Source code for torch.distributions.relaxed_categorical

import torch
from torch.distributions import constraints
from torch.distributions.categorical import Categorical
from torch.distributions.distribution import Distribution
from torch.distributions.transformed_distribution import TransformedDistribution
from torch.distributions.transforms import ExpTransform
from torch.distributions.utils import broadcast_all, clamp_probs

__all__ = ["ExpRelaxedCategorical", "RelaxedOneHotCategorical"]

class ExpRelaxedCategorical(Distribution):
    Creates a ExpRelaxedCategorical parameterized by
    :attr:`temperature`, and either :attr:`probs` or :attr:`logits` (but not both).
    Returns the log of a point in the simplex. Based on the interface to

    Implementation based on [1].

    See also: :func:`torch.distributions.OneHotCategorical`

        temperature (Tensor): relaxation temperature
        probs (Tensor): event probabilities
        logits (Tensor): unnormalized log probability for each event

    [1] The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
    (Maddison et al, 2017)

    [2] Categorical Reparametrization with Gumbel-Softmax
    (Jang et al, 2017)
    arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector}
    support = (
    )  # The true support is actually a submanifold of this.
    has_rsample = True

    def __init__(self, temperature, probs=None, logits=None, validate_args=None):
        self._categorical = Categorical(probs, logits)
        self.temperature = temperature
        batch_shape = self._categorical.batch_shape
        event_shape = self._categorical.param_shape[-1:]
        super().__init__(batch_shape, event_shape, validate_args=validate_args)

    def expand(self, batch_shape, _instance=None):
        new = self._get_checked_instance(ExpRelaxedCategorical, _instance)
        batch_shape = torch.Size(batch_shape)
        new.temperature = self.temperature
        new._categorical = self._categorical.expand(batch_shape)
        super(ExpRelaxedCategorical, new).__init__(
            batch_shape, self.event_shape, validate_args=False
        new._validate_args = self._validate_args
        return new

    def _new(self, *args, **kwargs):
        return self._categorical._new(*args, **kwargs)

    def param_shape(self):
        return self._categorical.param_shape

    def logits(self):
        return self._categorical.logits

    def probs(self):
        return self._categorical.probs

    def rsample(self, sample_shape=torch.Size()):
        shape = self._extended_shape(sample_shape)
        uniforms = clamp_probs(
            torch.rand(shape, dtype=self.logits.dtype, device=self.logits.device)
        gumbels = -((-(uniforms.log())).log())
        scores = (self.logits + gumbels) / self.temperature
        return scores - scores.logsumexp(dim=-1, keepdim=True)

    def log_prob(self, value):
        K = self._categorical._num_events
        if self._validate_args:
        logits, value = broadcast_all(self.logits, value)
        log_scale = torch.full_like(
            self.temperature, float(K)
        ).lgamma() - self.temperature.log().mul(-(K - 1))
        score = logits - value.mul(self.temperature)
        score = (score - score.logsumexp(dim=-1, keepdim=True)).sum(-1)
        return score + log_scale

[docs]class RelaxedOneHotCategorical(TransformedDistribution): r""" Creates a RelaxedOneHotCategorical distribution parametrized by :attr:`temperature`, and either :attr:`probs` or :attr:`logits`. This is a relaxed version of the :class:`OneHotCategorical` distribution, so its samples are on simplex, and are reparametrizable. Example:: >>> # xdoctest: +IGNORE_WANT("non-deterinistic") >>> m = RelaxedOneHotCategorical(torch.tensor([2.2]), ... torch.tensor([0.1, 0.2, 0.3, 0.4])) >>> m.sample() tensor([ 0.1294, 0.2324, 0.3859, 0.2523]) Args: temperature (Tensor): relaxation temperature probs (Tensor): event probabilities logits (Tensor): unnormalized log probability for each event """ arg_constraints = {"probs": constraints.simplex, "logits": constraints.real_vector} support = constraints.simplex has_rsample = True def __init__(self, temperature, probs=None, logits=None, validate_args=None): base_dist = ExpRelaxedCategorical( temperature, probs, logits, validate_args=validate_args ) super().__init__(base_dist, ExpTransform(), validate_args=validate_args)
[docs] def expand(self, batch_shape, _instance=None): new = self._get_checked_instance(RelaxedOneHotCategorical, _instance) return super().expand(batch_shape, _instance=new)
@property def temperature(self): return self.base_dist.temperature @property def logits(self): return self.base_dist.logits @property def probs(self): return self.base_dist.probs


Access comprehensive developer documentation for PyTorch

View Docs


Get in-depth tutorials for beginners and advanced developers

View Tutorials


Find development resources and get your questions answered

View Resources