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6c38b9be73
Fixes #144196 Extends #144106 and #144110 ## Open Problems: - [ ] Annotating with `numbers.Number` is a bad idea, should consider using `float`, `SupportsFloat` or some `Procotol`. https://github.com/pytorch/pytorch/pull/144197#discussion_r1903324769 # Notes - `beta.py`: needed to add `type: ignore` since `broadcast_all` is untyped. - `categorical.py`: converted `else` branches of mutually exclusive arguments to `if` branch[^2]. - ~~`dirichlet.py`: replaced `axis` with `dim` arguments.~~ #144402 - `gemoetric.py`: converted `else` branches of mutually exclusive arguments to `if` branch[^2]. - ~~`independent.py`: fixed bug in `Independent.__init__` where `tuple[int, ...]` could be passed to `Distribution.__init__` instead of `torch.Size`.~~ **EDIT:** turns out the bug is related to typing of `torch.Size`. #144218 - `independent.py`: made `Independent` a generic class of its base distribution. - `multivariate_normal.py`: converted `else` branches of mutually exclusive arguments to `if` branch[^2]. - `relaxed_bernoulli.py`: added class-level type hint for `base_dist`. - `relaxed_categorical.py`: added class-level type hint for `base_dist`. - ~~`transforms.py`: Added missing argument to docstring of `ReshapeTransform`~~ #144401 - ~~`transforms.py`: Fixed bug in `AffineTransform.sign` (could return `Tensor` instead of `int`).~~ #144400 - `transforms.py`: Added `type: ignore` comments to `AffineTransform.log_abs_det_jacobian`[^1]; replaced `torch.abs(scale)` with `scale.abs()`. - `transforms.py`: Added `type: ignore` comments to `AffineTransform.__eq__`[^1]. - `transforms.py`: Fixed type hint on `CumulativeDistributionTransform.domain`. Note that this is still an LSP violation, because `Transform.domain` is defined as `Constraint`, but `Distribution.domain` is defined as `Optional[Constraint]`. - skipped: `constraints.py`, `constraints_registry.py`, `kl.py`, `utils.py`, `exp_family.py`, `__init__.py`. ## Remark `TransformedDistribution`: `__init__` uses the check `if reinterpreted_batch_ndims > 0:`, which can lead to the creation of `Independent` distributions with only 1 component. This results in awkward code like `base_dist.base_dist` in `LogisticNormal`. ```python import torch from torch.distributions import * b1 = Normal(torch.tensor([0.0]), torch.tensor([1.0])) b2 = MultivariateNormal(torch.tensor([0.0]), torch.eye(1)) t = StickBreakingTransform() d1 = TransformedDistribution(b1, t) d2 = TransformedDistribution(b2, t) print(d1.base_dist) # Independent with 1 dimension print(d2.base_dist) # MultivariateNormal ``` One could consider changing this to `if reinterpreted_batch_ndims > 1:`. [^1]: Usage of `isinstance(value, numbers.Real)` leads to problems with static typing, as the `numbers` module is not supported by `mypy` (see <https://github.com/python/mypy/issues/3186>). This results in us having to add type-ignore comments in several places [^2]: Otherwise, we would have to add a bunch of `type: ignore` comments to make `mypy` happy, as it isn't able to perform the type narrowing. Ideally, such code should be replaced with structural pattern matching once support for Python 3.9 is dropped. Pull Request resolved: https://github.com/pytorch/pytorch/pull/144197 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
94 lines
2.6 KiB
Python
94 lines
2.6 KiB
Python
# mypy: allow-untyped-defs
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from typing import Optional, Union
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import torch
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from torch import Tensor
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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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from torch.types import _Number, _size
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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-deterministic")
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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) -> Tensor:
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return self.rate.reciprocal()
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@property
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def mode(self) -> Tensor:
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return torch.zeros_like(self.rate)
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@property
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def stddev(self) -> Tensor:
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return self.rate.reciprocal()
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@property
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def variance(self) -> Tensor:
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return self.rate.pow(-2)
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def __init__(
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self,
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rate: Union[Tensor, float],
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validate_args: Optional[bool] = None,
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) -> 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().__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: _size = torch.Size()) -> Tensor:
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shape = self._extended_shape(sample_shape)
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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.log1p(-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) -> tuple[Tensor]:
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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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