[Op] DeepSeekV3.2 support bmm_transpose operator (vllm-project#4631)

### What this PR does / why we need it?
DeepSeekV3.2 support bmm_transpose operator.

- vLLM version: v0.12.0
- vLLM main:
vllm-project/vllm@ad32e3e

Signed-off-by: ZYang6263 <[email protected]>
Signed-off-by: ZYang6263 <[email protected]>
Co-authored-by: wangxiyuan <[email protected]>

Author: ZYang6263

vllm_ascend/attention/sfa_v1.py

@@ -484,14 +484,15 @@ def get_and_maybe_dequant_weights(layer: LinearBase):
                 self._process_weights_for_fused_mlapo(act_dtype)
 
     def _v_up_proj(self, x):
-        if self.W_UV.shape[0] * self.W_UV.shape[1] < 65536:
-            x = x.view(-1, self.local_num_heads, self.kv_lora_rank)
-            x = torch_npu.npu_transpose_batchmatmul(x,
-                                                    self.W_UV,
-                                                    perm_x1=[1, 0, 2],
-                                                    perm_x2=[0, 1, 2],
-                                                    perm_y=[1, 0, 2])
-            x = x.reshape(-1, self.local_num_heads * self.v_head_dim)
+        if x.dtype in [torch.float16, torch.bfloat16] \
+                and hasattr(torch.ops._C_ascend, "batch_matmul_transpose"):
+            x = x.view(-1, self.num_heads, self.kv_lora_rank)
+            b, _, _ = x.shape
+            res = torch.empty((b, self.num_heads, self.v_head_dim),
+                              dtype=x.dtype,
+                              device=x.device)
+            torch.ops._C_ascend.batch_matmul_transpose(x, self.W_UV, res)
+            x = res.reshape(-1, self.num_heads * self.v_head_dim)
         else:
             # Convert from (B, N, L) to (N, B, L)
             x = x.view(-1, self.local_num_heads,