[torch.distributions] Implement positive-semidefinite constraint #71375
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
CI Flow Status⚛️ CI FlowRuleset - Version:
You can add a comment to the PR and tag @pytorchbot with the following commands: # ciflow rerun, "ciflow/default" will always be added automatically
@pytorchbot ciflow rerun
# ciflow rerun with additional labels "-l <ciflow/label_name>", which is equivalent to adding these labels manually and trigger the rerun
@pytorchbot ciflow rerun -l ciflow/scheduled -l ciflow/slowFor more information, please take a look at the CI Flow Wiki. |
🔗 Helpful links
💊 CI failures summary and remediationsAs of commit 7e20f50 (more details on the Dr. CI page):
🕵️ 1 new failure recognized by patternsThe following CI failures do not appear to be due to upstream breakages:
|
|
@pytorchbot ciflow rerun |
|
@neerajprad May I ask your opinions on this PR? |
neerajprad
reviewed
Jan 18, 2022
|
@pytorchbot ciflow rerun |
samdow
added
the
triaged
neerajprad
reviewed
Jan 19, 2022
This reverts commit 58df1f0.
neerajprad
approved these changes
Jan 19, 2022
|
@neerajprad has imported this pull request. If you are a Meta employee, you can view this diff on Phabricator. |
facebook-github-bot
pushed a commit
that referenced
this pull request
Jan 20, 2022
) Summary: While implementing #70275, I thought that it will be useful if there is a `torch.distributions.constraints` to check the positive-semidefiniteness of matrix random variables. This PR implements it with `torch.linalg.eigvalsh`, different from `torch.distributions.constraints.positive_definite` implemented with `torch.linalg.cholesky_ex`. Currently, `torch.linalg.cholesky_ex` returns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism. cc neerajprad Pull Request resolved: #71375 Reviewed By: H-Huang Differential Revision: D33663990 Pulled By: neerajprad fbshipit-source-id: 02cefbb595a1da5e54a239d4f17b33c619416518
pytorchmergebot
pushed a commit
that referenced
this pull request
Jan 20, 2022
) Summary: While implementing #70275, I thought that it will be useful if there is a `torch.distributions.constraints` to check the positive-semidefiniteness of matrix random variables. This PR implements it with `torch.linalg.eigvalsh`, different from `torch.distributions.constraints.positive_definite` implemented with `torch.linalg.cholesky_ex`. Currently, `torch.linalg.cholesky_ex` returns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism. cc neerajprad Pull Request resolved: #71375 Reviewed By: H-Huang Differential Revision: D33663990 Pulled By: neerajprad fbshipit-source-id: 02cefbb595a1da5e54a239d4f17b33c619416518 (cherry picked from commit 43eaea5)
|
Thank you for the reviews and feedbacks. |
nonconvexopt
deleted the
torch.distributions.constraints.positive_semidefinite
branch
2 tasks
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 3, 2022
…375) Summary: While implementing pytorch/pytorch#70275, I thought that it will be useful if there is a `torch.distributions.constraints` to check the positive-semidefiniteness of matrix random variables. This PR implements it with `torch.linalg.eigvalsh`, different from `torch.distributions.constraints.positive_definite` implemented with `torch.linalg.cholesky_ex`. Currently, `torch.linalg.cholesky_ex` returns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism. cc neerajprad Pull Request resolved: pytorch/pytorch#71375 Reviewed By: H-Huang Differential Revision: D33663990 Pulled By: neerajprad fbshipit-source-id: 02cefbb595a1da5e54a239d4f17b33c619416518 (cherry picked from commit 43eaea5)
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 3, 2022
…375) Summary: While implementing pytorch/pytorch#70275, I thought that it will be useful if there is a `torch.distributions.constraints` to check the positive-semidefiniteness of matrix random variables. This PR implements it with `torch.linalg.eigvalsh`, different from `torch.distributions.constraints.positive_definite` implemented with `torch.linalg.cholesky_ex`. Currently, `torch.linalg.cholesky_ex` returns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism. cc neerajprad Pull Request resolved: pytorch/pytorch#71375 Reviewed By: H-Huang Differential Revision: D33663990 Pulled By: neerajprad fbshipit-source-id: 02cefbb595a1da5e54a239d4f17b33c619416518 (cherry picked from commit 43eaea5)
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 9, 2022
…375) Summary: While implementing pytorch/pytorch#70275, I thought that it will be useful if there is a `torch.distributions.constraints` to check the positive-semidefiniteness of matrix random variables. This PR implements it with `torch.linalg.eigvalsh`, different from `torch.distributions.constraints.positive_definite` implemented with `torch.linalg.cholesky_ex`. Currently, `torch.linalg.cholesky_ex` returns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism. cc neerajprad Pull Request resolved: pytorch/pytorch#71375 Reviewed By: H-Huang Differential Revision: D33663990 Pulled By: neerajprad fbshipit-source-id: 02cefbb595a1da5e54a239d4f17b33c619416518 (cherry picked from commit 43eaea5)
cyyever
pushed a commit
to cyyever/pytorch_private
that referenced
this pull request
Feb 9, 2022
…375) Summary: While implementing pytorch/pytorch#70275, I thought that it will be useful if there is a `torch.distributions.constraints` to check the positive-semidefiniteness of matrix random variables. This PR implements it with `torch.linalg.eigvalsh`, different from `torch.distributions.constraints.positive_definite` implemented with `torch.linalg.cholesky_ex`. Currently, `torch.linalg.cholesky_ex` returns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism. cc neerajprad Pull Request resolved: pytorch/pytorch#71375 Reviewed By: H-Huang Differential Revision: D33663990 Pulled By: neerajprad fbshipit-source-id: 02cefbb595a1da5e54a239d4f17b33c619416518 (cherry picked from commit 43eaea5)
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.
While implementing #70275, I thought that it will be useful if there is a
torch.distributions.constraintsto check the positive-semidefiniteness of matrix random variables.This PR implements it with
torch.linalg.eigvalsh, different fromtorch.distributions.constraints.positive_definiteimplemented withtorch.linalg.cholesky_ex.Currently,
torch.linalg.cholesky_exreturns only the order of the leading minor that is not positive-definite in symmetric matrices and we can't check positive semi-definiteness by the mechanism.cc @neerajprad