-
Notifications
You must be signed in to change notification settings - Fork 25.6k
[c10d] Add a logger for all nccl collectives with its time duration when completed #156008
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
Conversation
🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/156008
Note: Links to docs will display an error until the docs builds have been completed. ❌ 2 Cancelled JobsAs of commit 163c971 with merge base 9ed0060 ( CANCELLED JOBS - The following jobs were cancelled. Please retry:
This comment was automatically generated by Dr. CI and updates every 15 minutes. |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
PR is already accepted internally.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
PR is already accepted internally.
…hen completed (#156008) Summary: Pull Request resolved: #156008 We want to build a logging table for tracking the collective time spent on GPU for all internal workloads. Since we have a cudaEventQuery for both the start and end of a collective (We rolled out ECudaEventStart (enableTiming) fully already), we plan to add this logging table inside the watchdog of PyTorch ProcessGroupNCCL so that we get to know the duration of collectives. Test Plan: CI + dry run. Rollback Plan: Reviewed By: uthakore Differential Revision: D76552340
This pull request was exported from Phabricator. Differential Revision: D76552340 |
…hen completed (pytorch#156008) Summary: Pull Request resolved: pytorch#156008 We want to build a logging table for tracking the collective time spent on GPU for all internal workloads. Since we have a cudaEventQuery for both the start and end of a collective (We rolled out ECudaEventStart (enableTiming) fully already), we plan to add this logging table inside the watchdog of PyTorch ProcessGroupNCCL so that we get to know the duration of collectives. Test Plan: CI + dry run. Rollback Plan: Reviewed By: fegin, H-Huang, uthakore Differential Revision: D76552340
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
This pull request was exported from Phabricator. Differential Revision: D76552340 |
…hen completed (pytorch#156008) Summary: Pull Request resolved: pytorch#156008 We want to build a logging table for tracking the collective time spent on GPU for all internal workloads. Since we have a cudaEventQuery for both the start and end of a collective (We rolled out ECudaEventStart (enableTiming) fully already), we plan to add this logging table inside the watchdog of PyTorch ProcessGroupNCCL so that we get to know the duration of collectives. Test Plan: CI + dry run. Rollback Plan: Reviewed By: fegin, H-Huang, uthakore Differential Revision: D76552340
This pull request was exported from Phabricator. Differential Revision: D76552340 |
@pytorchbot merge -i (Initiating merge automatically since Phabricator Diff has merged, merging with -i because oss signals were bypassed internally) |
Merge startedYour change will be merged while ignoring the following 2 checks: pull / linux-jammy-py3.9-gcc11-pch / build, pull / linux-jammy-xpu-2025.1-py3.9 / build Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
Summary: We want to build a logging table for tracking the collective time spent on GPU for all internal workloads. Since we have a cudaEventQuery for both the start and end of a collective (We rolled out ECudaEventStart (enableTiming) fully already), we plan to add this logging table inside the watchdog of PyTorch ProcessGroupNCCL so that we get to know the duration of collectives.
Test Plan:
CI + dry run.
Rollback Plan:
Differential Revision: D76552340
cc @H-Huang @awgu @wanchaol @fegin @wz337 @wconstab @d4l3k