[Fixbug] Fix shape not match when sliding_window and dynamic batch_size (#2830)

### What this PR does / why we need it?
Fix shape not match when test LLM-Research/Phi-4-mini-instruct accuarcy 

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

Users can't set dynamic batch_size or use lm_eval test accuracy when
using models(sliding_window)

### How was this patch tested?
accuarcy of LLM-Research/Phi-4-mini-instruct is ok :
```
vllm (pretrained=LLM-Research/Phi-4-mini-instruct,max_model_len=4096,dtype=auto,tensor_parallel_size=1), gen_kwargs: (None), limit: None, num_fewshot: 5, batch_size: auto
|Tasks|Version|     Filter     |n-shot|  Metric   |   |Value |   |Stderr|
|-----|------:|----------------|-----:|-----------|---|-----:|---|-----:|
|gsm8k|      3|flexible-extract|     5|exact_match|↑  |0.8105|±  |0.0108|
|     |       |strict-match    |     5|exact_match|↑  |0.8097|±  |0.0108|
```


- vLLM version: v0.10.2
- vLLM main:
3c96e7b8a1

Signed-off-by: hfadzxy <starmoon_zhang@163.com>

tests/ut/attention/test_attention_v1.py

@@ -435,6 +435,41 @@ class TestAscendAttentionBackendImpl(TestBase):
         mock_fused_infer_attention_score.assert_called_once()
         assert output.shape == (10, 8 * 64)
 
+    @patch('torch_npu._npu_reshape_and_cache')
+    @patch('torch_npu._npu_paged_attention')
+    @patch('torch_npu.npu_fused_infer_attention_score')
+    def test_forward_decode_only_swa_seq_len_mismatch(
+            self, mock_fused_infer_attention_score, mock_paged_attention,
+            mock_npu_reshape_and_cache):
+        """Test forward pass in DecodeOnly state when seq)len_mismatch"""
+        query = torch.randn(10, 8 * 64)
+        key = torch.randn(10, 8 * 64)
+        value = torch.randn(10, 8 * 64)
+        kv_cache = torch.empty(2, 5, 128, 8, 64)
+
+        metadata = self.attn_metadata
+        metadata.attn_state = AscendAttentionState.DecodeOnly
+        metadata.seq_lens = torch.tensor([10])  # len == 1 != query.size(0)==10
+        metadata.block_tables = torch.zeros(1, 5, dtype=torch.long)
+        metadata.num_actual_tokens = 10
+        metadata.slot_mapping = torch.zeros(10, dtype=torch.long)
+
+        mock_fused_infer_attention_score.return_value = (torch.ones(10, 8,
+                                                                    64), 1)
+
+        output = self.impl_swa.forward(self.layer_no_quant,
+                                       query,
+                                       key,
+                                       value,
+                                       kv_cache,
+                                       metadata,
+                                       trace_flag=False)
+
+        mock_paged_attention.assert_called_once()
+        mock_fused_infer_attention_score.assert_not_called()
+
+        assert output.shape == (10, 8 * 64)
+
     @patch('vllm_ascend.attention.attention_v1.is_310p', return_value=False)
     @patch('torch_npu._npu_reshape_and_cache')
     @patch('vllm_ascend.attention.attention_v1.vanilla_chunked_prefill')

vllm_ascend/attention/attention_v1.py

@@ -378,7 +378,8 @@ class AscendAttentionBackendImpl(AttentionImpl):
             # seq_lens_tensor needs to be transferred to the device for 310P.
             attn_metadata.seq_lens = \
                 attn_metadata.seq_lens.to(device=query.device)
-        if self.sliding_window is not None:
+        if self.sliding_window is not None and attn_metadata.seq_lens.shape[
+                0] == query.size(0):
             batch_size = attn_metadata.seq_lens.shape[0]
             block_size = 128
             query = query.view(batch_size, 1, self.num_heads * self.head_size)