Fix sample_all_priors to not sample one value for all lengthscales #2404
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## main #2404 +/- ##
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This pull request was exported from Phabricator. Differential Revision: D58855726 |
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…eta-pytorch#2404) Summary: Pull Request resolved: meta-pytorch#2404 meta-pytorch#2371 added support for `sample_all_priors` to handle multivariate priors, but these changes resulted in sampling the same value for all entries of a multi-dimensional hyperparameter if a univariate prior is used. In particular, this means that `sample_all_priors` will sample exactly the same lengthscale for all dimensions when using a univariate prior. This diff changes this behavior to instead sample according to the shape of the closure when a univariate prior is specified. This results in sampling different lengthscales for each dimension and batch dimension. Differential Revision: D58855726
…eta-pytorch#2404) Summary: Pull Request resolved: meta-pytorch#2404 meta-pytorch#2371 added support for `sample_all_priors` to handle multivariate priors, but these changes resulted in sampling the same value for all entries of a multi-dimensional hyperparameter if a univariate prior is used. In particular, this means that `sample_all_priors` will sample exactly the same lengthscale for all dimensions when using a univariate prior. This diff changes this behavior to instead sample according to the shape of the closure when a univariate prior is specified. This results in sampling different lengthscales for each dimension and batch dimension. Differential Revision: D58855726
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This pull request was exported from Phabricator. Differential Revision: D58855726 |
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This pull request has been merged in 965f154. |
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Summary:
#2371 added support for
sample_all_priorsto handle multivariate priors, but these changes resulted in sampling the same value for all entries of a multi-dimensional hyperparameter if a univariate prior is used. In particular, this means thatsample_all_priorswill sample exactly the same lengthscale for all dimensions when using a univariate prior.This diff changes this behavior to instead sample according to the shape of the closure when a univariate prior is specified. This results in sampling different lengthscales for each dimension and batch dimension.
Differential Revision: D58855726