The `PositiveDefiniteTransform` is required to transform from an unconstrained space to positive definite matrices, e.g. to support testing the Wishart mode in #76690. It is a simple extension of the `LowerCholeskyTransform`.
I've also added a small test that ensures the generated data belong to the domain of the associated transform. Previously, the data generated for the inverse transform of the `LowerCholeskyTransform` wasn't part of the domain, and the test only passed because the comparison uses `equal_nan=True`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/76777
Approved by: https://github.com/lezcano, https://github.com/fritzo, https://github.com/soumith
### Description
Across PyTorch's docstrings, both `callable` and `Callable` for variable types. The Callable should be capitalized as we are referring to the `Callable` type, and not the Python `callable()` function.
### Testing
There shouldn't be any testing required.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/82487
Approved by: https://github.com/albanD
Summary:
This PR fixes https://github.com/pytorch/pytorch/issues/53485 by allowing zero rates for the Poisson distribution. This implementation is consistent with `scipy.stats.poisson` which admits zero rates. In addition to addressing the aforementioned issue, this PR makes two supporting changes:
1. add a `nonnegative` constraint to enforce non-negative rates for the Poisson distribution.
2. adjust the evaluation of the gradient of `xlogy` such that it is well defined for `x == 0 and y == 0`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/61511
Reviewed By: ejguan
Differential Revision: D30352917
Pulled By: albanD
fbshipit-source-id: f3d33da58360e80d75eb83519f199b93232a2a2d
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:
This adds a transform to convert a real vector of (D * (D-1))/2 dimension into the cholesky factor of a D x D correlation matrix. This follows the implementation in [NumPyro](https://github.com/pyro-ppl/numpyro/blob/master/numpyro/distributions/transforms.py) by fehiepsi. This is needed for the LKJDistribution which will be added in a subsequent PR.
Also in line with the ongoing effort to refactor distributions test, this moves the transforms test into its own file that uses pytest with parametrized fixtures.
For review:
fehiepsi - could you help review the math?
fritzo - do you have any suggestions for what to do about the event dimension (more details are in the comment below)?
ezyang - could you review the changes in `run_test.py`? Instead of a separate `PYTEST_TESTS`, I have clubbed these tests in `USE_PYTEST_LIST` to avoid duplicate logic. The only difference is that we do not anymore check if pytest is not installed and exclude the tests in the list. I figured that if existing tests are already using pytest, this should not matter.
TODOs (probably not all can be satisfied at the same time):
- [x] Use operations that are JIT friendly, i.e. the transform works with different sized input under JIT.
- [x] Resolve test failures - currently `arange(scalar_tensor)` fails on certain backends but this is needed for JIT. Maybe we should only support same sized tensor under JIT?
- [x] Add tests to check that the transform gives correct gradients and is in agreement with the `log_det_jacobian`.
- [x] Add `input_event_dim` and `output_event_dim` to `CorrCholeskyTransform`.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/48041
Reviewed By: zhangguanheng66
Differential Revision: D25262505
Pulled By: neerajprad
fbshipit-source-id: 5a57e1c19d8230b53592437590b9169bdf2f71e9
Summary:
This adds calls to `super().__init__()` in three classes in torch.distributions.
This is needed when `Distribution` and `Transform` objects are used with multiple inheritance, as e.g. combined with `torch.nn.Module`s. For example
```py
class MyModule(torch.distributions.Transform, torch.nn.Module):
...
```
cc martinjankowiak esling who have wanted to use this pattern, e.g. in #16756
Pull Request resolved: https://github.com/pytorch/pytorch/pull/16772
Differential Revision: D13978633
Pulled By: soumith
fbshipit-source-id: 8bc6cca1747cd74d32135ee2fe588bba2ea796f1
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
* 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
