Source code for torch.distributions.continuous_bernoulli
# mypy: allow-untyped-defs
import math
from numbers import Number
import torch
from torch.distributions import constraints
from torch.distributions.exp_family import ExponentialFamily
from torch.distributions.utils import (
broadcast_all,
clamp_probs,
lazy_property,
logits_to_probs,
probs_to_logits,
)
from torch.nn.functional import binary_cross_entropy_with_logits
__all__ = ["ContinuousBernoulli"]
[docs]class ContinuousBernoulli(ExponentialFamily):
r"""
Creates a continuous Bernoulli distribution parameterized by :attr:`probs`
or :attr:`logits` (but not both).
The distribution is supported in [0, 1] and parameterized by 'probs' (in
(0,1)) or 'logits' (real-valued). Note that, unlike the Bernoulli, 'probs'
does not correspond to a probability and 'logits' does not correspond to
log-odds, but the same names are used due to the similarity with the
Bernoulli. See [1] for more details.
Example::
>>> # xdoctest: +IGNORE_WANT("non-deterministic")
>>> m = ContinuousBernoulli(torch.tensor([0.3]))
>>> m.sample()
tensor([ 0.2538])
Args:
probs (Number, Tensor): (0,1) valued parameters
logits (Number, Tensor): real valued parameters whose sigmoid matches 'probs'
[1] The continuous Bernoulli: fixing a pervasive error in variational
autoencoders, Loaiza-Ganem G and Cunningham JP, NeurIPS 2019.
https://arxiv.org/abs/1907.06845
"""
arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
support = constraints.unit_interval
_mean_carrier_measure = 0
has_rsample = True
def __init__(
self, probs=None, logits=None, lims=(0.499, 0.501), validate_args=None
):
if (probs is None) == (logits is None):
raise ValueError(
"Either `probs` or `logits` must be specified, but not both."
)
if probs is not None:
is_scalar = isinstance(probs, Number)
(self.probs,) = broadcast_all(probs)
# validate 'probs' here if necessary as it is later clamped for numerical stability
# close to 0 and 1, later on; otherwise the clamped 'probs' would always pass
if validate_args is not None:
if not self.arg_constraints["probs"].check(self.probs).all():
raise ValueError("The parameter probs has invalid values")
self.probs = clamp_probs(self.probs)
else:
is_scalar = isinstance(logits, Number)
(self.logits,) = broadcast_all(logits)
self._param = self.probs if probs is not None else self.logits
if is_scalar:
batch_shape = torch.Size()
else:
batch_shape = self._param.size()
self._lims = lims
super().__init__(batch_shape, validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None):
new = self._get_checked_instance(ContinuousBernoulli, _instance)
new._lims = self._lims
batch_shape = torch.Size(batch_shape)
if "probs" in self.__dict__:
new.probs = self.probs.expand(batch_shape)
new._param = new.probs
if "logits" in self.__dict__:
new.logits = self.logits.expand(batch_shape)
new._param = new.logits
super(ContinuousBernoulli, new).__init__(batch_shape, validate_args=False)
new._validate_args = self._validate_args
return new
def _new(self, *args, **kwargs):
return self._param.new(*args, **kwargs)
def _outside_unstable_region(self):
return torch.max(
torch.le(self.probs, self._lims[0]), torch.gt(self.probs, self._lims[1])
)
def _cut_probs(self):
return torch.where(
self._outside_unstable_region(),
self.probs,
self._lims[0] * torch.ones_like(self.probs),
)
def _cont_bern_log_norm(self):
"""computes the log normalizing constant as a function of the 'probs' parameter"""
cut_probs = self._cut_probs()
cut_probs_below_half = torch.where(
torch.le(cut_probs, 0.5), cut_probs, torch.zeros_like(cut_probs)
)
cut_probs_above_half = torch.where(
torch.ge(cut_probs, 0.5), cut_probs, torch.ones_like(cut_probs)
)
log_norm = torch.log(
torch.abs(torch.log1p(-cut_probs) - torch.log(cut_probs))
) - torch.where(
torch.le(cut_probs, 0.5),
torch.log1p(-2.0 * cut_probs_below_half),
torch.log(2.0 * cut_probs_above_half - 1.0),
)
x = torch.pow(self.probs - 0.5, 2)
taylor = math.log(2.0) + (4.0 / 3.0 + 104.0 / 45.0 * x) * x
return torch.where(self._outside_unstable_region(), log_norm, taylor)
@property
def mean(self):
cut_probs = self._cut_probs()
mus = cut_probs / (2.0 * cut_probs - 1.0) + 1.0 / (
torch.log1p(-cut_probs) - torch.log(cut_probs)
)
x = self.probs - 0.5
taylor = 0.5 + (1.0 / 3.0 + 16.0 / 45.0 * torch.pow(x, 2)) * x
return torch.where(self._outside_unstable_region(), mus, taylor)
@property
def stddev(self):
return torch.sqrt(self.variance)
@property
def variance(self):
cut_probs = self._cut_probs()
vars = cut_probs * (cut_probs - 1.0) / torch.pow(
1.0 - 2.0 * cut_probs, 2
) + 1.0 / torch.pow(torch.log1p(-cut_probs) - torch.log(cut_probs), 2)
x = torch.pow(self.probs - 0.5, 2)
taylor = 1.0 / 12.0 - (1.0 / 15.0 - 128.0 / 945.0 * x) * x
return torch.where(self._outside_unstable_region(), vars, taylor)
@lazy_property
def logits(self):
return probs_to_logits(self.probs, is_binary=True)
@lazy_property
def probs(self):
return clamp_probs(logits_to_probs(self.logits, is_binary=True))
@property
def param_shape(self):
return self._param.size()
[docs] def sample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
with torch.no_grad():
return self.icdf(u)
[docs] def rsample(self, sample_shape=torch.Size()):
shape = self._extended_shape(sample_shape)
u = torch.rand(shape, dtype=self.probs.dtype, device=self.probs.device)
return self.icdf(u)
[docs] def log_prob(self, value):
if self._validate_args:
self._validate_sample(value)
logits, value = broadcast_all(self.logits, value)
return (
-binary_cross_entropy_with_logits(logits, value, reduction="none")
+ self._cont_bern_log_norm()
)
[docs] def cdf(self, value):
if self._validate_args:
self._validate_sample(value)
cut_probs = self._cut_probs()
cdfs = (
torch.pow(cut_probs, value) * torch.pow(1.0 - cut_probs, 1.0 - value)
+ cut_probs
- 1.0
) / (2.0 * cut_probs - 1.0)
unbounded_cdfs = torch.where(self._outside_unstable_region(), cdfs, value)
return torch.where(
torch.le(value, 0.0),
torch.zeros_like(value),
torch.where(torch.ge(value, 1.0), torch.ones_like(value), unbounded_cdfs),
)
[docs] def icdf(self, value):
cut_probs = self._cut_probs()
return torch.where(
self._outside_unstable_region(),
(
torch.log1p(-cut_probs + value * (2.0 * cut_probs - 1.0))
- torch.log1p(-cut_probs)
)
/ (torch.log(cut_probs) - torch.log1p(-cut_probs)),
value,
)
[docs] def entropy(self):
log_probs0 = torch.log1p(-self.probs)
log_probs1 = torch.log(self.probs)
return (
self.mean * (log_probs0 - log_probs1)
- self._cont_bern_log_norm()
- log_probs0
)
@property
def _natural_params(self):
return (self.logits,)
def _log_normalizer(self, x):
"""computes the log normalizing constant as a function of the natural parameter"""
out_unst_reg = torch.max(
torch.le(x, self._lims[0] - 0.5), torch.gt(x, self._lims[1] - 0.5)
)
cut_nat_params = torch.where(
out_unst_reg, x, (self._lims[0] - 0.5) * torch.ones_like(x)
)
log_norm = torch.log(torch.abs(torch.exp(cut_nat_params) - 1.0)) - torch.log(
torch.abs(cut_nat_params)
)
taylor = 0.5 * x + torch.pow(x, 2) / 24.0 - torch.pow(x, 4) / 2880.0
return torch.where(out_unst_reg, log_norm, taylor)
