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4618371da5
This is a new version of #15648 based on the latest master branch. Unlike the previous PR where I fixed a lot of the doctests in addition to integrating xdoctest, I'm going to reduce the scope here. I'm simply going to integrate xdoctest, and then I'm going to mark all of the failing tests as "SKIP". This will let xdoctest run on the dashboards, provide some value, and still let the dashboards pass. I'll leave fixing the doctests themselves to another PR. In my initial commit, I do the bare minimum to get something running with failing dashboards. The few tests that I marked as skip are causing segfaults. Running xdoctest results in 293 failed, 201 passed tests. The next commits will be to disable those tests. (unfortunately I don't have a tool that will insert the `#xdoctest: +SKIP` directive over every failing test, so I'm going to do this mostly manually.) Fixes https://github.com/pytorch/pytorch/issues/71105 @ezyang Pull Request resolved: https://github.com/pytorch/pytorch/pull/82797 Approved by: https://github.com/ezyang
79 lines
3.3 KiB
Python
79 lines
3.3 KiB
Python
import torch
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from torch._six import nan
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from torch.distributions import constraints
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from torch.distributions.uniform import Uniform
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from torch.distributions.transformed_distribution import TransformedDistribution
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from torch.distributions.transforms import AffineTransform, PowerTransform
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from torch.distributions.utils import broadcast_all, euler_constant
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__all__ = ['Kumaraswamy']
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def _moments(a, b, n):
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"""
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Computes nth moment of Kumaraswamy using using torch.lgamma
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"""
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arg1 = 1 + n / a
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log_value = torch.lgamma(arg1) + torch.lgamma(b) - torch.lgamma(arg1 + b)
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return b * torch.exp(log_value)
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class Kumaraswamy(TransformedDistribution):
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r"""
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Samples from a Kumaraswamy distribution.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = Kumaraswamy(torch.tensor([1.0]), torch.tensor([1.0]))
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>>> m.sample() # sample from a Kumaraswamy distribution with concentration alpha=1 and beta=1
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tensor([ 0.1729])
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Args:
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concentration1 (float or Tensor): 1st concentration parameter of the distribution
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(often referred to as alpha)
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concentration0 (float or Tensor): 2nd concentration parameter of the distribution
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(often referred to as beta)
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"""
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arg_constraints = {'concentration1': constraints.positive, 'concentration0': constraints.positive}
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support = constraints.unit_interval
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has_rsample = True
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def __init__(self, concentration1, concentration0, validate_args=None):
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self.concentration1, self.concentration0 = broadcast_all(concentration1, concentration0)
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finfo = torch.finfo(self.concentration0.dtype)
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base_dist = Uniform(torch.full_like(self.concentration0, 0),
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torch.full_like(self.concentration0, 1),
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validate_args=validate_args)
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transforms = [PowerTransform(exponent=self.concentration0.reciprocal()),
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AffineTransform(loc=1., scale=-1.),
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PowerTransform(exponent=self.concentration1.reciprocal())]
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super(Kumaraswamy, self).__init__(base_dist, transforms, validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(Kumaraswamy, _instance)
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new.concentration1 = self.concentration1.expand(batch_shape)
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new.concentration0 = self.concentration0.expand(batch_shape)
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return super(Kumaraswamy, self).expand(batch_shape, _instance=new)
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@property
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def mean(self):
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return _moments(self.concentration1, self.concentration0, 1)
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@property
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def mode(self):
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# Evaluate in log-space for numerical stability.
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log_mode = self.concentration0.reciprocal() * \
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(-self.concentration0).log1p() - (-self.concentration0 * self.concentration1).log1p()
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log_mode[(self.concentration0 < 1) | (self.concentration1 < 1)] = nan
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return log_mode.exp()
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@property
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def variance(self):
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return _moments(self.concentration1, self.concentration0, 2) - torch.pow(self.mean, 2)
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def entropy(self):
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t1 = (1 - self.concentration1.reciprocal())
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t0 = (1 - self.concentration0.reciprocal())
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H0 = torch.digamma(self.concentration0 + 1) + euler_constant
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return t0 + t1 * H0 - torch.log(self.concentration1) - torch.log(self.concentration0)
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