remove torch.cat and replace it by TensorList[0]

Signed-off-by: huangxialu <huangxialu1@huawei.com>

Author: loukong33

vllm_ascend/ops/fused_moe.py

@@ -204,11 +204,10 @@ def fused_experts_with_mc2(
         group_list_type=1,
         group_type=0,
         group_list=group_list,
-    )
+    )[0]
 
     # TODO: Remove this in the future.
-    gate_up_out = torch.cat(gate_up_out_list, dim=0)
-    gate_up_out = torch_npu.npu_swiglu(gate_up_out)
+    gate_up_out = torch_npu.npu_swiglu(gate_up_out_list)
 
     w2 = w2.transpose(1, 2)
     down_out_list = torch_npu.npu_grouped_matmul(
@@ -218,9 +217,7 @@ def fused_experts_with_mc2(
         group_list_type=1,
         group_type=0,
         group_list=group_list,
-    )
-
-    down_out_list = torch.cat(down_out_list, dim=0)
+    )[0]
 
     # moeCombine
     kwargs_mc2 = {
@@ -311,9 +308,8 @@ def apply_mlp(
         group_list_type=group_list_type,
         group_type=0,
         group_list=group_list,
-    )
+    )[0]
 
-    hidden_states = torch.cat(hidden_states, dim=0)
     hidden_states = torch_npu.npu_swiglu(hidden_states)
 
     w2 = w2.transpose(1, 2)
@@ -324,9 +320,8 @@ def apply_mlp(
         group_list_type=group_list_type,
         group_type=0,
         group_list=group_list,
-    )
+    )[0]
 
-    hidden_states = torch.cat(hidden_states, dim=0)
     return hidden_states
 
 
@@ -416,23 +411,19 @@ def fused_experts_with_all2all(
         group_list_type=0,
         group_type=0,
         group_list=expert_tokens,
-    )
+    )[0]
 
-    # TODO: Remove this in the future.
-    hidden_states = torch.cat(gate_up_out_list, dim=0)
-    hidden_states = torch_npu.npu_swiglu(hidden_states)
+    hidden_states = torch_npu.npu_swiglu(gate_up_out_list)
 
     w2 = w2.transpose(1, 2)
-    down_out_list = torch_npu.npu_grouped_matmul(
+    hidden_states = torch_npu.npu_grouped_matmul(
         x=[hidden_states],
         weight=[w2],
         split_item=2,
         group_list_type=0,
         group_type=0,
         group_list=expert_tokens,
-    )
-
-    hidden_states = torch.cat(down_out_list, dim=0)
+    )[0]
 
     if expert_map is not None:
         resorted_idx = torch.argsort(sorted_idx)
@@ -822,11 +813,9 @@ def fused_experts(
         group_list_type=0,
         group_type=0,
         group_list=expert_tokens,
-    )
+    )[0]
 
-    # TODO: Remove this in the future.
-    gate_up_out = torch.cat(gate_up_out_list, dim=0)
-    gate_up_out = torch_npu.npu_swiglu(gate_up_out)
+    gate_up_out = torch_npu.npu_swiglu(gate_up_out_list)
 
     w2 = w2.transpose(1, 2)
     down_out_list = torch_npu.npu_grouped_matmul(
@@ -836,9 +825,7 @@ def fused_experts(
         group_list_type=0,
         group_type=0,
         group_list=expert_tokens,
-    )
-
-    down_out_list = torch.cat(down_out_list, dim=0)
+    )[0]
 
     if expert_map is not None:
         weighted_down_out = down_out_list * sorted_weights.unsqueeze(1)