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fix ci issue distributed 4 gpu test #20204
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fix ci issue distributed 4 gpu test #20204
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Signed-off-by: yewentao256 <zhyanwentao@126.com>
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Summary of Changes
Hello @yewentao256, I'm Gemini Code Assist1! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!
This pull request addresses a CI issue encountered during distributed 4-GPU testing by enhancing the data_parallel.py example with new configurable parameters. These parameters allow for more precise control over GPU memory allocation and sequence limits during model warm-up, mitigating potential out-of-memory errors and improving the stability of distributed inference tests.
Highlights
- CI Issue Resolution: This pull request aims to fix a continuous integration (CI) issue related to distributed 4-GPU testing, specifically addressing potential memory constraints that might cause test failures.
- New Configuration Arguments: I've introduced two new command-line arguments to
examples/offline_inference/data_parallel.py:--max-num-seqsand--gpu-memory-utilization. These provide finer control over memory usage during distributed inference. - Memory Management: The
--max-num-seqsargument (default 64) allows users to limit the maximum number of sequences used during engine warm-up, which can significantly reduce peak memory consumption. The--gpu-memory-utilizationargument (default 0.8) enables setting the fraction of GPU memory vLLM is allowed to allocate, providing more headroom to avoid CUDA OOM errors.
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Code Review
This pull request introduces two new command-line arguments, --max-num-seqs and --gpu-memory-utilization, to the data parallel example script. This provides more control over memory usage, which is intended to fix a CI test failure.
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Thanks @yewentao256!
| parser.add_argument( | ||
| "--max-num-seqs", | ||
| type=int, | ||
| default=64, | ||
| help=("Maximum number of sequences to be processed in a single iteration."), | ||
| ) |
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Are both of these args required to avoid the OOM? 64 is quite small for batch mode, would be good if we could fix just with the gpu_memory_utilization reduction...
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yeah, also seems that we need much more memory during initialization than before. I was about to investigate more into this, but didn't get time to do so. Wondering if @yewentao256 could dig further into this?
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Yeah I am happy to dig further, but what is the expected result for this? To reduce the memory usage? But it is kind of like a tradeoff between speed and efficiency I am afraid.
Basically, the original cause of this OOM issue is from #18724, which I think it is reasonable to adopt. @houseroad
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I unblocked the 4-GPUs test so that we can verify it passes. |
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The test passes so I'm merging this to unblock CI first. Let's fix the underlying issue in another PR.
Purpose
Fixes #20138
Test