[Model] Remove unnecessary CUDA sync of GLM-4.1V image and video preprocess

[what-in-the-nim]

This PR removes unnecessary CUDA sync in _process_image_input and _process_video_input of vllm/model_executor/models/glm4_1v.py by utilising grid_thw_list.

Related PR #22792
Related issue #23884

[github-actions]

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[Copilot]

Pull Request Overview

This PR removes unnecessary CUDA synchronization in GLM-4.1V model by optimizing the image and video preprocessing methods. The changes prevent implicit CUDA syncs that occur when calling .tolist() on GPU tensors during size calculations.

• Converts grid_thw tensor to list once at the beginning of each method
• Replaces tensor operations with CPU-based calculations for computing split sizes
• Uses the pre-converted list for both visual processing and size calculations

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[Copilot]

Creating a new tensor from the list and then converting back to list is inefficient. Consider using numpy operations or native Python calculations since grid_thw_list is already a Python list.

[Copilot]

Creating a new tensor from the list and then converting back to list is inefficient. Consider using numpy operations or native Python calculations since grid_thw_list is already a Python list.

[gemini-code-assist]

Code Review

This pull request introduces a performance optimization in vllm/model_executor/models/glm4_1v.py by removing an unnecessary CUDA synchronization. The change involves pre-computing a list from the grid_thw tensor at the beginning of the _process_image_input and _process_video_input functions. This allows the subsequent calculation of sizes to be performed on the CPU, overlapping with the main GPU-bound visual processing task and thus avoiding a synchronization point after the main kernel launch. The implementation is correct and should lead to improved performance. Additionally, the change improves robustness by using torch.long for calculations, preventing potential overflows, and enhances readability by grouping (merge_size * merge_size).