[generate_vmap_rule] support for jvp #91211
Closed
Conversation
This file contains hidden or 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
Support for jvp is very similar to support for backward(): - We need to vmap over a version of the original autograd.Function's jvp method that does not take ctx as input. - On the output, we need to reductify to ensure the output tangent has the same shape as the output. This reductify does not have the extra reduction semantics, because PyTorch forward-mode AD requires the output tangent to have the same exact shape as the output. - setup_context needs to tell us the bdims of the saved_tensors (necessary for vmap over jvp_no_context), as well as the output shapes (necessary for reductify). Test Plan: - Added jvp support to the *GenVmapAutogradFunction [ghstack-poisoned]
This was referenced Dec 20, 2022
Closed
zou3519
added a commit
that referenced
this pull request
Dec 20, 2022
Support for jvp is very similar to support for backward(): - We need to vmap over a version of the original autograd.Function's jvp method that does not take ctx as input. - On the output, we need to reductify to ensure the output tangent has the same shape as the output. This reductify does not have the extra reduction semantics, because PyTorch forward-mode AD requires the output tangent to have the same exact shape as the output. - setup_context needs to tell us the bdims of the saved_tensors (necessary for vmap over jvp_no_context), as well as the output shapes (necessary for reductify). Test Plan: - Added jvp support to the *GenVmapAutogradFunction ghstack-source-id: ed2abc4 Pull Request resolved: #91211
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/91211
Note: Links to docs will display an error until the docs builds have been completed. ❌ 1 FailuresAs of commit 3fef822: NEW FAILURES - The following jobs have failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
zou3519
added
the
release notes: torch.func
Support for jvp is very similar to support for backward(): - We need to vmap over a version of the original autograd.Function's jvp method that does not take ctx as input. - On the output, we need to reductify to ensure the output tangent has the same shape as the output. This reductify does not have the extra reduction semantics, because PyTorch forward-mode AD requires the output tangent to have the same exact shape as the output. - setup_context needs to tell us the bdims of the saved_tensors (necessary for vmap over jvp_no_context), as well as the output shapes (necessary for reductify). Test Plan: - Added jvp support to the *GenVmapAutogradFunction [ghstack-poisoned]
zou3519
added a commit
that referenced
this pull request
Dec 27, 2022
Support for jvp is very similar to support for backward(): - We need to vmap over a version of the original autograd.Function's jvp method that does not take ctx as input. - On the output, we need to reductify to ensure the output tangent has the same shape as the output. This reductify does not have the extra reduction semantics, because PyTorch forward-mode AD requires the output tangent to have the same exact shape as the output. - setup_context needs to tell us the bdims of the saved_tensors (necessary for vmap over jvp_no_context), as well as the output shapes (necessary for reductify). Test Plan: - Added jvp support to the *GenVmapAutogradFunction ghstack-source-id: 1bb23bd Pull Request resolved: #91211
Support for jvp is very similar to support for backward(): - We need to vmap over a version of the original autograd.Function's jvp method that does not take ctx as input. - On the output, we need to reductify to ensure the output tangent has the same shape as the output. This reductify does not have the extra reduction semantics, because PyTorch forward-mode AD requires the output tangent to have the same exact shape as the output. - setup_context needs to tell us the bdims of the saved_tensors (necessary for vmap over jvp_no_context), as well as the output shapes (necessary for reductify). Test Plan: - Added jvp support to the *GenVmapAutogradFunction [ghstack-poisoned]
zou3519
added a commit
that referenced
this pull request
Dec 27, 2022
Support for jvp is very similar to support for backward(): - We need to vmap over a version of the original autograd.Function's jvp method that does not take ctx as input. - On the output, we need to reductify to ensure the output tangent has the same shape as the output. This reductify does not have the extra reduction semantics, because PyTorch forward-mode AD requires the output tangent to have the same exact shape as the output. - setup_context needs to tell us the bdims of the saved_tensors (necessary for vmap over jvp_no_context), as well as the output shapes (necessary for reductify). Test Plan: - Added jvp support to the *GenVmapAutogradFunction ghstack-source-id: e09940f Pull Request resolved: #91211
Contributor
Author
|
@pytorchbot merge -f "check was cancelled, idk why" |
Collaborator
Merge startedYour change will be merged immediately since you used the force (-f) flag, bypassing any CI checks (ETA: 1-5 minutes). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
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.
Stack from ghstack:
Support for jvp is very similar to support for backward():
method that does not take ctx as input.
the same shape as the output. This reductify does not have the
extra reduction semantics, because PyTorch forward-mode AD requires the
output tangent to have the same exact shape as the output.
(necessary for vmap over jvp_no_context), as well
as the output shapes (necessary for reductify).
Test Plan: