[Kernel] sliding window support in paged_attention_v1/v2 kernels#4768
Closed
mmoskal wants to merge 2 commits intovllm-project:mainfrom
Closed
[Kernel] sliding window support in paged_attention_v1/v2 kernels#4768mmoskal wants to merge 2 commits intovllm-project:mainfrom
mmoskal wants to merge 2 commits intovllm-project:mainfrom
Conversation
Collaborator
|
cc @zhuohan123 (great timing!) |
Collaborator
|
btw, we need to make sure this can be compatible with this PR #4681, that we plan to merge soon.. I think this can make things much cleaner |
5 tasks
fadara01
added a commit
to fadara01/vllm
that referenced
this pull request
Oct 28, 2025
- Enables Sliding Window Attention (SWA) for the CPU backend. - Enables models with hybrid local-global attention (i.e., those with multiple KV cache groups) for the CPU backend. - Fixes a bug in `TorchSDPAMetadataBuilderV1` where `query_start_loc_cpu` was updated in-place despite being shared between multiple KV cache groups. - Adds a unit test for the decode phase in the CPU attention backend. - Adds reference implementations used to test paged attention to a common utils module and reuses it for the decode attention CPU test. SWA is enabled by truncating full blocks/pages that are outside the window (similar to vllm-project#23010). Hence, the implementation is block-granular and may attend to at most (block_size - 1) extra tokens; this did not affect the accuracy tests I ran. This is the best we can do with the current paged-attention API/implementation, which does not support SWA natively See: vllm-project#4768 for context and the attempt to enable native SWA support in paged_attn. Signed-off-by: Fadi Arafeh <fadi.arafeh@arm.com>
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
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.
The paged attention (v1 and v2) decode kernel does not support sliding window natively - the way it works now it just takes all the blocks passed in (up to seq_len). With v1 manager, the sliding window uses blocks in a "ring buffer" fashion, so this is not a problem. With the new block manager (see #4545) we need potentially to start attention computation in the middle of a block, otherwise we pay attention to a few tokens too many. It doesn't seem to affect this test though.
Related: #3385
This is just a rough draft - didn't even try to compile.
@cadedaniel is there another issue for this already?
BEFORE SUBMITTING, PLEASE READ THE CHECKLIST BELOW AND FILL IN THE DESCRIPTION ABOVE
PR Checklist (Click to Expand)
Thank you for your contribution to vLLM! Before submitting the pull request, please ensure the PR meets the following criteria. This helps vLLM maintain the code quality and improve the efficiency of the review process.
PR Title and Classification
Only specific types of PRs will be reviewed. The PR title is prefixed appropriately to indicate the type of change. Please use one of the following:
[Bugfix]for bug fixes.[CI/Build]for build or continuous integration improvements.[Doc]for documentation fixes and improvements.[Model]for adding a new model or improving an existing model. Model name should appear in the title.[Frontend]For changes on the vLLM frontend (e.g., OpenAI API server,LLMclass, etc.)[Kernel]for changes affecting CUDA kernels or other compute kernels.[Core]for changes in the core vLLM logic (e.g.,LLMEngine,AsyncLLMEngine,Scheduler, etc.)[Hardware][Vendor]for hardware-specific changes. Vendor name should appear in the prefix (e.g.,[Hardware][AMD]).[Misc]for PRs that do not fit the above categories. Please use this sparingly.Note: If the PR spans more than one category, please include all relevant prefixes.
Code Quality
The PR need to meet the following code quality standards:
format.shto format your code.docs/source/if the PR modifies the user-facing behaviors of vLLM. It helps vLLM user understand and utilize the new features or changes.Notes for Large Changes
Please keep the changes as concise as possible. For major architectural changes (>500 LOC excluding kernel/data/config/test), we would expect a GitHub issue (RFC) discussing the technical design and justification. Otherwise, we will tag it with
rfc-requiredand might not go through the PR.What to Expect for the Reviews
The goal of the vLLM team is to be a transparent reviewing machine. We would like to make the review process transparent and efficient and make sure no contributor feel confused or frustrated. However, the vLLM team is small, so we need to prioritize some PRs over others. Here is what you can expect from the review process:
action-requiredlabel on the PR if there are changes required. The contributor should address the comments and ping the reviewer to re-review the PR.Thank You
Finally, thank you for taking the time to read these guidelines and for your interest in contributing to vLLM. Your contributions make vLLM a great tool for everyone!