150 lines
5.2 KiB
Python
150 lines
5.2 KiB
Python
from numbers import Number
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import torch
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from torch.distributions import constraints
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from torch.distributions.distribution import Distribution
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from torch.distributions.transformed_distribution import TransformedDistribution
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from torch.distributions.transforms import SigmoidTransform
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from torch.distributions.utils import (
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broadcast_all,
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clamp_probs,
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lazy_property,
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logits_to_probs,
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probs_to_logits,
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)
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__all__ = ["LogitRelaxedBernoulli", "RelaxedBernoulli"]
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class LogitRelaxedBernoulli(Distribution):
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r"""
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Creates a LogitRelaxedBernoulli distribution parameterized by :attr:`probs`
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or :attr:`logits` (but not both), which is the logit of a RelaxedBernoulli
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distribution.
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Samples are logits of values in (0, 1). See [1] for more details.
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Args:
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temperature (Tensor): relaxation temperature
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probs (Number, Tensor): the probability of sampling `1`
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logits (Number, Tensor): the log-odds of sampling `1`
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[1] The Concrete Distribution: A Continuous Relaxation of Discrete Random
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Variables (Maddison et al, 2017)
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[2] Categorical Reparametrization with Gumbel-Softmax
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(Jang et al, 2017)
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"""
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arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
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support = constraints.real
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def __init__(self, temperature, probs=None, logits=None, validate_args=None):
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self.temperature = temperature
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if (probs is None) == (logits is None):
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raise ValueError(
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"Either `probs` or `logits` must be specified, but not both."
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)
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if probs is not None:
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is_scalar = isinstance(probs, Number)
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(self.probs,) = broadcast_all(probs)
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else:
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is_scalar = isinstance(logits, Number)
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(self.logits,) = broadcast_all(logits)
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self._param = self.probs if probs is not None else self.logits
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if is_scalar:
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batch_shape = torch.Size()
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else:
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batch_shape = self._param.size()
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super().__init__(batch_shape, validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(LogitRelaxedBernoulli, _instance)
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batch_shape = torch.Size(batch_shape)
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new.temperature = self.temperature
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if "probs" in self.__dict__:
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new.probs = self.probs.expand(batch_shape)
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new._param = new.probs
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if "logits" in self.__dict__:
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new.logits = self.logits.expand(batch_shape)
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new._param = new.logits
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super(LogitRelaxedBernoulli, new).__init__(batch_shape, validate_args=False)
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new._validate_args = self._validate_args
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return new
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def _new(self, *args, **kwargs):
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return self._param.new(*args, **kwargs)
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@lazy_property
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def logits(self):
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return probs_to_logits(self.probs, is_binary=True)
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@lazy_property
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def probs(self):
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return logits_to_probs(self.logits, is_binary=True)
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@property
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def param_shape(self):
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return self._param.size()
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def rsample(self, sample_shape=torch.Size()):
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shape = self._extended_shape(sample_shape)
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probs = clamp_probs(self.probs.expand(shape))
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uniforms = clamp_probs(
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torch.rand(shape, dtype=probs.dtype, device=probs.device)
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)
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return (
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uniforms.log() - (-uniforms).log1p() + probs.log() - (-probs).log1p()
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) / self.temperature
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def log_prob(self, value):
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if self._validate_args:
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self._validate_sample(value)
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logits, value = broadcast_all(self.logits, value)
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diff = logits - value.mul(self.temperature)
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return self.temperature.log() + diff - 2 * diff.exp().log1p()
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class RelaxedBernoulli(TransformedDistribution):
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r"""
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Creates a RelaxedBernoulli distribution, parametrized by
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:attr:`temperature`, and either :attr:`probs` or :attr:`logits`
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(but not both). This is a relaxed version of the `Bernoulli` distribution,
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so the values are in (0, 1), and has reparametrizable samples.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> m = RelaxedBernoulli(torch.tensor([2.2]),
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... torch.tensor([0.1, 0.2, 0.3, 0.99]))
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>>> m.sample()
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tensor([ 0.2951, 0.3442, 0.8918, 0.9021])
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Args:
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temperature (Tensor): relaxation temperature
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probs (Number, Tensor): the probability of sampling `1`
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logits (Number, Tensor): the log-odds of sampling `1`
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"""
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arg_constraints = {"probs": constraints.unit_interval, "logits": constraints.real}
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support = constraints.unit_interval
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has_rsample = True
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def __init__(self, temperature, probs=None, logits=None, validate_args=None):
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base_dist = LogitRelaxedBernoulli(temperature, probs, logits)
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super().__init__(base_dist, SigmoidTransform(), validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(RelaxedBernoulli, _instance)
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return super().expand(batch_shape, _instance=new)
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@property
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def temperature(self):
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return self.base_dist.temperature
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@property
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def logits(self):
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return self.base_dist.logits
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@property
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def probs(self):
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return self.base_dist.probs
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