[Bugfix]:replace npu_incre_flash_attention with npu_fused_infer_atten… (#2901)

### What this PR does / why we need it?
[Bugfix]:replace npu_incre_flash_attention with
npu_fused_infer_attention_score in order to be able to tiling update

### Does this PR introduce _any_ user-facing change?
No
### How was this patch tested?


- vLLM version: v0.10.2
- vLLM main:
2b85697031

Signed-off-by: p00465316 <panchao13@huawei.com>
Co-authored-by: p00465316 <panchao13@huawei.com>

tests/ut/torchair/test_torchair_attention.py

@@ -0,0 +1,95 @@
+from unittest.mock import MagicMock, patch
+
+import torch
+from vllm.attention.backends.abstract import AttentionType
+from vllm.distributed.parallel_state import GroupCoordinator
+
+from tests.ut.base import TestBase
+from vllm_ascend.attention.attention_v1 import AscendAttentionState
+from vllm_ascend.torchair.torchair_attention import \
+    AscendAttentionTorchairBackendImpl
+
+
+class TestAscendAttentionTorchairBackendImpl(TestBase):
+
+    @patch("torch.zeros")
+    @patch('vllm.distributed.parallel_state._TP',
+           new_callable=lambda: MagicMock(spec=GroupCoordinator))  # TODO
+    @patch("vllm.distributed.get_tensor_model_parallel_world_size",
+           return_value=2)  # TODO
+    @patch("vllm.config.get_current_vllm_config")  # TODO
+    @patch("vllm_ascend.torchair.torchair_mla.get_ascend_config")  # TODO
+    def setUp(self, ascend_config, vllm_config, mock_get_tp_size, mock_tp,
+              mock_zeros):
+        mock_tp.world_size = 2  # TODO
+        ascend_config.torchair_graph_config.enabled = True  # TODO
+        ascend_config.torchair_graph_config.enable_kv_nz = False  # TODO
+        speculative_config = MagicMock()
+        speculative_config.num_speculative_tokens = 4
+        vllm_config.speculative_config = speculative_config
+
+        num_heads = 32
+        head_size = 128  # TODO
+        scale = 0.1  # TODO
+        num_kv_heads = 4
+        kv_cache_dtype = "auto"
+        attn_type = AttentionType.DECODER
+        mock_zeros.return_value = torch.ones((),
+                                             device='cpu',
+                                             dtype=torch.int32)
+
+        self.impl = AscendAttentionTorchairBackendImpl(
+            num_heads=num_heads,
+            head_size=head_size,
+            scale=scale,
+            num_kv_heads=num_kv_heads,
+            alibi_slopes=None,
+            sliding_window=None,
+            kv_cache_dtype=kv_cache_dtype,
+            blocksparse_params=None,
+            logits_soft_cap=None,
+            attn_type=attn_type,
+            kv_sharing_target_layer_name=None)
+
+    @patch("torch_npu.npu_scatter_nd_update_")
+    @patch("torch_npu.npu_fused_infer_attention_score")
+    def test_forward_with_decode_only(self, mock_fused, _):
+        layer = MagicMock()
+        layer._k_scale_float = 1.0
+        layer._v_scale_float = 1.0
+
+        seq_len = 1
+        num_tokens = 100
+        num_blocks = 256
+        block_size = 4
+
+        query = torch.randn(num_tokens, seq_len,
+                            self.impl.num_heads * self.impl.head_size)
+        key = torch.randn(num_tokens, seq_len,
+                          self.impl.num_kv_heads * self.impl.head_size)
+        value = torch.randn(num_tokens, seq_len,
+                            self.impl.num_kv_heads * self.impl.head_size)
+        kv_cache = (torch.randn(num_blocks, block_size,
+                                self.impl.num_heads * self.impl.head_size),
+                    torch.randn(num_blocks, block_size,
+                                self.impl.num_heads * self.impl.head_size))
+        output = torch.randn(num_tokens, self.impl.num_heads,
+                             self.impl.head_size)
+
+        decode = MagicMock()  # TODO
+        decode.seq_lens_list = [2] * num_tokens
+        decode.block_table = torch.ones(num_tokens, 8, dtype=torch.int32)
+        decode.attn_mask = None
+
+        metadata = MagicMock()
+        metadata.attn_state = AscendAttentionState.DecodeOnly
+        metadata.slot_mapping = torch.arange(num_tokens, dtype=torch.int32)
+        metadata.decode = decode
+
+        mock_fused.return_value = (torch.ones(num_tokens, self.impl.num_heads,
+                                              self.impl.head_size),
+                                   torch.ones(1))
+
+        result = self.impl.forward(layer, query, key, value, kv_cache,
+                                   metadata, output, False)
+        self.assertEqual(result.shape[0], num_tokens)

vllm_ascend/torchair/torchair_attention.py

@@ -439,17 +439,24 @@ class AscendAttentionTorchairBackendImpl(AttentionImpl):
             block_size = key_cache.shape[1]
             query = query.view(num_tokens, 1,
                                self.num_heads * self.head_size).contiguous()
-            output = torch_npu.npu_incre_flash_attention(
-                query,
-                key_cache,
-                value_cache,
-                num_key_value_heads=self.num_kv_heads,
+            output, _ = torch_npu.npu_fused_infer_attention_score(
+                query=query,
+                key=key_cache,
+                value=value_cache,
+                query_rope=None,
+                key_rope=None,
                 num_heads=self.num_heads,
-                actual_seq_lengths=seq_lens,
-                scale_value=self.scale,
-                block_table=block_table,
+                num_key_value_heads=self.num_kv_heads,
                 input_layout='BSH',
-                block_size=block_size)
+                atten_mask=decode_meta.attn_mask,
+                sparse_mode=0,
+                scale=self.scale,
+                antiquant_mode=0,
+                antiquant_scale=None,
+                block_table=block_table,
+                block_size=block_size,
+                actual_seq_lengths_kv=seq_lens,
+            )
         else:
             raise NotImplementedError(
                 "Torchair graph mode with non-MLA attention backend is still experimental."