### What this PR does / why we need it?
- Use upstream util function (`_pre_process()` and `_post_process()`) to
reduce redundant codes. (Find more details at
https://github.com/vllm-project/vllm/blob/main/vllm/model_executor/layers/rotary_embedding/common.py#L184-L213)
- Merge Q/K split to simplify the logic of calling
`torch_npu.npu_rotary_mul()` for better performance (TPOT has been
reduced by **6.22%**).
### Does this PR introduce _any_ user-facing change?
no.
### How was this patch tested?
#### ✅ Functional test
Launch the server:
```bash
export VLLM_USE_MODELSCOPE=True
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct \
--dtype bfloat16 \
--limit-mm-per-prompt '{"image": 1}' \
--max-model-len 16384 \
--max-num-batched-tokens 16384
```
Query the server:
```bash
curl -X POST http://localhost:8000/v1/chat/completions \
-H "Content-Type: application/json" \
-d '{
"model": "/root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct",
"messages": [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": [
{"type": "image_url", "image_url": {"url": "https://modelscope.oss-cn-beijing.aliyuncs.com/resource/qwen.png"}},
{"type": "text", "text": "What is the text in the illustrate? How does it look?"}
]}
],
"max_tokens": 100
}'
```
Output:
```
{"id":"chatcmpl-b2911ab6989ef098","object":"chat.completion","created":1768202780,"model":"/root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct","choices":[{"index":0,"message":{"role":"assistant","content":"The text in the illustration is \"TONGYI Qwen.\" The word \"TONGYI\" is written in blue, and \"Qwen\" is written in gray. The text appears to be part of a logo or branding design, with \"TONGYI\" being more prominent and \"Qwen\" being slightly smaller and positioned below it. The font style is modern and clean, with \"TONGYI\" having a slightly bolder appearance compared to \"Qwen.\"","refusal":null,"annotations":null,"audio":null,"function_call":null,"tool_calls":[],"reasoning":null,"reasoning_content":null},"logprobs":null,"finish_reason":"length","stop_reason":null,"token_ids":null}],"service_tier":null,"system_fingerprint":null,"usage":{"prompt_tokens":78,"total_tokens":178,"completion_tokens":100,"prompt_tokens_details":null},"prompt_logprobs":null,"prompt_token_ids":null,"kv_transfer_params":null}
```
#### ✅ Benchmark
Run:
```bash
export VLLM_USE_MODELSCOPE=False
export HF_ENDPOINT="https://hf-mirror.com"
vllm bench serve \
--model /root/.cache/modelscope/hub/models/Qwen/Qwen2.5-VL-7B-Instruct \
--backend openai-chat \
--endpoint /v1/chat/completions \
--dataset-name hf \
--hf-split train \
--dataset-path lmarena-ai/vision-arena-bench-v0.1 \
--num-prompts 10 \
--no-stream
```
Before this PR:
```
============ Serving Benchmark Result ============
Successful requests: 10
Failed requests: 0
Benchmark duration (s): 5.96
Total input tokens: 7191
Total generated tokens: 996
Request throughput (req/s): 1.68
Output token throughput (tok/s): 167.05
Peak output token throughput (tok/s): 261.00
Peak concurrent requests: 10.00
Total token throughput (tok/s): 1373.16
---------------Time to First Token----------------
Mean TTFT (ms): 964.43
Median TTFT (ms): 858.48
P99 TTFT (ms): 1691.45
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 63.08
Median TPOT (ms): 40.86
P99 TPOT (ms): 241.30
---------------Inter-token Latency----------------
Mean ITL (ms): 40.16
Median ITL (ms): 33.61
P99 ITL (ms): 250.30
==================================================
```
After this PR:
```
============ Serving Benchmark Result ============
Successful requests: 10
Failed requests: 0
Benchmark duration (s): 5.71
Total input tokens: 7191
Total generated tokens: 996
Request throughput (req/s): 1.75
Output token throughput (tok/s): 174.45
Peak output token throughput (tok/s): 279.00
Peak concurrent requests: 10.00
Total token throughput (tok/s): 1433.95
---------------Time to First Token----------------
Mean TTFT (ms): 992.14
Median TTFT (ms): 938.30
P99 TTFT (ms): 1728.71
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms): 59.16
Median TPOT (ms): 37.65
P99 TPOT (ms): 234.89
---------------Inter-token Latency----------------
Mean ITL (ms): 36.55
Median ITL (ms): 30.73
P99 ITL (ms): 170.72
==================================================
```
- vLLM version: v0.13.0
- vLLM main:
2f4e6548ef
---------
Signed-off-by: shen-shanshan <467638484@qq.com>
d350c2ada6
