[MM][Perf] Minor Optimization on Qwen3-VL fast_pos_embed_interpolate

[ywang96]

Purpose

Test Plan

10 QPS of VisionArena on Qwen3-VL 4B on A100

Test Result

Main

============ Serving Benchmark Result ============
Successful requests:                     1000      
Request rate configured (RPS):           10.00     
Benchmark duration (s):                  101.85    
Total input tokens:                      94327     
Total generated tokens:                  120882    
Request throughput (req/s):              9.82      
Output token throughput (tok/s):         1186.81   
Peak output token throughput (tok/s):    2862.00   
Peak concurrent requests:                133.00    
Total Token throughput (tok/s):          2112.91   
---------------Time to First Token----------------
Mean TTFT (ms):                          229.53    
Median TTFT (ms):                        180.19    
P99 TTFT (ms):                           928.83    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          40.65     
Median TPOT (ms):                        36.29     
P99 TPOT (ms):                           87.93     
---------------Inter-token Latency----------------
Mean ITL (ms):                           39.96     
Median ITL (ms):                         17.36     
P99 ITL (ms):                            186.27    
==================================================

This branch

============ Serving Benchmark Result ============
Successful requests:                     1000      
Request rate configured (RPS):           10.00     
Benchmark duration (s):                  101.66    
Total input tokens:                      94327     
Total generated tokens:                  120735    
Request throughput (req/s):              9.84      
Output token throughput (tok/s):         1187.67   
Peak output token throughput (tok/s):    2310.00   
Peak concurrent requests:                124.00    
Total Token throughput (tok/s):          2115.57   
---------------Time to First Token----------------
Mean TTFT (ms):                          203.78    
Median TTFT (ms):                        162.26    
P99 TTFT (ms):                           848.32    
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          36.27     
Median TPOT (ms):                        31.53     
P99 TPOT (ms):                           80.10     
---------------Inter-token Latency----------------
Mean ITL (ms):                           36.00     
Median ITL (ms):                         16.07     
P99 ITL (ms):                            170.49    
==================================================

MMMU matched

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[gemini-code-assist]

Code Review

This pull request introduces a performance optimization to the fast_pos_embed_interpolate method in vllm/model_executor/models/qwen3_vl.py. The changes refactor the method to perform computations on the GPU using vectorized PyTorch operations, avoiding expensive list manipulations and CPU-GPU data transfers. A constant num_grid_per_side is now pre-calculated in the __init__ method to avoid repeated calculations. The new implementation is more efficient and readable, leveraging batched tensor operations for embedding lookups and calculations, which should lead to the performance improvements shown in the PR description. The logic appears correct and functionally equivalent to the previous implementation. I have no high or critical severity comments on these changes.