[Core] Remove host GPU sync in merge_multimodal_embeddings

[lgeiger]

Purpose

Indexing (index_put_) a GPU tensor with a boolean tensor causes a host/GPU sync since the kernel internally calls aten::nonzero.

In the case of merge_multimodal_embeddings we've already computed the number of non-zero elements so we can use nonzero_static which can be done directly on the GPU removing the host/GPU sync as the tensor has a static size.

I've generated a profile of gemma3-4b-it model with multimodal input and one can see that the CPU overhead of aten::nonzero is gone and aten:: _index_put_impl_ is also a tiny bit faster.

---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
                             Name    Self CPU %      Self CPU   CPU total %     CPU total  CPU time avg     Self CUDA   Self CUDA %    CUDA total  CUDA time avg   # of Calls
---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------
   (main)           aten::nonzero         0.04%       1.111ms         0.07%       1.894ms      16.914us      57.570us         0.00%      57.570us       0.514us           112
(this PR)    aten::nonzero_static         0.01%     177.279us         0.02%     451.676us      50.186us      79.582us         0.00%      79.582us       8.842us             9
   (main)  aten::_index_put_impl_         0.04%     978.786us         0.18%       4.783ms      79.714us     418.913us         0.02%     504.228us       8.404us            60
(this PR)  aten::_index_put_impl_         0.04%     918.248us         0.15%       3.915ms      64.179us     423.943us         0.02%     452.073us       7.411us            61
---------------------------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------  ------------

Test Plan

Covered by existing unittests

Test Result

See CI

[github-actions]

👋 Hi! Thank you for contributing to the vLLM project.

💬 Join our developer Slack at https://slack.vllm.ai to discuss your PR in #pr-reviews, coordinate on features in #feat- channels, or join special interest groups in #sig- channels.

Just a reminder: PRs would not trigger full CI run by default. Instead, it would only run fastcheck CI which starts running only a small and essential subset of CI tests to quickly catch errors. You can run other CI tests on top of those by going to your fastcheck build on Buildkite UI (linked in the PR checks section) and unblock them. If you do not have permission to unblock, ping simon-mo or khluu to add you in our Buildkite org.

Once the PR is approved and ready to go, your PR reviewer(s) can run CI to test the changes comprehensively before merging.

To run CI, PR reviewers can either: Add ready label to the PR or enable auto-merge.

🚀

[gemini-code-assist]

Summary of Changes

Hello @lgeiger, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request implements a performance optimization in the _merge_multimodal_embeddings function by removing an unnecessary host/GPU synchronization. By switching from aten::nonzero to aten::nonzero_static for obtaining indices from a boolean mask, the operation remains on the GPU, leading to reduced CPU overhead and slightly faster execution of the indexing operation, as shown in the provided profiling results.

Highlights

• Performance Optimization: Replaced the use of aten::nonzero with aten::nonzero_static within the _merge_multimodal_embeddings function. This change eliminates a host/GPU synchronization that occurred when indexing a GPU tensor with a boolean mask, as nonzero_static can be executed entirely on the GPU.
• Code Change: Modified the indexing logic in vllm/model_executor/models/utils.py to explicitly obtain indices using is_multimodal.nonzero_static(size=num_expected_tokens) before performing the assignment inputs_embeds[indices.squeeze(1)] = flattened. This is functionally equivalent to the original boolean indexing but avoids the performance penalty.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point in your pull request via creating an issue comment (i.e. comment on the pull request page) using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in issue comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist is currently in preview and may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments to provide feedback.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configureGemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[gemini-code-assist]

Code Review

This pull request introduces an effective optimization to the _merge_multimodal_embeddings function by replacing boolean indexing with nonzero_static. This change is well-justified by the goal of eliminating a host/GPU synchronization point, as nonzero_static can operate directly on the GPU when the number of non-zero elements is known beforehand.

The provided profiling data clearly demonstrates the benefits, showing a reduction in CPU overhead associated with aten::nonzero and a slight performance improvement in aten::_index_put_impl_.

The code modification is concise, targeted, and the accompanying comment clearly explains its purpose and equivalence to the original logic. The use of num_expected_tokens with nonzero_static is appropriate and the subsequent indexing with indices.squeeze(1) is correct.

No issues of medium, high, or critical severity were identified in this review. The change enhances performance while maintaining correctness and readability.

[DarkLight1337]

Thanks, this looks reasonable. I wonder whether this is only true for the current PyTorch version though. I'm worried that at some point we would step into the territory of over-optimizing for specific cases.

cc @ywang96

[Isotr0py]

I wonder whether this is only true for the current PyTorch version though.

I think this is fine, because nonzero_static was added in pytorch intentionally to reduce torch export runtime at first. (pytorch/pytorch#97417)

I'm worried that at some point we would step into the territory of over-optimizing for specific cases.

For this PR, I think we have better to benchmark on more models to ensure the speedup.

[DarkLight1337]

Closing as superseded by #22105