[main] remove torch.cat and replace it by List[0] (#2153)

### What this PR does / why we need it?
torch_npu.npu_grouped_matmul:

https://www.hiascend.com/document/detail/zh/Pytorch/710/apiref/torchnpuCustomsapi/context/torch_npu-npu_grouped_matmul.md

According to the document, when `split_item` is 2 or 3,
`torch_npu.npu_grouped_matmul` will return a list which has one element.
Therefore, the `torch.cat` after `torch_npu.npu_grouped_matmul` is
unnecessary.

### Does this PR introduce _any_ user-facing change?
not involved

### How was this patch tested?
ut and e2e covered: `tests/ut/ops/test_fused_ops.py`,
`tests/e2e/singlecard/ops/test_fused_moe.py`

**performance**:
(qwen3 30B, 2k->20k)

base:
Total Token throughput (tok/s):          667.76 

remove cat:
Total Token throughput (tok/s):          680.82 


- vLLM version: v0.10.0
- vLLM main:
vllm-project/vllm@fa00c5d

Signed-off-by: huangxialu <[email protected]>

Author: loukong33

tests/ut/ops/test_fused_ops.py

@@ -112,7 +112,7 @@ def mock_moe_env(mocker: MockerFixture):
                 torch.randn(16, 2)
         )), \
         patch("torch_npu.npu_grouped_matmul", return_value=(
-                (torch.randn(8, 2), torch.randn(8, 2))
+                [torch.randn(16, 2)]
         )), \
         patch("torch_npu.npu_swiglu", return_value=(
                 torch.randn(16, 2)

vllm_ascend/ops/fused_moe.py

@@ -205,11 +205,9 @@ 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(
@@ -219,9 +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 = {
@@ -312,9 +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)
@@ -325,9 +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
 
 
@@ -417,23 +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)
@@ -823,11 +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(
@@ -837,9 +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)