[Not to Land (Hopefully)]Revert PT Pin to 0912 #5987
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
Summary: Original PR: https://github.com/pytorch/executorch/pull/5824/files Changes picked up in the revert: pytorch@f005dd5#diff-3b0b2409eb2a7cb2dfd94e84c17f54f48243649eb0874b78422b2f1411283d43L169 Differential Revision: D64053532
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/executorch/5987
Note: Links to docs will display an error until the docs builds have been completed. ❌ 3 New FailuresAs of commit b2e7687 with merge base 77b1f08 ( NEW FAILURES - The following jobs have failed:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
facebook-github-bot
added
the
CLA Signed
|
This pull request was exported from Phabricator. Differential Revision: D64053532 |
Jack-Khuu
changed the title
|
Not to land (hopefully) Testing CI sanctity (whether there were stray deps updates being pulled in) |
facebook-github-bot
pushed a commit
that referenced
this pull request
Oct 8, 2024
Summary: Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Differential Revision: D64074891
larryliu0820
added a commit
that referenced
this pull request
Oct 8, 2024
Summary: Pull Request resolved: #6026 Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Differential Revision: D64074891
facebook-github-bot
pushed a commit
that referenced
this pull request
Oct 8, 2024
Summary: Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Reviewed By: Jack-Khuu Differential Revision: D64074891
larryliu0820
added a commit
that referenced
this pull request
Oct 8, 2024
Summary: Pull Request resolved: #6026 Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Reviewed By: Jack-Khuu Differential Revision: D64074891
facebook-github-bot
pushed a commit
that referenced
this pull request
Oct 8, 2024
Summary: Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Reviewed By: Jack-Khuu Differential Revision: D64074891
facebook-github-bot
pushed a commit
that referenced
this pull request
Oct 9, 2024
Summary: Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Reviewed By: digantdesai, Jack-Khuu, tugsbayasgalan Differential Revision: D64074891
larryliu0820
added a commit
that referenced
this pull request
Oct 9, 2024
Summary: Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Reviewed By: digantdesai, Jack-Khuu, tugsbayasgalan Differential Revision: D64074891
facebook-github-bot
pushed a commit
that referenced
this pull request
Oct 9, 2024
Summary: Pull Request resolved: #6026 Did a bunch of debugging on OSS CI:https://github.com/pytorch/executorch/actions/runs/11241297226/job/31252590975 Was able to confirm although the problem happens in `ConvertToLinear` but the root cause is we are partitioning the graph differently between these two pytorch nightly: dev20240916 and dev20240917. The exported graph looks the same but the partitioner was behaving differently and causes the `ConvertToLinear` pass to error out. We can't really revert back to dev20240916 nightly because it breaks other CI jobs, see #5987. The current approach I'm taking avoids decomposing linear by using `to_edge_lower_and_transform` API. This avoids jumping into the rabbit hole of debugging the partitioning & tagging logic. Reviewed By: digantdesai, Jack-Khuu, tugsbayasgalan Differential Revision: D64074891 fbshipit-source-id: c434f9f5fc240b9268a7419fc66ee4a365ae1664
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:
Original PR: https://github.com/pytorch/executorch/pull/5824/files
Changes picked up in the revert:
f005dd5#diff-3b0b2409eb2a7cb2dfd94e84c17f54f48243649eb0874b78422b2f1411283d43L169
Differential Revision: D64053532