Fix: Defensively close GPU device FDs in dataloader worker processes - #3684
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Phlip79
marked this pull request as draft
March 4, 2026 23:48
hexinw-nvidia
marked this pull request as ready for review
March 5, 2026 18:08
This ensures workers do not keep references into NVIDIA memory space after fork.
This helps ensure GPU memory can be reclaimed even if a dataloader worker is
delayed or fails to exit.
How to Reproduce / Validate:
1. Force a long-running dataloader worker
Modify GPTDataset.__getitem__ to insert:
time.sleep(3600)
This simulates a stuck dataloader worker (e.g., blocked in I/O).
2. Start training
Launch a 1-node Megatron-LM job with:
--num-workers > 0
3. Verify dataloader workers are alive
On the GPU node:
sudo fuser -v /dev/nvidia*
You should see the dataloader worker processes listed.
With this patch, they should not retain active /dev/nvidia* file
descriptors even though they are running.
4. Trigger a rank failure
Send SIGTERM to one of the training ranks:
kill -15 <rank_pid>
5. Observe GPU memory reclaim
Run:
nvidia-smi
The corresponding rank’s GPU memory usage should return to 0
immediately (assuming no other GPU-holding child processes such as async
checkpoint workers are present in this test).
6. Baseline (without this patch)
Repeat the same steps without this change.
After killing the rank in step 4, you will observe that GPU memory
remains non-zero in nvidia-smi, because the dataloader worker still
holds /dev/nvidia* references.
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asolergi-nv
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Mar 11, 2026
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/ok to test d70e411 |
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/ok to test d70e411 |
@chtruong814, there was an error processing your request: See the following link for more information: https://docs.gha-runners.nvidia.com/cpr/e/2/ |
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/ok to test 1136a37 |
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🔄 Merge queue validation started! You can track the progress here: https://github.com/NVIDIA/Megatron-LM/actions/runs/23256564704 |
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…VIDIA#3684) Co-authored-by: Charlie Truong <chtruong@nvidia.com>
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…VIDIA#3684) Co-authored-by: Charlie Truong <chtruong@nvidia.com> Signed-off-by: Jon Barker <jbarker@aws-cmh-slurm-1-vscode-02.cm.cluster>
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…VIDIA#3684) Co-authored-by: Charlie Truong <chtruong@nvidia.com>
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This ensures workers do not keep references into NVIDIA memory space after fork. This helps ensure GPU memory can be reclaimed even if a dataloader worker is delayed or fails to exit.
How to Reproduce / Validate:
Force a long-running dataloader worker
Modify GPTDataset.__getitem__ to insert:
time.sleep(3600)
This simulates a stuck dataloader worker (e.g., blocked in I/O).
Start training
Launch a 1-node Megatron-LM job with:
--num-workers > 0
Verify dataloader workers are alive
On the GPU node:
sudo fuser -v /dev/nvidia*
You should see the dataloader worker processes listed.
With this patch, they should not retain active /dev/nvidia* file
descriptors even though they are running.
Trigger a rank failure
Send SIGTERM to one of the training ranks:
kill -15 <rank_pid>
Observe GPU memory reclaim
Run:
nvidia-smi
The corresponding rank’s GPU memory usage should return to 0
immediately (assuming no other GPU-holding child processes such as async
checkpoint workers are present in this test).
Baseline (without this patch)
Repeat the same steps without this change.
After killing the rank in step 4, you will observe that GPU memory remains non-zero in nvidia-smi, because the dataloader worker still holds /dev/nvidia* references.