Skip to content

Conversation

@jkaniecki
Copy link

Cherry-pick of
6e1be4e


Cherry-pick of
vllm-project@6e1be4e

---------

Signed-off-by: Jan Kaniecki <[email protected]>
Signed-off-by: Jan Kaniecki <[email protected]>
Co-authored-by: Copilot <[email protected]>
Copy link
Contributor

Copilot AI left a comment

Choose a reason for hiding this comment

The reason will be displayed to describe this comment to others. Learn more.

Pull request overview

This PR adds support for chunked attention to the vLLM-Gaudi implementation, cherry-picked from the upstream vllm-gaudi repository. Chunked attention divides attention computation into smaller chunks, which can help with memory efficiency and performance for long sequences.

Key changes:

  • Added chunked attention bias computation for both prefill and decode phases
  • Extended attention metadata structures to include chunked attention fields
  • Integrated chunked attention configuration detection and layer setup

Reviewed changes

Copilot reviewed 3 out of 3 changed files in this pull request and generated 4 comments.

File Description
vllm_gaudi/v1/worker/hpu_model_runner.py Core implementation of chunked attention including bias computation, block mapping, metadata updates, and model initialization logic
vllm_gaudi/v1/attention/backends/hpu_attn.py Updated decode metadata factory method to accept chunked attention parameters
vllm_gaudi/attention/backends/hpu_attn.py Added chunked attention metadata fields and logic to select appropriate attention blocks during forward pass

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

@github-actions
Copy link

github-actions bot commented Dec 4, 2025

✅ CI Passed

All checks passed successfully against the following vllm commit:
1b7c7f5159484063af28cb47809d79e83d3301ec

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment

Labels

None yet

Projects

None yet

Development

Successfully merging this pull request may close these issues.

1 participant