Summary:
A number of derived distributions use base distributions in their
implementation.
We add what we hope is a comprehensive test whether all distributions
actually honor skipping validation of arguments in log_prob and then
fix the bugs we found. These bugs are particularly cumbersome in
PyTorch 1.8 and master when validate_args is turned on by default
In addition one might argue that validate_args is not performing
as well as it should when the default is not to validate but the
validation is turned on in instantiation.
Arguably, there is another set of bugs or at least inconsistencies
when validation of inputs does not prevent invalid indices in
sample validation (when with validation an IndexError is raised
in the test). We would encourage the implementors to be more
ambitious when validation is turned on and amend sample validation
to throw a ValueError for consistency.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/53600
Reviewed By: mrshenli
Differential Revision: D26928088
Pulled By: neerajprad
fbshipit-source-id: 52784a754da2faee1a922976e2142957c6c02e28
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:
This PR:
- adds a `.expand` method for `TransformedDistribution` along the lines of #11341.
- uses this method to simplify `.expand` in distribution classes that subclass off of `TransformedDistribution`.
- restores testing of `TransformedDistribution` fixtures.
- fixes some bugs wherein we were not setting certain attributes in the expanded instances, and adds tests for `.mean` and `.variance` which use these attributes.
There are many cases where users directly use `TransformedDistribution` rather than subclassing off it. In such cases, it seems rather inconvenient to have to write a separate class just to define a `.expand` method. The default implementation should suffice in these cases.
cc. fritzo, vishwakftw, alicanb
Pull Request resolved: https://github.com/pytorch/pytorch/pull/11607
Differential Revision: D9818225
Pulled By: soumith
fbshipit-source-id: 2c4b3812b9a03e6985278cfce0f9a127ce536f23
Summary:
This implements the two-parameter Weibull distribution, with scale $\lambda$ and shape $k$ parameters as described on [Wikipedia](https://en.wikipedia.org/wiki/Weibull_distribution).
**Details**
- We implement as a transformed exponential distribution, as described [here](https://en.wikipedia.org/wiki/Weibull_distribution#Related_distributions).
- The `weibull_min` variance function in scipy does not yet support a vector of distributions, so our unit test uses a scalar distribution instead of a vector.
Example of the bug:
```
>>> sp.stats.expon(np.array([0.5, 1, 2])).var() # fine
array([1., 1., 1.])
>>> sp.stats.weibull_min(c=np.array([0.5, 1, 2])).var() # buggy
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/local/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py", line 490, in var
return self.dist.var(*self.args, **self.kwds)
File "/usr/local/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py", line 1242, in var
res = self.stats(*args, **kwds)
File "/usr/local/lib/python3.7/site-packages/scipy/stats/_distn_infrastructure.py", line 1038, in stats
if np.isinf(mu):
ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all()
```
Pull Request resolved: https://github.com/pytorch/pytorch/pull/9454
Differential Revision: D8863574
Pulled By: SsnL
fbshipit-source-id: 1ad3e175b469eee2b6af98e7b379ea170d3d9787
