[Perf] Add new npu_fused_infer_attention_score op to improve perfomance in splitfuse cases and resolve long-seq mask problems (#2962)

### What this PR does / why we need it?
Add new npu_fused_infer_attention_score op to improve perfomance in
splitfuse cases and resolve long-seq mask problems .

1. The original op's performance is suboptimal in certain scenarios,
necessitating optimization through the _new op_
(npu_fused_infer_attention_score)。
2. For ultra-long sequences (128k), the original operator will allocate
a large attn_mask, which consumes excessive CPU memory. In contrast, the
_new op_ supports a fixed-size compressed mask, effectively resolving
this issue.

NOTE1: The current PR retains the original logic and uses a version
check of the CANN package to determine whether the _new op_ can be
enabled. This ensures no impact on existing users. In future versions,
this version check and the original logic will be deprecated, and the
_new op_ scheduling will be uniformly adopted.
NOTE2: This pr relies on future CANN version, which is not available
now.
NOTE3: To enable the new op in chunked prefill, the parameter
additional_config should be set like `--additional-config
'{"ascend_scheduler_config":
{"enabled":true,"enable_chunked_prefill":true}}' \` at least.

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




- vLLM version: v0.10.2
- vLLM main:
6c5f82e5aa

---------

Signed-off-by: tangtianyi <tangtianyi4@huawei.com>
Signed-off-by: Angazenn <supperccell@163.com>
Co-authored-by: Angazenn <supperccell@163.com>

vllm_ascend/attention/attention_v1.py

@@ -456,18 +456,43 @@ class AscendAttentionBackendImpl(AttentionImpl):
             attn_metadata.seq_lens = \
                 attn_metadata.seq_lens.to(device=query.device)
 
-        torch_npu._npu_paged_attention_splitfuse(
-            query=query,
-            key_cache=self.key_cache,
-            value_cache=self.value_cache,
-            mask=attn_metadata.attn_mask,
-            block_table=attn_metadata.block_tables,
-            seq_len=attn_metadata.query_lens,
-            context_lens=attn_metadata.seq_lens,
-            num_kv_heads=self.num_kv_heads,
-            num_heads=self.num_heads,
-            scale_value=self.scale,
-            out=output)
+        if torch.version.cann.startswith("8.3"):
+            # TODO:The npu_fused_infer_attention_score op is planned to
+            # be utilized in a wider range in upcoming versions.
+            num_block, block_size, _, _ = self.key_cache.shape  # type: ignore
+            key = self.key_cache.view(  # type: ignore
+                num_block, block_size, -1)
+            value = self.value_cache.view(  # type: ignore
+                num_block, block_size, -1)
+
+            output, _ = torch_npu.npu_fused_infer_attention_score(
+                query=query,
+                key=key,
+                value=value,
+                atten_mask=attn_metadata.attn_mask,
+                block_table=attn_metadata.block_tables,
+                input_layout="TND",
+                block_size=block_size,
+                actual_seq_lengths=attn_metadata.query_start_loc[1:],
+                actual_seq_lengths_kv=attn_metadata.seq_lens,
+                num_key_value_heads=self.num_kv_heads,
+                num_heads=self.num_heads,
+                scale=self.scale,
+                sparse_mode=3,
+            )
+        else:
+            torch_npu._npu_paged_attention_splitfuse(
+                query=query,
+                key_cache=self.key_cache,
+                value_cache=self.value_cache,
+                mask=attn_metadata.attn_mask,
+                block_table=attn_metadata.block_tables,
+                seq_len=attn_metadata.query_lens,
+                context_lens=attn_metadata.seq_lens,
+                num_kv_heads=self.num_kv_heads,
+                num_heads=self.num_heads,
+                scale_value=self.scale,
+                out=output)
         return output
 
     def forward(
@@ -561,12 +586,18 @@ class AscendAttentionBackendImpl(AttentionImpl):
                                                    output)
             # Normal V1 situation.
             else:
+                if torch.version.cann.startswith("8.3"):
+                    # npu_fused_infer_attention_score does not support cases
+                    # where query.shape[0] != attn_metadata.query_start_loc[-1].
+                    # Thus we need unpad it here.
+                    num_tokens = attn_metadata.query_start_loc[-1]
+                    query = query[:num_tokens]
                 output = self._forward_v1_style(query, attn_metadata, output)
 
         # to make in-place change to the output tensor
         if hasattr(layer, 'quant_method') and use_kv_cache_int8:
             output = output.view(num_tokens, self.num_heads, self.head_size)
-        ori_output[:, :, :] = output[:num_tokens, :, :]
+        ori_output[:num_tokens, :, :] = output[:num_tokens, :, :]
         return output.view(num_tokens, self.hidden_size)