qwen3_moe/qwen25 support torchair graph (#2403)

### What this PR does / why we need it?
Added support for the TorchAir graph mode in qwen3_moe and qwen2.5
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
```bash
llm = LLM(
    model=model,
    tensor_parallel_size=GPUs_per_dp_rank,
    enforce_eager=False,
    enable_expert_parallel=True,
    max_model_len=4096,
    max_num_seqs=16,
    trust_remote_code=trust_remote_code,
    gpu_memory_utilization=0.4,
    additional_config={
             "torchair_graph_config": {
                 "enabled": True,
                 "use_cached_graph": False,
                 "graph_batch_sizes_init": False,
                 "graph_batch_sizes": [16]
             },
             "ascend_scheduler_config": {
                 "enabled": True,
                 "chunked_prefill_enabled":True,
             },
             "refresh": True,
    },
)
```

- vLLM version: v0.10.0
- vLLM main:
b87cb97a53

Signed-off-by: taoyuxiang <oui.nicholas.tao@gmail.com>

vllm_ascend/torchair/torchair_attention.py

@@ -332,8 +332,9 @@ class AscendAttentionTorchairBackendImpl(AttentionImpl):
             shape = [batch_size * seq_len, num_heads, head_size]
         """
         num_tokens = query.shape[0]
-        use_kv_cache_quant = kv_cache is not None and kv_cache[0].numel(
-        ) > 0 and kv_cache[0].dtype == torch.int8
+        use_kv_cache_quant = (kv_cache is not None and len(kv_cache) > 0
+                              and kv_cache[0].numel() > 0
+                              and kv_cache[0].dtype == torch.int8)
         if output is None:
             output = torch.empty(num_tokens,
                                  self.num_heads,