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
This PR fixes#69466 and introduces some other minor changes. Tests are somewhat more involved because a reference implementation in `scipy` is not available; tests proceed differently for discrete and continuous distributions.
For continuous distributions, we evaluate the gradient of the `log_prob` at the mode. Tests pass if the gradient is zero OR (the mode is at the boundary of the support of the distribution AND the `log_prob` decreases as we move away from the boundary to the interior of the support).
For discrete distributions, the notion of a gradient is not well defined. We thus "look" ahead and behind one step (e.g. if the mode of a Poisson distribution is 9, we consider 8 and 10). If the step ahead/behind is still within the support of the distribution, we assert that the `log_prob` is smaller than at the mode.
For one-hot encoded distributions (currently just `OneHotCategorical`), we evaluate the underlying mode (i.e. encoded as an integral tensor), "advance" by one label to get another sample that should have lower probability using `other = (mode + 1) % event_size` and re-encode as one-hot. The resultant `other` sample should have lower probability than the mode.
Furthermore, Gamma, half Cauchy, and half normal distributions have their support changed from positive to nonnegative. This change is necessary because the mode of the "half" distributions is zero, and the mode of the gamma distribution is zero for `concentration <= 1`.
cc @fritzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76690
Approved by: https://github.com/neerajprad
Summary:
Fixes https://github.com/pytorch/pytorch/issues/50496
Fixes https://github.com/pytorch/pytorch/issues/34859
Fixes https://github.com/pytorch/pytorch/issues/21596
This fixes many bugs involving `TransformedDistribution` and `ComposeTransform` when the component transforms changed their event shapes. Part of the fix is to introduce an `IndependentTransform` analogous to `distributions.Independent` and `constraints.independent`, and to introduce methods `Transform.forward_shape()` and `.inverse_shape()`. I have followed fehiepsi's suggestion and replaced `.input_event_dim` -> `.domain.event_dim` and `.output_event_dim` -> `.codomain.event_dim`. This allows us to deprecate `.event_dim` as an attribute.
## Summary of changes
- Fixes `TransformDistribution` and `ComposeTransform` shape errors.
- Fixes a behavior bug in `LogisticNormal`.
- Fixes `kl_divergence(TransformedDistribution, TransformedDistribution)`
- Adds methods `Transform.forward_shape()`, `.inverse_shape()` which are required for correct shape computations in `TransformedDistribution` and `ComposeTransform`.
- Adds an `IndependentTransform`.
- Adds a `ReshapeTransform` which is invaluable in testing shape logic in `ComposeTransform` and `TransformedDistribution` and which will be used by stefanwebb flowtorch.
- Fixes incorrect default values in `constraints.dependent.event_dim`.
- Documents the `.event_dim` and `.is_discrete` attributes.
## Changes planned for follow-up PRs
- Memoize `constraints.dependent_property` as we do with `lazy_property`, since we now consult those properties much more often.
## Tested
- [x] added a test for `Dist.support` vs `Dist(**params).support` to ensure static and dynamic attributes agree.
- [x] refactoring is covered by existing tests
- [x] add test cases for `ReshapedTransform`
- [x] add a test for `TransformedDistribution` on a wide grid of input shapes
- [x] added a regression test for https://github.com/pytorch/pytorch/issues/34859
cc fehiepsi feynmanliang stefanwebb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/50581
Reviewed By: ezyang, glaringlee, jpchen
Differential Revision: D26024247
Pulled By: neerajprad
fbshipit-source-id: f0b9a296f780ff49659b132409e11a29985dde9b
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:
Some internal tests were sporadically failing for https://github.com/pytorch/pytorch/pull/45648. The cause of this is a bug in `Binomial.__init__` that references the lazy `logits` attribute and sets it when not needed. This cleans up the `is_scalar` logic too which isn't needed given that `broadcast_all` will convert `Number` to a `tensor`.
The reason for the flakiness is the mutation of the params dict by the first test, which is fixed by doing a shallow copy. It will be better to convert this into a pytest parameterized test once https://github.com/pytorch/pytorch/pull/45648 is merged.
cc. fritzo, ezyang
Pull Request resolved: https://github.com/pytorch/pytorch/pull/46055
Reviewed By: agolynski
Differential Revision: D24221151
Pulled By: neerajprad
fbshipit-source-id: 15aae90a692ee6aed729c9f1d2d1b1388170a3c0
Summary:
I have experienced that sometimes both were in `__dict__`, but it chose to copy `probs` which loses precision over `logits`. This is especially important when training (bayesian) neural networks or doing other type of optimization, since the loss is heavily affected.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/18614
Differential Revision: D14793486
Pulled By: ezyang
fbshipit-source-id: d4ff5e34fbb4021ea9de9f58af09a7de00d80a63
Summary:
This issue was discovered by fehiepsi in https://github.com/uber/pyro/issues/1706 with the `log_prob` computation for Binomial, ~and can be seen with `torch.float32` when we have a combination of low probability value and high `total_count` - a test is added to capture this (since scipy only uses float64, the comparison is done using relative tolerance).~
The problem is in the code that tries to pull out the minimum values amongst the logits (written by me earlier, presumably to avoid numerical instability issues), but it is not needed.
EDIT: After a few attempts, I have been unable to reliably show that the change is more numerically stable, and have removed my previous test which fails on linux. The reason is that the issue manifests itself when `total_count` is high and `probs` is very low. However, the precision of `lgamma` when `total_count` is high is bad enough to wash away any benefits. The justification for this still stands though - (a) simplifies code (removes the unnecessary bit), (b) is no worse than the previous implementation, (c) has better continuity behavior as observed by fehiepsi in the issue above.
cc. fehiepsi, alicanb, fritzo
Pull Request resolved: https://github.com/pytorch/pytorch/pull/15962
Differential Revision: D13709541
Pulled By: ezyang
fbshipit-source-id: 596c6853b6e4d5fba42336afa168a665ab6fbde2
Summary:
This adds tests in tests/test_distributions.py to ensure that all methods of `Distribution` objects are jittable.
I've replaced a few samplers with jittable versions:
- `.uniform_()` -> `torch.rand()`
- `.exponential_()` -> `-(-torch.rand()).log1p()`
- `.normal_()` -> `torch.normal(torch.zeros(...), torch.ones(...), ...)`
Some jit failures remain, and are marked in test_distributions.py
- `Cauchy` and `HalfCauchy` do not support sampling due to missing `.cauchy_()`
- `Binomial` does not support `.enumerate_support()` due to `arange` ignoring its first arg.
- `MultivariateNormal`, `LowRankMultivariateNormal` do not support `.mean`, `.entropy`
- [x] Currently some tests fail (I've skipped those) due to unavailability of `aten::uniform` and `aten::cauchy` in the jit. Can someone suggest how to add these? I tried to add declarations to `torch/csrc/ir.cpp` and `torch/csrc/passes/shape_analysis.cpp`, but that resulted in "Couldn't find operator" errors.
- [x] There are still lots of `TracerWarning`s that something doesn't match something. I'm not sure whether these are real.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11560
Differential Revision: D9816327
Pulled By: apaszke
fbshipit-source-id: 72ec998ea13fc4c76d1ed003d9502e0fbaf728b8
Summary:
This works around #11535 by avoiding `arange(n, out=x)` and `eye(n, out=x)` in `torch.distributions`. I've confirmed that the `.enumerate_support()` methods are now jittable.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11542
Differential Revision: D9777805
Pulled By: apaszke
fbshipit-source-id: fa38f2f1acfc0a289f725fd8c92478573cfdbefb
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:
This adds an optional `expand=True` kwarg to the `distribution.expand_support()` method, to get a distribution's support without expanding the values over the distribution's `batch_shape`.
- The default `expand=True` preserves the current behavior, whereas `expand=False` collapses the batch dimensions.
e.g.
```python
In [47]: d = dist.OneHotCategorical(torch.ones(3, 5) * 0.5)
In [48]: d.batch_shape
Out[48]: torch.Size([3])
In [49]: d.enumerate_support()
Out[49]:
tensor([[[1., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[1., 0., 0., 0., 0.]],
[[0., 1., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 1., 0., 0., 0.]],
[[0., 0., 1., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 1., 0., 0.]],
[[0., 0., 0., 1., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 1., 0.]],
[[0., 0., 0., 0., 1.],
[0., 0., 0., 0., 1.],
[0., 0., 0., 0., 1.]]])
In [50]: d.enumerate_support().shape
Out[50]: torch.Size([5, 3, 5])
In [51]: d.enumerate_support(expand=False)
Out[51]:
tensor([[[1., 0., 0., 0., 0.]],
[[0., 1., 0., 0., 0.]],
[[0., 0., 1., 0., 0.]],
[[0., 0., 0., 1., 0.]],
[[0., 0., 0., 0., 1.]]])
In [52]: d.enumerate_support(expand=False).shape
Out[52]: torch.Size([5, 1, 5])
```
**Motivation:**
- Currently `enumerate_support` builds up tensors of size `support + batch_shape + event_shape`, but the values are *repeated* over the `batch_shape` (adding little in the way of information). This can lead to expensive matrix operations over large tensors when `batch_shape` is large (see, example above), often leading to OOM issues. We use `expand=False` in Pyro for message passing inference. e.g. when enumerating over the state space in a Hidden Markov Model. This creates sparse tensors that capture the markov dependence, and allows for the possibility of using optimized matrix operations over these sparse tensors. `expand=True`, on the other hand, will create tensors that scale exponentially in size with the length of the Markov chain.
- We have been using this in our [patch](https://github.com/uber/pyro/blob/dev/pyro/distributions/torch.py) of `torch.distributions` in Pyro. The interface has been stable, and it is already being used in a few Pyro algorithms. We think that this is more broadly applicable and will be of interest to the larger distributions community.
cc. apaszke, fritzo, alicanb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11231
Differential Revision: D9696290
Pulled By: soumith
fbshipit-source-id: c556f8ff374092e8366897ebe3f3b349538d9318
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
