Clarify safety of CUDA graph memory pool sharing across graphs that are replayed in arbtirary order. #166975
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🔗 Helpful Links🧪 See artifacts and rendered test results at hud.pytorch.org/pr/166975
Note: Links to docs will display an error until the docs builds have been completed. ✅ No FailuresAs of commit cc9386b with merge base fee1ac9 ( This comment was automatically generated by Dr. CI and updates every 15 minutes. |
eellison
approved these changes
Nov 4, 2025
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Merge startedYour change will be merged once all checks pass (ETA 0-4 Hours). Learn more about merging in the wiki. Questions? Feedback? Please reach out to the PyTorch DevX Team |
BoyuanFeng
approved these changes
Nov 4, 2025
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Some users at pytorch conference were asking me about whether it is safe to share a memory pool among cuda graphs that never run concurrently, but may run in arbitrary order, if they don't depend upon each other's output. Even though your capture order doesn't match replay order in this situation, this is safe. However, our documents confusingly said this wasn't allowed. This update is intended to help with that. Since vLLM essentially depends upon this behavior, I call it out specifically.