[Refactor] Add expert processed token count output for DispatchFFNCombine/DispatchFFNCombineBF16 (#6402)

### What this PR does / why we need it?
Add New Output for Expert Token Count
An additional output tensor expert_token_nums is added to both operators
to meet the requirement of tracking token distribution among experts:

Tensor Name: expert_token_nums
Dimension: 1D tensor
Shape: (local_expert_num,)
Data Type: int32
Semantics: Represents the number of tokens actually received by each
expert on the current card.
### Does this PR introduce _any_ user-facing change?

### How was this patch tested?

- vLLM version: v0.14.1
- vLLM main:
dc917cceb8

---------

Signed-off-by: guanguan0308 <1546542263@qq.com>
Signed-off-by: guanguan0308 <162653673+guanguan0308@users.noreply.github.com>

csrc/torch_binding.cpp

@@ -725,7 +725,7 @@ void batch_matmul_transpose(const at::Tensor &tensor_a, const at::Tensor &tensor
     return;
 }
 
-at::Tensor& dispatch_ffn_combine(
+std::tuple<at::Tensor&, at::Tensor&> dispatch_ffn_combine(
     const at::Tensor& x,
     const at::TensorList& weight1,
     const at::TensorList& weight2,
@@ -735,7 +735,8 @@ at::Tensor& dispatch_ffn_combine(
     const at::Tensor& probs,
     c10::string_view group,
     int64_t max_output_size,
-    at::Tensor& out
+    at::Tensor& out,
+    at::Tensor& expert_token_nums
 ) {
     char *group_ep_ptr = const_cast<char *>(group.data());
     bool is_int8 = weight1[0].dtype() == at::kChar;
@@ -750,7 +751,8 @@ at::Tensor& dispatch_ffn_combine(
                  probs,
                  group_ep_ptr,
                  max_output_size,
-                 out);
+                 out,
+                 expert_token_nums);
     } else {
         EXEC_NPU_CMD(aclnnDispatchFFNCombineBF16,
                  x,
@@ -762,9 +764,10 @@ at::Tensor& dispatch_ffn_combine(
                  probs,
                  group_ep_ptr,
                  max_output_size,
-                 out);
+                 out,
+                 expert_token_nums);
     }    
-    return out;
+    return {out, expert_token_nums};
 }
 
 at::Tensor npu_lightning_indexer(
@@ -1452,7 +1455,7 @@ TORCH_LIBRARY_EXPAND(CONCAT(_C, _ascend), ops)
     ops.def(
         "dispatch_ffn_combine(Tensor x, Tensor[] weight1, Tensor[] weight2, Tensor expert_idx,"
         "                     Tensor[] scale1, Tensor[] scale2, Tensor probs, str group,"
-        "                     int max_output_size, Tensor! out) -> Tensor"
+        "                     int max_output_size, Tensor! out, Tensor! expert_token_nums) -> (Tensor out, Tensor expert_token_nums)"
     );
     ops.impl("dispatch_ffn_combine", torch::kPrivateUse1, &vllm_ascend::dispatch_ffn_combine);