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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
73 lines
2.6 KiB
Python
73 lines
2.6 KiB
Python
from numbers import Number
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import math
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import torch
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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, ExpTransform
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from torch.distributions.utils import broadcast_all, euler_constant
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__all__ = ['Gumbel']
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class Gumbel(TransformedDistribution):
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r"""
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Samples from a Gumbel Distribution.
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Examples::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = Gumbel(torch.tensor([1.0]), torch.tensor([2.0]))
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>>> m.sample() # sample from Gumbel distribution with loc=1, scale=2
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tensor([ 1.0124])
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Args:
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loc (float or Tensor): Location parameter of the distribution
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scale (float or Tensor): Scale parameter of the distribution
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"""
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arg_constraints = {'loc': constraints.real, 'scale': constraints.positive}
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support = constraints.real
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def __init__(self, loc, scale, validate_args=None):
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self.loc, self.scale = broadcast_all(loc, scale)
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finfo = torch.finfo(self.loc.dtype)
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if isinstance(loc, Number) and isinstance(scale, Number):
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base_dist = Uniform(finfo.tiny, 1 - finfo.eps)
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else:
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base_dist = Uniform(torch.full_like(self.loc, finfo.tiny),
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torch.full_like(self.loc, 1 - finfo.eps))
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transforms = [ExpTransform().inv, AffineTransform(loc=0, scale=-torch.ones_like(self.scale)),
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ExpTransform().inv, AffineTransform(loc=loc, scale=-self.scale)]
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super(Gumbel, 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(Gumbel, _instance)
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new.loc = self.loc.expand(batch_shape)
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new.scale = self.scale.expand(batch_shape)
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return super(Gumbel, self).expand(batch_shape, _instance=new)
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# Explicitly defining the log probability function for Gumbel due to precision issues
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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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y = (self.loc - value) / self.scale
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return (y - y.exp()) - self.scale.log()
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@property
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def mean(self):
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return self.loc + self.scale * euler_constant
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@property
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def mode(self):
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return self.loc
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@property
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def stddev(self):
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return (math.pi / math.sqrt(6)) * self.scale
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@property
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def variance(self):
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return self.stddev.pow(2)
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def entropy(self):
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return self.scale.log() + (1 + euler_constant)
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