[Core][MM] Use non-blocking CPU-GPU copy of multimodal data

[lgeiger]

Purpose

On main group_mm_kwargs_by_modality currently causes unnecessary CPU/GPU sync. This PR makes the copy non blocking to prevent this. Looks like this was missed in #25654 /cc @DarkLight1337

Test Plan

 VLLM_TORCH_PROFILER_DIR="vllm_profile" vllm serve Qwen/Qwen3-VL-30B-A3B-Instruct-FP8 --limit-mm-per-prompt.video 0 --max-model-len 10000

vllm bench serve --backend openai-chat --model Qwen/Qwen3-VL-30B-A3B-Instruct-FP8 --endpoint /v1/chat/completions --dataset-name hf --dataset-path lmarena-ai/VisionArena-Chat --hf-split train --num-prompts 5 --profile

Test Result

Before:

After:

[gemini-code-assist]

Code Review

This pull request introduces a performance optimization by enabling non-blocking CPU-to-GPU copies for multimodal data. The change correctly adds non_blocking=True to the torch.Tensor.to() call within group_mm_kwargs_by_modality, which prevents unnecessary CPU-GPU synchronization when using pinned memory. This is a valuable improvement that aligns the behavior of the new data path with the legacy one. The code change is correct and the performance gains are demonstrated by the provided profiler results. The pull request is well-justified and ready for merging.

[DarkLight1337]

Good catch