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>
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: https://github.com/pytorch/pytorch/pull/144110
Approved by: https://github.com/Skylion007
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
Summary:
There is a very common error when writing docs: One forgets to write a matching `` ` ``, and something like ``:attr:`x`` is rendered in the docs. This PR fixes most (all?) of these errors (and a few others).
I found these running ``grep -r ">[^#<][^<]*\`"`` on the `docs/build/html/generated` folder. The regex finds an HTML tag that does not start with `#` (as python comments in example code may contain backticks) and that contains a backtick in the rendered HTML.
This regex has not given any false positive in the current codebase, so I am inclined to suggest that we should add this check to the CI. Would this be possible / reasonable / easy to do malfet ?
Pull Request resolved: https://github.com/pytorch/pytorch/pull/60474
Reviewed By: mrshenli
Differential Revision: D29309633
Pulled By: albanD
fbshipit-source-id: 9621e0e9f87590cea060dd084fa367442b6bd046
Summary:
Fixes https://github.com/pytorch/pytorch/issues/50378.
Additionally, this has some minor fixes:
- [x] Fix mean for half-cauchy to return `inf` instead of `nan`.
- [x] Fix constraints/support for the relaxed categorical distribution.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/51053
Reviewed By: heitorschueroff
Differential Revision: D26077966
Pulled By: neerajprad
fbshipit-source-id: ca0213baa9bbdbc661aebbb901ab5e7fded38a5f
Summary:
Addresses https://github.com/pytorch/pytorch/issues/50496
This fixes a number of inconsistencies in torch.distributions.constraints as used for parameters and supports of probability distributions.
- Adds a `constraints.independent` and replaces `real_vector` with `independent(real, 1)`. (this pattern has long been used in Pyro)
- Adds an `.event_dim` attribute to all constraints.
- Tests that `constraint.check(data)` has the correct shape. (Previously the shapes were incorrect).
- Adds machinery to set static `.is_discrete` and `.event_dim` for `constraints.dependent`.
- Fixes constraints for a number of distributions.
## Tested
- added a new check to the constraints tests
- added a new check for `.event_dim`
cc fehiepsi feynmanliang stefanwebb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50547
Reviewed By: VitalyFedyunin
Differential Revision: D25918330
Pulled By: neerajprad
fbshipit-source-id: a648c3de3e8704f70f445c0f1c39f2593c8c74db
Summary:
This adds a `.expand` method for distributions that is akin to the `torch.Tensor.expand` method for tensors. It returns a new distribution instance with batch dimensions expanded to the desired `batch_shape`. Since this calls `torch.Tensor.expand` on the distribution's parameters, it does not allocate new memory for the expanded distribution instance's parameters.
e.g.
```python
>>> d = dist.Normal(torch.zeros(100, 1), torch.ones(100, 1))
>>> d.sample().shape
torch.Size([100, 1])
>>> d.expand([100, 10]).sample().shape
torch.Size([100, 10])
```
We have already been using the `.expand` method in Pyro in our [patch](https://github.com/uber/pyro/blob/dev/pyro/distributions/torch.py#L10) of `torch.distributions`. We use this in our models to enable dynamic broadcasting. This has also been requested by a few users on the distributions slack, and we believe will be useful to the larger community.
Note that currently, there is no convenient and efficient way to expand distribution instances:
- Many distributions use `TransformedDistribution` (or wrap over another distribution instance. e.g. `OneHotCategorical` uses a `Categorical` instance) under the hood, or have lazy parameters. This makes it difficult to collect all the relevant parameters, broadcast them and construct new instances.
- In the few cases where this is even possible, the resulting implementation would be inefficient since we will go through a lot of broadcasting and args validation logic in `__init__.py` that can be avoided.
The `.expand` method allows for a safe and efficient way to expand distribution instances. Additionally, this bypasses `__init__.py` (using `__new__` and populating relevant attributes) since we do not need to do any broadcasting or args validation (which was already done when the instance was first created). This can result in significant savings as compared to constructing new instances via `__init__` (that said, the `sample` and `log_prob` methods will probably be the rate determining steps in many applications).
e.g.
```python
>>> a = dist.Bernoulli(torch.ones([10000, 1]), validate_args=True)
>>> %timeit a.expand([10000, 100])
15.2 µs ± 224 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)
>>> %timeit dist.Bernoulli(torch.ones([10000, 100]), validate_args=True)
11.8 ms ± 153 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
```
cc. fritzo, apaszke, vishwakftw, alicanb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11341
Differential Revision: D9728485
Pulled By: soumith
fbshipit-source-id: 3b94c23bc6a43ee704389e6287aa83d1e278d52f
Summary:
`__repr__` currently fails for distributions with lazy attributes in PyTorch master, throwing a `KeyError`. This fixes the issue.
**Additionally:**
- Added `logits` to `arg_constraints` for distributions that accept either `probs` or `logits`. This is both to have `__repr__` display the `logits` param when available, and to be able to do validation checks (e.g. NaN checks) when the logit parametrization is used. fritzo, alicanb - I think there were reasons why we had not done so in the first place, but I am unable to recall now. It passes all the tests, but let me know if there is something that I am missing at the moment.
- There are certain distributions, e.g. `OneHotCategorical` which won't show any parameters because it uses a `categorical` instance under the hood and neither `logits` / `probs` in `arg_constraints` are present in the instance's `__dict__`. This isn't addressed in this PR.
cc. vishwakftw, fritzo, nadavbh12, apaszke
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11263
Differential Revision: D9654959
Pulled By: apaszke
fbshipit-source-id: 16f5b20243fe8e2c13e9c528050d4df0b8ea6e45
* Codemod to update our codebase to 0.4 standard
* Update some of the test scri[ts
* remove Variable in test_clip_grad_value
* fix _symbolic_override_wrapper_maker
