tiny = np.finfo(dtype_util.as_numpy_dtype(self.dtype)).tiny
eps = np.finfo(dtype_util.as_numpy_dtype(self.dtype)).eps
cdf_samples = tf.clip_by_value(cdf_samples, tiny, 1 - eps)
du = tf.exp(0.5 * (std_samples**2 - upper_broadcast**2) +
tf.math.log(cdf_samples))
dl = tf.exp(0.5 * (std_samples**2 - lower_broadcast**2) +
tf.math.log1p(-cdf_samples))
# Reduce the gradient across the samples
grad_u = tf.reduce_sum(dy * du, axis=-1)
grad_l = tf.reduce_sum(dy * dl, axis=-1)
return [grad_l, grad_u]
def _maybe_assert_valid_sample(self, counts):
"""Check counts for proper shape, values, then return tensor version."""
if not self.validate_args:
return []
assertions = distribution_util.assert_nonnegative_integer_form(counts)
assertions.append(
assert_util.assert_equal(
self.total_count,
tf.reduce_sum(counts, axis=-1),
message='counts must sum to `self.total_count`'))
return assertions
def _maybe_assert_valid_sample(self, counts):
"""Check counts for proper shape, values, then return tensor version."""
if not self.validate_args:
return counts
counts = distribution_util.embed_check_nonnegative_integer_form(counts)
return distribution_util.with_dependencies([
assert_util.assert_equal(
self.total_count,
tf.reduce_sum(counts, axis=-1),
message='counts last-dimension must sum to `self.total_count`'),
], counts)
def _maybe_assert_valid_sample(self, x):
"""Checks the validity of a sample."""
if not self.validate_args:
return []
return [
assert_util.assert_positive(x, message='samples must be positive'),
assert_util.assert_near(
tf.ones([], dtype=self.dtype),
tf.reduce_sum(x, axis=-1),
message='sample last-dimension must sum to `1`'),
]
def _sample_one_batch_member(args):
logits, num_cat_samples = args[0], args[1] # [K], []
# x has shape [1, num_cat_samples = num_samples * num_trials]
x = tf.random.categorical(logits[tf.newaxis, ...],
num_cat_samples,
seed=seed)
x = tf.reshape(x, shape=[num_samples,
-1]) # [num_samples, num_trials]
x = tf.one_hot(
x, depth=num_classes) # [num_samples, num_trials, num_classes]
x = tf.reduce_sum(x, axis=-2) # [num_samples, num_classes]
return tf.cast(x, dtype=dtype)
def _covariance(self):
static_event_ndims = tensorshape_util.rank(self.event_shape)
if static_event_ndims is not None and static_event_ndims != 1:
# Covariance is defined only for vector distributions.
raise NotImplementedError("covariance is not implemented")
with tf.control_dependencies(self._runtime_assertions):
# Law of total variance: Var(Y) = E[Var(Y|X)] + Var(E[Y|X])
probs = distribution_utils.pad_mixture_dimensions(
distribution_utils.pad_mixture_dimensions(
self.mixture_distribution.probs_parameter(), self,
self.mixture_distribution, self._event_ndims), self,
self.mixture_distribution, self._event_ndims) # [B, k, 1, 1]
mean_cond_var = tf.reduce_sum(
probs * self.components_distribution.covariance(),
axis=-3) # [B, e, e]
var_cond_mean = tf.reduce_sum(
probs *
_outer_squared_difference(self.components_distribution.mean(),
self._pad_sample_dims(self._mean())),
axis=-3) # [B, e, e]
return mean_cond_var + var_cond_mean # [B, e, e]
def _event_shape_tensor(self):
event_sizes = tf.nest.map_structure(tensorshape_util.num_elements,
self._distribution.event_shape)
if any(s is None for s in tf.nest.flatten(event_sizes)):
event_sizes = tf.nest.map_structure(
lambda static_size, shape_tensor: # pylint: disable=g-long-lambda
(tf.reduce_prod(shape_tensor)
if static_size is None else static_size),
event_sizes,
self._distribution.event_shape_tensor())
return tf.reduce_sum(tf.nest.flatten(event_sizes))[tf.newaxis]
def _variance(self):
probs = self._categorical.probs_parameter()
outcomes = tf.broadcast_to(self.outcomes,
shape=dist_util.prefer_static_shape(probs))
if dtype_util.is_integer(outcomes.dtype):
if self._validate_args:
outcomes = dist_util.embed_check_integer_casting_closed(
outcomes, target_dtype=probs.dtype)
outcomes = tf.cast(outcomes, dtype=probs.dtype)
square_d = tf.math.squared_difference(
outcomes,
self._mean(probs)[..., tf.newaxis])
return tf.reduce_sum(probs * square_d, axis=-1)
def maybe_assert_categorical_param_correctness(is_init, validate_args, probs,
logits):
"""Return assertions for `Categorical`-type distributions."""
assertions = []
# In init, we can always build shape and dtype checks because
# we assume shape doesn't change for Variable backed args.
if is_init:
x, name = (probs, 'probs') if logits is None else (logits, 'logits')
if not dtype_util.is_floating(x.dtype):
raise TypeError(
'Argument `{}` must having floating type.'.format(name))
msg = 'Argument `{}` must have rank at least 1.'.format(name)
ndims = tensorshape_util.rank(x.shape)
if ndims is not None:
if ndims < 1:
raise ValueError(msg)
elif validate_args:
x = tf.convert_to_tensor(x)
probs = x if logits is None else None # Retain tensor conversion.
logits = x if probs is None else None
assertions.append(
assert_util.assert_rank_at_least(x, 1, message=msg))
if not validate_args:
assert not assertions # Should never happen.
return []
if logits is not None:
if is_init != tensor_util.is_ref(logits):
logits = tf.convert_to_tensor(logits)
assertions.extend(
distribution_util.assert_categorical_event_shape(logits))
if probs is not None:
if is_init != tensor_util.is_ref(probs):
probs = tf.convert_to_tensor(probs)
assertions.extend([
assert_util.assert_non_negative(probs),
assert_util.assert_near(
tf.reduce_sum(probs, axis=-1),
np.array(1, dtype=dtype_util.as_numpy_dtype(probs.dtype)),
message='Argument `probs` must sum to 1.')
])
assertions.extend(
distribution_util.assert_categorical_event_shape(probs))
return assertions
def _forward_log_det_jacobian(self, x):
# CholeskyToInvCholesky.forward(X) is equivalent to
# 1) M = CholeskyOuterProduct.forward(X)
# 2) N = invert(M)
# 3) Y = CholeskyOuterProduct.inverse(N)
#
# For step 1,
# |Jac(outerprod(X))| = 2^p prod_{j=0}^{p-1} X[j,j]^{p-j}.
# For step 2,
# |Jac(inverse(M))| = |M|^{-(p+1)} (because M is symmetric)
# = |X|^{-2(p+1)} = (prod_{j=0}^{p-1} X[j,j])^{-2(p+1)}
# (see http://web.mit.edu/18.325/www/handouts/handout2.pdf sect 3.0.2)
# For step 3,
# |Jac(Cholesky(N))| = -|Jac(outerprod(Y)|
# = 2^p prod_{j=0}^{p-1} Y[j,j]^{p-j}
n = tf.cast(tf.shape(x)[-1], x.dtype)
y = self._forward(x)
return ((self._cholesky.forward_log_det_jacobian(x, event_ndims=2) -
(n + 1.) *
tf.reduce_sum(tf.math.log(tf.linalg.diag_part(x)), axis=-1)) -
(self._cholesky.forward_log_det_jacobian(y, event_ndims=2) -
(n + 1.) *
tf.reduce_sum(tf.math.log(tf.linalg.diag_part(y)), axis=-1)))
def _forward_log_det_jacobian(self, x):
# This code is similar to tf.math.log_softmax but different because we have
# an implicit zero column to handle. I.e., instead of:
# reduce_sum(logits - reduce_sum(exp(logits), dim))
# we must do:
# log_normalization = 1 + reduce_sum(exp(logits))
# -log_normalization + reduce_sum(logits - log_normalization)
n = prefer_static.shape(x)[-1]
log_normalization = tf.math.softplus(
tf.reduce_logsumexp(x, axis=-1, keepdims=True))
return tf.squeeze(
(-log_normalization +
tf.reduce_sum(x - log_normalization, axis=-1, keepdims=True)),
axis=-1) + 0.5 * tf.math.log(tf.cast(n + 1, dtype=x.dtype))
