Fix cuda memory allocation issue caused by fused_linear_act.py#1822
Merged
emailweixu merged 1 commit intopytorchfrom Nov 12, 2025
Merged
Fix cuda memory allocation issue caused by fused_linear_act.py#1822emailweixu merged 1 commit intopytorchfrom
emailweixu merged 1 commit intopytorchfrom
Conversation
In the previous implementation, fused_linear_act.StaticState will always allocate a cuda tensor once it is imported. The simple act of allocating a small tensor will cause torch to allocate several hundred MB cuda memory. This can become very bad if there are a lot of subprocesses. Fix is simple, only create the tensor when it is needed.
Haichao-Zhang
approved these changes
Nov 12, 2025
This file contains hidden or 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
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
2 participants
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Outdated suggestions cannot be applied.This suggestion has been applied or marked resolved.Suggestions cannot be applied from pending reviews.Suggestions cannot be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.
In the previous implementation, fused_linear_act.StaticState will always allocate a cuda tensor once it is imported. The simple act of allocating a small tensor will cause torch to allocate several hundred MB cuda memory. This can become very bad if there are a lot of subprocesses.
Fix is simple, only create the tensor when it is needed.