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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
88 lines
2.6 KiB
Python
88 lines
2.6 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.exp_family import ExponentialFamily
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from torch.distributions.utils import broadcast_all
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__all__ = ['Exponential']
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class Exponential(ExponentialFamily):
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r"""
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Creates a Exponential distribution parameterized by :attr:`rate`.
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterinistic")
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>>> m = Exponential(torch.tensor([1.0]))
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>>> m.sample() # Exponential distributed with rate=1
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tensor([ 0.1046])
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Args:
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rate (float or Tensor): rate = 1 / scale of the distribution
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"""
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arg_constraints = {'rate': constraints.positive}
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support = constraints.nonnegative
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has_rsample = True
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_mean_carrier_measure = 0
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@property
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def mean(self):
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return self.rate.reciprocal()
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@property
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def mode(self):
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return torch.zeros_like(self.rate)
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@property
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def stddev(self):
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return self.rate.reciprocal()
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@property
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def variance(self):
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return self.rate.pow(-2)
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def __init__(self, rate, validate_args=None):
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self.rate, = broadcast_all(rate)
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batch_shape = torch.Size() if isinstance(rate, Number) else self.rate.size()
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super(Exponential, 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(Exponential, _instance)
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batch_shape = torch.Size(batch_shape)
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new.rate = self.rate.expand(batch_shape)
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super(Exponential, 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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if torch._C._get_tracing_state():
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# [JIT WORKAROUND] lack of support for ._exponential()
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u = torch.rand(shape, dtype=self.rate.dtype, device=self.rate.device)
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return -(-u).log1p() / self.rate
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return self.rate.new(shape).exponential_() / self.rate
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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 self.rate.log() - self.rate * value
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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 1 - torch.exp(-self.rate * value)
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def icdf(self, value):
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return -torch.log(1 - value) / self.rate
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def entropy(self):
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return 1.0 - torch.log(self.rate)
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@property
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def _natural_params(self):
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return (-self.rate, )
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def _log_normalizer(self, x):
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return -torch.log(-x)
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