added type hints to lazy_property
#144106
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/144106
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….distributions`. (#144110) Fixes #76772, #144196 Extends #144106 - added type annotations to `lazy_property`. - added type annotation to all `@property` and `@lazy_property` inside `torch.distributions` module. - added simply type-check unit test to ensure type inference is working. - replaced deprecated annotations like `typing.List` with the corresponding counterpart. - simplified `torch.Tensor` hints with plain `Tensor`, otherwise signatures can become very verbose. Pull Request resolved: #144110 Approved by: https://github.com/Skylion007
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…`. (#144197) Fixes #144196 Extends #144106 and #144110 ## Open Problems: - [ ] Annotating with `numbers.Number` is a bad idea, should consider using `float`, `SupportsFloat` or some `Procotol`. #144197 (comment) # 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 <python/mypy#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: #144197 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
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…`. (pytorch#144197) Fixes pytorch#144196 Extends pytorch#144106 and pytorch#144110 ## Open Problems: - [ ] Annotating with `numbers.Number` is a bad idea, should consider using `float`, `SupportsFloat` or some `Procotol`. pytorch#144197 (comment) # 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.~~ pytorch#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`. pytorch#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`~~ pytorch#144401 - ~~`transforms.py`: Fixed bug in `AffineTransform.sign` (could return `Tensor` instead of `int`).~~ pytorch#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 <python/mypy#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: pytorch#144197 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
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…`. (pytorch#144197) Fixes pytorch#144196 Extends pytorch#144106 and pytorch#144110 ## Open Problems: - [ ] Annotating with `numbers.Number` is a bad idea, should consider using `float`, `SupportsFloat` or some `Procotol`. pytorch#144197 (comment) # 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.~~ pytorch#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`. pytorch#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`~~ pytorch#144401 - ~~`transforms.py`: Fixed bug in `AffineTransform.sign` (could return `Tensor` instead of `int`).~~ pytorch#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 <python/mypy#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: pytorch#144197 Approved by: https://github.com/malfet Co-authored-by: Aaron Gokaslan <aaronGokaslan@gmail.com>
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Partial fix for #76772, it remains to add type hints to all the properties of the predefined distribution objects.
EDIT: #144110 builds on top of this PR and provides these type hints.
cc @fritzo @neerajprad @alicanb @nikitaved @ezyang @malfet @xuzhao9 @gramster