[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_mask.py

@@ -39,11 +39,22 @@ class AttentionMaskBuilder:
         self,
         max_seq_len: int,
         dtype: torch.dtype,
+        device: torch.device = None,
     ):
+        # NOTE: The device argument specifies the target NPU
+        # to be used for the newly added FIA operator.
+        # Only pass this parameter when using the new FIA operator.
+
         attn_mask = _generate_attn_mask(max_seq_len, dtype)
 
         self._seq_len_cached = attn_mask.shape[0]
         self.attn_mask_cache = attn_mask
+        self.device = device
+        if torch.version.cann.startswith("8.3"):
+            assigned_mask_dim = 2048
+            self.chunked_prefill_attn_mask = torch.triu(
+                torch.ones(assigned_mask_dim, assigned_mask_dim),
+                diagonal=1).to(torch.int8).to(device)
 
     @staticmethod
     def get_mask_scale_factor(dtype: torch.dtype = torch.float16):
@@ -66,24 +77,28 @@ class AttentionMaskBuilder:
 
     def get_splitfuse_attn_mask(
         self,
-        seq_lens: torch.Tensor,
-        position: torch.Tensor,
-        dtype: torch.dtype,
-        device: torch.device,
+        seq_lens: torch.Tensor = None,
+        position: torch.Tensor = None,
+        dtype: torch.dtype = None,
+        device: torch.device = None,
     ) -> torch.Tensor:
-        if dtype not in [torch.float16, torch.bfloat16]:
-            raise ValueError(
-                "splitfuse_attn_mask now only supports bf16 and fp16")
-        max_seq_len = max(seq_lens, default=0)
-        self._update_attn_cache(max_seq_len, dtype)
-        # FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
-        # is not the same. Fix this in the future when kernel is ready.
-        mask_scale_factor = AttentionMaskBuilder.get_mask_scale_factor(dtype)
-        attn_mask = torch.index_select(self.attn_mask_cache,
-                                       dim=0,
-                                       index=position)[:, :max_seq_len]
-        attn_mask *= mask_scale_factor
-        return attn_mask.contiguous().to(device, non_blocking=True)
+        if torch.version.cann.startswith("8.3"):
+            return self.chunked_prefill_attn_mask
+        else:
+            if dtype not in [torch.float16, torch.bfloat16]:
+                raise ValueError(
+                    "splitfuse_attn_mask now only supports bf16 and fp16")
+            max_seq_len = max(seq_lens, default=0)
+            self._update_attn_cache(max_seq_len, dtype)
+            # FIXME: Currently the mask value of chunked-prefill situation and Prefill-Only situation
+            # is not the same. Fix this in the future when kernel is ready.
+            mask_scale_factor = AttentionMaskBuilder.get_mask_scale_factor(
+                dtype)
+            attn_mask = torch.index_select(self.attn_mask_cache,
+                                           dim=0,
+                                           index=position)[:, :max_seq_len]
+            attn_mask *= mask_scale_factor
+            return attn_mask.contiguous().to(device, non_blocking=True)
 
     def _update_attn_cache(self, seqlen: int, dtype: torch.dtype):
         if seqlen > self._seq_len_cached: