Event Logging for NCCL Async Error Handling Process Crash #47244
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
This is an event-logging based update that should allow us to collect high-quality data about how many times the NCCL Async Error Handling mechanism is triggered. This logs an event called `ProcessGroupNCCL.WorkNCCL.handleNCCLGuard`, which is recorded as an entry in the `scuba_caffe2_pytorch_usage_stats` Scuba table. This Scuba entry will also contain metadata like workflow status, entitlement, hostnames, and workflow names, which will give us insight into what workloads/domains and machines are benefiting from async error handling. It also contains the Flow Run ID, which can be used as a join key with the `fblearner_workflow_run_status` scuba table for additional information like final error message, etc. We can easily quantify the number of times the async handling code was triggered by querying the `scuba_caffe2_pytorch_usage_stats` table. As a demonstration, I ran the following workflow with this diff patched: f229675892 Since the workflow above causes a desync, the `handleNCCLGuard` event is logged in scuba soon. See here for the filtered table: https://www.fburl.com/scuba/scuba_caffe2_pytorch_usage_stats/tmp1uvio As you can see, there are 4 entries. The workflow above uses 3 GPUs, 2 of which run into the desync scenario and are crashed using async error handling. We make this fail twice before succeeding the 3rd time, hence 4 entries. Differential Revision: [D24688739](https://our.internmc.facebook.com/intern/diff/D24688739/) [ghstack-poisoned]
osalpekar
requested review from
mingzhe09088,
mrshenli,
pietern,
pritamdamania87,
rohan-varma and
zhaojuanmao
as code owners
facebook-github-bot
added
cla signed
oncall: distributed
osalpekar
added a commit
that referenced
this pull request
Nov 2, 2020
This is an event-logging based update that should allow us to collect high-quality data about how many times the NCCL Async Error Handling mechanism is triggered. This logs an event called `ProcessGroupNCCL.WorkNCCL.handleNCCLGuard`, which is recorded as an entry in the `scuba_caffe2_pytorch_usage_stats` Scuba table. This Scuba entry will also contain metadata like workflow status, entitlement, hostnames, and workflow names, which will give us insight into what workloads/domains and machines are benefiting from async error handling. It also contains the Flow Run ID, which can be used as a join key with the `fblearner_workflow_run_status` scuba table for additional information like final error message, etc. We can easily quantify the number of times the async handling code was triggered by querying the `scuba_caffe2_pytorch_usage_stats` table. As a demonstration, I ran the following workflow with this diff patched: f229675892 Since the workflow above causes a desync, the `handleNCCLGuard` event is logged in scuba soon. See here for the filtered table: https://www.fburl.com/scuba/scuba_caffe2_pytorch_usage_stats/tmp1uvio As you can see, there are 4 entries. The workflow above uses 3 GPUs, 2 of which run into the desync scenario and are crashed using async error handling. We make this fail twice before succeeding the 3rd time, hence 4 entries. Differential Revision: [D24688739](https://our.internmc.facebook.com/intern/diff/D24688739/) ghstack-source-id: 115708632 Pull Request resolved: #47244
jiayisuse
approved these changes
Nov 2, 2020
💊 CI failures summary and remediationsAs of commit 6885538 (more details on the Dr. CI page):
ci.pytorch.org: 1 failedThis comment was automatically generated by Dr. CI (expand for details).Follow this link to opt-out of these comments for your Pull Requests.Please report bugs/suggestions on the GitHub issue tracker or post in the (internal) Dr. CI Users group. This comment has been revised 1 time. |
Codecov Report
@@ Coverage Diff @@
## gh/osalpekar/100/base #47244 +/- ##
======================================================
Coverage 60.81% 60.81%
======================================================
Files 2748 2748
Lines 254027 254027
======================================================
+ Hits 154490 154495 +5
+ Misses 99537 99532 -5 |
|
This pull request has been merged in 8b13ab9. |
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:
This is an event-logging based update that should allow us to collect high-quality data about how many times the NCCL Async Error Handling mechanism is triggered. This logs an event called
ProcessGroupNCCL.WorkNCCL.handleNCCLGuard, which is recorded as an entry in thescuba_caffe2_pytorch_usage_statsScuba table. This Scuba entry will also contain metadata like workflow status, entitlement, hostnames, and workflow names, which will give us insight into what workloads/domains and machines are benefiting from async error handling. It also contains the Flow Run ID, which can be used as a join key with thefblearner_workflow_run_statusscuba table for additional information like final error message, etc. We can easily quantify the number of times the async handling code was triggered by querying thescuba_caffe2_pytorch_usage_statstable.As a demonstration, I ran the following workflow with this diff patched: f229675892
Since the workflow above causes a desync, the
handleNCCLGuardevent is logged in scuba soon. See here for the filtered table: https://www.fburl.com/scuba/scuba_caffe2_pytorch_usage_stats/tmp1uvioAs you can see, there are 4 entries. The workflow above uses 3 GPUs, 2 of which run into the desync scenario and are crashed using async error handling. We make this fail twice before succeeding the 3rd time, hence 4 entries.
Differential Revision: D24688739