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b13cd141b3
Adds suppressions to pyrefly will typecheck clean: https://github.com/pytorch/pytorch/issues/163283 Test plan: dmypy restart && python3 scripts/lintrunner.py -a pyrefly check step 1: delete lines in the pyrefly.toml file from the `project-excludes` field step 2: run pyrefly check step 3: add suppressions, clean up unused suppressions before: https://gist.github.com/maggiemoss/4b3bf2037014e116bc00706a16aef199 after: 0 errors (4,263 ignored) Pull Request resolved: https://github.com/pytorch/pytorch/pull/164748 Approved by: https://github.com/oulgen
77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
# mypy: allow-untyped-defs
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from typing import Optional, Union
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from torch import Tensor
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from torch.distributions import constraints
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from torch.distributions.normal import Normal
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from torch.distributions.transformed_distribution import TransformedDistribution
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from torch.distributions.transforms import ExpTransform
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__all__ = ["LogNormal"]
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class LogNormal(TransformedDistribution):
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r"""
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Creates a log-normal distribution parameterized by
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:attr:`loc` and :attr:`scale` where::
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X ~ Normal(loc, scale)
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Y = exp(X) ~ LogNormal(loc, scale)
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Example::
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>>> # xdoctest: +IGNORE_WANT("non-deterministic")
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>>> m = LogNormal(torch.tensor([0.0]), torch.tensor([1.0]))
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>>> m.sample() # log-normal distributed with mean=0 and stddev=1
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tensor([ 0.1046])
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Args:
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loc (float or Tensor): mean of log of distribution
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scale (float or Tensor): standard deviation of log of the distribution
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"""
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arg_constraints = {"loc": constraints.real, "scale": constraints.positive}
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# pyrefly: ignore # bad-override
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support = constraints.positive
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has_rsample = True
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# pyrefly: ignore # bad-override
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base_dist: Normal
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def __init__(
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self,
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loc: Union[Tensor, float],
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scale: Union[Tensor, float],
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validate_args: Optional[bool] = None,
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) -> None:
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base_dist = Normal(loc, scale, validate_args=validate_args)
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super().__init__(base_dist, ExpTransform(), validate_args=validate_args)
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def expand(self, batch_shape, _instance=None):
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new = self._get_checked_instance(LogNormal, _instance)
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return super().expand(batch_shape, _instance=new)
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@property
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def loc(self) -> Tensor:
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return self.base_dist.loc
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@property
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def scale(self) -> Tensor:
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return self.base_dist.scale
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@property
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def mean(self) -> Tensor:
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return (self.loc + self.scale.pow(2) / 2).exp()
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@property
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def mode(self) -> Tensor:
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return (self.loc - self.scale.square()).exp()
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@property
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def variance(self) -> Tensor:
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scale_sq = self.scale.pow(2)
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return scale_sq.expm1() * (2 * self.loc + scale_sq).exp()
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def entropy(self):
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return self.base_dist.entropy() + self.loc
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