[DP][ray] Support different VLLM_RAY_DP_PACK_STRATEGY #23849
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This pull request refactors the data parallel placement group creation logic in Ray to ensure that dp_size_local ranks are strictly packed onto the same node. This is an important change for use cases like DeepEP. While the overall direction is correct, I've identified a critical bug in the implementation that prevents scheduling on any node other than the master node, which would break multi-node data parallelism. My review includes a suggested fix for this issue.
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Signed-off-by: Rui Qiao <ruisearch42@gmail.com>
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cc @njhill |
cjackal
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kouroshHakha
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vllm/envs.py
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| # - "strict": | ||
| # allocate exactly data-parallel-size-local DP ranks to each picked node; | ||
| # This environment variable is ignored if data-parallel-backend is not Ray. | ||
| "VLLM_RAY_DP_PACK_STRATEGY": lambda: os.getenv("VLLM_RAY_DP_PACK_STRATEGY", "fill"), |
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shouldn't the default be strict?
kouroshHakha
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LGTM |
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Thanks @ruisearch42 @kouroshHakha
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Purpose
Currently we only strictly pack dp_size_local for the master node. However, in case of DeepEP it assumes EP ranks [0, 7] are on the same node, (same for [8, 15], etc.) and uses cuda IPC for communication among them. If this is not satisfied, a runtime error is raised because cuda IPC does not work cross-node. This PR fixes the issue by restricting the placement.
Test Plan
Test Result
Essential Elements of an Effective PR Description Checklist
supported_models.mdandexamplesfor a new model.