[MM][Perf] Parallelize Q/K/V padding in AscendMMEncoderAttention for better performance (#6204)

### What this PR does / why we need it?

Currently, we pad the last dim of qkv to 128 before flash attention (in
`AscendMMEncoderAttention`) to get better performance on Ascend NPU.
However, the qkv padding is executed serially, which may lead to more
overhead when launching `aclnnConstantPadNd` (launch 3 times).

Since the three operations are mutually independent, we stack qkv first
and then pad them in one kernel launch. With this optimization, **TTFT**
has been reduced by **3.15%**, **peak throughput** has been increased by
**4.20%**.

---

### Does this PR introduce _any_ user-facing change?

No.

### How was this patch tested?

Launch the server:

```bash
vllm serve /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-Instruct \
--dtype bfloat16 \
--limit-mm-per-prompt '{"image": 1}' \
--max-model-len 16384 \
--max-num-batched-tokens 16384
```

Run benchmark:

```bash
vllm bench serve \
--model /root/.cache/modelscope/hub/models/Qwen/Qwen3-VL-8B-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 1000 \
--no-stream
```

Before this PR:

```
============ Serving Benchmark Result ============
Successful requests:                     1000      
Failed requests:                         0         
Benchmark duration (s):                  122.33    
Total input tokens:                      66638     
Total generated tokens:                  122845    
Request throughput (req/s):              8.17      
Output token throughput (tok/s):         1004.18   
Peak output token throughput (tok/s):    3073.00   
Peak concurrent requests:                1000.00   
Total token throughput (tok/s):          1548.90   
---------------Time to First Token----------------
Mean TTFT (ms):                          51757.16  
Median TTFT (ms):                        44853.42  
P99 TTFT (ms):                           110700.14 
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          226.06    
Median TPOT (ms):                        206.85    
P99 TPOT (ms):                           935.31    
---------------Inter-token Latency----------------
Mean ITL (ms):                           208.82    
Median ITL (ms):                         96.37     
P99 ITL (ms):                            2183.13   
==================================================
```

After this PR:

```
============ Serving Benchmark Result ============
Successful requests:                     1000      
Failed requests:                         0         
Benchmark duration (s):                  121.47    
Total input tokens:                      66638     
Total generated tokens:                  122860    
Request throughput (req/s):              8.23      
Output token throughput (tok/s):         1011.47   
Peak output token throughput (tok/s):    3202.00   
Peak concurrent requests:                1000.00   
Total token throughput (tok/s):          1560.08   
---------------Time to First Token----------------
Mean TTFT (ms):                          50125.08  
Median TTFT (ms):                        46270.85  
P99 TTFT (ms):                           108107.12 
-----Time per Output Token (excl. 1st token)------
Mean TPOT (ms):                          227.11    
Median TPOT (ms):                        205.13    
P99 TPOT (ms):                           816.08    
---------------Inter-token Latency----------------
Mean ITL (ms):                           204.60    
Median ITL (ms):                         92.66     
P99 ITL (ms):                            2219.02   
==================================================
```
- vLLM version: v0.14.0
- vLLM main:
d68209402d

Signed-off-by: shen-shanshan <467638484@qq.com>

vllm_ascend/ops/mm_encoder_attention.py

@@ -106,10 +106,12 @@ class AscendMMEncoderAttention(MMEncoderAttention):
         if enable_pad:
             origin_shape = q.shape[-1]
             pad_len = MAX_PAD_SIZE - origin_shape
-            # q, k, v: [b * s, head, head_dim] -> [b * s, head, MAX_PAD_SIZE]
-            q = F.pad(q, (0, pad_len), mode="constant", value=0)
-            k = F.pad(k, (0, pad_len), mode="constant", value=0)
-            v = F.pad(v, (0, pad_len), mode="constant", value=0)
+            # Merge qkv to reduce the overhead of launching npu pad operation.
+            # [3, b*s, head, head_dim]
+            qkv = torch.stack([q, k, v], dim=0)
+            # qkv: [3, b * s, head, head_dim] -> [3, b * s, head, MAX_PAD_SIZE]
+            qkv = F.pad(qkv, (0, pad_len), mode="constant", value=0)
+            q, k, v = qkv.unbind(dim=0)
 
         context_layer = torch.empty_like(q)