[MM][Model][Perf] Remove Qwen2.5-VL modeling files and add patch for VisionAttention

[shen-shanshan]

What this PR does / why we need it?

• Patch Qwen2_5_VisionAttention with AscendQwen2_5_VisionAttention.
• Replace AscendQwen2_5_VisionTransformer with Qwen2_5_VisionTransformer in vllm.
• Move padding logic (q/k/v and cos/sin) before FA to forward() of Qwen2_5_VisionAttention.
• Covert cu_seqlens in Qwen2_5_VisionAttention from cumulative form to intervals and move it to cpu (compatible for npu FA).
• Remove Qwen2.5-VL modeling files.
• Remove Qwen2.5-VL (without padding) modeling files.
• Remove related UT.
• Make set_forward_context pluggable when getting MM embedding. Find more details at [Platform] Make forward context manager pluggable for other device vllm#29388.
• Simplify padding logic for FA.
• Add patch for [Model][Perf] Use cos and sin cache in QwenVL vllm#28798.

Does this PR introduce any user-facing change?

No.

How was this patch tested?

• Functional test (eager mode)
• Functional test (graph mode)
• Benchmark

✅ Functional Test - Eager Mode

Run:

vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct \
--max_model_len 16384 \
--max-num-batched-tokens 16384 \
--tensor-parallel-size 2 \
--enforce-eager

Output:

{"id":"chatcmpl-dcec377ffdda48f99252e6c45a418721","object":"chat.completion","created":1763971067,"model":"/root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the image appears to be a series of characters that are not standard letters or symbols. It looks like a pattern or a code rather than readable text. The characters seem to be a mix of shapes and lines, possibly representing a specific language or a stylized form of writing, but without additional context, it's difficult to determine its meaning or purpose. If you have any specific questions about the pattern or need help with a particular aspect of the image, please let me know!","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning_content":null},"logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":78,"total_tokens":177,"completion_tokens":99,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}

✅ Functional Test - Graph Mode

Run:

vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct \
--max_model_len 16384 \
--max-num-batched-tokens 16384 \
--tensor-parallel-size 2

Output:

{"id":"chatcmpl-dcec377ffdda48f99252e6c45a418721","object":"chat.completion","created":1763971067,"model":"/root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the image appears to be a series of characters that are not standard letters or symbols. It looks like a pattern or a code rather than readable text. The characters seem to be a mix of shapes and lines, possibly representing a specific language or a stylized form of writing, but without additional context, it's difficult to determine its meaning or purpose. If you have any specific questions about the pattern or need help with a particular aspect of the image, please let me know!","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning_content":null},"logprobs":null,"finish_reason":"stop","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":78,"total_tokens":177,"completion_tokens":99,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}

✅ Benchmark

After replacing the whole modeling file with that in latest vllm, we have got a considerable performance improvement.

• Mean TTFT has has been reduced 24.76%.
• Mean TPOT has been reduced 18.03.

Before removing:

============ Serving Benchmark Result ============
Successful requests:                     200       
Failed requests:                         0         
Request rate configured (RPS):           16.00     
Benchmark duration (s):                  45.35     
Total input tokens:                      20026     
Total generated tokens:                  20430     
Request throughput (req/s):              4.41      
Output token throughput (tok/s):         450.48    
Peak output token throughput (tok/s):    2055.00   
Peak concurrent requests:                194.00    
Total Token throughput (tok/s):          892.06    
---------------Time to First Token----------------
Mean TTFT (ms):                          11300.73  
Median TTFT (ms):                        11307.59  
P99 TTFT (ms):                           23844.70  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          243.95    
Median TPOT (ms):                        235.41    
P99 TPOT (ms):                           454.32    
---------------Inter-token Latency----------------
Mean ITL (ms):                           219.73    
Median ITL (ms):                         79.90     
P99 ITL (ms):                            666.86    
==================================================

After removing:

============ Serving Benchmark Result ============
Successful requests:                     200       
Failed requests:                         0         
Request rate configured (RPS):           16.00     
Benchmark duration (s):                  37.55     
Total input tokens:                      20026     
Total generated tokens:                  19561     
Request throughput (req/s):              5.33      
Output token throughput (tok/s):         520.90    
Peak output token throughput (tok/s):    1950.00   
Peak concurrent requests:                191.00    
Total Token throughput (tok/s):          1054.19   
---------------Time to First Token----------------
Mean TTFT (ms):                          8501.95   
Median TTFT (ms):                        8653.75   
P99 TTFT (ms):                           16959.75  
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          200.08    
Median TPOT (ms):                        193.59    
P99 TPOT (ms):                           358.28    
---------------Inter-token Latency----------------
Mean ITL (ms):                           173.30    
Median ITL (ms):                         81.29     
P99 ITL (ms):                            574.16    
==================================================

• vLLM version: v0.11.2
• vLLM main: https://github.com/vllm-project/vllm/commit/v0.11.2

[gemini-code-assist]

Code Review

This pull request refactors the Ascend-specific implementation for Qwen2_5_VisionAttention. The changes move the custom attention logic from subclassing Qwen2_5_VisionAttention to monkey-patching it, and switches from weight padding to activation padding. This simplifies the code in vllm_ascend/models/qwen2_5_vl.py by removing a lot of redundant code.

However, the new implementation in vllm_ascend/patch/worker/patch_qwen2_5_vl.py introduces a critical bug in the forward method due to incorrect state management. Instance attributes are modified in a way that will cause incorrect behavior on subsequent calls. I've provided a detailed comment and a suggested fix for this issue.

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