bugfix for qwen2_vl (#301)

### What this PR does / why we need it?
this pr fixes the error while inferring Qwen2_VL.
### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?
We've tested it on benchmark, it meets our satisfaction and is equal to
gpu.
---------

Signed-off-by: zouyida <zouyida@huawei.com>

vllm_ascend/models/qwen2_vl.py

@@ -40,6 +40,23 @@ from vllm.multimodal import MULTIMODAL_REGISTRY
 
 class CustomQwen2VisionAttention(Qwen2VisionAttention):
 
+    def __init__(
+        self,
+        embed_dim: int,
+        num_heads: int,
+        projection_size: int,
+        quant_config: Optional[QuantizationConfig] = None,
+        prefix: str = "",
+    ) -> None:
+        super().__init__(
+            embed_dim,
+            num_heads,
+            projection_size,
+            quant_config,
+            prefix,
+        )
+        self.cu_seqlens = None
+
     def forward(
         self,
         x: torch.Tensor,
@@ -47,6 +64,8 @@ class CustomQwen2VisionAttention(Qwen2VisionAttention):
         rotary_pos_emb: torch.Tensor,
     ) -> torch.Tensor:
 
+        self.cu_seqlens = cu_seqlens
+
         # [s, b, c] --> [s, b, 3 * head * head_dim]
         x, _ = self.qkv(x)
 
@@ -72,7 +91,7 @@ class CustomQwen2VisionAttention(Qwen2VisionAttention):
             query=q,
             key=k,
             value=v,
-            seq_len=cu_seqlens,
+            seq_len=self.cu_seqlens,
             scale_value=self.hidden_size_per_attention_head**-0.5,
             num_heads=self.num_attention_heads_per_partition,
             num_kv_heads=self.num_attention_heads_per_partition,