Clean up is_fully_bayesian #1992
Closed
Conversation
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
facebook-github-bot
added
the
CLA Signed
|
This pull request was exported from Phabricator. Differential Revision: D50884342 |
Summary: X-link: pytorch/botorch#2108 This attempts to clean up the usage of `is_fully_bayesian` and also separately treat fully Bayesian models from ensemble models. The main changes in diff are to: - Add an `_is_fully_bayesian` attribute to `Model`. This is `True` for fully Bayesian models that rely on Pyro/NUTS to be fitted (they need some special handling for fitting and `state_dict` loading/saving. - Add an `_is_ensemble` attribute to `Model`. This indicates whether the model is a collection of multiple models that are stored in an additional batch dimension. This is hopefully a better classification, but I'm open to a different name here. - Rename `FullyBayesianPosterior` to `GaussianMixturePosterior` since that is more descriptive and plays better with the other changes. Reviewed By: esantorella Differential Revision: D50884342
dme65
force-pushed
the
export-D50884342
branch
from
7ecfad5 to
1a78f27
Compare
|
This pull request was exported from Phabricator. Differential Revision: D50884342 |
|
The failures here are expected and they should go away once the BoTorch part of this diff lands. |
facebook-github-bot
pushed a commit
to pytorch/botorch
that referenced
this pull request
Nov 16, 2023
Summary: X-link: facebook/Ax#1992 Pull Request resolved: #2108 This attempts to clean up the usage of `is_fully_bayesian` and also separately treat fully Bayesian models from ensemble models. The main changes in diff are to: - Add an `_is_fully_bayesian` attribute to `Model`. This is `True` for fully Bayesian models that rely on Pyro/NUTS to be fitted (they need some special handling for fitting and `state_dict` loading/saving. - Add an `_is_ensemble` attribute to `Model`. This indicates whether the model is a collection of multiple models that are stored in an additional batch dimension. This is hopefully a better classification, but I'm open to a different name here. - Rename `FullyBayesianPosterior` to `GaussianMixturePosterior` since that is more descriptive and plays better with the other changes. Reviewed By: esantorella Differential Revision: D50884342 fbshipit-source-id: 0ba603416c1823026c4fdf2e445cefdf8036cda8
|
This pull request has been merged in 3985791. |
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Summary:
This attempts to clean up the usage of
is_fully_bayesianand also separately treat fully Bayesian models from ensemble models.The main changes in diff are to:
_is_fully_bayesianattribute toModel. This isTruefor fully Bayesian models that rely on Pyro/NUTS to be fitted (they need some special handling for fitting andstate_dictloading/saving._is_ensembleattribute toModel. This indicates whether the model is a collection of multiple models that are stored in an additional batch dimension. This is hopefully a better classification, but I'm open to a different name here.FullyBayesianPosteriortoGaussianMixturePosteriorsince that is more descriptive and plays better with the other changes.Reviewed By: esantorella
Differential Revision: D50884342