[Perf] vectorize PCP/DCP loops in mla_v1.py (#5003)

### What this PR does / why we need it?
- Replace nested PCP/DCP Python loops with fully vectorized tensor
operations

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

---------

Signed-off-by: F.Liu <liufeng248@huawei.com>
Co-authored-by: F.Liu <liufeng248@huawei.com>

vllm_ascend/attention/mla_cp.py

@@ -1,4 +1,4 @@
-from typing import ClassVar, List, Optional, Tuple, TypeVar
+from typing import ClassVar, Optional, Tuple, TypeVar
 
 import numpy as np
 import torch
@@ -1120,26 +1120,37 @@ class AscendMlaCPImpl(AscendMLAImpl):
                 lse=softmax_lse)
 
         # Update out&lse
-        attn_out_lse_list = self._process_attn_out_lse(attn_output,
-                                                       softmax_lse,
-                                                       decode_meta)
-        attn_output = self._npu_attention_update(attn_out_lse_list)
+        attn_out_lse = self._process_attn_out_lse(attn_output, softmax_lse,
+                                                  decode_meta)
+        attn_output = self._npu_attention_update(attn_out_lse)
         return self._v_up_proj(attn_output)
 
-    def _npu_attention_update(
-            self, attn_out_lse_list: List[torch.Tensor]) -> torch.Tensor:
-        attn_out_split_cp = []
-        attn_lse_split_cp = []
-
-        for attn_out_lse in attn_out_lse_list:
-            attn_out_allgather, attn_lse_allgather = self._out_lse_reshape(
-                *torch.split(attn_out_lse, [self.kv_lora_rank, 1], dim=-1))
-            attn_out_split_cp.append(attn_out_allgather)
-            attn_lse_split_cp.append(attn_lse_allgather)
-        attn_out, _ = torch_npu.npu_attention_update(attn_lse_split_cp,
-                                                     attn_out_split_cp, 0)
-        attn_out = attn_out.view(-1, attn_out_lse_list[0].shape[1],
-                                 self.kv_lora_rank)
+    def _npu_attention_update(self,
+                              attn_out_lse: torch.Tensor) -> torch.Tensor:
+        # [PCP * S, DCP * H, D+1]
+        B_total, H_total, D_plus_1 = attn_out_lse.shape
+        S = B_total // self.pcp_size
+        H = H_total // self.dcp_size
+        D = self.kv_lora_rank
+        assert D_plus_1 == D + 1
+        # [PCP, S, DCP, H, D+1]
+        x = attn_out_lse.view(self.pcp_size, S, self.dcp_size, H, D_plus_1)
+        # [PCP, DCP, S, H, D+1]
+        x = x.permute(0, 2, 1, 3, 4).contiguous()
+        # Flatten [N, S, H, D+1], N = pcp_size * dcp_size
+        x = x.view(-1, S, H, D_plus_1)
+        # Split out lse
+        out_flat, lse_flat = torch.split(x, [D, 1],
+                                         dim=-1)  # [N, S, H, D], [N, S, H, 1]
+        #    out: [N, S, H, D] -> [N, S*H, D]
+        #    lse: [N, S, H, 1] -> [N, S*H]
+        out_flat = out_flat.flatten(1, 2)  # [N, S*H, D]
+        lse_flat = lse_flat.flatten(1, -1)  # [N, S*H]
+        #  unbind to list
+        out_list = out_flat.unbind(0)  # [S*H, D]
+        lse_list = lse_flat.unbind(0)  # [S*H]
+        attn_out, _ = torch_npu.npu_attention_update(lse_list, out_list, 0)
+        attn_out = attn_out.view(-1, H, D)
         return attn_out
 
     def _out_lse_reshape(self, attn_out: torch.Tensor,
@@ -1155,8 +1166,7 @@ class AscendMlaCPImpl(AscendMLAImpl):
         attn_output: torch.Tensor,
         softmax_lse: torch.Tensor,
         decode_meta: AscendMLADecodeMetadata,
-    ) -> List[torch.Tensor]:
-        attn_out_lse_list = []
+    ) -> torch.Tensor:
         out_mask = decode_meta.batch_seq_mask[:, None,
                                               None].expand_as(attn_output)
         attn_output = torch.where(out_mask, 0, attn_output)
@@ -1175,30 +1185,14 @@ class AscendMlaCPImpl(AscendMLAImpl):
             dist.all_to_all_single(attn_out_lse_all2all,
                                    attn_out_lse,
                                    group=self.dcp_group)
-            # permute: [num_heads, v_head_dim+1, bs] -> [bs, num_heads, v_head_dim+1]
-            attn_out_lse_all2all = attn_out_lse_all2all.permute([2, 0, 1])
-            if self.pcp_size > 1:
-                attn_out_lse = attn_out_lse_all2all.contiguous()
-            attn_out_lse_list = list(
-                torch.chunk(attn_out_lse_all2all, self.dcp_size, dim=1))
+            attn_out_lse = attn_out_lse_all2all.permute([2, 0, 1])
 
         if self.pcp_size > 1:
-            # AllGather out&lse within PCP group
-            attn_out_lse_list = [
-                torch.empty_like(attn_out_lse) for _ in range(self.pcp_size)
-            ]
-            dist.all_gather(attn_out_lse_list,
-                            attn_out_lse,
-                            group=self.pcp_group)
-        if self.dcp_size > 1 and self.pcp_size > 1:
-            attn_out_lse_list_pcp_dcp = []
-            for s in attn_out_lse_list:
-                attn_out_lse_list_split = list(
-                    torch.chunk(s, self.dcp_size, dim=1))
-                attn_out_lse_list_pcp_dcp += attn_out_lse_list_split
-            attn_out_lse_list = attn_out_lse_list_pcp_dcp
+            # AllGather out&lse within CP group
+            attn_out_lse = get_pcp_group().all_gather(
+                attn_out_lse.contiguous(), dim=0)
 
-        return attn_out_lse_list
+        return attn_out_lse
 
     def _reorg_kvcache(
         self,