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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
89 lines
3.1 KiB
Python
89 lines
3.1 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.utils import broadcast_all
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__all__ = ['Laplace']
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class Laplace(Distribution):
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r"""
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Creates a Laplace distribution parameterized by :attr:`loc` and :attr:`scale`.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = Laplace(torch.tensor([0.0]), torch.tensor([1.0]))
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>>> m.sample() # Laplace distributed with loc=0, scale=1
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tensor([ 0.1046])
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Args:
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loc (float or Tensor): mean of the distribution
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scale (float or Tensor): scale 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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has_rsample = True
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@property
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def mean(self):
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return self.loc
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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 variance(self):
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return 2 * self.scale.pow(2)
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@property
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def stddev(self):
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return (2 ** 0.5) * self.scale
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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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if isinstance(loc, Number) and isinstance(scale, Number):
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batch_shape = torch.Size()
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else:
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batch_shape = self.loc.size()
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super(Laplace, self).__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(Laplace, _instance)
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batch_shape = torch.Size(batch_shape)
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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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super(Laplace, 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 rsample(self, sample_shape=torch.Size()):
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shape = self._extended_shape(sample_shape)
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finfo = torch.finfo(self.loc.dtype)
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if torch._C._get_tracing_state():
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# [JIT WORKAROUND] lack of support for .uniform_()
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u = torch.rand(shape, dtype=self.loc.dtype, device=self.loc.device) * 2 - 1
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return self.loc - self.scale * u.sign() * torch.log1p(-u.abs().clamp(min=finfo.tiny))
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u = self.loc.new(shape).uniform_(finfo.eps - 1, 1)
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# TODO: If we ever implement tensor.nextafter, below is what we want ideally.
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# u = self.loc.new(shape).uniform_(self.loc.nextafter(-.5, 0), .5)
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return self.loc - self.scale * u.sign() * torch.log1p(-u.abs())
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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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return -torch.log(2 * self.scale) - torch.abs(value - self.loc) / self.scale
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def cdf(self, value):
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if self._validate_args:
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self._validate_sample(value)
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return 0.5 - 0.5 * (value - self.loc).sign() * torch.expm1(-(value - self.loc).abs() / self.scale)
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def icdf(self, value):
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term = value - 0.5
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return self.loc - self.scale * (term).sign() * torch.log1p(-2 * term.abs())
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def entropy(self):
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return 1 + torch.log(2 * self.scale)
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