* We are separating out the rocm jobs of the periodic workflow
* We are introducing a new label `ciflow/periodic-rocm-mi200` to allow us to run distributed tests only on ROCm runners, without triggering many other jobs on the `periodic.yml` workflow (via `ciflow/periodic`)
* This new workflow will also be triggered via the `ciflow/periodic`, thus maintaining the old status quo.
* We are reverting to the `linux.rocm.gpu.4` label since it targets a lot more CI nodes at this point than the K8s/ARC-based `linux.rocm.gpu.mi250.4` label, as that is still having some network/scaling issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166544
Approved by: https://github.com/jeffdaily
Co-authored-by: Jeff Daily <jeff.daily@amd.com>
This PR enables additional Inductor unit tests for Intel GPU. Due to the increased number of test cases, the number of runners has been extended from 8 to 12 to prevent CI timeouts.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166047
Approved by: https://github.com/jansel
Co-authored-by: Deng, Daisy <daisy.deng@intel.com>
Co-authored-by: Jason Ansel <jansel@jansel.net>
vllm-compile implies "module: vllm" and "oncall: pt2".
The volume of issues in Flex -> HigherOrderOperators is too noisy,
plus we have a different set of folks looking at each, so I'm going to
make that not automatic anymore. We can still manually label flex issues
as higher order operator issues.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/166172
Approved by: https://github.com/angelayi
* Moving rocm.yml from using persistent non-ARC runners from the combined MI2xx (MI210 + MI250) cluster to the ARC runners from the MI250 cluster. This halves the number of nodes, but provides access to approximately 4 times the runners, since every 8-GPU MI250 node now provides 8 1-GPU runners. This should help with concurrent capacity and queueing on the MI2xx jobs.
Tested here successfully: https://github.com/pytorch/pytorch/actions/runs/18620814622/job/53092469720
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165481
Approved by: https://github.com/jeffdaily
Co-authored-by: Jithun Nair <37884920+jithunnair-amd@users.noreply.github.com>
1. Run distributed job with B200 runner, periodically.
2. discovered generic distributed test issue that certain unit test hard-coded ranks, calling for require_exact_world_size(world_size) API instead of require_world_size(world_size).
Pull Request resolved: https://github.com/pytorch/pytorch/pull/159323
Approved by: https://github.com/eqy
Co-authored-by: Aidyn-A <aidyn.b.aitzhan@gmail.com>
Use linux.c7i.2xlarge as the default runner for the _linux-build.yml workflow. In testing we found that switching from c5 - c7i grants a 15-20% faster build times despite c7i costing 5% more. This should reduce costs of jobs using _linux-build.yml.
Relates to pytorch/test-infra#7175.
Pull Request resolved: https://github.com/pytorch/pytorch/pull/164747
Approved by: https://github.com/atalman
We noticed that disabling autorevert in any and all ci:sevs is too impactful, as ci: sevs are sometimes created just to communicate an action or a impactful change. But sometimes durring a SEV we might not want to disable autorevert anyways, a example is a ci: sev impacting jobs we don't use as basis for autorevert.
So, a note is added reminding the ci:sev author to optionally add this tag to disable auto-revert
Note: using this opportunity to fix the ci: disable-autorevert issues. As it is best for the title to be simple and the displayed message in the GitHub interface to be decorated with emoji :)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/165459
Approved by: https://github.com/malfet
