New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Intel GPU: specify the tolerance for torchbench models #125213
Closed
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
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/125213
Note: Links to docs will display an error until the docs builds have been completed. ✅ You can merge normally! (1 Unrelated Failure)As of commit 000d2cf with merge base 8320b77 (): FLAKY - The following job failed but was likely due to flakiness present on trunk:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
desertfire
approved these changes
Apr 30, 2024
@pytorchbot merge |
Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
andoorve
pushed a commit
to andoorve/pytorch
that referenced
this pull request
May 1, 2024
We encountered some model accuracy failures as the tolerance is critical. In general, we align with CUDA practice. This PR intends to adjust the tolerance for Torchbench models for training mode on Intel GPU devices and aligns with CUDA. Pull Request resolved: pytorch#125213 Approved by: https://github.com/desertfire
andoorve
pushed a commit
to andoorve/pytorch
that referenced
this pull request
May 1, 2024
We encountered some model accuracy failures as the tolerance is critical. In general, we align with CUDA practice. This PR intends to adjust the tolerance for Torchbench models for training mode on Intel GPU devices and aligns with CUDA. Pull Request resolved: pytorch#125213 Approved by: https://github.com/desertfire
facebook-github-bot
pushed a commit
to pytorch/benchmark
that referenced
this pull request
May 2, 2024
Summary: We encountered some model accuracy failures as the tolerance is critical. In general, we align with CUDA practice. This PR intends to adjust the tolerance for Torchbench models for training mode on Intel GPU devices and aligns with CUDA. X-link: pytorch/pytorch#125213 Approved by: https://github.com/desertfire Reviewed By: kit1980 Differential Revision: D56862220 fbshipit-source-id: a773ff0162da3bcac91834876c5ab0335c03ed53
pytorch-bot bot
pushed a commit
that referenced
this pull request
May 3, 2024
We encountered some model accuracy failures as the tolerance is critical. In general, we align with CUDA practice. This PR intends to adjust the tolerance for Torchbench models for training mode on Intel GPU devices and aligns with CUDA. Pull Request resolved: #125213 Approved by: https://github.com/desertfire
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Labels
ciflow/inductor
ciflow/trunk
Trigger trunk jobs on your pull request
ciflow/xpu
Run XPU CI tasks
Merged
module: dynamo
oncall: pt2
open source
topic: not user facing
topic category
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.
We encountered some model accuracy failures as the tolerance is critical. In general, we align with CUDA practice. This PR intends to adjust the tolerance for Torchbench models for training mode on Intel GPU devices and aligns with CUDA.
cc @ezyang @msaroufim @bdhirsh @anijain2305 @chauhang @voznesenskym @penguinwu @EikanWang @jgong5 @Guobing-Chen @XiaobingSuper @zhuhaozhe @blzheng @wenzhe-nrv @jiayisunx @chenyang78 @kadeng